The Deep Determinants of Economic Development in China: A Provincial Perspective

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CEAMeS Discussion Paper No. 12 / 2018

Linda Glawe, Helmut Wagner

The Deep Determinants of

Economic Development in China

A Provincial Perspective

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The Deep Determinants of Economic Development in China – A Provincial Perspective^*

Linda Glawe^a and Helmut Wagner^b

(Second Version, May 2018)

Abstract. There is a significant body of literature arguing that institutional quality is the key for long run economic growth and development. While the majority of these studies are based on cross-country growth regression, in our paper, we focus on the institution-economic growth nex- us within a particular country, namely China. China is often regarded as an exception by having achieved miraculous growth for more than three decades despite relatively low institutional quality. However, our key findings suggest that at the provincial level, institutional quality played in fact an important role for the economic success of a province in China, “trumping” geographical factors and integration which only have an indirect effect by influencing institutional quality. We employ instrumental variable estimation techniques to address the endogeneity problems regarding the institutions-development relationship.

Keywords: economic growth and development, deep determinants of economic growth, institu- tional quality, the Chinese economy

JEL Classification: O11, O43, O53

________________________

* We would like to thank all participants of the 2^nd CEAMeS Workshop on East Asia Macroeconomic Studies (Xiamen, 2018). Our special thanks are due to David Weil and Yanwu Wang for helpful com- ments.

a, b

University of Hagen, Faculty of Economics, Chair of Macroeconomics, 58084 Hagen, Germany, phone +4923319872640, fax +492331987391, e-mail linda.glawe@fernuni-hagen.de and

helmut.wagner@fernuni-hagen.de

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The Deep Determinants of Economic Development in China – A Provincial Perspective

1 Introduction

Since the 1990s, a considerable body of literature has emerged, focusing on the so-called ‘deep determinants’ (namely, geography, institutions, and integration) for explaining current cross- country differences in per capita income (see Easterly and Levine, 2003, and Spolaore and Wacziarg, 2013, for survey articles). While early contributions treat the deep determinants sepa- rately and disagree on which determinant is the most important, more recent studies taking into account the three determinants simultaneously, postulate the primacy of institutions (Rodrik et al.

2004; Bhattacharyya, 2004). Against this background, China appears to be an exception: Despite lacking institutional quality, the country has experienced rapid growth over the last three decades (Huang, 2008; Wedeman, 2012; Ahlers, 2014; Zhou, 2014). This phenomenon has left research- ers puzzled and is sometimes referred to as the “China paradox” (Rothstein, 2014). However, that does not mean that institutions did not matter for China; as we will see, our study reveals that institutions do have played an important role in China’s economic development process if we analyze the impact of the deep determinants within China. That is, in contrast to the majority of studies analyzing the deep determinants of economic development by using cross-country sam- ples, we choose a provincial perspective.¹

Figure 1. Provincial institutional quality and per capita income in 2010.

(a) Provincial per capita GDP (in US$) (b) Institutional quality index

Data Source: Provincial per capita income: NBS (2017), converted in US$ using the yuan-dollar exchange rates of the Federal Reserve Economic Data; Institutional quality index: Tang et al. (2014). Own representation.

1 There are some other studies that analyze institutional quality within China. Examples include Ji et al. (2013), Ang et al. (2014), and Zhou (2014). However, the latter two primarily focus on the impact of institutional quality on the firms’ R&D activity. Ji et al. (2013) analyze institutional quality within China in the context of the interplay between resource abundance, institutions, and economic growth in China with a focus on natural resources.

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China is a huge country consisting of 31 provinces, autonomous regions, and municipalities, some of which are themselves larger than some European countries – not only regarding the geographical size but also with respect to population, GDP, and even GDP per capita. Moreover, as depicted by Figure 1a, there is a very unequal economic development across provinces. Al- most two decades into the implementation of Deng Xiaoping's 'get rich first' policy, inland provinces fell far behind the prosperous coast. For example, Beijing’s average per capita income (US$

9,350) is more than 5.6 times that of Guizhou’s (US$ 1,659). Moreover, although Chinese provinces possess homogeneous constitution, law and governance structures (Ji et al., 2014), Figure 1b reveals that there are significant differences regarding institutional quality. In fact, Figures 1a and 1b depict a relatively similar pattern (Anhui province and Xinjiang province appear to be exceptions) and there is a strong positive correlation between institutional quality and the log of provincial per capita income (with a coefficient of 0.84).

