rafmacalaba/datause-encoder-data huggingface dataset:
4 The following section outlines the theoretical approach. Section III discusses the demand analysis. The data set is Indonesia's 1981 National Socio-Economic Survey (the SUSENAS). Section IV presents the estimated demand parameters, which Section V then uses to construct the spatial cost-of-living indlces. Section VI offers some conclusions.

# II. The Theory of Spatial Cost-of-Living Comparisons

Spatial cost-of-living indices are viewed here as special cases of the 'true cost-of-living indices' proposed by Konus (1939).9 This section discusses aspects of the theory relevant to our present interest.

The cost (or "expenditure") function for a household with characteristics denoted by the vector z is:

# c D C(U,p,Z)

which is the minimum cost of that household achieving utility u when facing the price vector p. It is intrinsic to our interest in spatial cost-ofliving comparisons that different households can face different prices. We shall view the price vector facing the household as a function (though not necessarily continuous) of the household's place of residence, denoted s:

# p = p(s)

# (2)

The spatial cost-of-living index for location or sector s is defined as the cost of a given utility level ur incurred by a reference household with characteristics zr when facing the prices p(s) which prevail at s, relative to the cost incurred by the same household when facing the
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- Li, X., Zhou, Y., Zhao, M., and Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. _Scientific Data_ , 7(1):168.

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49
Financial Regulation and Development and deposit rates with their respective minimum and maximum rates as published by the National Bank of Ethiopia (2013). Although some changes occurred in mid-2009, it is noticeable that over the period of the policy (2011–2013), rates are generally constant, at least in the first moment of their distributions and the respective supports. This is in line with the theoretical model, in which market prices were left constant over the policy change.

Figure 11: Climate and Policy Change <!-- Start of picture text --> 0 50 100<br>Time<br>Ethiopia Addis Ababa<br>26<br>24<br>22<br>20<br>Celsius Degree<br>18<br>16<br><!-- End of picture text --> _Note:_ This figure reports the monthly average temperature in Ethiopia (blue) and Addis Ababa (red) between January 2005 and August 2013. The policy change occurs in April 2011, Time 75, and there does not seem to be any response to weather changes. The data come from the Berkeley Earth project (http://berkeleyearth.org/). Alternative measures of temperatures were used from the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC), which are highly correlated with the current values (0.72 for Addis Ababa and 0.6 for Ethiopia) and highlight similar differences.

Thirdly, climate might be considered problematic, if the policy change occurred over periods of extensive temperature fluctuations, which might affect the industrial productivity and hence financial markets. Although Ethiopia is a country with a strongly heterogeneous climate, close to the equator and with strongly diverse altitudes, all of these characteristics make it suitable for important temperature fluctuations, which might be related to our study. From an analysis of average monthly temperatures for Ethiopia (blue) and Addis Ababa (red) between 2005 and 2013, as shown in Figure 11, we observe that while there is some substantial cyclical variation in temperature, there does not seem to be an exceptional increase in either the level or the volatility of temperatures over the period of the policy change.

In addition to this, natural disasters might lead to a change in the marginal value of public/private infrastructure and affect financial markets. The year of the policy change, 2011, was indeed marked 32
Table 6: Impact on Budgeting and Financial Planning

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Had a Financial<br>Goal in the last<br>6 Months|Plans to Have a<br>Financial Goal in<br>the next 2<br>Months|Discusses<br>Budgetting<br>Matters with<br>Migrant|Prepares Cash<br>Records|Budgeting and<br>Financial<br>Planning:<br>Aggregated|
|Migrant-Only Training (A)|0.044|-0.023|-0.040|0.014|-0.001|
||(0.047)|(0.034)|(0.040)|(0.024)|(0.025)|
|Family-Only Training (B)|0.052|0.033|-0.016|0.047*|0.029|
||(0.045)|(0.037)|(0.040)|(0.027)|(0.026)|
|Migrant and Family Training (C)|0.055|0.060|0.025|0.091***|0.058**|
||(0.048)|(0.039)|(0.040)|(0.030)|(0.026)|
|R-squared|0.064|0.096|0.005|0.015|0.025|
|Sample Size|1133|1133|1133|1133|1133|
|Number of Clusters|394|394|394|394|394|
|Mean of Dependent Variable in Control Group|0.526|0.226|0.767|0.059|0.395|
|**Test: A**-**B**=**0 (p**-**value)**|0.855|0.107|0.561|0.221|0.248|
|Test: A-C = 0 (p-value)|0.821|0.024|0.108|0.011|0.030|
|Test: B-C = 0 (p-value)|0.956|0.498|0.306|0.171|0.298|
|Test: A = B = C = 0(p-value)|0.594|0.112|0.432|0.015|0.093|

