rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Placebo test using initial employment share in other industries

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.:Growth inper|capita income(1|996-2011)|||
|Coefficients for: Sh<br>|are Employed_s_|_ector_ **+**HL*S|hare employed|_sector_|
|_Sector:_|||||
|Mining1996|0.398**|0.504**|0.351**|0.315**|
||[0.199]|[0.213]|[0.161]|[0.145]|
|Manufacturing1996|-0.128|-0.273**|-0.111|-0.208*|
||[0.135]|[0.129]|[0.133]|[0.118]|
|Agriculture1996|0.190|0.160|0.163|0.116|
||[0.126]|[0.126]|[0.130]|[0.124]|
|Construction1996|-0.102|-0.057|0.033|0.060|
||[0.152]|[0.150]|[0.147]|[0.139]|
|Retail1996|0.032|0.126|0.063|0.023|
||[0.147]|[0.147]|[0.140]|[0.126]|
|Community, social|-0.087|-0.147|-0.256**|-0.386***|
|& personal services1996|[0.124]|[0.127]|[0.107]|[0.097]|
|Observations|9,583|9,583|9,583|9,558|
|R-squared|0.595|0.604|0.638|0.715|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The coefficients presented correspond to the sum of the coefficient for the share of employment in given sector in 1996 (e.g. mining) and the interaction of the homeland indicator with the share employment in the sector in 1996. The dependent variable is the difference in log per capita income for 2011-1996. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 1019, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
# **Figure 1**

<!-- Start of picture text --> Major Rules and Regulations Submitted to OMB<br><!-- End of picture text --> <!-- Start of picture text --> Department of Agriculture<br>Department of Energy<br>Department of Health and Human Services<br>Department of Homeland Security<br>Department of Housing and Urban<br>Development<br>Department of Justice<br>Department of Labor<br>Department of Transportation<br>Environmental Protection Agency<br><!-- End of picture text --> Not surprisingly, a great deal has been written about the techniques use by the EPA and other U.S. regulatory agencies, as well as about the results of the analyses performed on regulations _before_ they are issued in the _Federal Register._ For a discussion of these issues, we refer the reader to Harrington et al. (2009). In contrast, there is a relatively small literature on regulatory studies performed _after_ the rules have been implemented. In the next section we present recent information on _ex post_ assessments and, in particular, we compare the _ex ante_ and _ex post_ analyses.

# **_Ex post_ v.** **_ex ante_ analysis of regulation**

_Ex post_ analysis of the costs (and other consequences) of government regulations is relatively uncommon, especially in comparison to the large number of _ex ante_ studies. Harrington, Morgenstern and Nelson (2000, hereafter HMN) surveyed the peer-reviewed and gray literature for published comparisons of _ex ante_ and _ex post_ cost estimates of specific social regulations.<sup>3</sup> The focus was on environmental and workplace safety regulations in the U.S., but all case studies that became known to the authors were

> 3 Also see earlier studies by Putnam, Hayes and Bartlett (1980), the U.S. Office of Technology Assessment (1995), and by Goodstein (1997).

9
and public capital stock and GDP per capita as the input, as estimated in IMF (2021). Orange diamonds indicate medians, and bars show the minimum-maximum range. Sample size includes 15 ECA and 16 euro area economies. C. GDP per capita at current market prices in percent of EU-27 total per capita (based on purchasing power standards). Aggregates calculated using real U.S. dollar GDP at average 2010-19 prices and market exchange rates. Sample size includes 8 ECA economies. D. Absorption rates of EU funds reflect the total net payments divided by planned EU spending for the 2014-20 EU spending program. “Best absorber” indicates the EU-27 country that achieved the highest absorption rate of EU funds.

E. Investments in transport and green projects in percent of Western Balkans GDP.
# **Convergence in Institutions and Market Outcomes: Cross-country and Time-series Evidence from the Business Environment and Enterprise Performance Surveys in Transition Economies**

## **Pradeep Mitra World Bank**

## **Alexander Muravyev IZA and DIW Berlin**

## **Mark E. Schaffer CERT, Heriot-Watt University and CEPR/IZA/WDI**

JEL classification: O12 (Microeconomic Analyses of Economic Development), P31 (Socialist Enterprises and Their Transitions), L11 (Production, Pricing, and Market Structure; Size Distribution of Firms), G32 (Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure).