Institutional quality can differ across provinces because of geographical, political, and historical reasons (Ji et al., 2014) or a mix of them. For example, in the course of the ‘get rich first’ policy under Deng Xiaoping, the government created a favorable policy environment for coastal provinces. This decision, in turn, was probably influenced by the favorable geographical location (proximity to ports etc.) of these provinces.

In our paper, we analyze in how far the deep determinants and particularly institutional quality can explain differences in economic performance across provinces in China by using or- dinary least squares (OLS) and two-stage least squares (2SLS) regression analysis. Moreover, we briefly discuss the implications for China’s future growth strategy.

The paper is organized as follows. In Section 2 we describe our data and provide some descriptive statistics. Section 3 presents our OLS and 2SLS regression results. Section 4 con- cludes.

2 Data and Descriptive Statistics

Descriptive statistics of the key variables are provided in Table 1. Our base sample consists of 31 provinces, including 4 municipalities and 5 autonomous regions (see Appendix A). The log of per capita provincial income (in current RMB) for 2010 is our measure of economic performance.² The government efficiency index for the year 2010 is used as a measure of institutional quality (𝐼𝑁𝑆) and is due to Tang et al. (2014). This standardized index varies between -1 (weak government efficiency) and +1 (strong government efficiency) and consists of four indicators, namely

“government public services”, “public infrastructure”, “government size”, and “welfare of resi- dents”, as well as various sub-indicators and 47 indexes (Tang et al., 2014: 146). It has to be noted that the set of institutions that matter for economic performance is far more complex and can- not be fully captured by this index. However, since data on the quality of provincial institutions in China is limited and government efficiency comprises important aspects of institutional quality such as the provision of comprehensive legal systems and public services (cf. Tang et al., 2014:

142), our choice of variable seems reasonable. Integration, measured as the (log) share of trade in

2 We also repeated our entire analysis using the log of per capita GDP in US dollar. However, our results remain unchanged.

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GDP (𝐼𝑁𝑇), is compiled using NBS (2017) provincial trade and GDP data. Geography (𝐺𝐸𝑂) is measured by latitude, that is the distance from the equator. This variable is often used to control for the effect of climate on economic development. Provincial mortality rates after the great famine (defined as deaths per 1,000 population) and ethnic fractionalization indexes for the years 1990 and 2000 (ranging between 0 and 1, where zero corresponds to a completely homogenous province) are used as instruments for institutions and are obtained from Meng et al. (2015) and Yeoh (2012), respectively. Moreover, we use the air distance to Beijing/Shanghai, whichever is less, (DIST) (calculated with the great-circle distance formula) as instrument for institutions.³ The dummy variable COAST indicates whether a province is located at the coast or not and is used as instrument for integration.

Table 1. Descriptive Statistics.

Base sample (1) Log of per capita gross provincial product (PCGDP) 10.30

(0.45) Institutional quality (government efficiency) (GE) -0.01

(0.30)

Log of the trade share (% of GDP) (TRADE) -1.72

(1.01)

Geography (Latitude) 0.37

(0.08)

Distance to Beijing/Shanghai (DISTANCE) 975.25

(703.48)

Coastal dummy (COAST) 0.39

(0.50) Famine mortality rate (FMR)

(defined as deaths per 1,000 population)

22.39 (15.12)

Ethnic fractionalization 1990 (EFI90) 0.18

(0.22)

Ethnic fractionalization 2000 (EFI00) 0.19

(0.21)

Observations 31

Notes: Variable definitions and sources are provided in the text. Standard errors are in parentheses.