This table presents pooled regression analysis across three follow-up survey rounds for measures of financial planning behavior. Column (5) is an aggregate measure of Columns (1) - (4). Separate regression analysis by follow-up survey round is presented in Appendix Table A6. All regressions include survey round dummies. Standard errors in parentheses, clustered at the individual level. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
# Research Conpaonents of World Dank Education Projects, since 1982.

|Area<br>l<br>I~<br>~ ~|1<br>Country and Loan/Credi<br>l<br>h~~~~umbel'<br> <br>_______<br>I<br>__|At proved<br>____|Research Cqmponents<br>_|Current Status of Studies<br>including<br>Compteted Doc--nets/Data<br>4C_.-lte|
|---|---|---|---|---|
|||||(vi) MOE (1987). "Production and utitization of<br>teachers." (doc. 9 F13375)|
|ASIA|Vanuatu CR 169L-VA|1989|Primary and Secondary Education.<br>Studies in: (i) the financing of education<br>(ii)<br>teacher incentive<br>system (sataries,<br>recruitment,<br>teacher deployment etc)<br>(Para 3.14 p.17;|Studies not initiated.|

49
Figure 1. Share of Female Ownership and Management in Firms integrating into GVCs <!-- Start of picture text --> 100%<br>90% 64% 64% 73% 74% 87% 89% 87% 88%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>36% 36%<br>20% 27% 26%<br>10%<br>0% 13% 11% 13% 12%<br>GVC1 GVC2 GVC3 GVC4 GVC1 GVC2 GVC3 GVC4<br>(a) Female Ownership (b) Female Top Manager<br>Yes No<br><!-- End of picture text --> Source: Constructed by the authors using the WBES.

Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

When we look at the relationship between female participation, firm size, and GVC integration, two remarks are worthwhile. First, for large firms and the strict definition of GVC, females (whether they are owners or managers) tend to be under-represented. Second, generally, for less restrictive definitions of GVC, we do not observe significant differences between small, medium, and large firms when it comes to female ownership and management. As for regional differences, Figure 3 shows that females are doing better in terms of ownership compared to management when it comes to GVC integration. Indeed, for all GVC definitions in different regions, the share of firms that are part of GVCs and that are owned by females is greater than the one of firms managed by females with a slightly lower figures for the most restrictive definition (GVC4) as it is shown in Figure 2. In addition, for the most restrictive definition, EAP followed by SAR are the best performers whereas MENA and LAC are the worst in terms of female ownership and management (see Figure 3). This is due to the low labor cost that helps develop value chains, especially in the textile and garments sector (Kumar, 2017). Indeed, South Asia has the second-highest level of GVC exports out of total exports among developing regions, chiefly thanks to final and intermediate apparel products (Lopez-Acevedo et al., 2017). This is why, as it was mentioned before, the MENA region’s problem of women’s participation is also reflected in their integration into GVCs as owners or managers.

6
<u>Table 2. Percent of Firms Choosing Their Biggest Obstacle (%)</u>

||All Countries|EAP|ECA|LAC|MENA|SAR|SSA|
|---|---|---|---|---|---|---|---|
|Access to finance|14.2|13.6|9.4|9|12.1|12.1|23.9|
|Land|2.9|5.6|1.7|1.4|1.5|4.6|4.3|
|Business licensing and permits|2.5|2.8|2.7|4.4|3.5|1|1.4|
|Corruption|6.5|6.6|3.8|9.7|9.7|10.5|7.3|
|Courts|0.9|0.8|1.2|1|1|0.4|0.6|
|Crime, theft, and disorder|2.9|3|1.7|6.7|1.6|2.7|3.3|
|Customs and trade regulations|3.3|2.9|2.6|4.2|3.4|1.8|4.5|
|Electricity|8.4|6.5|3.5|5.4|10.9|21.8|13.9|
|Inadequately educated workforce|10.3|7.6|19.7|7.8|8.8|2.9|1.8|
|Labor regulations|3.5|2.9|5.3|4.5|2.4|4.8|1|
|Political instability|11.9|15.3|9|11.1|19.5|20.7|10.8|
|Practices of the informal sector|11.5|14.4|11.6|17.4|6.8|4.6|10.6|
|Tax administration|3.9|2.4|4.5|4.4|2.7|2.2|4.3|
|Tax rates|13.2|10.6|18.6|10.9|11.7|6|9.7|
|Transportation|3.8|5|4.8|2.2|4.3|3.7|2.6|

