rafmacalaba/datause-encoder-data huggingface dataset:
Chinese provinces, for instance, is twice that of coastal areas (Cai et al. 2011). To understand the effects of tax burden on firm performance, one must take advantage of within-country variations, which holds the legal system (and therefore _de jure_ institutions) constant.<sup>5</sup> Moreover, some key measures of a country can only be obtained with firm-level data (Bigsten and Soderbom 2006). For example, researchers have often used dispersion of productivity within an industry of a country to capture industry-level competition, which can be constructed only by exploiting firm-level data. And, to understand what types of firms are credit-constrained or particularly vulnerable to government expropriation, only firm-level data can be relied on. Unfortunately, very little comparable firm-level data were available in developing countries—until now.

The past 10 years has witnessed an explosion of firm-level evidence on the effects of business environments, an explosion partly due to the data efforts of the World Bank, mainly the World Bank Enterprise Surveys (also known as the Investment Climate Surveys). These data efforts lead to consistent measures of business environments, which, in turn, usher in a substantial new literature on how business environments affect development at the firm level. To push the research forward, it is helpful to review what lessons have been learned.

In this paper, I survey evidence on how business environments affect economic development. Given the vastness of this task, one has to be selective. Thus, I focus on firm-level research related to the World Bank Investment Climate Surveys—with occasional discussions of outside firm-level research that are particularly relevant about the effects of business environments. This would automatically leave out studies based on the complementary Doing-Business Survey. A benefit of this omission is that we can focus only on _de facto_ institutions or regulations (in the case of Investment Climate Surveys) while ignoring new issues related to _de jure_ institutions or regulations (in the case of Doing-Business Surveys) (Hallward-Dreimier, et al. 2010). Several topics are too broad to cover, but there are excellent surveys already and not much is lost in skipping them. The

> at the macro level, in which case, it is only natural to rely on such data for identification. Often, studies based on macro data are useful first steps in our quest for understanding a specific topic. For instance, Knack and Keefer (1995, 1997) rely on macro indicators to show the potential importance of institutions and social capital, which then usher in numerous micro studies to examine the issues more closely.

> 5 Note that while the _de jure_ institutions are held constant within a country, the _de facto_ enforcements of institutions across regions are not (Hallward-Dreimer et al. 2010). Only to the extent that _de jure_ institutions capture a significant source of variations for _de facto_ institutions, relying on within-country variations mitigate the omitted variable bias due to the lack of control for institutions.

3
# **1 Introduction**

It is widely recognized that international capital flows have nontrivial consequences for macroeconomic outcomes. The history of financial crises taught us that the vulnerability to external shocks can vary greatly depending on which economic sector(s) are on the receiving side of capital inflows. For example, sovereign debt proved to be the Achilles’ heel in the Latin American crises, while private sector debt financed by capital inflows was the key source of fragility in the Asian financial crises. During the latest global financial crisis, in the US, the culprit was the domestic household debt held by US and global banks. By contrast, in the European countries, sovereigns’ and banks’ external borrowing played the central role, which culminated in a sudden stop. Yet, gross capital flows by _sector_ have received no attention in the empirical literature due to lack of data for a large set of countries and long time periods at the business cycle frequency. Our paper fills this gap.

