rafmacalaba/datause-encoder-data huggingface dataset:
and hygiene interventions, making separating the effect of safe feces containment difficult<sup>3</sup> (Andres et al. (2013)). A widely cited meta-analysis estimates the impact of sanitation on diarrheal disease to be 32 percent (Fewtrell et al. (2005)), but this estimate is based on just two studies. More recently, communities who were randomly assigned to receive a community led total sanitation and sanitation marketing (TSSM) intervention reported reductions in 2-day prevalence of diarrhea in children under 5 of 45 percent (Cameron and Shah (2013)). It is critically recognized that the consequences of poor sanitation extend beyond the burden of diarrhea. Exposure to fecal contamination in the environment and chronic diarrhea decreases the ability to absorb essential nutrients, leading to malnutrition (Checkley et al. (2008)).<sup>4</sup> Poor sanitation is increasingly recognized as a distal cause of underweight and stunting in children (Humphrey et al. (2009)). Malnourishment during childhood is associated with severe long-term consequences. These long-term consequences include poor cognitive development, lower school attendance, reduced human capital attainment, and potentially a higher risk of chronic disease in adulthood (Victora et al. (2008)). There is strong evidence that the individual benefit to having improved sanitation is potentially very large, but to what extreme is this benefit actually driven through a community benefit, or externality to private sanitation, is less well understood. It has been suggested that even a few remaining open defecators in a community risk bringing fecal matter back into the environment resulting in continued contamination of soil and water, and potentially infecting others in the community who practice safe sanitation. In other words, even if my household has chosen to practice safe sanitation, if others in my community continue to defecate in the open, what are the consequences for the welfare of my household?

Observational and quasi-experimental studies have looked at the impact of improved sanitation on child health outcomes including diarrhea, infant mortality, and stunting. However, most of these studies have focused on the effects at the household level and have not considered the importance, or additional effects, that may be present when an entire community has access to sanitation. The most relevant for the current study is a recent paper that used propensity score matching to estimate the effect of access to improved sanitation on diarrhea in children under five using the District Level Household Survey (DLHS-3) (Ku3Indeed, some have suggested there is too little evidence on the complementary effects of WSH interventions and that too much attention has been focused on understanding the epidemiology of single transmission pathways (see Eisenberg (2007 and 2012)).

> 4This effect also goes the other way, with poor nutritional status associated with increased risk of diarrhea (see for example Chen et al. (1981)).

6
wages (chasing a moving target) and partly to the better protection of civil servants' real wages compared with that of other groups since the start of the 1990 stabilization.

Table 6: Actual Government Wage Expenditures and its Components (percentage of GDP)

||1988|1989|1990|1991|1992|1993|1994|
|---|---|---|---|---|---|---|---|
|Net Government wages|3.0|4.3|4.1|6.0|5.3|5.3|5.2|
|Gross Government wages|n.a.|n.a.|n.a.|n.a.|6.7|6.7|6.7|
|Government wage cost|4.0|6.0|5.7|8.0|9.1|9.1|9.0|
|**Wedge between wage costs and net wages**|**1.0**|**1.7**|**1.6**|**2.0**|**3.8**|**3.8**|**3.8**|

n.a. not applicable All wage data include central and local govermment expenditures. Wage cost includes contributions to ZUS.

In 1992 net government wages stabilized at around 91 percent of enterprise wages, and rose slightly in 1994. The stabilization of government employees' (net of tax) share of GDP after 1992 still implies a slight improvement of their purchasing power since GDP started to grow again in 1992. There is no clear evidence that there are too many government employees or that they are overpaid. Government employment was apparently (and fortunately) not used as an unemploymentfighting (or hiding) device. Education, however, may have suffered from too many teachers per pupil: about 1 million teachers for about 11 million people under age 18 (not all of them enrolled in school).

For the purpose of this paper the main focus here is the government wage explosion in 1991, linked to the general wage explosion in the economy. In addition, it should be noted that- like pensioners-civil servants managed to protect or even raise their wages better than many other groups (admittedly, though, from a low level).