Source: Constructed by the authors using the WBES. Note: EAP stands for East Asia and Pacific, ECA Europe and Central Asia, LAC Latin America and the Caribbean, MENA Middle East and North Africa, SAR South Asia and SSA Sub-Saharan Africa.

When the gender dimension is considered, some obstacles turn to be more impeding than others. As such barriers are primarily faced by owners and managers, we do not include in this part female employees. Thus, Figure 5 shows that access to finance, tax rates, practices from informal competitors, political instability, inadequately educated labor force and corruption are the highest obstacles for firms that either owned or managed by women. In addition, electricity turns to be impeding for firms that are owned by women. This is why, we are going to focus only on these barriers in the empirical part, as it will be shown later.<sup>4</sup> Figure 5. Percent of Firms Choosing Their Biggest Obstacle and Gender (%) (a) Female owners (b) Female managers <!-- Start of picture text --> Courts Courts<br>Access to land Business licensing<br>Business licensing Access to land<br>Trade regulations Trade regulations<br>Tax administration Electricity<br>Crime, theft and disorder<br>Crime, theft and disorder<br>Transportation Tax administration<br>Labor regulations<br>Labor regulations<br>Corruption<br>Transportation<br>Electricity<br>Corruption<br>Political instability<br>Inadeq. educ. labor force<br>Inadeq. educ. labor force<br>Political instability<br>Informal comp.<br>Tax rates Informal comp.<br>Access to finance<br>Access to finance<br>Tax rates<br>0% 5% 10% 15% 20%<br>0% 5% 10% 15% 20%<br><!-- End of picture text --> Source: Constructed by the authors using the WBES.

4 The results of other barriers are available upon request.

9
**Table 1: The size of the Shadow economy in the New EU Member countries and Croatia (Shadow economy as a percentage of official GDP)**

|Countries|1999/00|2006/07|
|---|---|---|
|1. Slovakia|18.9|17.4|
|2. Czech Republic|19.1|18.2|
|3.Hungary|25.1|24.4|
|4. Slovenia|27.1|26.4|
|5.Poland|27.6|26.5|
|6. Lithuania|30.3|28.2|
|7.Estonia|38.4|36.0|
|8. Croatia|33.4|36.5|
|9. Latvia|39.9|37.1|
|10.Romania|34.4|37.4|
|11. Bulgaria|36.9|39.4|
|Unweighted average|30.1|29.8|
|Unweighted average<br>of 21 OECD<br>countries|16.8|14.8<br>(2004/2005)|

_Source:_ Schneider 2009.

10. In an international business survey for the IMD World Competitiveness Yearbook 2009, people were asked about the importance of tax evasion for hampering business activity. Among NM-EU countries, the level of tax evasion was seen as least problematic in the Czech Republic (ranked 11<sup>th</sup> among 57 countries), followed by the Slovak Republic (ranked 17<sup>th</sup> ), while in Hungary (ranked 53<sup>rd</sup> ), Romania (ranked 55<sup>th</sup> ) and Croatia (ranked 56<sup>th</sup> ) tax evasion was seen as most problematic (Table 2). It is unclear, however, if the survey participants in the various countries used the same unit of measurement when assessing tax evasion. For example, it is possible that in one country people are more sensitive to the problem of tax evasion than in another country, even if the actual level of tax evasion is lower. Some caution is therefore needed when comparing the country ranking within the NM-EU countries and also in comparison with other countries.