Keywords: transition, convergence, market structure, competition, enterprise finance, enterprise restructuring.
Table C2: Firm distribution and frequency Vietnam firm‐level data by year

|**Year**|**Freq. **|**Percent**|**Cum.**|
|---|---|---|---|
|2010|7,890|25.12|25.12|
|2011|8,292|26.40|51.52|
|2012|7,577|24.12|75.65|
|2013|7,649|24.35|100.00|
|Total|31,408|100.00||

Table C3: Summary statistics for the variable used for Vietnam (2010‐2013)

|**Variable**|**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Index * (D/T)|31,397|0.07|0.03|0.03|0.16|
|R&D|31,381|0.09|0.28|0.00|1.00|
|R&D new|2,700|0.57|0.50|0.00|1.00|
|Patent Nat.|31,397|0.01|0.10|0.00|1.00|
|Patent Int.|31,397|0.00|0.06|0.00|1.00|
|Collab.|31,247|0.01|0.10|0.00|1.00|

38
VKIICY RESEARItH WORKINM PAPER 1550

# Summary findings

lUsing data frorni the World Bank's Operations Evaluation D)epartnmcilt, Ishalan, Kaufmann, and Pritchett examine the link between the performance of Bank-financed projects and varioLus inLdicators of coountry governance. They find that: T'here is a strong statistical, and possibly causal, link berveen civil liberties and project performance. After conltrolling for a variety of determiiinalnts of project performance, they find that in countries with the best civil liberties records projects have an economic rate of return between 8 and 22 percentage points higher than the rate of return in countries with the worst civil liberties. (The average rate of return in the sample is 16 percent.)

* The type of political regime (whether authoritarian or democratic) and the status of more purely political liberties do not appear to significantly affect project performance.

- T'his paper -- a prodUct of the Poverty and Human Resources Division, Policy Research Department is part of a larger effort in the department to understand the donor and country determinants of aid effectiveness. The study was funded by the Bank's Research Support Budget under the research project "Bank Project Effectiveness and Country Policy Envirolnmeit" (RPO 679-49). Copies of this paper are available free from the World Bank, 1818 H StreetNW, Washington, DC 2043.3. Please contact Sheila Fallon, room N8-030, telephone 202-473-8009, fax 202-522-1123, Internet address sfallon@(aworldbank.org. (44 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about deuelopmrpen t issues. At obje ctive of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry thc names of the authors and should be used and cited accordingly. The findings, interpretations, and conclusions are the authors' ownr atnd should no?t be attributed to the World Bank, its Executive Board of Directors, or any of its member countries._

## Produced by the Policy Research Dissemination Center
Table A3: Robustness to imputing missing income responses by community mean

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.:Growth inper capita income(1|996-2011)||||
|Homeland (=1)|2.162***|2.300***|1.852***|1.472*|
||[0.567]|[0.577]|[0.563]|[0.753]|
|Share Employed mining1996|-0.523***|-0.354*|-0.779***|-0.198|
||[0.179]|[0.212]|[0.235]|[0.216]|
|Homeland*Share Employed mining1996|0.975***|0.897***|1.189***|0.621***|
||[0.257]|[0.289]|[0.263]|[0.227]|
|Observations|9,583|9,583|9,583|9,558|
|R-squared|0.585|0.593|0.631|0.716|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 2011-1996. Per capita income imputes missing by the average per capita income of a community (instead of with zero). 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
# **Graph (3a): Scaled Residuals - 3 Lags VAR- Closed Economy VAR**

<!-- Start of picture text --> 0.2<br>0.25 r rm2 r rgdp<br>0.1<br>0.00 0.0<br>-0.1<br>-0.25<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>20<br>r INFL 2.5 r Interestrt<br>0 0.0<br>-2.5<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>5.0<br>r DISC<br>2.5<br>0.0<br>-2.5<br>1970 1980 1990 2000 2010<br><!-- End of picture text -->

# **Graph (3b): Long Run Stability Test of - 3 Lags VAR- Closed Economy VAR**

<!-- Start of picture text -->
1.00 Roots of companion matrix<br>0.75<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>-0.75<br>-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0<br><!-- End of picture text -->

