Financing Fertility through Bank Competition? Evidence from a Quasi-Natural Experiment in China

doi:10.21203/rs.3.rs-3593574/v1

Using the bank deregulation in China as an exogenous shock to bank competition, we investigate how it affects local fertility. We show that the deregulation significantly increases city-level birth rates. This effect is more pronounced among cities with more employment, more household savings, and higher housing affordability. We argue that the deregulation improves local capital supply and boosts local economic vibrancy, which increases households’ income and willingness of fertility. Consistent with this notion, we find that the effect of bank deregulation on birth rates is greater for cities with more entrepreneurship, more private businesses, and more innovative activities. This study sheds light upon the role of financial development in affecting local fertility.

JEL Classification: G21; J13; R11

Bank competition

Economic vibrancy

Household income

Fertility

For the last 70 years, fertility rates have dramatically decreased across countries. The “jaw-dropping” global crash in new-born babies triggered widespread concerns about its implications for societies¹. When discussing whether the declining fertility would recover, stay or continue to fall, most studies refer to Easterlin’s (1968) relative income hypothesis that young couples tend to have more children if income is high relative to aspirations and jobs are plentiful. But when jobs are scarce, couples would maintain living standards and rather have fewer children². Economic burdens, as argued in Lutz et al. (2006), is considered one of the major components that results in a downward spiral in births in many countries (the “low fertility trap”). More children would have been born into poor families if those families have higher socioeconomic status (Chen and Zhou 2007).

Besides fruitful studies on the institutional determinants that affect fertility³, an emerging strand of literature analyzes the fertility impacts of financial interventions. For example, Powell-Jackson et al. (2015) find that cash incentive programs encourage uptake of maternity services, especially among less educated and poorer women. Grossman (2019) documents that welfare programs that give sizable grants and tax breaks to high-poverty reduce fertility rates. Olson et al. (2019) show that cash transfer program that support poor families for their children’s school attendance reduces fertility among eligible teens. These fiscal interventions, however, are government-led. It is unclear whether market dynamics in the financial sector could have fertility effects. In this study, we exploit a quasi-natural experiment in China where the financial system is dominated by the banking sector, and evaluates how financial development affects local fertility.

Like many other emerging countries, China’s economic development relies on its great population for the last few decades. Despite the most populated country in the world, China is under the pressure of declining fertility which has fallen below the warning line⁴. To study the influence of financial development on local fertility, one advantage of exploiting the bank deregulation in China is that the Chinese financial markets are dominated with the banking sector. The deregulation allows Chinese commercial banks (in particular, joint-stock banks and city commercial banks) to set up new branches across cities, which significantly boosts city-level bank competition. A long strand of literature documents that bank competition reduces borrowing costs, boosts local economy and activate local labor markets⁵. For example, Hu et al. (2020) document that the bank deregulation in China entices low-income parents to substitute out of childrearing and into employment with adverse effects on children’s education. Following this vein, we test whether the bank deregulation affects local fertility rates.

We compile a data set of 200,000 branch-level information of 154 commercial banks in China from the website of China Banking Regulatory Commission (CBRC), and we collect data about city information, including a city’s birth rates and other socioeconomic factors from China Regional Statistical Yearbook. Following Chong et al. (2013), we estimate city-level bank competition using the branch-based Herfindahl Hirsch Index (HHI). Then, we construct a generalized difference-in-differences (DiD) framework using the 2009 bank deregulation as an external shock to bank competition. We investigate, subsequent to the bank deregulation, whether cities with greater bank competition witness greater increases in birth rates.

We document that in the three years following the deregulation, cities with more intense bank competition experience a greater increase in birth rates. Specifically, a one-standard deviation increase in a city’s bank competition is associated with 3.11% increase in the city’s birth rates, or equivalently 1,464 new-born babies given that an average city in our sample has 4.2 million residents. This result remains robust using dynamic tests, using alternative measurements, and using peer cities’ average bank competition as the instrument variable for identification. Overall, these results suggest that increased bank competition can help increase local fertility.

We argue that bank competition encourages local fertility because it increases local households’ income and relieves their financial constraints. Consistent with this argument, we show that in the three years following the deregulation, cities with more bank competition experience greater increases in employment rates and in labor wage. A one-standard deviation increase in a city’s bank competition is associated with 1.1% increase in employment rate and 3.9% increase in labor wage, and the wage increase is mainly driven by urban residents. Also, we show that the positive effect of bank deregulation on birth rates is more pronounced among cities with higher employment, more household savings, and higher housing affordability. These results suggest that bank deregulation reduces households’ financial concerns, so that they can maintain living standards and are likely to raise more children.

To understand the channel through which bank competition increases household income, we conjecture that bank competition may increases local capital supply and boost the vibrancy of local economy (Akins et al., 2016; Fu et al., 2014). Existing literature documents that bank competition provides more capital access to innovative businesses and entrepreneurial businesses (Banerjee and Newman 1993; Cornaggia et al. 2015). With improved economic vibrancy, we expect increased local labor income, and thus higher birth rates. To evaluate this argument, we assign the sample cities into two groups by local economic vibrancy. We show that the effect of bank deregulation on birth rates is greater among cities with more startups, more private businesses, and more innovation. To the extent that these businesses are more vibrant than mature, state-owned, and conventional businesses, we argue that bank competition provides more capital to local economy, raises local households’ income and then encourages fertility willingness as a result.

Besides the quantity of bank capital, the deregulation may encourage commercial banks to improve governance and provide better quality services to local customers (Besanko and Thakor, 1992), for example, by providing more diverse financial products. With more sound bank services, local households are more able to withstand unexpected income shocks and smooth consumption across time (Eswaran and Kotwal 1989)⁶. Therefore, households would have reduced financial concerns and more fertility tendency. We hand collect the information about the service quality of commercial banks in China from Chnbrand, a leading Chinese brand consulting company. We find that in Chinese banking markets, joint-stock banks have the best quality services and highest customer satisfaction among other types of banks, and we find that the positive effect of bank deregulation on local birth rates is more prominent in cities dominated with joint-stock banks. This result is consistent with the idea that bank service quality affects local households’ fertility tendency.

Local governments often put forward fiscal policies to encourage fertility. Lastly, we test whether bank competition complements or substitutes for government support. We assign the cities into two groups by fiscal expenditures on education, considering that the government’s greater support to education can encourage households to raise more children. We find that the effect of bank competition on local birth rates is driven by the cities with lower fiscal expenditures on education, suggesting that bank competition substitutes for the government to encourage local fertility.

Our paper is closely related to the literature on the economic effects of bank competition. Existing literature documents that competition improves access to capital of local businesses. Bensanko and Thakor (1992) argue that bank entry lowers borrowing costs, increases deposit rates, and thus improves social welfare. Dick and Lehnert (2010) show that bank competition improves credit conditions. Also, bank competition can better discipline bank governance so that local banking markets are stabilized (Akins et al.2016; Fu, Lin, and Molyneux 2014). In line with these studies, we exploit the bank deregulation in China as an exogenous shock that boosts bank competition in local banking markets, and show that the deregulation increases local employment and labor income. More importantly, we argue that such a wealth effect on local households increases local fertility.