Our paper’s contributions are twofold. First, we introduce a new comprehensive dataset on _gross_ capital inflows and outflows at the _quarterly_ frequency starting 1996 for a balanced panel of 85 countries for inflows and 31 countries for outflows, decomposing both inflows and outflows by borrowing and lending sectors. Second, using this dataset we document four new stylized facts. Our facts are as follows: First, the co-movement of capital inflows and outflows is solely driven by flows to and from banks. Second, procyclicality of capital inflows is driven by banks and corporates in all the countries, whereas sovereigns’ external liabilities move acyclically in advanced countries and countercyclically in emerging markets. Third, procyclicality of capital outflows is driven by advanced countries’ banks and emerging countries’ sovereigns. Fourth, capital inflows and outflows decline for banks and 1
<!-- Start of picture text --> Figure 4: Mean Survival Time based on Fixed Costs by Sector<br>Construction<br>Retail<br>Other Services<br>Manufacturing of Food and Beverages<br>Manufacturing of Metals and Fabricated Metal Products<br>Other Manufacturing<br>Manufacturing of Textiles, Apparel, and Leather Products<br>Manufacturing of Machinery, Computing, and Electrical ...<br>Manufacturing of Chemical, Plastic, and Mineral Products<br><!-- End of picture text --> <!-- Start of picture text --> 16 20 24<br>Weeks<br><!-- End of picture text --> <!-- Start of picture text --> 28 32 36 40<br><!-- End of picture text --> <!-- Start of picture text --> 0 4 8 12<br><!-- End of picture text --> <!-- Start of picture text --> Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345<br><!-- End of picture text --> _Note_ : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345 The differences across countries between the average and median survival time persist (Figure 5). While the median business in Turkey is estimated to run out of cash in 9 weeks, businesses on average have the means to survive for about 20 weeks, or nearly twice as long. Results for other countries are more similar: in both Kazakhstan and Ukraine the median and the average firm will run out of cash in about 10 weeks.

<!-- Start of picture text --> Figure 5: Mean Survival Time based on Fixed Costs by Country<br><!-- End of picture text -->

**Figure 5: Mean Survival Time based on Fixed Costs by Country**

<!-- Start of picture text --> Ukraine<br>Kazakhs ...<br>Greece<br>12 16 20 24<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> 28<br><!-- End of picture text --> <!-- Start of picture text --> 32<br><!-- End of picture text --> <!-- Start of picture text --> 36<br><!-- End of picture text --> <!-- Start of picture text --> Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345<br><!-- End of picture text --> _Note_ : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345 <!-- Start of picture text --> 40<br><!-- End of picture text --> 11
extended vector of controls. _F_ -tests with eight degrees of freedom show that the higher-order interaction does not improve the fit – that is, that _β_ 2 _,_ 2 through _β_ 2 _,_ 5 and _β_ 3 _,_ 2 through _β_ 3 _,_ 5 are all zero – are rejected, for example with _F_ = 8 _._ 50, _p <_ 0 _._ 0001 in the case of country fixed with no controls.

Figure 1 shows that although adding controls and changing the fineness of fixed effects shifts the estimated function vertically, which changes the _level_ of the sanitation-health – gradient, the shape of the function that is, the dependence of the health-open defecation gradient on population density – remains similar. Across model specifications, the association between open defecation and infant mortality, for example, is about twice as steep in places with the average population density of Bangladesh (or in the similarly dense, largely rural Indian states of Uttar Pradesh and Bihar) as it is in places with the average population density of sub-Saharan Africa. Moreover, the function curves convexly, so the effect of population density is even greater at higher levels of population density. Because the average population density in the Bangladesh data used in section 4 is especially high, these estimates predict a particularly steep sanitation-health gradient and large interaction with population density in that context.

# **4 Population density, sanitation and child height in Bangladesh**

Section 3 showed that higher population density is robustly and uniquely associated with a steeper sanitation-health gradient. This section uses variation across time and place in local – open defecation within Bangladesh a country where open defecation has fallen sharply – over recent decades in order to provide further evidence for the internal validity of the sanitation-population density interaction.

Bangladesh is an apt case study to further interrogate the sanitation-population density interaction for two reasons. First, unlike many DHS surveys, the Bangladesh DHS surveys 17
# **Table 3 Average Distance Across Asset Classes**

This table presents the average distance across asset classes, for each fund type. First, we calculated the distance of portfolio shares across asset classes for all PFA pairs and then we averaged the value of the distance across all PFA pairs.

