rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8: The Dutch disease hypothesis: manufacturing output shares (** % **non-oil GDP) regressions**

||Marginal Efects|
|---|---|
|Population (logarithm)|-0.0851***<br>(0.0314)|
|GDP per capita|0.0872***<br>(0.0253)|
|Real efective exchange rate|0.0004<br>(0.0003)|
|Oil rents (% of GDP)|0.0017**<br>(0.0007)|
|Monthly minimum wage (2011 US dollars)|-0.0000735<br>(0.0002)|
|Observations|27|

Marginal Effects are reported. Standard errors in parentheses. Results based on fixed effects fractional logistic regressions in equation (2) for Cameroon, Ghana, Nigeria, and South Africa. _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 010 sub-Saharan Africa’s manufacturing experience.

# **7 Concluding Remarks**

This paper investigates the cross-country patterns and trends in the share of manufacturing in national output and employment among sub-Saharan Africa. Rodrik (2016) claims that several developing countries including those from subSaharan Africa are prematurely deindustrializing because manufacturing has begun to shrink at levels of income that are much lower than those at which the advanced economies started to deindustrialize. We study the extent to which African countries differ regarding the scale, timing and causes of “deindustrialization” using recent panel data methods for fractional responses. We use two main measures of industrialization, namely the share of manufacturing valued added in GDP at constant prices and the share of manufacturing employment in economywide employment. Our data come from a variety of sources and comprises of an unbalanced panel of 41 African countries. We also explore potential explanations or correlates of deindustrialization such as natural resource endowment. Our key finding is that deindustrialization does not appear to be the common experience of the majority of Sub-Saharan African countries. Only the Southern SSA subregion appears to have deindustrialized over the period under study. We do not, however, find evidence that this deindustrialization of the southern subregion has occured 24
18 promoting community organization, conducting necessary hydrological and topographic studies, raising external funds, providing the engineering designs and standards, assisting in construction, and contributing technical services for maintenance (Briscoe de Ferranti, 1988). To further reduce the transaction costs of developing water supplies, government staff train community leaders in technical and organizational skills. The villagers organize themselves in order to: participate in project design and planning, contribute data to pre-project studies, provide labor for project construction, and operate and maintain the system.

In 1977 the World Bank approved the first of two loans for rural water supply and sanitation projects in Paraguay. Over the next thirteen years, the two projects have served 98 poor rural communities of between 400 to 4,000 people (over 250,000 in total) (World Bank, 1991a and 1987). In these projects, the communities are responsible for all O&M and pay a portion of the construction costs. To receive support, a community must form a committee, or junta, and agree to contribute a minimum of 22 percent of investment cost, that is, 10 percent in cash and labor during construction and the remainder as long-term debt (with interest). The fees are set with a concern for fairness since the socioeconomic level of the community is taken into account. But the fee still must cover debt payment and O&M, and also contribute to a fund for major repairs and replacement parts.

While there were delays in implementing the projects, mainly due to weaknesses at the government rural water supply agency, the projects have been an overall successful. The juntas are motivated, function well, and manage the systems satisfactorily. The fee structures appear to be fair and affordable. By 1985, nearly all of the juntas that were formed under the first project had budget surpluses. Results from the second project show total contributions ranging from 18 to 68 percent (weighted average of 50 percent) of investment costs.

In the southern coastal area of Kenya, the World Bank teamed up with UNDP and its affiliate agency, PROWWESS (the Promotion of the Role of Women in Water Supply and Environment
# **1 Introduction**

We explore the impact of online international trade on the wage gap between skilled and unskilled workers. In a sample of 22 developing countries for which we have data on cross border online trade, and labor surveys to estimate the wage gap, we find that a one percent increase in the share of online exports over GDP reduces the wage gap by 0.01 percent. This can be explained by the information providing mechanisms in online platforms that reduce the fixed cost of exporting, allowing smaller firms with a more unskilled workforce to export. We provide evidence supporting this mechanism as the reduction in the wage gap associated with online exports is larger in countries with a larger share of small This result is important for at least three reasons. First, while world trade increased by 92 percent (8 percent annual growth) between 2004 and 2012, cross-border online trade grew more than 7 times faster (see Figure 1 where the left axis shows the growth in world trade, and the right axis the growth in online trade). Even if cross-border e-commerce only represents around 20 percent of online sales, it has been rapidly growing and its share is expected to double over the next 5 years (UNCTAD, 2015). For policy makers, it is important to assess early on the consequences of such a rapidly growing phenomenon. Second, the wage gap has been declining in the developing world over the last decade (Cruz and Milet, 2017). Our sample is no exception, with an average annual decline of 1 percent as can be seen from Figure 2. Understanding the forces behind the decline in the wage gap, and the role played by employment in small firms, can provide useful tools in the fight against income inequality. Third, it contributes to the debate on how technological progress affects skilled and unskilled workers, and ultimately income inequality. Does technological progress promote or reduce income inequality? While the development of the Internet has often been seen as skilled biased and contributing to income inequality (Akerman et al., 2015), this paper suggests otherwise. Online platforms that allow small firms to access world markets help unskilled workers and contribute to a reduction in income inequality.<sup>1</sup> We face several challenges. First, data on online cross border trade flows are not yet collected by any national or international agency, although some proposals have been put forward (UNCTAD, 2015). As a proxy we use cross-border flows on the eBay platform made available by Lendle et al.