Aside from the studies on the environmental determinants of fertility such as Rosenzweig and Schultz (1983) and Sellers and Gray (2019), our paper also adds to the literature on the institutional factors that affect fertility. Prior studies on economic determinants of fertility decisions mainly analyze households’ abortion costs (Joyce, Tan, and Zhang 2013), parental involvement (Levine 2003), and households’ wealth (Olson, Clark, and Reynolds 2019). For example, Schultz (2005) proposes the life cycle savings theory that household savings are substitutes for a family’s child raising, and thus savings and population growth are negatively correlated. This theory is empirically tested using household data in Asian countries (Mason and Kinugasa 2008). In this study, we test the role of financial factors in affecting city-level fertility. Specifically, we investigate the market structure of local banking markets. We provide robust results that bank competition affects local labor markets. We argue that economic considerations are an important determinant of local households’ fertility decisions. A more developed banking market can help increase a family’s fertility tendency.

More broadly, our paper adds to the emerging literature on the role of financial markets in societal evolution. To address the debate about whether finance benefits society (Sapienza and Zingales 2013; Zingales 2015), recent studies evaluate the effect of financial development on a number of social factors, for example, on local income (Beck et al. 2010), inequality (Greenwood and Jovanovic 1990; Banerjee and Newman 1993; Aghion and Bolton 1997; Beck, Demirgüç-Kunt, and Levine 2007; Beck, Levine, and Levkov 2010; Levine, Levkov, and Rubinstein 2014; Levine et al. 2015), crime (Garmaise and Moskowitz 2006), gender gap (Beck, Behr, and Guettler 2013), education (Laeven and Popov 2016), and entrepreneurship (Levine and Rubinstein 2017). Among these studies, access to finance is widely recognized as essential to relieve households’ financial constraints and improve their standards of living. In this article, we exploit the bank deregulation in China, and evaluate whether financial development may affect households’ income, and thus improve local fertility. We argue that bank competition activates local labor markets, and banking markets can substitute for government support to encourage fertility.

2.1 Declining fertility in China

The rapid growth of working age population used to be an engine for the economic growth of China over the past four decades. That is, an adequate supply of labor makes investment the main driver of GDP growth. However, since 2004, the population structure of China is ever changing: the population is aging, the birth rate goes down, and the working age population stops growing. In terms of the relationship between fertility and economic development, the quantity-quality trade-off theory in Becker (1960) argues that fertility rates are negatively associated with socioeconomic development. On the other hand, recent studies find a U-shaped relationship between fertility rates and socioeconomic conditions. At certain levels of economic conditions, families with more socioeconomic resources raise more children (Myrskylä et al., 2009).

An emerging literature analyzes the reasons behind the declining fertility. Based on a 2017 China Fertility Survey, He et al. (2018) analyze the nationwide fertility rates, fertility desires, and the availability of childbearing and parenting services. They find that financial burdens are the major concern that discourages households’ fertility desires. Using the same survey data, Qiao et al. (2021) find that financial considerations are crucial for families to consider having a second child. Therefore, they suggest that policy makers should adopt supportive policies to relieve working age families from economic concerns about the costs of raising a family. These survey-based studies indicate that financial concerns significantly affect households’ fertility decisions. It is likely that financial development plays an important role in shaping local population growth.

2.2 The bank deregulation in China

China’s financial markets are dominated by the banking system (Allen, Qian, and Gu 2017). Its banking sector is dominated by six national state-owned commercial banks, also known as the Big Six (i.e., Industrial and Commercial Bank of China, Agricultural Bank of China, Bank of China, China Construction Bank, Bank of Communication, and Postal Savings Bank of China). The Big Six owns the majority of market shares in Chinese banking markets. There are two major shareholders of the Big Six, one is the Ministry of Finance, and the other is the China Investment Corporation, the largest state-owned Chinese sovereign wealth fund.

Since 1988, twelve national joint-stock banks have been established⁷. The joint-stock banks compete with the Big Six for large enterprises and with city commercial banks for medium-to-small enterprises (SMEs) (Chong, Lu, and Ongena 2013). City commercial banks are another important type of commercial banks in China. They conduct loan businesses mostly with SMEs. As of 2018, there are in total 137 city commercial banks in China, most of which were restructured from urban credit cooperatives.

Among the three types of commercial banks in China, the Big Six national state banks are burdened with policy targets such as employment and funding state-owned enterprises (Berger et al. 2009). Joint-stock banks have diversified ownership structure, so they are more efficient and more profitable (Lin and Zhang 2009). City commercial banks are well recognized for superior operating efficiency in reallocating financial resources in Chinese banking markets⁸. City commercial banks’ performance depends on their governance and the institutional environments they work with. Studies argue city commercial banks have lower performance if they operate in the west of China (Ferri 2009), face poor law enforcement (Zhang, Wang, and Qu 2012), have fewer strategic investors (Sun et al. 2013), and are more connected to local governments (Chen et al. 2020).

Before 2009, joint-stock banks and city commercial banks are imposed with tight restrictions on setting up branches across cities. Such restrictions were removed in 2009. In April 2009, the Chinese Bank Regulatory Committee (CBRC) put forward a crucial order deregulating the banking sector (See the CBRC Order [2009] No.143)⁹. The order stated that joint-stock banks and city commercial banks can freely set up branches in cities where they already established branches, or in all cities of a province where they already have branches in the capital city of the province. This policy provided joint-stock banks and city commercial banks with greater flexibility setting up branches across cities, and there is no longer capital requirement for the newly created branches¹⁰. The deregulation is considered as a milestone in the development of commercial banks and invigorating competition in China’s banking sector (Gao et al. 2019).

3.1 Sample

To construct our sample, we collect the branch information of commercial banks in China from CBRC’s official website. This data covers over 200,000 branches of 4,150 financial institutions from 2000 to 2018. The branch level information includes full names, branch IDs, branch addresses, and exact opening and closing dates. From these data, we can learn how many new branches a specific bank sets up in a given year and in a given city. In our study, we restrict our sample to 155 commercial banks (including the Big Six, 12 joint-stock banks, and 137 city commercial banks¹¹). Then we collect from China Regional Statistical Yearbook demographic factors including GDP per capita, geographic characteristics, population growth, industry composition, population composition, and fiscal expenditures.

Traditional literature measures bank competition using banks’ market shares in the local credit market of deposits, loans, or the number of branches (Degryse et al., 2009). In our data set, the branch-level information about deposits and loans is not available, we follow Degryse et al. (2009), Chong et al. (2013), Gao (2019) and Liu et al. (2020) that measure the Herfindahl-Hirschman Index (HHI) with a bank’s number of branches in a city. The underlying assumption is that bank branches are homogenous in deposits and loans. The city-year panel data of bank concentration not only allows us to assess the variation of bank concentration across cities, but also the trend of bank concentration across time. The bank competition of a city’s banking market is calculated as follows:

${Bank competition}_{j,t}$ = 1-${HHI}_{j,t}=$1-$\sum _{i=1}^{{N}_{j}}({{\#branch}_{ij,t}/\sum _{i=1}^{{N}_{j}}{branch}_{ij,t})}^{2}$, (1)

in which ${\#branch}_{ij}$ indicates in year t, the number of branches for bank i in city j. ${N}_{j}$ indicates the number of bank names in city j. The value of ${HHI}_{j,t}$ ranges from 0 to 1. Higher the HHI’s value is, the less competitive the city’s banking markets are.

We process the data with the following steps: 1) We divide all nominal price-related variables (such as housing prices and wages) with the province-level CPI in 2000; 2) We exclude the observations with missing information; 3) We winsorize continuous variables at the 1% and the 99% percentile. Eventually, our sample has 288 cities and 2,523 observations.