||Average Distance|Across Asset Classes|
|---|---|---|
||December 2005|2003-2005|
||(1)|(2)|
|Fund A|7.4%|10.6%|
|Fund B|7.4%|10.0%|
|Fund C|9.7%|10.0%|
|Fund D|16.1%|12.7%|
|Fund E|15.1%|13.7%|
# Policy Research Working Paper Series

|T|itle|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS1478 <br>|Promoting Growth in Sri Lanka:<br>Lessons from East Asia|SadiqAhmed<br>Priya Ranjan|June 1995|A. Bhalla<br>82168|
|WPS1479<br>|IsThere<br>a Commercial Case for<br>Tropical Timber Certification?|Panayotis N. Varangis<br>Rachel Crossley<br>Carlos A. Primo Braga|June 1995|J. Jacobson<br>33710|
|WPS1480|Debt as a Control Device in<br>Transitional Economies: The<br>Experiences of Hungary and Poland|Herbert L. Baer<br>Cheryl W. Gray|June 1995|G. Evans<br>85783|
|WPS1481|Corporate Control in Central Europe<br>=<br>and Russia: Should Banks Own<br>Shares?|Peter Dittus<br>Stephen Prowse|June 1995|G. Evans<br>85783|
|WPS1482|A Measure of Stock Market<br>Integration for Developed and<br>Emerging Markets|Robert A. Korajczyk|June 1995|P. Sintim-Aboagye<br>38526|
|WPS1483|Costa Rican Pension System:<br>Options for Reform|Asli Demirgtig-Kunt<br>Anita Schwarz|June 1995|P. Sintim-Aboagye<br>38526|
|WPS1484|The Uruguay Round and South Asia:<br>An Overview of the Impact and<br>Opportunities|Nader Majd|July 1995|J. Ngaine<br>37947|
|WPS1485|Aggregate Agricultural Supply<br>Response in Developing Countries:<br>A Survey of Selected Issues|Maurice Schiff<br>Claudio E. Montenegro|July 1995|J. Ngaine<br>37947|
|WPS1486|The Emerging Legal Framework for<br>Private Sector Development in<br>Viet Nam’s Transitional Economy|Pham van Thuyet|July 1995|G. Evans<br>85783|
|WPS1487|Decomposing Social Indicators Using<br>Distributional Data|Benu Bidani<br>Martin Ravallion|July 1995|P. Sader<br>33902|
|WPS1488|Estimating the World atWork|Deon Filmer|July 1995|M. Geller<br>31393|
|WPS1489|Educational Attainment in Developing<br>Countries: New Estimates and<br>Projections Disaggregated by Gender|Vinod Ahuja<br>Deon Filmer|July 1995|M. Geller<br>31393|
|WPS1480|Trade Reform Design as a Signal to<br>Foreign Investors: Lessons for<br>Economies in Transition|Eric Bond<br>Steve Chiu<br>Antonio Estache|July 1995|A. Estache<br>81442|
|WPS1491|Equilibrium Incentives for Adopting<br>CleanerTechnology Under Emissions<br>Pricing|PeterW. Kennedy<br>Benoit Laplante|August 1995|E. Schaper<br>33457|
where public facilities were located in close proximity to large local populations annual caseloads were low (World Bank, 1994b). Based on detailed case studies the study identifies two principal reasons. First, many public facilities were lacking equipment, medicines, and appropriate health workers. Second, and more importantly, detailed assessments on the way resources were combined to produce health outcomes showed that poor functioning contributed to a large degree to the low public facility utilization. Respondents in one case-study felt that public facilities were of low quality while "[tihey were confident that they could get considerate and unrushed care in a pleasant and informal setting in the private practice of doctors, bidans [midwives] and nurses" (World Bank, 1994b).

A recent study in El Salvador found remarkably similar results. Respondents consistently complained about the low quality of public health posts and units, especially relative to higher level services from health centers and hospitals. Typical answers were along the lines of: Health posts operate only twice a week. Consultation is only until noon. The doctor is not always there. Sometimes only the nurse assistant is present. Waiting time is three hours on average. Only those who arrive by 8 get a consultation (World Bank, 1997).