> 1As noted by Varian (2005): “ _big enterprises were the first to reap the benefits of this technological progress. But the impact of information technology on small and medium-size enterprises may yet turn out to have the most impact on the economy_ ”. He also remarked more recently that small businesses are taking advantage of technologies which used to be the sole privilege of large companies: “ _[. . . ] even the smallest company can now afford a communications and computational infrastructure that would have been the envy of a large corporation 15 years ago_ ”(Varian, 2011).

1
productivity with respect to local firms when domestic infrastructure or access to finance is worse. Foreign firms are more able to withstand a weaker business environment in general, but need more help in day-to-day interactions with the government.

The business environment effects also differ greatly by firm size. Particularly important elements for relatively small firms include corruption, availability of both basic and modern infrastructure, a better human capital endowment, and access to land. Access to finance is also important for relatively smaller firms for firm growth, but important for large firms for productivity. Like foreign firms, relatively large firms benefit more from physical safety.

The main contributions of this paper are four. First, by using multiple sources of indicators of the business environment from three popular data sets, we are able to ascertain the robustness and the importance of various aspects of the business environment. Second, we offer novel evidence for the importance of contract enforcement in understanding the effects of de jure indicators of the business environment. Third, we offer evidence that the effects of the business environment differ greatly for domestic private and foreign firms, with the latter being better able to withstand a worse business environment in general, but need more help in day-to-day interactions with the government. Fourth, we provide evidence how the effects of the business environment differ by firm size.

The rest of this paper is organized as follows. Section 2 describes our hypotheses. Section 3 discusses the data and measurements. Section 4 presents basic results of the effect of various aspects of the business environment on firm performance, and how these effects interact with government capacity. Ownership-specific and size-varying business environment effects are explored in sections 5 and 6, respectively. Section 7 concludes.

# **2. Hypotheses**

As mentioned earlier, this analysis relies three groups of BE indicators to gauge their effects on firm performance. How should the three groups of BE indicators be associated with firm performance differently? Does it matter whether the indicators are _de jure_ or _de facto_ ?

The DB indicators are de jure measures, and are designed to reflect what a typical small or medium-sized firm (by international standards) would do based on the official laws and regulations. The actual BE that a firm faces could differ systematically from what is implied by the DB measures based on varying enforcement by various branches and/or agents of the government, or by the same government officials with respect to different firms. The enforcement could reflect whether the firm is well connected to the government, and/or 4
per month, much higher than the market interest rate, and cannot rule out linearity in returns.

We then set out a model consistent with our data which can be used to investigate the proximate causes of such high returns to capital. The two main theories we consider are imperfect credit markets and imperfect insurance markets. Examination of the heterogeneity of treatment effects leads us to find that returns to capital are higher for entrepreneurs with higher ability and with fewer other wage workers in the household. We do not find significant interaction effects consistent with risk aversion or uncertainty limiting investment in the enterprise. This suggests that the reason for high returns to capital is missing credit markets rather than missing insurance markets, and that the household internal capital market is a binding constraint.

We begin by describing the experiment. We follow in Section 3 with a description of the data. Section 4 estimates the treatment effect and Section 5 the return to capital. Sections 6 and 7 consider robustness to spillovers, attrition, reporting differences, different treatments, and nonlinearities. In Section 8, we develop a simple model of household investment which incorporates both the riskiness of investment and factors affecting the shadow value of capital. The model provides several testable implications, which we confront with the data. Section 9 concludes.