3.2 Model specification

To test the relationship between the competition of local banking markets and local fertility, we construct the baseline model specification as follows:

$${Birth rate}_{i,t}={\alpha }_{i}{{+\beta }_{1}{Bank competition}_{i}\times Post}_{t}+\theta {Control}_{i,t-1}+{\gamma }_{i}+{\tau }_{t}+{\epsilon }_{i,t}$$

2

where ${Birth rate}_{i,t}$ indicates the birth rates of city i in year t¹². It is measured with the number of new born babies divided by the number of non-alien residents. This data is from China Regional Statistical Yearbook.${Bank competition}_{i}$is the level of competition for the banking market of city i calculated using Eq. (1). The panel regression runs from four years prior to the deregulation to four years after. Post_t is an indicator of one for city years after the deregulation. To measure the bank competition of each city before deregulation, we take the four-year average of the city’s Bank competition over 2005–2008. If bank competition boosts a city’s birth rates, we should expect${\beta }_{1}>0$. Considering that cities differ from one another in economic conditions, we include a series of control variables that might affect local fertility. ${Control}_{i,t-1}$ includes one-year-lagged city-level industry composition (FIE, SIE, TIE), population density (PD), GDP per capita (GDP), fiscal expenses on technology (Science), the number of educational institutions (Pschool, Mschool, University), the number of hospitals (Hospital), gender ratio (Gender), and senior ratio (Age). We present the summary statistics of key variables in Table 1. Detailed variable definitions are shown in Appendix A.

Table 1

**Summary Statistics** This table reports the summary statistics about key variables. Detailed variable definitions are provided in Appendix A.
	Mean	Median	Min	Max	Sd	N
Birth rate	0.0112	0.0106	0.0037	0.0259	0.0037	2523
Bank competition	0.8620	0.8738	0.7235	0.9321	0.0447	2523
FIE	0.0377	0.0154	0.0004	0.4071	0.0664	2523
SIE	0.4271	0.4201	0.1223	0.7526	0.1383	2523
TIE	0.5339	0.5388	0.2250	0.8186	0.1295	2523
PD	0.0415	0.0335	0.0017	0.2094	0.032	2523
GDP	2.2479	1.5313	0.3351	13.1327	2.1645	2523
Science	0.0487	0.0383	0.0097	0.2583	0.0377	2523
University	0.0159	0.0081	0.0000	0.0966	0.0204	2523
Mschool	0.5759	0.5593	0.2466	1.0631	0.1515	2523
Pschool	2.4329	2.1975	0.3604	9.1929	1.5853	2523
Hospital	0.0059	0.0045	0.0016	0.0477	0.0058	2523
Senior	0.1244	0.123	0.0670	0.1730	0.0207	2523
Gender	1.0381	1.036	0.9465	1.1523	0.0329	2523
Employment	0.1003	0.0772	0.0240	0.5057	0.0749	2523
Wage	2.4994	2.3426	0.8765	5.4408	1.0477	2523
Rural Wage	0.4777	00.431	0.1417	1.2498	0.2133	2523
Urban Wage	1.2345	1.1567	0.5537	2.6951	0.4215	2523

We report in Table 2 the estimates about the effect of bank deregulation on city birth rates. Column (1) does not include any control variables or any fixed effects. The results show that the deregulation significantly increases a city’s birth rates. Columns (2) and (3) include city and year fixed effects and other control variables that may be concurrently associated with city birth rates and bank competition, such as local GDP (GDP) and industry composition (FIE, SIE, TIE)). The results show that the positive effect of bank deregulation on birth rates remains significantly positive. In column (3), the results show that subsequent to the deregulation, a one-standard deviation increase in bank competition, the city’s birth rate increases by 0.35% (= 0.0078*0.0447), equivalent to 3.1% (= 0.35%/1.12%) of the average birth rate. Given that in our sample, an average city’s population is 4.2 million, a one-standard deviation increase in bank competition is associated with 1,464 more new born babies of the city. In column (4), we classify the cities into whether each city has a high level of bank competition relative to the sample median. The estimates remain positive at the 1% level. Overall, the results in Table 2 are consistent with the argument that bank deregulation has a positive effect on local birth rates.

Table 2

**Bank Competition and Birth Rates** This table reports the estimates about the effect of bank deregulation on a city’s birth rates.Bank competition is measured with average bank concentration of a city between 2005 and 2008. The measure is computed using Eq. (1). *Post* is an indicator of one for city-years after 2009 when Chinese joint-stock banks and city commercial banks are allowed to set up new branches across cities. Column (1) reports results without controlling for any fixed effects. Column (2) controls for city fixed effects. Column (3) includes both city and year fixed effects. In Column (4), *High_Bank competition* is a proxy of one if a city’s average levels of bank competition during 2005–2008 is above sample median. Detailed variable definitions are in Appendix A. Standard errors clustered at the city level are reported in parentheses; *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Variable	Birth rate_it
	(1)	(2)	(3)	(4)
Bank competition×Post _it	0.0009***	0.0012***	0.0078**
	(0.0002)	(0.0003)	(0.0036)
High_Bank competition×Post _it				0.0008***
				(0.0003)
FIE_it−−1		-0.0151	-0.0120	-0.0115
		(0.0148)	(0.0147)	(0.0147)
SIE_it−1		-0.0085	-0.0080	-0.0074
		(0.0104)	(0.0113)	(0.0113)
TIE_it−1		-0.0112	-0.0088	-0.0081
		(0.0100)	(0.0107)	(0.0107)
PD_it−1		-0.0364***	-0.0402***	-0.0403***
		(0.0080)	(0.0083)	(0.0084)
GDP_it−1		0.0001	0.0001	0.0002
		(0.0003)	(0.0003)	(0.0003)
Science_it−1		-0.0007	-0.0110	-0.0116
		(0.0164)	(0.0180)	(0.0183)
University_it−1		-0.0075	-0.0065	-0.0081
		(0.0220)	(0.0194)	(0.0197)
Mschool_it−1		0.0043***	0.0054***	0.0052***
		(0.0012)	(0.0012)	(0.0012)
Pschool_it−1		0.0002	0.0003**	0.0004**
		(0.0001)	(0.0002)	(0.0002)
Hospital_it−1		-0.0187	-0.0179	-0.0171
		(0.0151)	(0.0139)	(0.0141)
Senior		0.0130*	-0.0028	-0.0026
		(0.0066)	(0.0082)	(0.0082)
Gender		0.0016	0.0031	0.0030
		(0.0038)	(0.0041)	(0.0041)
Constant	0.0109***	0.0160	0.0117	0.0143
	(0.0002)	(0.0117)	(0.0126)	(0.0128)
Year FE	NO	NO	Yes	Yes
City FE	NO	Yes	Yes	Yes
Obs.	2523	2239	2239	2239
R²	0.0098	0.6377	0.6617	0.6623

To evaluate the dynamic effect of bank deregulation on local birth rates, we decompose the dummy, Post, into a series of seven year dummies. We define 2009 as the base year and plot the coefficient estimates of each year dummy along with the confidence intervals at the 5% level in Fig. 1. The figure shows that before the bank deregulation, the estimates are not significantly different from zero, suggesting that bank competition is not significantly associated with birth rate. The estimates become significantly greater than zero for the years after deregulation, and there do not appear reversals three years after the deregulation. Therefore, the results from dynamic tests indicate that the positive impact of bank deregulation on local birth rates is not short-lived.