3 OLS and 2SLS results

Our regression model to estimate the effect of the deep determinants (and especially institutional quality) on the log provincial per capita income (𝑙𝑜𝑔 𝑦_𝑖) is given by the following equation:

3 The great-circle distance (in km) (𝐷) between two provinces can be obtained via the following formula: 𝐷 = arccos (sin(𝜙₁) ∗ sin(𝜙₂) + cos(𝜙₁) ∗ cos(𝜙₂) ∗ cos(𝜆₁− 𝜆₂)) ∗ R_⊕, where 𝜙₁(𝜙₂) is the latitude of province 1 (province 2) and 𝜆₁ (𝜆₂) is the longitude of province 1 (province 2). R_⊕ denotes the earth radius (approximately 6.371 km). Using an alternative formula developed by Thaddeus Vincenty which is not based on a sphere but on an ellipsoid (Vincenty 1975) does not affect our results; the significance levels stay the same and even the coefficients are almost identical compared to those obtained via the great-circle distance formula.

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5 (1) 𝑙𝑜𝑔 𝑦_𝑖 = 𝛼 + 𝛽𝐼𝑁𝑆_𝑖+ 𝛾𝐼𝑁𝑇_𝑖 + 𝛿𝐺𝐸𝑂_𝑖 + 𝜀_𝑖,

where 𝐼𝑁𝑆_𝑖 is the institutional measure, in particular the government efficiency index (𝐺𝐸). 𝐼𝑁𝑇_𝑖 and 𝐺𝐸𝑂_𝑖 denote the remaining two deep determinants, namely integration, i.e. the log of the trade share in provincial income and the geographical measure, i.e. the latitude of a province. 𝜀_𝑖 denotes the random error term. We use standardized measures of our three regressors, which enables us to directly compare the estimated coefficients.⁴

Figure 2. Bivariate OLS relationships between the deep determinants and per capita income.

Before we discuss our estimation results, we take a look on the scatter plots depicting the bivariate relationship between each of the deep determinants and the log of provincial per capita income. As depicted by Figure 2, there is a strong positive relationship between institutions and income as well as between openness and income and a moderate positive relationship between latitude and income. The OLS estimates of equation (1) reported in Column (6) of Table 2 con-

4 The standardized variable 𝑥^∗ is obtained by using the following formula: 𝑥^∗=^𝑥−𝜇

𝑠𝑑, where 𝑥 denotes the original variable and 𝜇 (𝑠𝑑) is the mean (standard deviation) of 𝑥.

9.51010.51111.5Log GDP per capita in 2010

-1 -.5 0 .5

Government Efficiency in 2010

-3 -2 -1 0 1

Log Openness in 2010

.2 .3 .4 .5

Distance from the Equator

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firm these findings: The coefficients of institutions (0.22) and openness (0.21) are highly significant and have the expected positive sign. The coefficient of latitude is positive, however, it is insignificant at the 10-percent level. Interestingly, latitude is highly significant if we only include trade and geography in the regression (Column 5), however its coefficient is insignificant and much smaller when only including institutions and geography (Column 4).

Table 2. OLS estimates (standardized variables).

(1) (2) (3) (4) (5) (6)

Institutions (GE) 0.3782***

(8.10)

0.2727***

(5.32)

0.3766***

(7.40)

0.2244***

(3.86)

Integration (TRADE) 0.3393***

(6.05)

0.1704***

(3.32)

0.3510***

(7.31)

0.2055***

(3.78)

Geography (Latitude) 0.0045

(0.09)

0.1647***

(3.43)

0.0738 (1.61)

R-squared 0.6935 0.5582 0.7802 0.6936 0.6890 0.7802

Observations 31 31 31 31 31 31

Note: Dependent variable: Log per capita GDP. The independent variables are all scaled in the sense that they pre- sent deviations from the mean divided by the standard deviation. T-statistics are in parentheses. Significance at the 10, 5, and 1 percent levels are denoted by *, **, and ***, respectively.