56
Table IV.2 (cont'd)

||||S|ubsadlzation||
|---|---|---|---|---|---|
|Title of Project.<br>Country|Contraceptives<br>Donated|Company Subsidy|Donor Subsidy<br>for Serviceg|Client Pays Fees|Pollcy<br>*nd Estimates<br>of<br>Coats/Revenues<br>or BWget|
|uNOTlrCPfITAhXINY|EXlS<br>IN<br>**_DAN*L?_**PL*L|IN (cont-d)||||
|Thaillnd:<br>Family Planning<br>Narket Survey<br>q<br>Marketing<br>Capa-<br>bility Evaluation|N/A|N/A|N/A|N/A|Project cost- $59.038|
|Ibxicos<br>Nedical Service<br>NicGenterprises<br>In metropolitan<br>Areas|In first year<br>only|N/A|Only for first<br>M.D|Yeo|Project cost- $150,039|
|Self-Financing<br>Family Planning<br>Project|Sold to NDs at<br>cost and donated<br>to Social<br>Security<br>na|Social Security<br>services only|so|**eas**butbelow<br>market price for<br>IUDbecauseof<br>subsidy.<br>Consul-<br>tationfeefor<br>private<br>M6Dm|Over 3130,096/year|
|SOCIALhAWING||||||
|**N15erla:**<br>Sterling Products<br>Private Sector<br>Family Planning<br>Project|Tea to both dis-<br>tributlon projects|**Tea.**for provider<br> <br>time at health<br>clinic|Teo|Clients pay for<br>pharmacy&chemSet<br>shop products for<br>benefits project|Sales from distribution<br> <br>project grossed $700,000<br>In first four months.<br>Total project cost-|
|Chan&<br>**Social**Marketing<br>Program|Tea|_N/A_|N/A|YeT,butbelow<br>maiket price|$1,000,000 over fouryears|
|Nigeria:<br>lbedanMarket<br>Project|Teo|N/A|_NIA_|Tes|Three quarters of revenue<br>goes beck to project,<br>one-<br>fourth to traders|
|DominicanRepublic<br>||||||
|Contraceptive<br>Social Marketing|No|No|No only paying<br>for advertising|Yea,butless than<br>*rket<br>price|Not yet determined|
(2006): "Global income inequality : what it is and why it matters," Policy research working paper series, The World Bank.

- Milanovic, B. and Yitzhaki, S. (2002) “Decomposing World Income Distribution: Does the World Have a Middle Class?” _Review of Income and Wealth_ 48 (2): 155–78. June.

- Olivera, M. and Lora, M. (2005) “The Electoral Consequences of the Washington Consensus”, _Inter-American Development Bank Working Paper_ 530

- Pritchett, L. (1997) “Divergence, Big Time”, _Journal of Economic Perspectives_ Summer 1997, pp3-17

- Quah, D. T. (1996): "Twin Peaks: Growth and Convergence in Models of Distribution Dynamics," _Economic Journal,_ 106(437), 1045-55.

- ———. (1997) “Empirics for Growth and Distribution: Polarization, Stratification, and Convergence Clubs,” _Journal of Economic Growth_ , vol. 2, no. 1, pp. 27-59.

- Ravallion, Martin. 2004. “Competing Concepts of Inequality in the Globalization Debate.”  In Susan Margaret Collins and Carol Graham, (ed.), Brookings Trade Forum 2004. Washington, DC: Brookings Institution.

- Ravallion, Martin, 2003. “Inequality convergence,” _Economics Letters_ , Elsevier, vol. 80(3), pages 351-356, September.

- Sala-i-Martin, X. (2002a) “The Disturbing ‘Rise’ of Global Income Inequality.” _NBER Working Paper_ 8904, National Bureau of Economic Research, Cambridge, MA. April.

- ———. (2002b) “The World Distribution of Income (estimated from Individual Country Distributions).” _NBER Working Paper_ 8933, National Bureau of Economic Research, Cambridge, MA. May.

- Sala-I Martin, X. (2006): "The World Distribution of Income: Falling Poverty and Convergence, Period," _The Quarterly Journal of Economics,_ 121(2), 351-397.

- Theil, H. and Seale, J.L. (1994) “The geographic distribution of world income, 1950– 1990”, _De Economist_ 142, pp. 387–419

- van der Mensbrugghe, Dominique (2005) “ _The Linkage Model Technical Documentation_ .” Washington, DC: World Bank.

- World Bank (2005): _World Development Report 2006: Equity and Development._ Oxford University Press for the World Bank, Oxford and New York.

- World Bank (2006): _Global Economic Prospects 2007: Managing the Next Wave of Globalization_ .

24
# **Learning from Power Sector Reform: The Case of Uganda**

**Catrina Godinho, Anton Eberhard**<sup>**1**</sup> **,**<sup>**2**</sup>

<u>Keywords: power sector reform, electric utility, power generation, energy access, state-owned</u> enterprise, regulation, electricity pricing.