One may have reversal causality concerns about the model specification. A city with higher birth rate may have more demand for financial services. It is not impossible that, in response to the deregulation, banks self-select to have more branches in high-birth rate cities. To address this issue, we utilize as the instrument variable the average bank competition levels of peer cities in the same province. The capital markets in China are segmented (Sun and Tong 2000). Due to the favoritism of local governments, it is much more difficult for a city to obtain financial resources from other provinces than from its own province. It is reasonable to believe that cities in the same province (peer cities) have similar banking market characteristics, and that peer cities’ average level of bank competition is unlikely to be correlated with a city’s birth rate, except through the city’s birth rate itself. Therefore, we instrument a city’s level of bank competition with its peer cities’ mean bank competition and perform two-staged least square estimation. Table 4 shows that a city’s bank competition is positively associated with the peer cities’ average level of bank competition, statistically significant at the 1% level. Column (2) reports the estimates of the second stage estimation. It shows that the instrumented bank competition is positively associated with the city’s birth rate, significant at the 5% level. The coefficient magnitude is greater than that reported in Table 3. These results support the argument that the bank deregulation increases local birth rates.

Table 3

**Instrumental Variable** This table reports the results about the two stage least square (2SLS) estimation. We instrument a city’s level of bank competition with the average bank competition levels of other cities in the same province. Column (1) reports the results of the first stage estimation. Column (2) reports the results of the second stage estimation. The control variables are the same as those in Table 2. Year fixed effects and city fixed effects are included in the regressions. Standard errors clustered at the city level are reported in the parentheses below coefficients. Detailed variable definitions are included in Appendix A. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Variable	First stage	Second stage
	(1) Bank competition_it	(2) Birth rate_it
Bank competition_iv_it	0.5878***
	(0.0748)
Bank competition×Post _it		0.0125**
		(0.0061)
Controls	Yes	Yes
Year FE	Yes	Yes
City FE	Yes	Yes
Obs.	2226	2226
R²	0.2209	0.6715

We utilize alternative measures to evaluate the robustness of our results. First, the HHI based on number of a city’s bank branches does not account for size differences across cities. We employ bank density, measured with the number of bank branches per square kilometers of a city to proxy for the city’s bank competition. Higher bank density indicates higher bank competition of a city. The results, which remain significantly positive, are reported in column (1) of Table 4.

We utilize a balanced four-year window to estimate the effect of bank competition on a city’s birth rates. To check whether our results remain significant in a longer time period, we extend the sample period from 2000 to 2018¹³. The estimates of extended period are reported in columns (2) and (3) of Table 4. Column 2 shows the overall relationship between a city’s bank competition and the birth rates. It shows that a one standard-deviation increase in a city’s level of bank competition is associated with 19.2% (= 0.047*0.045/0.011) increase in the birth rate. Column (3) presents that subsequent to bank competition (Post = 1), the magnitude increases to 29.0% (= 0.071*0.045/0.011) relative to the average. These results suggest that the positive effect of bank competition on local birth rates is likely to persist in a long term.

Table 4

**Robustness** This table reports the results of robustness tests. Column (1) uses *Bank density* as an alternative measure of *Bank competition*. A higher value of *Bank density* indicates a city’s greater bank competition. Columns (2) and (3) report the results of the baseline regression using an extended sample period over 2000–2018. Column (2) reports the overall association between a city’s birth rates and its bank competition. Column (3) reports the estimates about the effect of bank deregulation on city-level birth rates. The control variables are the same as those in Table 2. Year fixed effects and city fixed effects are included in the regressions. Standard errors clustered at the city level are reported in the parentheses below coefficients. Detailed variable definitions are included in Appendix A. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Variable		Birth rate_it
	(1) Bank density	(2) 2000–2018	(3) 2000–2018
Bank competition×Post _it	0.0056**		0.0707**
	(0.0023)		(0.0310)
Bank competition _it		0.0467***
		(0.0111)
Controls	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
City FE	Yes	Yes	Yes
Obs.	2239	4945	4945
R²	0.6608	0.7772	0.7774

5.1 Bank deregulation and household income

To the extent that economic burdens affect a household’s fertility decisions, we argue that bank competition increases local birth rates because it increases local households’ income, relieving the households’ financial constraints. To evaluate this channel, we investigate the effect of bank deregulation on the city’s employment and labor income. The results reported in Table 5 show that subsequent to bank deregulation, cities with higher levels of bank competition have significantly higher employment and higher average salary. A one standard-deviation increase in bank competition is associated with 1.1% higher employment rate and 3.9% higher salary. Moreover, the income increase is more pronounced among urban residents while there is no significant income change for rural residents (columns 3 and 4).

Moreover, we assign the cities into groups relative to the median by the cities’ employment rate, savings rate, and housing affordability (measured with the city’s average housing price scaled by the city’s average income). Table 6 shows that the positive effect of bank deregulation on the city’s birth rate is more pronounced among cities with higher employment, higher savings rate, and higher housing affordability. Combining the results in Table 5, these results suggest that the bank deregulation significantly improves local residents income, and the residents with increased wealth are more willing to have more children. Overall, we argue that relieving households’ financial constraints is likely to be the channel via which bank deregulation increases local birth rates.

Table 5

**Bank Competition and Local Labor Markets** This table reports estimates about the effect of bank competition on local labor markets. Column (1) reports the estimates about the effect on the city’s employment rate. Column (2) reports the estimates about the effect on the city’s average wage. Column (3) reports the estimates about the effect on the average wage of rural residents in the city. Column (4) reports the estimates about the effect on the average wage of urban residents in the city. The control variables are the same as those in Table 2. Year fixed effects and city fixed effects are included in the regressions. Standard errors clustered at the city level are reported in the parentheses below coefficients. Detailed variable definitions are included in Appendix A. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
	(1) Employment	(2) Wage	(3) Rural Wage	(4) Urban Wage
Bank competition×Post _it	0.0886***	0.6152**	-0.0548	0.2848**
	(0.0323)	(0.2804)	(0.0683)	(0.1352)
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Obs.	2239	2239	2229	2229
R²	0.9262	0.9439	0.9652	0.9654

Table 6

**Bank Competition, Living Pressure, and Birth Rates** This table reports the estimates about the effect of bank deregulation on city-level birth rates between cities at different levels of living pressure. Columns (1) and (2) measure living pressure with the city’s employment rate. Columns (3) and (4) measure living pressure with the city’s average household savings rate. Columns (5) and (6) measure living pressure with the city’s average housing rents divided by the average wage. The control variables are the same as those in Table 2. Year fixed effects and city fixed effects are included in the regressions. Standard errors clustered at the city level are reported in the parentheses below coefficients. Detailed variable definitions are included in Appendix A. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
			Birth rate_it
	Low Emp.	High Emp.	Low Saving	High Saving	Low Housing	High Housing
	(1)	(2)	(3)	(4)	(5)	(6)
Bank competition×Post _it	0.0064	0.0107**	-0.0044	0.0152***	0.0134**	-0.0002
	(0.0054)	(0.0042)	(0.0059)	(0.0054)	(0.0052)	(0.0046)
Coeff. Diff.	p-value=-0.0043**		p-value = 0.0196***		p-value = 0.0136***
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	1118	1107	1049	1039	1118	1079
R²	0.6144	0.7501	0.6805	0.7111	0.6655	0.7175

5.2 Bank deregulation, economic vibrancy, and birth rates

We examine the way in which bank competition improves local households’ income. A long strand of literature shows that bank competition increases local capital supply, facilitating businesses, especially small-and-medium enterprises (SMEs), to access more financial resources (Bensanko and Thakor 1992; Dick and Lehnert 2010; Chong, Lu, and Ongena 2013). Building on these studies, we conjecture that local businesses would have increased growth opportunities, so that there may be increased job opportunities and higher salaries in local labor markets.