In contrast to, for example, one typical respondent in El Pinar "[The health center at] La Palma is a little hospital with very good services. It is well equipped. The fee is only c/3 for consultation and sometimes medication" (World Bank, 1997) The study in El Salvador assessed the impact of the very lowest level intervention, the health promoter who lives within the community and is supposed to be the first level of information and referral. The assessment found that focus group respondents had very little use for these promoters, and regression analysis found that health promoters from the public sector had little 23
# **Table 1: Summary Statistics, by Countrv**

Number of Bankruptcies was collected from sources listed in Appendix<sup>1.</sup> _BNKRPT_FRM_<sup>is the</sup> ratio of the number of bankruptcies to the number of firms.<sup>RealGDPpercapitainUS$</sup> _(RGDPPC),_ I-year growth rates of GDP _(GGDP),_ and real interest rates<sup>_(RINTEREST)_arefrom</sup> the International Financial Statistics. Statistics are reported as the average over<sup>theperiodof</sup> available years.

|**Country**|**Available**<br>**Years**|**Numberof**<br>**Bankruptcies**|BNKRPT_<br>**FRM(%)**|**RGDPPC**<br>(US$,**1995)**|**GGDP**<br>**(%)**|**RINTEREST**<br>**(%)**|
|---|---|---|---|---|---|---|
|_ARGENTINA_|92-99|2144.38|0.12|7,567.47|6.37|4.26|
|_AUSTRALLA_|90-99|5166.50|2.10|21,120.16|4.47|8.50|
|_AUSTRIA_|90-99|2065.10|1.33|28,774.78|2.58|3.73|
|_BELGIUM_|90-99|4850.20|2.59|26,784.65|2.10|3.32|
|_CANADA_|90-98|12696.67|2.96|19,280.07|1.93|6.29|
|_CHILE_|90-99|88.60|0.28|4,195.95|7.66|13.22|
|_COLOMBIA_|96-99|225.50|0.16|2,411.81|2.81|19.99|
|_CZECHREPUBLIC_|92-96|1729.40|1.49|4,755.23|-0.78|-2.33|
|_DENMARK_|90-99|2375.90|1.53|33,582.58|1.93|7.77|
|_FINLAND_|90-98|5106.11|4.14|25,587.28|1.39|6.34|
|_FRANCE_|90-99|51671.75|2.62|26,293.72|1.65|6.64|
|_GERMANY_|92-98|21152.57|1.03|29,782.87|1.46|8.43|
|_GREECE_|90-94|857.4|0.29|10,987.33|1.20|9.18|
|_HONGKONG_|90-98|1205.44|0.55|22,610.76|4.98|2.33|
|_HUNGARY_|92-96|8425.40|1.99|4,252.39|-2.22|4.74|
|_IRELAND_|90-99|788.60|2.74|17,580.34|6.47|5.23|
|_ITALY_|90-96|8663.14|0.54|18,431.58|1.61|7.64|
|_JAPAN_|90-99|14000.60|0.22|41,709.64|2.18|3.61|
|_NETHERLANDS_|90-99|4154.56|1.30|9,780.62|5.75|3.29|
|_NEWZEALAND_|93-98|716.00|3.67|27,106.94|3.44|5.63|
|_NORWAY_|90-98|3546.56|1.83|15,981.80|3.54|9.42|
|_PERU_|93-99|145.14|0.05|31,786.32|3.44|8.02|
|_POLAND_|90-96|3319.57|0.23|2,180.54|4.92|26.87|
|_PORTUGAL_|91-99|516.44|0.08|2,984.64|0.99|-5.34|
|_RUSSIA_|95-98|2770.75|0.31|10,791.99|2.73|7.95|
|_SINGAPORE_|90-99|227.80|3.06|2,274.35|-4.80|51.49|
|_SOUTHAFRICA_|90-99|2918.60|4.62|21,414.20|8.06|4.03|
|_SOUTHKOREA_|90-98|162.67|0.17|3,944.43|1.38|6.54|
|_SPAIN_|90-99|518.60|0.02|14,764.81|2.59|5.91|
|_SWEDEN_|90-99|13917.10|7.61|27,088.16|1.36|7.57|
|_SWITZERLAND_|90-98|9212.56|3.33|44,345.91|1.08|3.71|
|_THAILAND_|90-99|346.73|0.12|2,480.25|6.03|7.34|
|_TURKEY_|98-99|1496.00|0.86|3,149.98|5.31|-5.44|
|_UNITEDKINGDOM_|923-98|46583.83|1.85|18,942.57|2.61|3.75|
|_UNITEDSTATES_|90-99|55752.60|3.65|27,344.91|2.97|5.56|

