rafmacalaba/datause-encoder-data huggingface dataset:
(Figure 3). However, these cuts were not enough to offset the decline in revenues, resulting in a fiscal deficit that has averaged 10 percent over the last thirty years.

**Figure 3. Sri Lanka: Drivers of Fiscal Outcomes**

<!-- Start of picture text --> (a). Government Revenues and Capital Expenditure (1977-2010) (b). Sri Lanka: Overseas Development Assistance(ODA) and Military<br>(As a percent of GDP) Expenditures ( % of GDP)<br>30 30 14 7<br>12 6<br>25<br>25 10 5<br>20<br>8 4<br>20 15<br>6 3<br>10<br>4 2<br>15<br>5<br>2 1<br>10 0 0 0<br>Revenues-L/Axis Capital Expenditure-R/Axis ODA-L/Axis Military Expenditures-R/Axis<br>Source: CBSL<br>1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009<br><!-- End of picture text --> A direct consequence is that interest costs have grown from 4.6 percent of GDP in 1985 to an average 6 percent of GDP for much of the recent decade. Rising interest costs started to kick in during the 1990s when there was a dramatic fall in concessional sources of financing as the conflict intensified. Overseas development assistance fell from on average 8.5 percent of GDP in the 1980s to just 2 percent of GDP by 2000, as shown in Figure 3 (b). Non-concessional external debt and much more expensive domestic debt became the main sources of financing in these years, helping to drive up both the stock of debt and interest costs.

# **_b. Debt Dynamics_**

We focus on government debt dynamics since the late 1990s, which have been driven by the interaction among aid inflows, the conflict, inflation and economic growth outcomes. Going forward, debt dynamics are likely to be quite different from the historical evolution described here. In particular, faster growth and raising primary fiscal surpluses will need to be the focal points if Sri Lanka is to attain its goal of reducing government indebtedness to 60 percent by 2016.

11
CIRI measure of women’s social rights are qualitatively similar. For CIRI measure of women’s political rights, we find a negative correlation between female employment and more political rights for women but this again is not robust to the control for GDP per capita.

We looked at the various interaction terms and found that the only significant interaction for CIRI measure of women’s economic rights is with the dummy for small vs. other firms (Table 9). Regression results in Table 9 show that women’s employment is not significant (at the 10 percent level) for small or remaining firms once we control for GDP per capita (column 2). Furthermore, the interaction term between CIRI and _Small_ that even retains significance when we control for GDP per capita becomes insignificant once we control for the interaction term between _Small_ and our _Non-discrimination_ variable (column 4). For the other two CIRI variables (women’s social and political rights), we find no evidence of any significant difference in their relationship with female employment in small vs. large firms. Summarizing, while the CIRI measures contain useful information regarding economic conditions affecting men vs. women, our results seem to suggest that they are too broad measures and therefore not properly insulated from the wider macro-economic factors in the country that could affect female vs. male employment.

# 5.6 _Gender discrimination and competition_

Above, we argued that gender discrimination is less likely in markets that are more competitive. Hence, one possibility is that non-discrimination may be particularly effective in boosting women’s employment in the relatively less competitive markets. We test for this hypothesis using concentration ratios at the industry level. These concentration ratios or the Herfindahl index is constructed using ES data on annual sales of the firm. To this end we create industrycountry cells where industries are as listed in Table 1. Next, we compute total sales of all firms in 21
# **INTRODUCTION AND SUMMARY**

In the three years preceding the recent crisis, Egyptian GDP growth averaged over 7 percent per year. The adoption of market-oriented policies, together with a boom in commodity prices and in capital inflows lifted growth to unprecedented levels during three consecutive years. Foreign direct investment flowed into the country and capital formation turned into the driver of growth. The virtuous circle of capital flows, investment, and growth, seemed to have no end. There were some signs of trouble, however: rising inflation, an appreciating currency, and high unemployment rates, especially among women. These challenges seemed manageable, and the diagnosis was that the trickle-down effect from growth would allow polishing off these rough edges. The future seemed to have finally arrived in Egypt.

The global shock that hit Egypt in mid 2008 was mostly an aggregate demand shock that reduced demand for Egyptian exports and lowered private investment due to increased uncertainty and weakened foreign direct investment. The shock led to a revision of expectations and questioning on the growth strategy, on the extent to which the adverse global economic surrounding would affect Egypt, on the country’s growth prospects and on the policy options ahead.

This boom-bust cycle is not the first one in Egypt, and it is certainly nothing new to emerging economies. The objective of this report is to analyze the current slowdown within a historical perspective, and to frame it within the country’s long-term growth challenges. Our analysis of Egyptian growth since the 1990s shows that there are clearly differentiable boom- busts periods tracking international capital flows cycles. The boom periods are associated with rising total factor productivity (TFP), market-oriented reforms and fiscal contraction, while the bust periods are associated with falling productivity, higher public spending and sluggish economic reform. In spite of the procyclical productivity behavior, the growth decomposition exercise shows that capital accumulation is the main driver of growth in Egypt.