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# **CITIES WITHOUT LAND MARKETS: LOCATION AND LAND USE IN THE SOCIALIST CITY**

by

# Alain BERTAUD* and Bertrand RENAUD**

* Principal Urban Planner Infrastructure and Urban Development Department ** Housing Finance Advisor Financial Sector Development Department The World Bank H Street N. W. Washington DC 20433

The empirical findings presented in this paper result from a program of technical cooperation carried out between the Russian Federation and the World Bank. We are especially grateful to Joseph K. Eckert, Natalya V. Kalinina, Mikhail P. Berezin, Olga Kaganova and Oleg Matiukhin for their assistance during this period; particularly to J. Eckert for the use of the price gradient data.
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 29: In a pure DC reform for only new entrants’ replacement rates*  for non-switchers are higher than for switchers****

<!-- Start of picture text --> 120.0%<br>100.0%<br>80.0%<br>60.0%<br>40.0%<br>20.0%<br>0.0%<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br>Year<br>non-sw itchers switchers<br>Replacement rate<br><!-- End of picture text -->

**Notes** : * Average pension as percentage of average group wage; ** Comparison of replacement rates in Reform I.2 for teachers – switchers with non-switchers. **Source** : WB calculations based on PROST.

The cost of wage indexation in the remaining old scheme was estimated by comparing Reform I.2 and Reform I.3. With the real wage growth assumed to be positive throughout the simulation period, wage indexation may increase pension expenditure by up to 0.1% of GDP, as illustrated in Figure 30 and Figure 31. On the other hand, price indexation may drastically reduce average replacement rates for the stock of retirees remaining in the defined benefit scheme (Figure 32 and Figure 33).

- 62 -
Moreover, in countries where official creditor financing of government investment is large, predicted disbursements will also be a strong instrument for changes in government investment.<sup>11</sup> We, therefore, employ the predicted disbursements on loans from official creditors to identify the exogenous component of government investment.

There are several challenges associated with using predicted disbursements as an instrument for public investment. First, as described in Kraay (2014), for the first-stage regression, predicted disbursements are correlated with overall government spending, which comprise investment and non-investment spending. Hence, if one is regressing private investment on public investment and using predicted disbursements as an instrument, it may be the case that non-investment government spending affects private investment, and if the latter is not controlled for, could lead to a violation of the exclusion restriction. However, controlling for non-investment government spending in the first-stage regression can be problematic as non-investment government spending might be correlated with the error-term in the first-stage regression. Second, it may be that predicted loan disbursements acts as a signal to private investors that the involvement of multilateral creditors may lower policy uncertainty. We attempt to address these challenges by employing an instrument-free estimator in section 6.3.

investment. This empirical strategy is guided by two arguments in Kraay (2012). First, although loan commitments are made before subsequent shocks are realized, the shocks may be persistent and possibly predictable. Second, if loan commitments are correlated with contemporaneous macroeconomic shocks, they will also be correlate with future shocks, which will violate the exclusion restriction. Consequently, including the lagged dependent variable in the regression helps address this possibility and improves the credibility of our identifying assumption.

# 4 Main results

Based on the two-stage least squares (2SLS) regression, using predicted disbursement as the instrumental variable, the coefficient on public investment is 1.6 (in the case of _h_ =0) and The cumulative is statistically significant at the 5% level of significance (table 2; panel B).<sup>12</sup> impact of public investment on private investment rises to a peak of 2.4 after two years. The estimates have two economic meanings, as highlighted in section 2. Specifically, recall that a positive coefficient on public investment in the private investment equation means that government investment is an Edgeworth-Pareto complement to private investment, suggesting that an increase in public investment raises the marginal product of private investment. Additionally, the estimated positive coefficient implies the crowding-in of private investment following an increase in public investment. In particular, the results

> 11 Total disbursements average 77 percent of public investment and 2.8 percent of GDP for country-year pairs. 12 In annex C we look at the impact of alternative censoring rules applied to the loan-level data.