27
41 _Social Capital, Household Welfare and Poverty in Indonesia_

**Table 10: Social Capital and Asset Accumulation**

||**Asset Ow**|**nership!'**|**Increasing**|**Savings2'**|
|---|---|---|---|---|
|Intercept|-0.1972|(1.26)|-|_|
|**_Social Capital Dimensions_**|||||
|Number of Memberships|0.0023|_(0.50)_|0.0101|(2.86)|
|Membership in Financial Associations|-||0.0594|(2.13)|
|Heterogeneity Index|0.0018|(2.23)|0.0002|(0.31)|
|Meeting Attendance|0.0002|(0.08)|-0.0044|(1.85)|
|Index of Participation in Decision Making|0.0018|(4.30)|0.0003|(0.80)|
|Cash Contribution Score|-0.0025|(0.44)|0.0009|(0.21)|
|Work Contribution Score|0.0007|(0.38)|-0.0019|(1.09)|
|Community Orientation|-0.0006|(0.93)|-0.0007|(1.49)|
|Household Size|0.0243|(3.61)|-0.0009|(0.18)|
|Years of Education|0.0527|(7.83)|-0.0024|(0.50)|
|Asset Score||-|0.0497|(2.22)|
|Female Head of Household|-0.0909|(1.87)|0.0231|(0.59)|
|Age of Head of Household|0.0280|(4.28)|-0.0057|(1.13)|
|Age of Head of Household Squared|-0.0002|(3.42)|0.0000|(0.81)|
|Farmer Household|-0.3116|(7.74)|-0.0631|(2.21)|
|Jawa Tengah|-0.2092|(4.72)|0.2267|(5.13)|
|Nusa Tenggara Timur|-0.6212|(13.37)|0.1161|(2.70)|
|Number of Observations|1137||1137||
|R-squared|0.37||_||
|F-statistic|46.8||_||
|Log Likelihood|_||-430.3||
|Chi-squared|||140.7||
|<br>Probability > Chi-squared|||0.00||

Notes: 1. OLS model with asset score (principal component weights) as dependent variable; reported are coefficients and t-values based on robust standard errors.

2. Probit model of households who increased savings in the past year; reported are probability derivatives at the mean of the explanatory variables (or for 0 to I change in the case of dummy variables) and z-srores based on robust standard errors.

Another aspect of asset accumulation is the ability to have savings. While the LLI questionnaire did not record the amount of savings, it did ask whether households had been able to increase savings in the past year. Households with more memberships in local associations were significantly more able to do so than others (Column 2, Table 10). The effect was especially strong from memberships in credit and savings associations indicating that such organizations do in fact achieve their professed objective. The initial wealth position of the household also mattered, as richer
# **ANNEX 1: Summary Statistics**