We have to be careful when making statements about the causality of these relationships, inter alia due to the problems of reverse causality (richer economies can afford better institutions) as well as of omitted independent variables correlated with institutions. Therefore, in a next step, we treat the institutional quality as well as integration as endogenous. We employ a two-stage-least-squares (2SLS) model. In particular, we use the distance to Beijing/Shanghai as an instrument for institutional quality and the coastal dummy as an instrument for integration. We also tested various other instruments for institutional quality, namely settler mortality rates after the great famine for the year 1960 as well as indexes of ethnic fractionalization. Our regression results using these alternative instruments (and the corresponding scatter plots between the re- spective instruments and institutional quality) are provided in the Appendix B. However, although all instruments are negatively correlated with institutional quality (at the 1- or 5-percent level), the F-statistics are (partly significantly) below the threshold of 10 suggested by Staiger and Stock (1997). Indeed, the distance to Beijing/Shanghai is the only instrument that fulfills the no-weak-instrument criteria.

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Figure 3. Institutional quality and the distance to Shanghai/Beijing

Figure 3 depicts the negative correlation between the distance to Beijing/Shanghai and the government efficiency indicator. The first stage regressions are given by equations (2) and (3).

(2) 𝐼𝑁𝑆_𝑖 = 𝜗 + 𝜌𝐷𝐼𝑆𝑇_𝑖+ 𝜃𝐶𝑂𝐴𝑆𝑇_𝑖 + 𝜎𝐺𝐸𝑂_𝑖 + 𝜀_𝐼𝑁𝑆_𝑖, (3) 𝐼𝑁𝑇_𝑖 = 𝜑 + 𝜇𝐶𝑂𝐴𝑆𝑇_𝑖 + 𝜋𝐷𝐼𝑆𝑇_𝑖 + 𝜌𝐺𝐸𝑂_𝑖 + 𝜀_𝐼𝑁𝑇_𝑖,

where 𝐷𝐼𝑆𝑇_𝑖 is the distance to Beijing/Shanghai (whichever province is nearer), 𝐶𝑂𝐴𝑆𝑇_𝑖 is the coastal dummy variable. Our full 2SLS model is given by equations (1)–(3). The exclusive restriction is that 𝐷𝐼𝑆𝑇_𝑖 and 𝐶𝑂𝐴𝑆𝑇_𝑖 does not appear in equation (1).

Our regression results are presented in Table 3. Panel A gives the 2SLS estimates and Panel B provides the corresponding first-stage relationships. Notably, for all columns, the coefficients of institutional quality are larger than the corresponding OLS estimates reported in Table 2, suggesting that the attenuation bias due to measurement error is more important than reverse causality or omitted variables biases (As argued by Acemoglu et al. (2001), in reality, we have a set of institutions that apply and not only one single measure which, therefore, can only capture a part of the “true institutions”.). As in Table 2, in all columns, institutional quality is highly significant. However, in contrast to our OLS regression results, integration turns insignificant at the 10- percent level once we control for institutions (see Columns 5 and 6) and the coefficient is reduced (for example from 0.21 to 0.15 in Column 6). As before, latitude is insignificant at the 10-percent level as long as we control for institutions. Thus, the “primacy of institutions” also applies to the cross-provincial analysis.⁵

5 Using the average of the government efficiency index for the years 2001 to 2010 does not change our results regarding the primacy of institutions.

-1-.50.5Government Efficiency in 2010

0 1000 2000 3000

Distance to Beijing/Shanghai

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8 Table 3. 2SLS estimates (standardized variables).

(1) (2) (3) (4) (5) (6)

Panel A: 2SLS

(6.99)

0.4163***

(6.40)

0.3457***

(5.54)

0.3174***

(3.19)

(5.53)

0.4193***

(7.04)

0.1257 (1.60)

0.1546 (1.58)

Geography (Latitude) -0.0098

(-0.19)

0.1695***

(3.58)

0.0367 (0.65)

R-squared 0.6877 0.5489 0.6869 0.6665 0.7643 0.7801

Underidentification test (p-value) 0.0000 0.0000 0.0000 0.0000 0.0005 0.0033

Test for endogeneity (p-value) 0.3505 0.2964 0.3498 0.2964 0.2863 0.1440

Panel B: First-Stage for Institutions and Trade

Column (1) (2) (3) (4) (5) (5) (6) (6)