11
Figure 3 shows the development in employment by industrial sector in the period from 1964 to 1994.2 The total number of formal employees in Zimbabwe reached 1.263.700 in 1994, starting at 736.600 in 1964.

The service sector is the largest employer followed by the agricultural and manufactwuing sectors.<sup>3</sup> Employment increased for all three major sectors over the thirty year period. There is no clear trend in agriculture, leading to only a minor increase (10 percent') as compared to 160 percent and 125 percent increase in the manufacturing and service sectors, respectively.

**Figure 3 Employment by sector in thousands (1964-94)**

<!-- Start of picture text --> 6.5<br>5.5 Agricufture<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~anufacuturin<br>_ > G_ t~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~lectricity<br>4.5 Distribution<br>0 = _ > + ~~~~~~~~~~~~~~~~~~~~~~~~~~Mining<br>3.5 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Service<br>2.5<br>1.5<br>C (CO C O N I ND CD Co N CD tO O (N<br><!-- End of picture text --> Data source: CSO. Note: Series are log (employees in thousands).

2 The service sector includes public administration, education, health, private domestic, and other.

3 Industry is an aggregation of the following seven sectors: mining and quarrying; manufacturing; electricity and water; construction; distribution, restaurants and hotels; finance; insurance and real estate; and, transport and communications.

6
**Table 1: Loans Requiring Collateral in the MENA Region (%)**

|**Country**|**Loans to Firms requiring collateral (%)**|
|---|---|
|Algeria|79|
|Egypt|84.4|
|Jordan|97.6|
|Lebanon|67.5|
|Morocco|90|
|Oman|73.8|
|West Bank/Gaza|84.5|

_Source:_ WB Enterprise Surveys 2006-10 The importance of collateral is also supported by empirical studies conducted with financial institutions in OECD and emerging market countries on the role of collateral in the overall credit decision and risk management process. Prevailing lending practices in a diverse group of countries reveal that, while their primary focus is on the capacity to repay the loan, availability of collateral is also a condition precedent to lending.<sup>12</sup> Collateral and credit information<sup>13</sup> are critical elements of a functioning credit system. The lack of these elements creates information asymmetry and a risk premium for borrowers who want to access credit. Credit is constrained in markets with information asymmetry (Stiglitz and Weiss, 1981). Moral hazard and adverse selection will be reduced if collateral frameworks and credit information systems are improved, creating a more robust financial sector in the region.

# **Part of the Solution to Increase Access to Credit Lies in Reforming Secured Transactions**

**Laws and Registries** _._ Providing legal structures through which movable assets in emerging markets can be effectively used as collateral will significantly improve access to finance to those firms that need it the most.  Even in the most advanced jurisdictions where reliable credit information and a wide range of financial products are available, only the largest and best connected businesses can obtain unsecured loans. The rest have to offer collateral. A sound legal and institutional infrastructure is critical to maximize the economic potential of movable assets so that they can be used as collateral. Effective secured transactions laws are a crucial component of a healthy financial sector and business climate. In their absence, entrepreneurs are unable to leverage current assets into

> 12 See ―How to Expand Credit to SMEs – Creative Solutions to the Puzzle of Collateral Lending‖, Alvarez de la Campa, Alejandro. IFC Smart Lessons, Washington D.C. 2007.

> 13 Credit bureaus, which maintain information on borrowers, have been developed in several MENA countries. Banks can make more prudent lending decisions if there is a repository of information on borrowers regarding their creditworthiness—their history of repaying debts.  However, a number of MENA countries are still missing proper credit information system deepening the information asymmetry gap.

5
(undercapitalized and overcapitalized banks) increased. The negative skewness of banking system capitalization before Basel III implementation changed into positive skewness (more banks with above-average capitalization)—an even greater positive than the one of the control group.