If this conjecture holds true, we expect that the effect of bank deregulation on birth rates to be more pronounced for cities with more economic vibrancy. We use four measures to proxy for a city’s economic vibrancy. First is the city’s number of newly registered startup enterprises (Startup). Second is the city’s privately-owned assets scaled by the city’s total assets (Private). We believe that a city is more vibrant with more startups and privately-owned businesses than one with more mature firms and state-owned businesses. Also, we argue that compared with mature firms, startup businesses have more difficulty accessing bank capital (Patti and Dell’Ariccia 2004), and that in Chinese credit markets, private businesses are at a disadvantage of capital access compared to state-owned businesses (Cull and Xu 2003). Bank deregulation is expected to relieve the financial constraints of startups and private-owned businesses. Therefore, we expect the effect of bank deregulation on local birth rates to be greater among cities with more startups and privately-owned businesses.

We employ as a third measure of economic vibrancy a city’s innovative activities, proxied with the number of approved patents (Patent) and trademarks (Trademark). Similar to the argument above, compared to conventional businesses, innovation has high uncertainty and thus financing costs. Following Deng et al. (2020), we expect the deregulation would have a larger impact on birth rates among cities with more patents and more trademarks. Table 7 show that the positive effect of bank deregulation on local birth rates is more pronounced among cities with more startup firms (column 2) and with more private business (column 4). Also, Table 8 shows that the effect of bank competition on birth rates is greater among cities with more patents and trademarks. Overall, these results support our notion that bank competition alleviates local enterprises’ financial constraints, boosting local entrepreneurship, provide more job opportunities, and activate local labor markets.

One may concern that startup businesses and innovative activities are mainly performed by young people who are at the age of fertility. It is possible that bank competition directly alleviates these households’ financial concerns apart from the corporate financing channel. For example, the bank deregulation may encourage local banks to provide more personal loans to local households, encouraging the households to have children. Due to data availability, in this article, we are unable to evaluate the personal financing channel, but it should not weaken the corporate financing channel since we show increased employment and labor income after the deregulation.

Table 7

**Bank Competition, Economic Vibrancy, and Birth Rates** This table reports the estimates about the effect of bank deregulation on city-level birth rates between cities at different levels of economic vibrancy. Columns (1) and (2) measure economic vibrancy with the entrepreneurship index released by Peking University. Columns (3) and (4) measure economic vibrancy with the assets of private enterprises scaled by the assets of all industrial enterprises in a city. The control variables are the same as those in Table 2. Year fixed effects and city fixed effects are included in the regressions. Standard errors clustered at the city level are reported in the parentheses below coefficients. Detailed variable definitions are included in Appendix A. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
		Birth rate_it
	Low Startup	High Startup	Low Private	High Private
	(1)	(2)	(3)	(4)
Bank competition×Post _it	-0.0011	0.0124**	0.0056	0.0113**
	(0.0040)	(0.0055)	(0.0036)	(0.0056)
Coeff. Diff.	p-value=-0.0135***		p-value=-0.0057*
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Obs.	1096	1105	1102	983
R²	0.7112	0.7235	0.7157	0.6555

Table 8

**Bank Competition, Innovation, and Birth Rates** This table reports the estimates about the effect of bank deregulation on city birth rates between cities with different intellectual properties. In columns (1) and (2), intellectual property is measured with the number of a city’s patents. In columns (3) and (4), intellectual property is measured with the number of a city’s trademark. The control variables are the same as those in Table 2. Year fixed effects and city fixed effects are included in the regressions. Standard errors clustered at the city level are reported in the parentheses below coefficients. Detailed variable definitions are included in Appendix A. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
		Birth rate_it
	Low Patent	High Patent	Low Trademark	High Trademark
	(1)	(2)	(3)	(4)
Bank competition×Post _it	0.0005	0.0173***	0.0025	0.0135**
	(0.0049)	(0.0052)	(0.0042)	(0.0055)
Coeff. Diff.	p-value=-0.0168***		p-value=-0.0110***
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Obs.	1093	1096	1098	1098
R²	0.6861	0.7179	0.6965	0.6775

5.3 Bank service quality

Since city-level data about personal financing is not available for time windows around bank deregulation, it is empirically difficult to directly examine whether bank competition affects birth rates through personal financing. However, we perform an explorative test on this consideration. A bank with sound personal financing services should have high customer satisfaction. If personal financing is a channel via which bank competition boosts birth rates, we expect that the effect to be stronger among cities with more banks of high service quality.

We hand collect the data about bank service quality from ChnChina, a leading brand rater and consulting agency in China. As reported in Appendix B, we summarize the brand evaluation in services and credit card separately for the Big Six, joint-stock banks, city commercial banks, rural commercial banks, and foreign banks. Appendix B shows that the best banks of service quality are mainly the national state banks and joint-stock banks.

For each city, we calculate the number of national state banks (i.e., the Big Six), joint-stock banks, and city commercial banks scaled by the city’s total number of commercial banks. Then, we use the bank type density to identify a city’s major type of commercial bank. Table 9 shows that the positive effect of bank deregulation on local birth rates is greater for cities dominated with national state banks (Bighhi = 1) and those dominated with joint-stock banks (Jshhi = 1). In particular, the economic magnitude of the effect is significantly larger among cities dominated with joint-stock banks than those dominated with national state banks. On the other hand, for the cities dominated with city commercial banks (Cchhi = 1), the sensitivity between bank competition and birth rates is significantly weakened, suggesting that bank service quality affects household fertility decisions. Birth rates may not increase after bank deregulation if local banking markets cannot provide better financial services.

Table 9

**Bank Heterogeneity** This table reports the estimates about the effect of bank deregulation on a city’s birth rates in cities dominated with different types of commercial banks. In each city year, we calculate the ratios of national state banks (the Big Six), joint-stock banks, and city commercial banks over the total number of commercial banks, so that we can identity the major type of bank of a city. Bighhi (Jshhi,Cchhi) indicates if a city’s major bank type is national state banks (joint-stock banks, city commercial banks). The control variables are the same as those in Table 2. Year fixed effects and city fixed effects are included in the regressions. Standard errors clustered at the city level are in the parentheses below coefficients. Detailed variable definitions are included in Appendix A. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
	Birth rate_it
	(1)	(2)	(3)	(4)
Bank competition×Post _it	0.0120***	0.0068*	0.0089**	0.0115***
	(0.0040)	(0.0036)	(0.0038)	(0.0040)
Bank competition×Post ×Bighhi_it	0.0034***			0.0031***
	(0.0010)			(0.0010)
Bank competition×Post ×Jshhi_it		0.0638**		0.0557**
		(0.0288)		(0.0260)
Bank competition×Post ×Cchhi_it			-0.0065**	-0.0054*
			(0.0031)	(0.0030)
Controls	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Obs.	2239	2239	2239	2239
R²	0.6655	0.6640	0.6628	0.6679

5.4 Bank competition and government intervention

Government often comes up with a series of policies to encourage local fertility. Since in this article we argue that bank competition also can increase local fertility, we test if bank competition complements or substitute for government interventions. We classify the cities by the fiscal expenditures on education as a fraction of a city’s GDP. Government’s educational expenditures matters for household fertility decisions. The results in Table 10 show that the positive effect of bank deregulation on birth rates is greater among cities with less educational expenses, suggesting that bank competition serves as a substitute for government intervention in boosting fertility.

To address the concern that the fiscal expenditures on education may capture other factors correlated with government intervention, we test the effect of fiscal expenditures on social security and fixed investment for robust check. The results in columns (3) through (6) in Table 10, show that these types of fiscal expenses do not make a significant difference in the relationship between bank competition and birth rates, suggesting that bank competition is likely to substitute for fiscal expenditures on education in boosting local birth rates.