The shock led to slower output growth without a corresponding fall in factor utilization. Hence, real production costs increased per unit of output. This brings into consideration the determinants of productivity growth in Egypt in the medium term, and highlights that policy responses to the crises need to support long-term productivity growth. The shock was confronted mostly by a fiscal stimulus package, which was prudent (equivalent to 1.5 percent of GDP). However, some elements of the stabilization package, which were adequate as temporary measures to help firms cope with the fall in productivity or the rise in real cost of production, such as freezing the energy price adjustment program and lowering interest rates, artificially lower the user cost of capital and induce a higher capital intensity than would prevail absent such policy interventions.

In spite of the transitory impact on the growth rate, the crisis will have a permanent effect on the GDP due mainly to the lower capital accumulation during the 2009-2011 period. To regain the 2
# **Figure 1 – The number of employed persons by enterprise ownership in Urban China (2001-2011)**

<!-- Start of picture text --> 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>State-owned Units Urban Collective-owned Units<br>Cooperative Units Joint Ownership Units<br>Limited Liability Corporations Share-holding Corporations Ltd.<br>Private Enterprises Units with Funds from Hong Kong, Macao & Taiwan<br>Foreign Funded Units Self-employed Individuals<br>Sources: China Statistical Yearbooks, 2002-2012.<br>250<br>200<br>150<br>100<br>Employed Persons (millon) 50<br>0<br><!-- End of picture text --> With the restructuring of the enterprise sector, firm turnovers and job churnings became normal phenomena. Creative destruction accounted for a higher share of the increase in productivity. Workers were no longer tied to one firm. The risk of being laid off by one firm was separated from the risk of being unemployed. Spurred by the rapid development in the coastal provinces or urban areas, millions of migrant workers left their hometowns in inland or rural areas to pursue better job opportunities. The supply of inexpensive labor served as a strong “push force” to further stimulate the economic growth and attracted more domestic private investment and foreign direct investment in the coastal urban areas. The spillover effects of foreign direct investment on domestic firms vary. Wei and Liu (2006) indicated that there are positive interindustry productivity spillovers from R&D and exports, and positive intra- and inter-industry productivity spillovers from foreign presence to domestic firms within regions. Du et al. (2011) elaborated on the different channels and mixed effects through forward linkages and backward linkages, and argued that positive spillovers are insignificant for horizontally integrated firms.

5
|p-value|0.29<br>0.37<br>0.81<br>0.31|0.66|0.80|0.15|0.74|0.91|0.07<br>0.43<br>0.19<br>0.31|ue of a F-test that|
|---|---|---|---|---|---|---|---|---|
|Mean<br>(SD)|29.41<br>(30.88)<br>0.52<br>(0.50)<br>0.20<br>(0.40)<br>0.13<br>(0.34)|0.50<br>|(0.50)<br>0.28<br>034|(.)<br>0.06<br>|(0.24)<br>0.15<br>|(0.35)<br>0.87<br>(0.33)|14.14<br>(7.00)<br>28.18<br>(25.54)<br>198.98<br>(137.43)<br>9.60<br>(8.54)|d report the p-val|
||Annual income in 2014 (000s pesos)<br>Reported liquidity constraints (1=Yes)<br>Ever applied for a loan (1=Yes)<br>Never takes risks (1=Yes)|Farmer sold maize in 2014 (1=Yes)|Share of maize production sold in 2014|Ever had extension services (1=Yes)|Ever done soil analysis (1=Yes)|Supported by a government program in 2014 (1=Yes)|Nitrogen (N) (ppm)<br>Phosphorus (P) (ppm)<br>Potassium (K) (ppm)<br>CEC (cmolc/kg)|iations. We also regress the baseline values of each variable on a set of treatment dummies an<br>t of the intervention. See Appendix Table|
|p-value|0.82<br>0.37<br>0.16<br>0.06|0.35|0.93|0.98|0.95|0.97|0.96<br>0.98<br>0.09<br>0.03|nd standard dev<br>5, before the star|
|Mean<br>(SD)|_Panel A: Farmer Characteristics_<br>Male (1=Yes)<br>0.83<br>(0.38)<br>Age (years)<br>56.95<br>(13.52)<br>Years as farmer<br>34.90<br>(16.15)<br>Completed primary school (1=Yes)<br>0.60<br>(0.49)|_Panel B: 2014 Practices & Yields_<br>Number of plots cultivated in 2014<br>2.09<br>|(1.27)<br>Total area cultivated (ha) in 2014<br>5.77<br>445|(.)<br>Number of fertilizations in 2014<br>1.58<br>|(0.54)<br>Number of days between sowing and 1st fertilization in 2014<br>36.32<br>|(21.48)<br>Self-reported yield (ton/ha) in 2014<br>2.06<br>(1.16)|_Panel C: Plot Characteristics_<br>Sand (%)<br>70.93<br>(9.96)<br>Clay (%)<br>14.71<br>(5.43)<br>Slope (degrees)<br>3.24<br>(3.43)<br>pH (1:2 water)<br>6.07<br>(0.71)|Note: This table reports summary statistics for the study sample of 678 farmers in 2014. For each variable, we report the mean a<br>all treatment coeffcients are jointly equal to zero. The data were collected using our Baseline survey conducted in February 201<br>reftab:defvars for the defnition of the variables.|