# **2. Description of Experiment**

_Selection of the participants_ We carried out a baseline survey of microenterprises in April 2005 as the first wave of the Sri Lanka Microenterprise Survey (SLMS).<sup>3</sup> Four additional waves of the panel survey were then conducted at quarterly intervals. The survey took place in three Southern and South-Western districts of Sri Lanka: Kalutara, Galle and Matara. The survey is designed to also study the process of recovery of microenterprises from the December 26, 2004 Indian Ocean tsunami, and so these districts were selected as ones where coastal areas had received some tsunami damage. We set out to draw a sample of firms with invested capital of 100,000 rupees (about US$1000) or less, excluding

> 3 Fieldwork was carried out by ACNielsen Lanka (Pvt) Ltd.

5
# Table A1: Balance check

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Con|trol|Offe|red||
||Mean|S.D.|Mean|S.D.|p-value|
|**Panel A. Socio-Demographic**||||||
|Female|0.50|0.50|0.50|0.50|0.32|
|Married|0.11|0.32|0.14|0.34|0.32|
|Has Children|0.12|0.33|0.12|0.32|0.74|
|Age|27.50|2.83|27.55|3.08|0.71|
|Province: East|0.08|0.27|0.09|0.28|0.64|
|Province: Kigali|0.49|0.50|0.44|0.50|0.12|
|Province: North|0.14|0.35|0.17|0.37|0.28|
|Province South|0.16|0.37|0.20|0.40|0.07|
|Province West|0.13|0.34|0.11|0.31|0.21|
|Mother is employee|0.04|0.21|0.06|0.23|0.42|
|Father is employee|0.07|0.25|0.07|0.25|0.98|
|Wealth Index|-0.07|1.43|0.05|1.43|0.19|
|**Panel B. Education**||||||
|Years since graduation|1.92|0.92|1.86|0.95|0.32|
|High Education Diploma|0.21|0.41|0.20|0.40|0.75|
|Bachelor Degree|0.77|0.42|0.77|0.42|0.83|
|Masters Graduate|0.02|0.14|0.02|0.15|0.78|
|Public School|0.74|0.44|0.76|0.43|0.44|
|Good English Language|0.97|0.17|0.97|0.16|0.72|
|Good French Language|0.44|0.50|0.44|0.50|0.96|
|Good Swahili Language|0.14|0.35|0.13|0.34|0.73|
|**Panel C. Employment**||||||
|Has ever worked|0.84|0.37|0.86|0.35|0.55|
|Not paid job last month|0.59|0.49|0.60|0.49|0.79|
|Works as employee|0.10|0.30|0.09|0.28|0.43|
|<br>Works as self-employed|0.04|0.21|0.06|0.25|0.18|
|<br>Works as family employee|0.09|0.28|0.08|0.28|0.79|
|Works as casual employee|0.18|0.38|0.17|0.37|0.73|
|Has formal job|0.03|0.18|0.02|0.14|0.21|
|Job accordance to degree|0.31|0.46|0.27|0.45|0.13|
|Labor earnings (monthly USD)|43.05|82.27|35.59|78.00|0.14|
|<br>**Panel D. Training**||||||
|<br>Willingness to pay for course (USD)|173.17|971.41|140.21|333.41|0.51|
|Training: get job faster|0.58|0.49|0.56|0.50|0.60|
|Training: get higher pay job|0.29|0.45|0.27|0.45|0.54|
|Training: get better job|0.35|0.48|0.39|0.49|0.23|
|<br>Prefers to attend Kigali|0.66|0.47|0.67|0.47|0.33|
|Prefers to attend North|0.19|0.39|0.18|0.38|0.12|
|Prefers to attend South|0.15|0.36|0.15|0.36|0.90|
|**Panel D. Joint test for panels A-D**<br>||||||
|p-value (joint F-test)|||||0.39|
|Observation|450||450||900|

Notes: the p-value reported in Column 5 is obtained from a regression of each variable on an assigned to treatment dummy with standard errors robust to heteroskedasticity, controlling for lottery dummies. We do not test for differences in means for females since the lottery was randomized for each gender separately. **p-value (joint F-test)** : corresponds to the orthogonality test in a regression of assigned to treatment dummy on covariates, the regression also controls for lottery dummies.