Against the general increase in liquidity in other EMDEs, the banks in countries that implemented Basel III show a much slower increase at the median, with a slight decline at the mean. In contrast to the other EMDEs, the dispersion of liquidity position shrunk overall in the Basel III countries. Given the mild decline in positive skewness, more banks with above-average liquidity slightly declined, similarly as in other EMDEs. We present these results in table 9.

Table 9: Higher moments of bank liquidity: Treated versus control group

|**Variable**|**Treate**<br>|**d group**<br>|**Control**<br>|**group**<br>|**Treated %**<br>|**Control**<br>|**DiD in**<br>|
|---|---|---|---|---|---|---|---|
||**2012**|**2016**|**2012**|**2016**|**change **|**% change **|**p.p. (%)**|
|Mean|13.87|12.95|7.25|11.10|-3|18|-21|
|Median|5.83|8.09|1.40|6.27|14|65|-51|
|Std. dev.|18.71|15.57|11.26|13.57|-8|8|-16|
|Skewness|2.07|1.92|2.11|1.80|-4|-7|3|
|Kurtosis|7.93|7.81|7.76|6.01|-1|-11|10|

_Note:_ The liquidity is measured by the ratio of liquid assets to total assets. The distribution of bank liquidity in the figures is based on the univariate kernel density estimation. DiD is calculated as the difference between the percentage change for the treated and control groups. DiD = difference-in-differences; p.p. = percentage points.

The evolution of loan loss reserves to total assets differs markedly between treated and control countries, as table 10 shows. In contrast to control countries, the mean loan loss reserves decreased somewhat in Basel III countries. The variability in the loan loss reserves dropped dramatically for Basel III implementors, while the opposite happened in other EMDEs. The decline in the positive skewness in treated countries indicates that there were fewer banks with low loan loss reserve ratios following Basel III implementation. Kurtosis declined significantly in the treated countries, indicating a lower occurrence of extreme values in loan loss reserves to total loans.

Table 10: Kernel density estimation of banks’ loan loss reserves: Treated versus control group

|**Variable**|**Treated**<br>**2012**|**group**<br>**2016**|**Control**<br>**2012**|**group**<br>**2016**|**Treated %**<br>**change**|**Control %**<br>**change**|**DiD in**<br>**p.p.**<br>**(%)**|
|---|---|---|---|---|---|---|---|
|Mean|3.32|2.64|4.38|8.99|-10|31|-41|
|Median|1.56|1.67|2.21|3.02|3|14|-11|
|Std. dev.|12.04|3.46|6.57|19.26|-54|47|-101|
|Skewness|14.56|4.82|4.21|4.61|-48|4|-52|
|Kurtosis|241.48|38.90|26.11|28.15|-79|3|-83|

22
the relative importance of human capital versus institutions (Glaeser et al., 2004; Acemoglu and Dell, 2010; Gennaioli et al., 2013), and the relative importance of finance versus property rights (Johnson et al., 2002; Cull and Xu, 2005). By controlling for all of these factors, our research suggests that access to finance and infrastructure could be more important than institutions and human capital, and that it is important to add the category of the agglomeration environment. The large global sample on which our results are based adds to the credibility of our study. To our knowledge, our paper is also the first that allows the BE effects to differ by income categories.

The remainder of the paper is structured as follows. Section 2 describes the data and our empirical methodology. Section 3 presents our baseline results based on the full global sample of firms. Section 4 analyzes differences between high-growth and other firms. Section 5 examines the heterogeneous effects of the BE across firm types, country income levels and industries. Finally, Section 6 concludes.

# **2. Data and Methodology**

The main data sources for this paper are the World Bank’s Enterprise Surveys (WBES) in 709 cities of 128 countries.<sup>4</sup> The WBES data are collected by the World Bank to benchmark the business climate in developing (and a few developed) countries across the world and to understand the determinants of firm performance. The richest country in the sample is Sweden with a GDP per capita of 46,000 U.S. dollars, and the poorest countries (i.e., Burundi, Liberia, Malawi, Ethiopia) have a GDP per capita around 200 U.S. dollars. The country population ranges from 52 thousand in St. Kitts and Nevis to 1.3 billion in China. In each country, the survey is based on the universe of eligible firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the non-agricultural private economy in the country.<sup>5</sup> Stratification is based on two criteria: the sector of activity and firm size. Typically, the stratified sampling yields between 100 and 1,000 firms per country, with 108 firms for the median city. Industries range from manufacturing and construction to services and retail and wholesale trade.<sup>6</sup> Each survey is largely a cross-sectional data set. However, firms are asked

> 4 The number of cities with BE information is around 650. 5 Thus wholly state-owned firms are not in the sample.