<u>JEL Codes: L9; Q4</u>

> 1 From the University of Cape Town, Graduate School of Business in South Africa.

> 2 Research on this paper was conducted by Dr. Geogrey Bakkabulindi in Uganda. Valuable guidance was provided by Raihan Elahi and Mbuso Gwafila of the World Bank Country Team for Uganda.
the distributional consequences of the observed productivity dynamics. In particular, we look at the relationship between the real exchange rate and inequality in the country and how productivity growth in the traded sector might also require social rebalancing.

# **2 Balassa-Samuelson in South Africa: Some preliminary observations**

The traditional Balassa-Samuelson analysis is based on an exogenous definition of the traded and non-traded sectors often identified as goods and services. The problem is that a strict definition of traded and non-traded sectors conflicts with a simple observation: in all sectors, the proportion of firms that do not trade internationally represents the vast majority of the firms, with maybe the exception of the mining sector. Consider, for example, the following table from (Edwards et al., 2018) The table shows that almost 70% of all manufacturing firms in South Africa do not trade. This is possible if we consider a world with heterogeneous firms, with different levels of productivity, producing heterogeneous products which do not have perfect substitutes. Less productive firms can survive because they are partly protected from external competition. This observation is not specific to South Africa: the literature shows that in the United States only 21% of manufacturing plants export and there is a lot of switching from traded to non-traded goods in the same plants.

Moreover, in principle all sectors have traded components. The following graph shows the proportion of sectoral product exported for primary, secondary, and tertiary sectors in South Africa in

||2009|2010|2011|2012|2013|Average|
|---|---|---|---|---|---|---|
|Exporter only|6.2|7.2|7.1|7.1|7.2|7.0|
|Exporter and importer|16.3|17.7|17.2|17.9|15.9|17.0|
|Importer only|9.2|8.4|8.2|7.7|7.7|8.2|
|Non-trader|68.4|66.6|67.5|67.2|69.2|67.8|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

Table 1: Manufacturing Firm Engagement in International Trade, (Share of firms, %)(source (Edwards et al., 2018)) 7
Table A.3: Descriptive statistics: Current migrants and return migrants

||Current|migrants|Return m|igrants||
|---|---|---|---|---|---|
||(1)<br>Mean|(2)<br>St. Dev.|(3)<br>Mean|(4)<br>St. Dev.|(5)<br>Diference|
|**_Individual characteristics_**||||||
|Age|34.511|9.647|42.997|10.068|-8.486***|
|Ever-married|0.748|0.435|0.939|0.239|-0.192***|
|No educational degree|0.195|0.397|0.275|0.447|-0.080***|
|Primary or preparatory education|0.089|0.285|0.153|0.360|-0.064***|
|Secondary education|0.470|0.499|0.414|0.493|0.056**|
|Above secondary education|0.246|0.431|0.158|0.365|0.088***|
|**_Relationship to head_**||||||
|Head|0.000|0.000|0.901|0.299|-0.901***|
|Spouse|0.579|0.494|0.000|0.000|0.579***|
|Son or daugther|0.355|0.479|0.089|0.285|0.266***|
|Brother or sister|0.029|0.168|0.006|0.077|0.023***|
|Other relations|0.037|0.188|0.004|0.067|0.032***|
|Observations|1,351||789|||

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

_Notes_ : Column 5 is t-test for whether the difference in means between the two groups is statistically significant.

Table A.4: Time variation in diplomatic representation

|Year|Percent of destination countries<br>with diplomatic representation in Egypt|
|---|---|
|1955|0.870|
|1960|0.760|
|1965|0.759|
|1970|0.862|
|1975|0.857|
|1980|0.457|
|1985|0.543|
|1990|0.865|
|1995|0.944|
|2000|0.944|
|2005|0.972|

_Notes_ : This table presents the share of countries with diplomatic representation in Egypt by year. Information on diplomatic representation from the Correlates of War Diplomatic exchange data set is available every five years between 1950 and 2005. We restrict the analysis to the full sample of all destination countries.