26
<!-- Start of picture text --> Panel A: Benchmark Panel B: Low I/S<br>to),! ro): |<br>|<br>||<br>=~ | ~ |<br>x | x |<br>hed eho<br>22<br>|<br>""<br>i——— Imports<br>> | * —— Consumption<br>Panel C: Unanticipated Panel D: Stock<br>ro) ! ro)<br>, | , —— Benchmark I/S<br>N N —— Lowl/S<br>a | a ———— Unanticipated<br>x | Go:<br>hed fhe<br>xe) | xe)<br>"<br>—— Imports " '<br>? ———= Consumption * |<br><!-- End of picture text -->
Table A 6: Impact of 3G/4G access on wage/salaried employment by group

||Gen|der|Consu|mption|Re|gion|
|---|---|---|---|---|---|---|
||Male|Female|Low|High|South|North|
|_Access to_:|||||||
|Medium or strong 3G/4G|0.007|0.002|0.001|0.010|-0.011|0.012*|
||(0.008)|(0.006)|(0.006)|(0.010)|(0.009)|(0.006)|
|Medium or strong 3G/4G|-0.001|0.002|-0.000|0.001|-0.017**|0.006|
|for one year|(0.009)|(0.006)|(0.007)|(0.010)|(0.009)|(0.008)|
|Medium or strong 3G/4G|<br>0.000|<br>0.008|<br>0.007|<br>-0.001|<br>-0.015*|<br>0.002|
|for two years or more|(0.009)|(0.005)|(0.007)|(0.009)|(0.008)|(0.009)|
|Medium or strong 3G/4G|0.011|0.016***|0.014*|0.014|0.003|0.009|
|for three years or more|(0.009)|(0.006)|(0.008)|(0.009)|(0.008)|(0.008)|
|# of observations|19,167|21,463|24,003|16,018|17,660|22,970|
||A|ge|Localit|yType|Edu|cation|
|||30 or|||Primary|Less than|
||Under 30|older|Rural|Urban|<br>or more|primary|
|_Access to_:|||||||
|Medium or strong 3G/4G|-0.001|0.008|0.008|-0.007|0.003|0.010|
||(0.007)|(0.007)|(0.006)|(0.014)|(0.007)|(0.010)|
|Medium or strong 3G/4G|0.007|-0.008|0.005|-0.013|-0.001|0.012|
|<br>for one year|(0.008)|(0.008)|(0.006)|(0.013)|(0.007)|(0.010)|
|Medium or strong 3G/4G|0.013*|-0.008|0.001|0.006|0.004|-0.001|
|for two years or more|(0.008)|(0.007)|(0.006)|(0.012)|(0.007)|(0.009)|
|Medium or strong 3G/4G|0.033***|-0.007|0.010|0.021*|0.015**|-0.004|
|for three years or more|(0.008)|(0.007)|(0.006)|(0.011)|(0.007)|(0.008)|
|# of observations|19,044|18,582|28,668|11,962|28,925|4,971|
||Lite|racy|Children|under 3|||
||Illiterate|Literate|No|Yes|||
|_Access to_:|||||||
|Medium or strong 3G/4G|0.004|0.005|-0.000|0.008|||
||(0.006)|(0.008)|(0.008)|(0.007)|||
|Medium or strong 3G/4G|0.003|-0.002|-0.000|0.002|||
|for one year|(0.006)|(0.008)|(0.008)|(0.008)|||
|Medium or strong 3G/4G|0.005|-0.001|0.002|0.006|||
|<br>for two years or more|(0.008)|(0.008)|(0.008)|(0.007)|||
|<br>Medium or strong 3G/4G|<br>0.004|<br>0.012|<br>0.018**|<br>0.012|||
|<br>for three years or more|(0.008)|(0.007)|(0.009)|(0.007)|||
|# of observations|11,719|28,161|17,798|22,832|||

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. All these regressions include the additional set of controls (e.g., access to electricity, ownership of dwelling, household size, and a wealth index) although the coefficients for these variables are not reported to save space. *** p<0.01, ** p<0.05, * p<0.1.

37
reflects countries' overall level of development, it would seem justified to eliminate the influence of this structural long-term relationship in order to obtain a universally applicable comparative output criterion. This was done by correcting PE for the "estimated" contribution of per capita GNP (see table 1).

# I. The Country Typology

The PER reports selected for review cover a sample of 36 countries, 19 of which are in the low-income range. This sample was subdivided with respect to each of the three typology parameters into "high" and "low" country groupings, depending on whether the parameter value for each country lies above or below the median value of the sample. "High" and "low" are thus relative terms that do not indicate value judgments. All country data are from the Bank's 1989 World Development Report, complemented from other sources where needed. Parameter values in the sample are shown in table 1.