|**Table A1.1 Summary Statistics** **(PFM**|**and Cou**|**ntry Characte**|**ristics)**||
|---|---|---|---|---|
|**Variable**<br>**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|**PEFA**<br>120|2.4|0.51|1.31|3.55|
|**GDP per capita (log)**<br>112|8.03|0.93|5.82|9.93|
|**Growth (per capita)**<br>118|3.3|3.02|-1.34|20.28|
|**Population (log)**<br>120|15.53|2.14|9.19|20.87|
|**SIDS**<br>120|0.24|0.43|0|1|
|**Resource**<br>120|0.21|0.41|0|1|
|**Tax**<br>94|16.81|6.75|0.65|37.8|
|**Aid**<br>116|9.16|13.32|-0.11|106.58|
|**Political Stability (WGI)**<br>119|3.75|2.50|0.03|9.82|
|**Regime (Freedom House)**<br>118|5.47|2.72|0|10|
|**Programmatic Parties**<br>105|0.27|0.24|0|1|
|**_Additional or alternative variables_**|||||
|**CPIA-13**<br>135|3.41|0.72|1|5.5|
|**Fragility**<br>120|0.23|0.42|0|1|
|**FS Index (Foreign Policy)**<br>83|83.09|14.44|41.2|111.1|
|**Regime (Polity IV)**<br>102|6.53|2.69|0.5|10|
|**Aid (Technical Cooperation)**<br>81|1.16|1.14|0.03|4.74|
|**Aid (CPA)**<br>81|9.86|24.93|0.05|210.18|
|**HIPC**<br>120|0.05|0.22|0|1|
|**Fiscal Shock**<br>49|0.76|1.13|0|4|
|**Growth Shock (4 year lag)**<br>120|0.1|0.3|0|1|
|**Growth Shock (5 year lag)**<br>120|0.03|0.16|0|1|

Note: These summary statistics are based on the year of the country's most recent PEFA assessment, except for CPIA-13, which was based on 2011.

**Table A1.2. Summary Statistics (PFM and Outcomes)**

|**Variable**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**PEFA**|106|2.30|0.52|1.33|3.55|
|**GDP per capita (log)**|106|8.04|0.97|5.77|9.91|
|**Deficit**|56|-1.10|3.81|-11.41|16.31|
|**PI-1**|102|2.81|1.01|1|4|
|**PI-2**|97|2.38|1.06|1|4|
|**Health efficiency**|60|79.90|12.44|56.71|98.18|
|**Education efficiency**|57|44.56|18.36|14.23|95.04|

Note: Summary statistics were formulated based on the year in which a country's first PEFA assessment was performed 43
# 2 Estimation of a Dynamic Binary Response Panel Data Model with an Endogenous Regressor

## 2.1 Dynamic Binary Response Panel Data Models

Dynamic binary response panel data models with unobserved heterogeneity have been used extensively in theoretical and empirical studies. Both parametric and semi-parametric methods have been proposed to solve the initial conditions problem and to obtain consistent estimates of model parameters when all explanatory variables other than the lagged dependent variable are strictly exogenous.<sup>2</sup> Semi-parametric methods allow estimation of parameters without specifying a distribution of the unobserved heterogeneity, but they are often overly restrictive with respect to the strictly exogenous covariates. Honoré and Kyriazidou (2000), for example, propose an approach that does not allow for discrete explanatory variables. More importantly, because semi-parametric methods do not specify the distribution of the unobserved heterogeneity, the absolute importance of any of the explanatory variables in a dynamic binary response panel data model cannot be determined. Models which do not place any assumption on either the unobserved e¤ects or the initial conditions, or their relationship to other covariates, are best described as ...xed e¤ects models, and the semi-parametric approach of Honoré and Kyriazidou (2000) falls into this class of models.<sup>3</sup> Due to their computational simplicity, parametric methods have received greater attention than semi-parametric methods. There are four main parametric approaches, all employing conditional maximum likelihood (CMLE) analysis, that have been employed for estimation of the dynamic nonlinear panel data models in which all covariates other than the lagged dependent variable are strictly exogenous. The ...rst approach treats the initial conditions for each cross-sectional unit - yi0 - as nonrandom variables. If, in addition, unobserved e¤ects, ci, are also assumed to be independent of the exogenous explanatory variables,

> 2 With a structural binary outcome model that allows for unobserved e¤ects, one must be concerned that bias could be introduced through a systematic relationship between an unobserved e¤ect and the initial value of the dependent variable. This is known as the initial conditions problem.

> 3We follow Chay and Hyslop (2000) in classifying models requiring no assumption on unobservable e¤ects or initial conditions as ...xed e¤ect models, and refer to random e¤ect models as those in which one speci...es a distribution of unobserved e¤ects and initial conditions given exogenous explanatory variables.

4