51
Table 2: Survival cohorts

||E|Senegal<br>ntry:200|1|E|Tanzania<br>ntry:200|4||Mali<br>Entry:200|5|E|Malawi<br>ntry:200|5|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Nr|Y-Exit|Exit|Nr|Y-Exit|Exit|Nr|Y-Exit|Exit|Nr|Y-Exit|Exit|
|**Firm**|||||||||||||
|2001|206||||||||||||
|2002|84|0.59|0.59||||||||||
|2003|57|0.32|0.72||||||||||
|2004|40|0.30|0.81|420|||||||||
|2005|35|0.13|0.83|194|0.54|0.54|273|||670|||
|2006|29|0.17|0.86|118|0.39|0.72|159|0.42|0.42|217|0.68|0.68|
|2007|24|0.17|0.88|85|0.28|0.80|123|0.23|0.55|154|0.29|0.77|
|2008|21|0.13|0.90|75|0.12|0.82|103|0.16|0.62|126|0.18|0.81|
|**Prod**|**uct**||||||||||||
|2001|2055||||||||||||
|2002|449|0.78|0.78||||||||||
|2003|192|0.57|0.91||||||||||
|2004|117|0.39|0.94|2656|||||||||
|2005|94|0.20|0.95|497|0.81|0.81|1047|||3322|||
|2006|78|0.17|0.96|200|0.60|0.92|305|0.71|0.71|325|0.90|0.90|
|2007|61|0.22|0.97|106|0.47|0.96|166|0.46|0.84|174|0.46|0.95|
|2008|54|0.11|0.97|71|0.33|0.97|123|0.26|0.88|127|0.27|0.96|
|**Prod**|**uct de**|**stinatio**|**ns**||||||||||
|2001|3326||||||||||||
|2002|718|0.78|0.78||||||||||
|2003|356|0.50|0.89||||||||||
|2004|245|0.31|0.93|4908|||||||||
|2005|167|0.32|0.95|837|0.83|0.83|1391|||3828|||
|2006|129|0.23|0.96|295|0.65|0.94|286|0.79|0.79|509|0.87|0.87|
|2007|101|0.22|0.97|167|0.43|0.97|122|0.57|0.91|316|0.38|0.92|
|2008|84|0.17|0.97|113|0.32|0.98|82|0.33|0.94|224|0.29|0.94|

Note: In the columns labelled _Nr_ we document for each origin country the number of firms products and destinations in the first available year, and follow this cohort of units over time. Column _Y-Exit_ shows the exit rate (ie. the share of units that left) with respect to the previous year, and column _Exit_ the exit rate with respect to the entry year.

13
selecting a location, entering a country and establishing an investment, running and expanding operations, and considering divestment. Concluding remarks and policy implications are offered in Section VI.

# **II. Data and Methodology**

The data used in this study are from the _Global Investment Competitiveness Survey_ . The GIC Survey captures perceptions of international business executives on the role of investment climate factors in their FDI decisions. The World Bank Group commissioned Kantar Public, a global survey firm, to conduct 30minute computer-assisted telephone interviews with 754 business executives involved with the operations of their MNC in developing countries. The sample frame consisted of nearly 8,000 eligible companies in the commercially available Dunn and Bradstreet database. The 754 respondents were investors with existing affiliates in at least one developing country. The interviews consisted of a screener phase, to ensure the eligibility of respondents. Respondents were a combination of executives at the global headquarters and executives at a foreign affiliate.

The interviews were conducted in 13 languages: Arabic, Bahasa Indonesia, Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish, and Turkish. The interviews were conducted between February and June 2017.

The survey complemented other existing investor surveys by focusing on investment climate variables, such as administrative and legal barriers, rather than broader economy-wide factors. These specific investment climate variables were areas actionable for policy makers.

The survey was composed of four sections:

1. _General information on the company and respondent_ , including sector, number of employees, and position of the respondent in the company.

2. _Importance of factors in investing in a developing country_ , where respondents rated the importance of country characteristics and investment policy factors on a scale from 1 to 4 from “not at all important” to “critically important.” “Critically important” means it is a dealbreaker—by itself, it could change the company’s decision about whether or not to invest in a country.

3. _Political risks and investment exit_ , where respondents identified experiences of political risks and the company’s course of action. They were also asked whether they had shut down a foreign affiliate in a developing country and their reasons for doing so.

4. _Investment in a specific developing country_ , where respondents selected a specific developing country where they were most familiar with the process of establishing an affiliate. Questions on the specific investment included sector, activity, motivation, reinvested earnings, efficiency of government agencies, incentives received, sources of inputs, and corporate programs for suppliers.

The survey was designed to provide a broad understanding of corporate perspectives and investor behavior and was not intended as a benchmarking tool to compare countries. Results are not representative at a country level.

3