The first step in the development of the typology is to combine the PRM and SRA criteria into a sector financing matrix, which characterizes each country according to its "high" or "low" status with respect to the policy determinants of overall sector resource availability. This matrix, shown in table 2, provides a rough comparative framework of basic priorities with regard to sector financing issues. The actual position of countries on the PRM and SRA scales is illustrated in figure 1.

Table 3 completes the country typology. It shows the net outcome of the interaction of PRM and SRA, that is, the overall level of sector resource mobilization relative to GNP (SRM), referenced against the selected educational output criterion (PE). The actual position of countries on the SRM and PE scales is illustrated in figure 2. In addition, table 2 indicates for each country the critical parameter(s)--PRM, SRA, or both -determining the high or low status of SRM.

Of the 36 countries Li the review, 20 have a comparatively low level of sector resource mobilization, including 12 of the 19 low-income countries.1Y On the educational output-side, however, 19 countries have a comparatively high primary enrollment rate, including 9 low-income countries.

Surprisingly, the largest category emerging from the typology in table 3 consists of 13 countries with low sector resource mobilization (SRM) and high sector output (PE). This may be interpreted in several ways, depending on country-specific circumstances: a relatively high proportion of SRM is allocated to primary education; central government spending on education in general, or on primary education in particular, represents only a small proportion of total spending because local government or private contributions are high; SRM has been declining in recent years, but this has so far affected quality rather than PE.

l/The median SRM (2.67) represents the product of the median PRM and median SRA, rather than the median of the product of the two medians.

68
WPS4190 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 4</mark>

# **THE AFTERMATH OF CIVIL WAR**<sup>*****</sup>

## **Siyan Chen**

_American University_

## **Norman V. Loayza**

_The World Bank_

## **Marta Reynal-Querol**

_Pompeu Fabra University, CEPR and CESifo_

### **Abstract**

_Using an “event-study” methodology, this paper analyzes the aftermath of civil war in a cross-section of countries. It focuses on those experiences where the end of conflict marks the beginning of a relatively lasting peace. The paper considers 41 countries involved in internal wars in the period 1960-2003. In order to provide a comprehensive evaluation of the aftermath of war, the paper considers a host of social areas represented by basic indicators of economic performance, health and education, political development, demographic trends, and conflict and security issues. For each of these indicators, the paper first compares the post- and pre-war situations and then examines their dynamic trends during the post-conflict period. The paper concludes that, even though war has devastating effects and its aftermath can be immensely difficult, when the end of war marks the beginning of lasting peace, recovery and improvement are indeed achieved._

### World Bank Policy Research Working Paper 4190, April 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * We are grateful to Ian Bannon, Paul Collier, Ibrahim Elbadawi, Jim de Melo, and participants of the PostConflict Conference at Oxford University, November 2005.
**Table 6. Firm Efficiency and Government Facilitation: Base Results**

||**lnlp**|**lnlp**|**lnlp**|**lnlp**|
|---|---|---|---|---|
|ln(firm age)|LProd style<br>-0.451***|TFP style<br>-0.430***|LProd style<br>-0.460***|TFP style<br>-0.437***|
||(0.047)|(0.045)|(0.052)|(0.046)|
|ln(L)|0.083***|0.143|0.077***|0.196|
||(0.029)|(0.215)|(0.029)|(0.161)|
|state|0.005|-0.330***|0.039|-0.295***|
||(0.114)|(0.107)|(0.108)|(0.105)|
|foreign|0.313***|0.067|0.337***|0.085|
||(0.091)|(0.084)|(0.087)|(0.084)|
|Ln(GDP per capita)|0.633***|0.405***|0.661***|0.419***|
||(0.077)|(0.071)|(0.107)|(0.070)|
|PS long tenure|-0.066|-0.012|-0.090|-0.048|
||(0.100)|(0.096)|(0.111)|(0.097)|
|loan help|0.440***|0.283***|||
||(0.086)|(0.075)|||
|info facilitation|0.149**|0.126**|||
||(0.072)|(0.064)|||
|loan help, city-ind avg|||1.700***|1.142***|
||||(0.384)|(0.340)|
|info facilitation, city-ind avg|||0.661***<br>(0.239)|0.613***<br>(0.209)|
|Industry dummies|Yes|Yes|Yes|Yes|
|industry dummies * (ln(K/L), lnL)|No|Yes|No|Yes|
|r2_a|0.181|0.351|0.192|0.358|
|N|4,817|4,709|4,817|4,709|

Note. *, ** and *** represent statistical significance at the 10, 5 and 1 percent, respectively. Whiteheteroskedasticity-corrected standard errors clustered at the firm level in parentheses.

37