> 6 See http:\\www.enterprisesurveys.org for a more detailed description of the WBES.

7
d’Ivoire, 55 percent in Peru, and 50 percent in Ghana. In the Mexican urban labor force survey used in this paper, 65 percent of households are followed for all five quarters. Thomas et al. (2001) report that the follow-up rate in the second round of the Indonesia Family Life Survey would have been 84 percent instead of 94 percent if they had not tracked households which move. Failure to follow households which move is likely to understate mobility in both the OLS and IV estimates, since it appears likely that households which move dwellings are likely to have experienced greater income changes than households which stay put. Although correction for attrition is possible under certain structural assumptions, most studies of mobility do not attempt to address this issue.<sup>10</sup> We therefore now investigate how much of the difference between our pseudopanel estimates and panel data estimates is due to non-random attrition rather than measurement error. We begin by examining the determinants of who attrits. Table 3 presents marginal effects from probit estimation of two types of attrition. Column 1 considers households which attrit after only one round of interviews. These household heads are younger, less likely to be married, have smaller household sizes and larger incomes than household heads who remain for two or more waves of the survey. However, while these differences are significant given the large number of observations, the magnitude of the effects is rather small. In Columns 2 through 5, we look at households which appear in the first two quarters of the survey and examine the determinants of attriting before their full five quarters are completed. This allows us to examine whether attrition is related to the change in income experienced by the household between the first two waves. We find that both the change in income or log income, and the absolute value of this change, are positively associated with subsequent attrition from the panel. However, a one standard deviation change in either the change in income or absolute value of the change in income is associated with less than a 0.01 increase in the probability of attrition. Figure 1 shows that the kernel density of the change in income between periods 1 and 2 is very similar for

> 10 An exception is Lokshin and Ravallion (2004), who include a correction for attrition in their study of non-linear income dynamics.

20
interviews in the major metropolitan areas and is available from 1987 to 1993. This permits the generation of time series of relative earnings for each sector at quarterly frequency. The NUES is also structured so as to generate panels which allow tracking a fifth of each sample across five quarters. Workers are matched by position in an identified household, sex, level of education, and age to ensure against generating spurious transitions. These panels permit sketching patterns of mobility among sectors that can be tracked across the seven year period. The analysis restricts itself to men aged 16-65 with a high school education or less.

While the term "informal" suffers from overly broad and imprecise usage, it refers here to owners and workers in firms under 16 employees who do not have social security or medical benefits and are therefore not protected.' Four sectors of paid work are studied that include formal salaried employment and three "informal" sectors: The self-employed including owners of informal firms; the informal salaried, those working in informal firms; and contract workers, those who do not receive a regular wage or salary, but who are paid as a percentage, by piece, on commission, or fixed contract. They may be affiliated with a larger firm that provides raw materials, but work independently and are those most likely to be involved in subcontracting relations. There is also a residual "other" category (not shown) that includes firm owners who are protected, and owners and unprotected workers in firms of over 16 people. The remainder of the interviewed population (to total 100%) is divided into three classes of individuals who are not working: those out of the labor 5 There appear to be workers at larger firms who do not report having benefits, however this is stretching our idea of informality beyond the smaller scale industries usually contemplated and may, in addition, be due to measurement error. Since it is often the wife of the worker who is at home at the time of the survey, itis entirely possible that they are not informed about their husbands true employment conditions. STPS also argued that there may be some very transitory workers for large firms not covered in violation of the law.

4