Dependent variable GE TRADE GE TRADE GE TRADE GE TRADE

DISTANCE -0.7721***

(-6.54)

-0.7281***

(-6.03)

-0.6356***

(-5.20)

-0.0680 (-0.53)

-0.5087***

(-4.23)

-0.0470 (-0.33)

COAST 0.7914***

(6.97)

0.8023***

(6.88)

0.3008**

(2.46)

0.7605***

(5.90)

0.4076***

(3.47)

0.7782***

(5.59)

Latitude 0.1632

(1.35)

0.0648 (0.56)

0.2912**

(2.68)

0.0481 (0.37)

F-stat 42.805 48.59 36.31 47.37 28.17 23.83 31.30 22.98

Observations 31 31 31 31 31 31 31 31

Note: Dependent variable: Log per capita GDP. The independent variables are all scaled in the sense that they present deviations from the mean divided by the stand- ard deviation. T-statistics are in parentheses. Significance at the 10, 5, and 1 percent levels are denoted by *, **, and ***, respectively.

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Regarding the first-stage relationships, there is a significant positive relationship between our instruments and the endogenous variables (that is between institutions and the distance to Beijing/Shanghai and between integration and the coastal dummy variable) for all columns.

Moreover, the coastal dummy is also significantly correlated with institutional quality while there is no significant relationship between the distance to Beijing/Shanghai and trade (see Columns 5 and 6). When simultaneously including all regressors, latitude is positively correlated with institutional quality at the 5-percent level (There is no such positive relationship regarding latitude and trade). We briefly turn to some diagnostic statistics: In all Columns, the F-statistics for our excluded instruments vary between 28 and 36 regarding our institutional variable and between 23 and 49 for our trade variable, and thus far exceed the critical threshold of 10 suggested by Staiger and Stock (1997).⁶ Moreover, the R-squared is reasonably high.

In a next step, we investigate the inter-relationships between institutions and integration, that is we (a) regress trade and geography on institutions and (b) regress institutions and geography on trade.⁷ Our results are presented in Table 4. Panel A presents the OLS regressions and Panel B the 2SLS regressions (the first-stages are provided in Panel C). We find that institutions and integration have a positive effect on each other. In particular, (when controlling for endogeneity) a unit increase in institutions increases the trade share by 0.64 units and a one unit increase in integration increases institutional quality by 0.83 units; both effects are highly significant. In combination with Column (6) of Table 3, we can calculate the total impacts on the log per capita income for institutional quality and integration: A unit (positive) shock to the institution quality equation and a unit shock to the integration equation both ultimately produce an increase in the log per capita income of approximately 0.42.⁸ However, if we only consider the statistically significant effects (at the 5-percent level), trade has no direct impact on income. Thus, the total impact of integration on income consists only of the indirect effect (via institutional quality) and is therefore reduced from 0.42 to 0.26. In contrast, the (statistically significant) total effect of institutions stems solely from the direct effect that it has on income and therefore corresponds to the estimate in Table 3, Column (6), namely 0.32. Although the estimated indirect effect of institutions on trade is statistically significant, this has no impact on income because the direct effect of trade is insignificant.⁹ Overall, institutions trump integration, albeit not to the same extent as in general cross-country studies. For example, Rodrik et al. (2004) show that integration has no effect on income at all once only the statistically significant effects are considered. The main rea- son for this difference is that in our case, trade has a large and highly significant impact on institutional quality.

6 It has to be noted that in the strict sense, this “rule of thumb” only applies to the single endogenous regressor case (as in Columns 1-4 of Table 3). However, as argued by Rodrik et al. (2004), F-statistics far exceeding the threshold (as in our case) are nonetheless a good sign that our results do not suffer from weak instruments.

7 Note that we follow Rodrik et al. (2004) by omitting the feedback effect from per capita income to institutions and integration.

8 In particular, we obtain these values by solving the system of equations implied by Column (6) of Table 3, Panel A, and by Columns (1) and (2) of Table 4, Panel B.