Table 10

**Bank Competition, Fiscal Expenditures, and Birth Rates** This table reports the estimates about the effect of bank deregulation on city birth rates across cities at different levels of fiscal expenditures. Columns (1) and (2) report the estimates between cities of low and high fiscal expenditures on education. Columns (3) and (4) report the estimates between cities of low and high fiscal expenditures on social security. Columns (5) and (6) report the estimates between cities of low and high fiscal expenditures on fixed investment. The control variables are the same as those in Table 2. Year fixed effects and city fixed effects are included in the regressions. Standard errors clustered at the city level are reported in the parentheses below coefficients. Detailed variable definitions are included in Appendix A. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
			Birth rate_it
	Low Edu.	High Edu.	Low Social	High Social	Low Invest	High Invest
	(1)	(2)	(3)	(4)	(5)	(6)
Bank competition×Post _it	0.0116***	0.0030	0.0068	0.0042	0.0070	0.0079
	(0.0043)	(0.0051)	(0.0055)	(0.0039)	(0.0048)	(0.0048)
Coeff. Diff.	p-value = 0.0086***		p-value = 0.0026		p-value=-0.0009
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	1092	1081	1106	1064	1223	993
R²	0.6705	0.7039	0.6519	0.7426	0.6409	0.7143

Exploiting the bank deregulation as an exogenous shock to bank competition, we address an important question about how financial development affects local fertility. Building on fruitful literature that documents the positive effect of bank competition on access to capital, we argue that the bank deregulation alleviates local households’ financial concerns and increases their fertility tendency.

There are four main findings in this article. First, the 2009 bank deregulation in China significantly boosts local birth rates, and this effect holds robust after we control for a series of local economic and demographic factors. Second, the bank deregulation significantly raises local employment and average wages. The effect of bank deregulation on birth rates is more prominent for cities with higher employment, higher household savings, and higher housing affordability, suggesting that bank competition reduces households’ financial concerns, increasing their fertility tendency. Third, the positive effect of bank deregulation on fertility is more pronounced among cities with more entrepreneurship and innovative businesses, which suggests that bank competition relieves local enterprises’ financial constraints and help to activate local economic vibrancy. Finally, the effect of bank deregulation on local birth rates is greater among the cities with less fiscal support from the government, suggesting that bank competition substitutes for government policies in support of local fertility.

The results in this study have profound implications for the interactions between financial development and population evolution. For example, competitive banking markets can benefit not only local economic vibrancy, but also can affect household decisions such as on fertility. Moreover, financial development may be an alternative to government policy that increases local fertility. Understanding the societal impact of finance sheds light upon alleviating fiscal burdens to support human development.

Author Contribution

D.L. wrote the text of manuscript.Y.C. executed the empirical data analysis.T.L. designed the research setup.