11. The EU Commission in the European Employment Observatory has published estimates of informal sectors, which are based on National Accounts statistics (EC 2007). These estimates confirm that informal sectors are relatively large in most of the new EU countries. According to these estimates, informal sectors are smaller than estimated by Schneider and the ranking across the NM-EU countries is also different. These estimates reveal that Estonia has the smallest informal sector among the new EU member countries and it is also lower than in some old EU member countries, including Sweden. Estonia is followed by the Czech Republic, Slovakia, Poland, Lithuania, Slovenia, Hungary, Latvia, and Bulgaria (Table 3).

**12.** Applying a regression model, Albu (2007) estimated for Romania the average share of informal income in total household income at between 17 and 18 percent in 2005, down from around 22 to 23 percent in 2000, which is also lower than the estimate by Schneider and close to the estimate of the EU study. It has also been estimated that in the Czech Republic, Hungary, and Poland around one tenth of the workforce are typically not reported for tax purposes and in Slovakia and Lithuania this share is 6 percent (OECD, 2008, EC 2007).

4
25 methods used were flawed. There are good reasons for this lack of success. First, studies that concentrate on the effects of individual health on simple output measures of labor productivity (e.g. the effects of anemia on labor productivity of sugarcane workers) might welL be missing the much larger potential effect that better health would have through contributing to entrepreneurship, willing sas to invest (including in education, job search and so on), and creativity in general; these effects would be extraordinarily difficult to capture empirically. Second, as noted by Over et. al. (1989), in a recent thoughtful summary of work on the consequences of health for development, negative consequences of poor health may be difficult to document empirically because households and entire communities adopt coping mechanisms when illness strikes.<sup>29</sup> Coping may reduce the costs of poor health, but may also itself entail costs. These costs may not be directly reflected in lost family income or reduced productivity. Costs may instead be reflected in reduced health of other family members, reduced consumption or foregone investment, reduced leisure of other family members, or even break-up of families. But costs such as these, especially costs imposed on other family members, have not generally been studied and are difficult to study without specially dcsigned data.<sup>30</sup> 29 For example, households that include sick members are more likely to receive private transfers from other households, as documented by Cox and Jimenez (1989).

30 Salkever (1982) estimated using U.S. data on families with handicapped children that the hidden costs included at least an estimated $1 billion (in 1975 dollars) of lost family income due to reduced maternal working hours. This appears to be one of the few such studies of the costs of foregone labor of parents of disabled children.
# Research Comporwnts of World Bank Education Projects, since 1982.

|Area|Country and Loan/Credit<br>Number|Approved|<br>Research Components|Current Status of Studies,<br>inctuding<br>CoWpIeted Documents/Date|
|---|---|---|---|---|
||||(il)<br>Tracer study of first<br>wndsecond class<br>graduates(para. 3.25<br>p. <sup>2 9 </sup>).||
|EIENA|Portugal<br>LH 2867-PO|1988|Technology Education Credit.<br>Evaluation<br>of<br>project<br>to introduce<br>computers to 40<br>secondary and 10 priury<br>schools, due fMrch<br>1994 (para 2.27, p.20)|Notyet initiated.|
|||||General coment:<br>There seems to be a reluctance<br>to evaluate,<br>especiatly<br>whenusing control<br>groups.|
|ENEEA|Tunisia<br>LU 2230-TUH|1983|Fifth<br>Education.<br>Agricuttural<br>education<br>studies:<br>(i)<br>analyze agricultural<br>mwpouer<br>stacks;<br>(ii)<br>determination<br>of contentandorientation<br>of technical<br>training<br>of instructors;<br>(iii)<br>evaluation<br>of agricultural<br>educationand<br>training<br>programs (para.2.04p.13).<br>(iv)<br>Studies of technician<br>level training<br>for<br>industry:<br>(a) assess infrastructure;<br>(b) evaluate<br>feasibility<br>of intervention<br>(pra<br>2.25p.18).|Diagnostic<br>part of studies<br>completed by the end of<br>1988. Conclusionsandrecomendations<br>untikely<br>to<br>becomeavailable<br>given the fact that<br>the study is<br>three years behind schedule;momentumseem to be<br>lost.<br>Studies of technician<br>level training<br>for<br>industry<br>completed.<br>TORsandoutlines<br>of proposed projects<br>**available**<br>in docsU350.016 (a) - (f).<br>(iii)<br>Ninistere<br>d'Agriculture<br>(1988).<br>"Adequation<br>formtion:<br>emploi danstedomine<br>agricole."<br>(doc.#H17138-44)|
|ENEIEA|Tunisia<br>LN 3054-TUN|1989|EducationandTraining<br>SectorLoan. No<br>research component.|Studies not yet initiated.|
|ENEMA<br>EENEA|Turkey LN 2399-TU<br> TurkeyLM2536-TU|1984<br>1985|Industrial<br>Training<br>Project.<br>Future project<br>development component (para. 2.14(d)<br>p.13).<br>Industrial<br>Schools Project.<br>(i)<br>Tracer study of<br>graduatesfrom four industrial<br>schools (due Dec||Project<br>iwopossl completed.<br>Alsoavailablein files:<br>"Research on the existing<br>post-secondary<br>school<br>systemandits<br>implications<br>for theVOK/UBtechnician<br>training<br>project."<br>Tekesis (1985)(doc.*<br>352.8;38)<br>(i)<br>Completion date postponed to 12/92|