9 Again, using the average of the government efficiency index for the years 2001 to 2010 does not change our results.

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Table 4. Inter-relationships between institutional quality and integration.

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Panel A: OLS Regression

Dependent variable Institutions Integration

Geography (Latitude) 0.4052***

(3.18)

-0.3369**

(-2.31)

(5.09)

R-square 0.5473 0.4828

Panel B: 2SLS Regression

Dependent variable Institutions Integration

Geography (Latitude) 0.4183***

(3.32)

-0.3006**

(-2.05)

(3.44)

(5.26)

R-square 0.5130 0.4739

Test for endogeneity (p-value) 0.0464 0.4067

Number of observations 31 31

Panel C: First-Stage for Institutions and Trade

Dependent variable Integration Institutions

Latitude 0.0648

(0.56)

0.1632 (1.35)

DISTANCE -0.7281***

(-6.03)

COAST 0.8023***

(6.88)

F-statistic 47.367 36.311

R-square 0.6303 0.6208

Note: Dependent variable: Log per capita GDP. The independent variables are all scaled in the sense that they pre- sent deviations from the mean divided by the standard deviation. T-statistics are in parentheses. Significance at the 10, 5, and 1 percent levels are denoted by *, **, and ***, respectively.

4 Conclusion

Our analysis reveals that institutional quality plays an important role regarding the (economic) success of a province in China, even more important than integration (what is probably surprising regarding China’s strong export performance). Moreover, we find that geography and integration both have (positive) indirect effects by influencing institutional quality.

Cross-country rankings reveal that China’s overall institutional quality is still relatively weak (see, for example, Wagner, 2015). The fact that China nonetheless achieved miraculous GDP growth on the national level for such a long period of time might (at least to some extent) stem from the fact that some provinces (primary those located in the East of China) have much

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better institutions than the rest of China: In 2010, the Eastern provinces recorded an average government efficiency index of 0.23 while the inland provinces had a much lower average value of only -0.18 (the mean of all provinces was around zero). In line with this finding, the Eastern provinces recorded on average a much higher average GDP per capita (US$5,777 versus US$2,968). However, in the future, it might probably not be enough to rely on the relatively good institutional quality and the exceptional economic performance of some provinces. Indeed, the Chinese government has already shifted its focus to the inland parts of China by pursuing various initiatives such as “China’s Western Development Strategy” since 1999 as well as the new “One Belt One Road” initiative. Our findings suggest that improving institutional quality is a key factor to successfully exploit the growth potential of the inland provinces.

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12 References

Acemoglu, D., Johnson, S. and Robinson, J. A. (2001) The Colonial Origins of Comparative De- velopment: An Empirical Investigation. American Economic Review 91(5): 1369-1401.

Ahlers, A. L. (2014) Rural Policy Implementation in Contemporary China. London: Routledge.

Bhattacharyya, S. (2004) Deep determinants of economic growth. Applied Economics Letters 11(9): 587-90.

Easterly, W. and Levine, R. (2003) Tropics, germs, and crops: how endowments influence economic development. Journal of Monetary Economics 50(1): 3-39.

Huang, Y. (2008) Capitalism with Chinese Characteristics: Entrepreneurship and the State.

Cambridge: Cambridge University Press.

Meng, X. Qian, N., and Yared, P. (2015) The Institutional Causes of Famine in China, 1959-61.

The Review of Economic Studies 82(4) 1568-611.

Rodrik, D., Subramanian, A., and Trebbi, F. (2004) Institutions Rule: The Primacy of Institutions Over Geography and Integration in Economic Development. Journal of Economic Growth 9:

131-65.

Rothstein, B. (2014) The Chinese Paradox of High Growth and Low Quality of Government: The Cadre Organization Meets Max Weber. Governance – An International Journal of Policy, Admin- istration, and Institutions 28(4): 533-48.

Spolaore, E. and Wacziarg, R. (2013) How Deep Are the Roots of Economic Development?

Journal of Economic Literature 51(2): 325-69.