32
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23
Table S.32: Checking on spillover effects - GPA z-scores: Case C <u>(OLS)</u>

|Dependent variables:|||GPA|z-scores|||
|---|---|---|---|---|---|---|
||Bengali|English|Math|General<br>science|Islam<br>studies|Bangladesh<br>general<br>studies|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|
|**December 2012**|||||||
|Share of treated students and their|-5.774|-0.015|-2.177|-5.327|-7.649**|-5.339**|
|siblings in each school-grade|(3.885)|(4.967)|(5.418)|(6.261)|(3.592)|(2.519)|
|Share of students in each school-grade that|4.358*|1.452|2.904|3.809|4.469*|3.105*|
|had no access to electricity at baseline|(2.393)|(2.938)|(2.866)|(3.143)|(2.519)|(1.537)|
|All products|-0.017|-0.039|0.002|-0.030|-0.092|-0.075|
||(0.081)|(0.078)|(0.069)|(0.072)|(0.073)|(0.089)|
|One product|-0.020|0.001|0.016|-0.004|-0.106|-0.083|
||(0.096)|(0.080)|(0.095)|(0.104)|(0.086)|(0.094)|
|R-squared|0.231|0.220|0.215|0.217|0.232|0.227|
|No. of obs.|849|849|849|849|849|849|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|
|**December 2013**|||||||
|Share of treated students and their|0.356|5.075|0.529|-2.639|-2.667|-1.866|
|siblings in each school-grade|(3.980)|(4.789)|(3.952)|(4.018)|(4.251)|(4.197)|
|Share of students in each school-grade that|0.069|-4.168|1.286|3.055|3.142|0.675|
|had no access to electricity at baseline|(2.418)|(2.926)|(2.321)|(2.336)|(2.451)|(2.475)|
|All products|-0.046|-0.064|-0.040|0.057|0.060|0.067|
||(0.087)|(0.064)|(0.058)|(0.084)|(0.082)|(0.069)|
|One product|0.024|-0.147**|-0.077|0.050|0.032|0.131|
||(0.087)|(0.069)|(0.072)|(0.079)|(0.080)|(0.090)|
|R-squared|0.257|0.263|0.395|0.305|0.266|0.272|
|No. of obs.|849|849|849|849|849|849|
||(3a)|(3b)|(3c)|(3d)|(3e)|(3f)|
|**December 2014**|||||||
|Share of treated students and their|-5.178**|1.034|3.136|-1.262|-4.742**|-5.355**|
|siblings in each school-grade|(1.951)|(1.861)|(2.725)|(2.142)|(2.195)|(2.577)|
|Share of students in each school-grade that|2.217*|-0.197|-1.304|-0.002|1.955*|1.560|
|had no access to electricity at baseline|(1.238)|(1.155)|(1.611)|(1.211)|(1.084)|(1.335)|
|All products|-0.078|-0.036|-0.105|-0.101|-0.053|-0.120|
||(0.082)|(0.085)|(0.088)|(0.081)|(0.089)|(0.090)|
|One product|0.059|0.042|0.033|-0.068|0.040|-0.025|
||(0.092)|(0.086)|(0.088)|(0.092)|(0.099)|(0.092)|
|R-squared|0.118|0.099|0.163|0.150|0.111|0.135|
|No. of obs.|845|845|845|845|845|845|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

77
in Malawi) we conduct regressions weighted by the inverse of the number of observations per country. Effectively, what we do when we apply this weighting scheme is to give equal weight to each country in our sample. These results are shown in Table 6.

Table 6 shows that the introduction of registries for movable assets continues to have a significant impact on most indicators of access to finance once we weight our estimations by the inverse of the number of firms sampled in each country. In particular, we find that the adoption of a collateral registry for movable assets has a positive impact on the likelihood that a firm has access to a loan, line of credit or overdraft; it significantly lowers the interest rate on loans, while at the same time increasing the maturity of the financing.