55
<!-- Start of picture text --> Current Account Balance (% of GDP)<br>12.0<br>10.0<br>“<br>60<br>“<br>20<br>a0 Oe a)<br>See<br>“<br>Source: IMEAFS HEAP mEAPex China MECA MMNA mLAC MSAR MSSA<br><!-- End of picture text --> <!-- Start of picture text --> External Debt (°3 of Exports of Goods and Services<br>200.0<br>180.0160.0 |rH er<br>woin ee<br>coool ie i i a<br>mFEEt se ee<br>TOUTE PT gy<br>a<br>bee Fl Uhh hf<br>40.0<br>o DELETE<br>*s ?§ *& &SF eCS *SF -C~ *<br>source: World Benk WD! wEAP ECA wna wLAC wSuR mess<br><!-- End of picture text -->
# **Warlords, State Failures, and the Rise of Communism in China**<sup>**1**</sup>

Zhangkai Huang Tsinghua University Meng Miao Remin University of China Yi Shao Peking University Lixin Colin Xu World Bank

**Key words** : famine, warlord, geography, inequality, communism. **JEL code** :  H41, P26, N15, N45

1 We appreciate the helpful suggestions from Bob Cull, Ducan Fisher, Steven Nafziger, Martin Reiser, Yuhua Wang, and seminar participants at Tsinghua University, Peking University, the Cheung Kong Graduate School of Business, The Economic History Society Annual Conference 2021, The Quantitative China Studies Seminar at New York University 2021. The views in this paper are the authors’ own, and do not implicate the World Bank or the country it represents. Zhangkai Huang is available at huangzhk@sem.tsinghua.edu.cn, Tsinghua University, School of Economics and Management; Meng Miao, miaomeng@ruc.edu.cn, Renmin University of China, School of Finance; Yi Shao, shaoyi@pku.edu.cn, Peking University, Guanghua School of Management; Lixin Colin Xu (corresponding author), lxu1@worldbank.org, the World Bank.
In this setting with occasionally binding constraints, we cannot assume _ψ_ to be differentiable everywhere. Thus, in theory, we cannot derive the first-order conditions of this problem since they would imply derivatives of _ψ_ . But since, in practice, _ψ_ is approximated by a spline, which is differentiable everywhere, we can solve the dynamic programming problem numerically by solving the first-order conditions of the approximated problem:

# **D Numerical algorithm**

The numerical algorithm used here is inspired by Fackler (2005) and Miranda and Glauber (1995). It is a projection method with a collocation approach. Since several models are solved, we present here a general method that can be applied to all of them. Following Fackler (2005), rational expectations problems can be expressed using three groups of equations. State variables _s_ are updated through a transition equation: where _x_ are response variables, _e_ are stochastic shocks, and next-period variables are indicated with a dot on top of the character. Response variables are defined by solving a system of complementarity equilibrium equations: Response variables can have lower and upper bounds, _<u>x</u>_ and _<u>x</u>_ <u>,</u> which can themselves be function of state variables.<sup>14</sup> For generality, equilibrium equations have been expressed as complementarity problems. In cases where response variables have no lower and upper bounds, equation (67) simplifies

> 14It is the case in equation (39) where lower and upper bounds on _P_ t _t_<sup>arefunctionsof</sup><sup>_P w_</sup> _t_<sup>.</sup>

32