Staiger, D. and Stock J.H. (1997) Instrumental Variables Regression with Weak Instruments.

Econometrica 65: 557-86.

Ji, K., Magnus, J.R., and Wang, W. (2014) Natural Resources, Institutional Quality, and Econom- ic Growth in China. Environmental and Resource Economics 57: 323-43.

Tang, R., Tang, T., and Lee, Z. (2014) The Efficiency of Provincial Governments in China from 2001 to 2010: Measurement and Analysis. Journal of Public Affairs 14(2): 42-53.

Vincenty, T. (1975) Direct and Inverse Solutions of Geodesics on the Ellipsoid with Applications of Nested Equations. Survey Review 23(176):88-93.

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Wagner, H. (2015) Structural Change and Mid-Income Trap – Under Which Conditions can Chi- na Succeed in Moving Towards Higher Income Status? The European Journal of Comparative Economics 12(2): 165-88.

Wedeman, A. (2012) Double Paradox: Rapid Growth and Rising Corruption in China. Ithaca, NY: Cornell University Press.

Yeoh, E. K.-K. (2012) Ethnic Fractionalization: The World, China and Malaysia in Perspective.

China-ASEAN Perspective Forum 2(1/2): 161-2016.

Zhou, Y. (2014) Role of Institutional Quality in Determining the R&D Investment of Chinese Firms. China & World Economy 22(4): 60-82.

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14 Appendix A

List of provinces for which we have data on the government efficiency (Tang et al., 2014)

Anhui, Beijing, Chongqing, Fujian, Gansu, Guangdong, Guangxi, Guizhou, Hainan, Hebei, Hei- longjiang, Henan, Hubei, Hunan, Inner Mongolia, Jiangsu, Jiangxi, Jilin, Liaoning, Ningxia, Qinghai, Shaanxi, Shandong, Shanghai, Shanxi, Sichuan, Tianjin, Tibet, Xinjiang, Yunnan, Zhejiang

Appendix B

Table B1. 2SLS regression (alternative instruments)

(1) (2) (3) (4) (5) (6)

Panel B: 2SLS Regression

(5.06)

0.7033***

(4.28)

0.4810***

(3.91)

0.4831***

(3.85)

0.4703***

(4.04)

0.4733***

(3.94)

Geography (Latitude) -1.1677

(-1.10)

-0.3824 (-0.44)

-0.3986 (-0.47)

R-squared 0.6102 0.5289 0.6533 0.6554 0.6524 0.6542

Underidentification test (p-value) 0.0109 0.0247 0.0283 0.0223 0.0213 0.0173 Endogeneity test (p-value) 0.0271 0.0177 0.3495 0.3358 0.3547 0.3446

Panel B: First-stage for institutions

FMR -0.4553***

(-2.82)

-0.4104**

(-2.35)

EFI90 -0.4038**

(-2.31)

-0.3942**

(-2.39)

EFI00 -0.4134**

(-2.45)

-0.3995**

(-2.50)

Latitude 1.7325

(0.72)

4.4678**

(-2.00)

4.5281**

(2.15)

F-stat 7.97 5.53 5.35 5.69 5.98 6.27

Observations 26 26 30 30 31 31

Note: Dependent variable: Log per capita GDP. FMR denotes the famine mortality rate (deaths per 1,000 population after the Great Famine in 1960). EFI90 (EFI00) is the ethnic fractionalization index (ranging from 0 to 1, a higher value indicating greater heterogeneity) for the year 1990 (2000). The independent variables are all scaled in the sense that they present deviations from the mean divided by the standard deviation. T-statistics are in parentheses. Signifi- cance at the 10, 5, and 1 percent levels are denoted by *, **, and ***, respectively.

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15 Figure B1. Alterative instruments for institutions

a) Mortality rates (after the Great Famine)

b) Ethnic fractionalization (1990) c) Ethnic fractionalization (2000)

0 20 40 60 80

Mortality rates

0 .2 .4 .6

Ethnic fractionalization 1990

0 .2 .4 .6

Ethnic fractionalization 2000