Second, we run estimations where we contrast results for the seven countries that introduced a collateral registry for movable assets against a matched sample of seven countries selected based on the availability of pre- and post-reform surveys, the geographic location, and GDP per capita of each reform country. These results are reported in Table 7. Despite a large drop in our sample size, our results remain robust. Thus, Table 7 confirms that the adoption of a collateral registry for movable assets has a positive impact on the likelihood that a firm has access to a loan, line of credit or overdraft; it increases the share of working capital financed by banks and it significantly lowers the interest rate on loans.

Third, because four out of the seven countries that introduced collateral registries also introduced other changes to their collateral legal framework, we run estimations with a control sample of countries that introduced reforms to their collateral laws but did not set up a movable collateral registry. The purpose of these estimations is to isolate the impact of setting up a registry for movable collateral from that of changes in collateral laws that are often undertaken at the same time the movable collateral registry is introduced. These results are presented in Table 13
**Appendix C.  Size of Population Aged 12-24 from 1950 to 2050, United**

<!-- Start of picture text --> Nations Medium Variant Projections, Classified by Categories for Timing of the<br>Category 1: Peak before 2000<br>Peak<br>Albania Armenia Barbados<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Belarus Bosnia and Herzegovina Bulgaria<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>China Croatia Cuba<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Czech Republic Estonia Georgia<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Guadeloupe Guyana Hungary<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Kazakhstan Korea, Dem. Rep. Latvia<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Year<br>United Nations Medium Variant Projections<br>2.5 2 1.4<br>21.5 1.5 1.21<br>1 1 .8<br>1.2 1.4 1<br>1 1.2 .8<br>1<br>.8<br>.8 .6<br>.6 .6 .4<br>2.5 1 2.5<br>2 2<br>.8<br>1.5 1.5<br>1 .6 1<br>1.4 1.2<br>1.2<br>1.2 1 1<br>1<br>.8 .8<br>.8<br>.6<br>.6 .6<br>2 2.5 1.2<br>Population aged 12-24 (1950=1.0)<br>2 1<br>1.5<br>1.5 .8<br>1 1 .6<br>2 2 1.2<br>1<br>1.5 1.5 .8<br>1 1 .6<br><!-- End of picture text --> 53
# **Urbanization without Growth:**

# **A not so uncommon Phenomenon**

Marianne Fay* and Charlotte Opal**

* World Bank (MFAY@Worldbank.org); ** Oxford University. This paper was written while Charlotte Opal was a summer intern at the World Bank

We are grateful to Vernon Henderson for the use of his data base and for his suggestions; to Bill Easterly and the participants of the macro brownbag lunch seminar at the World Bank for their comments; and to Christine Kessides for her support and interest. All remaining errors are ours. This research was supported by the Transport, Water, and Urban Division of the World Bank.
Second, we add new evidence to the corruption literature.<sup>5</sup> We show that accounting information disclosure can be detrimental to firm development if firms operate in a corrupt business environment with low institutional quality. Such disclosure allows corrupt bureaucrats to gain access to firm-level information and use it for endogenous harassment (Fisman and Svensson, 2007). Corruption can thus indirectly discourage the adoption of good policies (such as voluntary information disclosure) that would be efficient in economies with better institutions.

The rest of this paper is organized as follows. Section II describes data, variable definitions, and summary statistics. Section III examines the effect of disclosure on firm financial obstacles. Section IV examines its effect on firm corruption obstacles. Section V investigates the effect of voluntary disclosure on firm growth. Section VI analyzes how the costs and benefits of auditing hinge on property rights protection. Section VII concludes.

# **II. Data, Variables and Summary Statistics**

# **A. Data**

We construct firm-level variables from _World Bank Enterprise Survey (WBES)_ collected for 71,677 firms in 121 countries from 2006 to 2014.<sup>6</sup> Of the 121 countries, most were surveyed once; a few, in two to three waves. See Table A1 in the Appendix for the total number of firm observations, and the number and percentage of audited and unaudited firms by country.

WBES relies on standardized survey instruments in collecting firm-level data. The survey respondents are mainly business owners and/or firm top managers. The surveys focus on assessing the critical obstacles in the business environment that hinder firm growth, including access to finance and obstacles related to corruption, political instability, infrastructure, crime, competition, the labor

> 5 For papers and surveys on the literature of corruption, see Bardhan (1997), Cai, Fang and Xu (2011), Clarke and Xu (2004), Li, Xu and Zou (2000), Mauro (1995), Shleifer and Vishny (1993), and Svensson (2003, 2005),

> 6 For a literature survey of firm-level studies using the WBES data, see Xu (2011).

5