Aghion, P. & Bolton, P. (1997). A trickle-down theory of growth and development with debt overhang”. Review of Economic Studies. 64, 151–172.
Akins, B., L. Li, J. Ng, and T. O. Rusticus (2016). Bank Competition and Financial Stability： Evidence from the Financial Crisis, Journal of Financial and Quantitative Analysis, 51(1):1-28.
Allen, F., Qian, J. Q., & Gu, X. (2017). An overview of China's financial system. Annual Review of Financial Economics, 9, 191-231.
Banerjee, A., and A. Newman. (1993). Occupational Choice and the Process of Development, Journal of Political Economy, 101:274－298.
Beck, T., A. D. Kunt, and V. Maksimovic, 2004, “Bank Competition and Access to Finance: International Evidence”, Journal of Money, Credit, and Banking, 36(3) :627—648.
Beck, T., Behr, P. & Guettler, A. (2013). Gender and banking: Are women better loan officers? Review of Finance, 17(4), 1279–1321.
Beck, T., Demirgüç-Kunt, A. & Levine, R. (2007). Finance, inequality and the poor. Journal of Economic Growth, 12(1), 27–49.
Beck, T., Levine, R. & Levkov, A. (2010). Big bad banks? The winners and losers from bank deregulation in the United States. The Journal of Finance, 65(5), 1637–1667.
Becker, G. S. (1960). An economic analysis of fertility. In Demographic and Economic Change in Developed Countries (pp. 209-240). Columbia University Press.
Berger, A. N., Miller, N. H., Petersen, M. A., Rajan, R. G., & Stein, J. C. (2005). Does function follow organizational form? Evidence from the lending practices of large and small banks. Journal of Financial Economics, 76(2), 237-269.
Berger, A. N., Hasan, I., & Zhou, M. (2009). Bank ownership and efficiency in China: What will happen in the world’s largest nation? Journal of Banking & Finance, 33(1), 113-130.
Besanko, D., and A. V. Thakor. (1992), Banking Deregulation: Allocational Consequences of Relaxing Entry Barriers”, Journal of Banking & Finance, 16 (5):909—932.
Bundorf, M. K., Chun, N., Goda, G. S., & Kessler, D. P. (2009). Do markets respond to quality information? The case of fertility clinics. Journal of Health Economics, 28(3), 718-727.
Carbo-Valverde, S., Rodriguez-Fernandez, F. and Udell, G.F., (2009). Bank market power and SME financing constraints. Review of Finance, 13(2), pp.309-340.
Chava, S., A. Oettl, A. Subramanian, and K. Subramanian, (2013). Banking Deregulation and Innovation”, Journal of Financial Economics, 109(3):759-774.
Chen, Y., & Zhou, L. A. (2007). The long-term health and economic consequences of the 1959–1961 famine in China. Journal of Health Economics, 26(4), 659-681.
Chen, Z., Poncet, S., & Xiong, R. (2020). Local financial development and constraints on domestic private-firm exports: Evidence from city commercial banks in China. Journal of Comparative Economics, 48(1), 56-75.
Chong, T., Lu, L, and S. Ongena, (2013). Does Banking Competition Alleviate or Worsen Credit Constraints Faced by Small And MediumSized Enterprises? Evidence from China, Journal of Banking & Finance, 2013, 37(9): 3412-3424.
Cornaggia, J., Y. Mao, X. Tian, and B. Wolfe, (2015). Does Banking Competition Affect Innovation?, Journal of Financial Economics, 115(1) :189—209.
Cull, R., & Xu, L. C. (2003). Who gets credit? The behavior of bureaucrats and state banks in allocating credit to Chinese state-owned enterprises. Journal of Development Economics, 71(2), 533-559.
Cetorelli, N, and G. Michele, (2001). Banking Market Structure, Financial Dependence and Growth: International Evidence from Industry Data, Journal of Finance, 56: 617-648.
DeCicca, P., & Krashinsky, H. (2020). Does education reduce teen fertility? Evidence from compulsory schooling laws. Journal of Health Economics, 69, 102268.
Degryse, H., & Ongena, S. (2007). The impact of competition on bank orientation. Journal of Financial Intermediation, 16(3), 399-424.
Degryse, H., Laeven, L., & Ongena, S. (2009). The impact of organizational structure and lending technology on banking competition. Review of Finance, 13(2), 225-259.
Deng, S. Y., C. X. Mao, and C. Xia, (2020). Bank Geographic Diversification and Corporate Innovation: Evidence from the Lending Channel. Journal of Financial and Quantitative Analysis, 56(3): 1065 – 1096.
Dick A., Lehnert A. (2010). Personal bankruptcy and credit market competition [J]. The Journal of Finance. 65(2)：655-686.
Dynan, K., A. Mian, and D.M. Pence, (2012). Is a Household Debt Overhang Holding Back Consumption? Brookings Papers on Economic Activity,(1):299-362.
Easterlin, R. A. (1968). The American Baby Boom in Historical Perspective. American Economic Review. 51 (5): pp. 869–911.
Easterlin, R. A. (1975). An economic framework for fertility analysis. Studies in Family Planning, 6(3), 54-63.
Easterlin, R. A. (1978). What will 1984 be like? Socioeconomic implications of recent twists in age structure. Demography, 15(4), 397-432.
Easterlin, R., Pollak, R., & Wachter, M. L. (1980). Toward a more general economic model of fertility determination: Endogenous preferences and natural fertility. In Population and economic change in developing countries (pp. 81-150). University of Chicago Press.
Eswaran, M., and K. Ashok (1989) Credit as insurance in agrarian economies. Journal of Development Economics, 31(1): 37-53.
Ferri, G. (2009). Are new tigers supplanting old mammoths in China’s banking system? Evidence from a sample of city commercial banks. Journal of Banking & Finance, 33(1), 131-140.
Fletcher, J. M., & Yakusheva, O. (2016). Peer effects on teenage fertility: Social transmission mechanisms and policy recommendations. American Journal of Health Economics, 2(3), 300-317.
Fu, X., Y. Lin, and P. Molyneux (2014). Bank Competition and Financial Stability in Asia Pacific”, Journal of Banking & Finance, 38, 64—77.
Gao, H., Ru, H., Townsend, R., & Yang, X. (2019). Rise of bank competition: Evidence from banking deregulation in China (No. w25795). National Bureau of Economic Research.
Garmaise, M. & Moskowitz, T. (2006). Bank mergers and crime: The real and social effects of credit market competition. Journal of Finance, 61(2), 495–538.
Greenwood, J. & Jovanovic, B. (1990). Financial development, growth, and the distribution of income. Journal of Political Economy, 98, 1076–1107.
Grossman, D. (2019). The unintended effects of place based programs: fertility and health effects of urban empowerment zones. Journal of Health Economics, 63, 114-127.
Halla, M., Mayr, H., Pruckner, G. J., & Garcia-Gomez, P. (2020). Cutting fertility? Effects of cesarean deliveries on subsequent fertility and maternal labor supply. Journal of Health Economics, 72, 102325.
He, D., Zhang, X., Zhuang, Y., Wang, Z. and Yang, S., (2018). China fertility status report, 2006–2016: an analysis based on 2017 China fertility survey. Population Research, 42(6), pp.35-45.
Herrera-Almanza, C., & Rosales-Rueda, M. F. (2020). Reducing the cost of remoteness: community-based health interventions and fertility choices. Journal of Health Economics, 73, 102365.
Hill, M. J. (2015). Easterlin revisited: Relative income and the baby boom. Explorations in Economic History, 56, 71-85.
Hu, Q., Levine, R., Lin, C., & Tai, M. (2020). Finance and children’s academic performance (No. w26678). National Bureau of Economic Research.
Joyce, T., Tan, R., & Zhang, Y. (2013). Abortion before & after Roe. Journal of Health Economics, 32(5), 804-815.
Laeven, L. & Popov, A. (2016). A lost generation? Education decisions and employment outcomes during the US housing boom-bust cycle of the 2000s. American Economic Review. 106(5), 630–635.
Levine, P. B. (2003). Parental involvement laws and fertility behavior. Journal of Health Economics, 22(5), 861-878.
Levine, R., Levkov, A. & Rubinstein, Y. (2014). Bank deregulation and racial inequality in America. Critical Finance Review, 3(1), 1–48.
Levine, R., & Rubinstein, Y. (2017). Smart and illicit: who becomes an entrepreneur and do they earn more? The Quarterly Journal of Economics, 132(2), 963-1018.
Lin, J. Y., Sun, X., & Wu, H. X. (2015). Banking structure and industrial growth: Evidence from China. Journal of Banking & Finance, 58, 131-143.
Lin, X., & Zhang, Y. (2009). Bank ownership reform and bank performance in China. Journal of Banking & Finance, 33(1), 20-29.
Liu, G., Zhang, C., & Zhu, Y. (2020). The interest group theory of banking sector expansion in China: Evidence from a quasi-natural experiment. Journal of International Money and Finance, 107, 102225.
Lu, Y., & Slusky, D. J. (2019). The impact of women's health clinic closures on fertility. American Journal of Health Economics, 5(3), 334-359.
Lutz, W., Skirbekk, V., & Testa, M. R. (2006). The low-fertility trap hypothesis: Forces that may lead to further postponement and fewer births in Europe. Vienna Yearbook of Population Research, 167-192.
Macunovich, D. J. (1998). Fertility and the Easterlin hypothesis: An assessment of the literature. Journal of Population Economics, 11, 53-111.
Mason, A., & Kinugasa, T. (2008). East Asian economic development: two demographic dividends. Journal of Asian Economics, 19(5-6), 389-399.
Myrskylä, Mikko, Hans-Peter Kohler, and Francesco C. Billari. (2009). Advances in Development Reverse Fertility Declines. Nature, 460 (7256): 741–43.
Olson, Z., Clark, R. G., & Reynolds, S. A. (2019). Can a conditional cash transfer reduce teen fertility? The case of Brazil’s Bolsa Familia. Journal of Health Economics, 63, 128-144.
Patti, E. B. D., and G. Dell’ Arriccia, 2004, “Bank Competition and Firm Creation”, Journal of Money, Credit and Banking, 36(2):123-156.
Powell-Jackson, T., Mazumdar, S., & Mills, A. (2015). Financial incentives in health: new evidence from India's Janani Suraksha Yojana. Journal of Health Economics, 43, 154-169.
Qiao, J., Wang, Y., Li, X., Jiang, F., Zhang, Y., Ma, J., Song, Y., Ma, J., Fu, W., Pang, R. and Zhu, Z., (2021). A Lancet Commission on 70 years of women's reproductive, maternal, newborn, child, and adolescent health in China. The Lancet, 397(10293), pp.2497-2536.
Rosenzweig, M. R., & Schultz, T. P. (1983). Consumer demand and household production: the relationship between fertility and child mortality. The American Economic Review, 73(2), 38-42.
Ryan, R.M., O’Toole, C.M. and McCann, F., (2014). Does bank market power affect SME financing constraints?. Journal of Banking & Finance, 49, pp.495-505.
Sandner, M. (2019). Effects of early childhood intervention on fertility and maternal employment: Evidence from a randomized controlled trial. Journal of Health Economics, 63, 159-181.
Sapienza, Paola, and Luigi Zingales. (2013). Economic experts vs. average Americans, American Economic Review 103, 636–642.
Schmidt, L. (2007). Effects of infertility insurance mandates on fertility. Journal of Health Economics, 26(3), 431-446.
Schultz, T. P. (2005). Demographic determinants of savings: estimating and interpreting the aggregate association in Asia. China Economic Quarterly. 4(4), 991 -1018.
Sellers, S., & Gray, C. (2019). Climate shocks constrain human fertility in Indonesia. World Development, 117, 357-369.
Sun, Q., & Tong, W. H. (2000). The effect of market segmentation on stock prices: The China syndrome. Journal of Banking & Finance, 24(12), 1875-1902.
Sun, J., Harimaya, K., & Yamori, N. (2013). Local economic development, strategic investors, and efficiency of Chinese city commercial banks. Journal of Banking & Finance, 37(5), 1602-1611.
Weiss, LW. (1966). Concentration and labor earnings. The American Economic Review, 56(1/2), 96-117.
Zhang, J., Wang, P., & Qu, B. (2012). Bank risk taking, efficiency, and law enforcement: Evidence from Chinese city commercial banks. China Economic Review, 23(2), 284-295.
Zingales, L. (2015). Presidential address: Does finance benefit society? The Journal of Finance, 70(4), 1327-1363.

BBC News, “Fertility rate: ‘Jaw-dropping’ global crash in children being born”, July 15, 2020.
For example, see Easterlin (1968, 1975, 1978), Easterlin et al. (1980), Macunovich (1998); Hill (2015).
The Institutional factors analyzed in existing literature mainly include health care provision (Bundorf et al. 2009; Lu and Slusky 2019; Halla et al. 2020; Herrera-Almanza and Rosales-Rueda; 2020), law enforcement (Levine 2003; Schmidt 2007; Decicca and Krashinsky 2020), social interactions (Fletcher and Yakusheva 2016), and family planning (Sandner 2019).
See GRC Insights, “Has China Already Entered the ‘Low Fertility Trap’?”, August 25, 2021.
Existing studies show that reduced bank competition results in reduced capital supply (Cetorelli and Michelle 2001; Beck et al. 2004; Chong et al. 2013; Ryan et al. 2014), less entrepreneurship, decreased employment (Banerjee and Newman, 1993), and decreased innovation(Chava et al. 2013; Cornaggia et al. 2015; Deng et al. 2020). The tightened constraints are particularly tightened for small-and-medium sized enterprises (Weiss 1966; Beck et al. 2004; Berger et al. 2005; Carbo et al. 2009; Chong et al. 2013; Ryan et al. 2014).
Dynan et al. (2012) highly leveraged households are reluctant to access bank loans. Tightened financial constraints would negatively affect the households’ consumption levels.
The 12 joint-stock banks are China Merchants Bank (CMB), Shanghai Pudong Development Bank (SPDB), China CITIC Bank, Hua Xia Bank (HXBank), China Everbright Bank (CEB), China Minsheng Bank (CMBC), Industrial Bank (CIB), China Guangdong Development Bank (GDB), Ping An Bank (SPABank), China Zheshang Bank (CZBank), China Bohai Bank (BOHAIB), and Hengfeng Bank (EGB).
Existing studies on the efficiency of Chinese city commercial banks (CCB) include Ferri (2009), Mattews and Zhang (2010), Zhang, Wang, and Qu (2012), Sun, Harimaya, and Yamori (2013), Lin, Sun, and Wu (2015), Chen, Poncet, and Xiong (2020).
See the CBRC Order [2009] No.143, titled “Adjustment comment on the market access policy of setting up branches for small- and medium-sized commercial banks.”
Some argue that the bank deregulation actually started in 2006 when CBRC announced Order [2006] No.2, titled “The implementation of administrative licensing items on Chinese commercial banks”. This policy allows joint-stock banks to set up branches across regions, but it is conditional on a series of stringent requirements such as minimum capital requirement of the new branch. The policy does not bring significant changes in the branching of joint-stock banks. Also, the policy in 2006 did not involve city commercial banks.
We follow Chong et al. (2013) and do not include in our sample policy banks, rural credit cooperative, and credit unions considering these financial intermediaries’ policy orientation, and we only keep commercial banks in the study.
Ideally, fertility rate should be measured with the number of new born babies divided by the number of proper age females. Since the data is not available on the city level about proper-age females, we use birth rates to proxy for local fertility. Similar measurement is employed in health economics literature such as Levine (2003), Schmidt (2007), Bundorf et al. (2009), Joyce et al. (2013), and Olson et al. (2019).
Before 2013, data about a city’s birth rates are report in China Regional Statistical Yearbook. Starting from 2017, this data is reported in China City Yearbook. For the data points missing between 2013 and 2017, we apply linear interpolation to fill the gap.

Appendix A. Variable Definition

Variable	Definition
Birth rate	City-level birth rate measured with the number of new born babies scaled by the city’s population
Bank competition	City-level average bank competition levels between 2005 and 2008. Bank competition is measured with the branch-based Herfindahl Hirsch Index (HHI) following Eq. (1)
FIE	Number of employees in agricultural industries scaled by total eligible employees in a city’s labor market
SIE	Number of employees in manufacturing industries scaled by total eligible employees in a city’s labor market
TIE	Number of employees in service industries scaled by total eligible employees in a city’s labor market
PD	Population density measured with the number of ten thousand population scaled by the city’s geographic size in square kilometers
GDP	Ln (City GDP in ten thousand yuan/City population + 1)
Science	City-level fiscal expenditures on science and technology in ten thousand yuan, scaled by the city’s population
University	Number of universities (or colleges) in a city per ten thousand number of population
Mschool	Number of middle schools and high schools in a city per ten thousand number of population
Pschool	Number of elementary schools in a city per ten thousand number of population
Hospital	Number of hospitals and healthcare centers in a city per ten thousand number of population
Senior	Province level number of population aged over 64 scaled by total population
Gender	Province level number of male scaled by number of female
Employment (Emp.)	The number of a city’s registered employees scaled by the city’s average number of residents
(Rural/Urban) Wage	Average wage (in ten thousand yuan) of a city’s (rural/urban) residents at work scaled by the city’s average population
Edu.(Social/Invest)	City level fiscal expenditures on education (social security/fixed investment) divided by the city’s population
Saving	City-level: (Total income - total consumption)/Total income
Housing	City level: average housing price divided by average income
Startup	The entrepreneurship index released by Peking University. It is computed using the log number of a city’s newly registered startup companies
Private	For enterprises with total assets above 20 million yuan, the ratio of private enterprises’ total assets divided by all industrial enterprises’ total assets
Patent	The innovation index released by Peking University. The index is computed using the log number of a city’s newly filed patent applications
Trademark	The trademark index released by Peking University. The index is computed using the log number of a city’s newly granted trademarks

Appendix B. Brand Evaluation of Commercial Banks in China

This table reports the brand evaluation for different types of commercial banks in China. Using the evaluation reports by Chnbrand, a leading brand rating and consulting agency in China, we summarize the brand evaluation in services and credit card separately for six national state banks (the Big Six), the joint-stock banks, city commercial banks, rural commercial banks, and foreign banks. BPI indicates a bank’s brand quality. CSI indicates a bank’s customer satisfaction. NPS indicates a bank’s popularity among customers. Higher are the indexes, higher brand evaluation a bank receives. We consider the top twenty banks on each ranking list and report the fraction for each type of banks under each category.

	BPI (2011–2021)		CSI (2015–2020)		NPS (2015–2021)
	Service	Credit Card	Service	Credit Card	Service	Credit Card
National State	48.53%	43.42%	49.32%	43.21%	51.22%	45.45%
Joint-stock	50.00%	51.97%	49.32%	51.85%	47.56%	48.86%
City Commercial	0.74%	3.29%	1.37%	4.94%	1.22%	5.68%
Rural Commercial	0	0	0	0	0	0
Foreign	0.74%	1.32%	0	0	0	0

No competing interests reported.

Financing Fertility through Bank Competition? Evidence from a Quasi-Natural Experiment in China

Status:

Version 1

Abstract

Figures

1. Introduction

2. Institutional Background

2.1 Declining fertility in China

2.2 The bank deregulation in China

3. Sample and Methodology

3.1 Sample

3.2 Model specification

4. Main Results

5. Mechanisms

5.1 Bank deregulation and household income

5.2 Bank deregulation, economic vibrancy, and birth rates

5.3 Bank service quality

5.4 Bank competition and government intervention

6. Concluding Remarks

Declarations

Author Contribution

References

Footnotes

Appendix

Appendix A. Variable Definition

Appendix B. Brand Evaluation of Commercial Banks in China

Additional Declarations

Status:

Version 1