rafmacalaba/datause-encoder-data huggingface dataset:
marked effect on graduates’ choices. In particular, the lower are the relative wages or wage growth in teaching, the less likely is a graduate to choose that career. These earning effects operate on initial choices and choices made later in an individual’s career. Dolton and Chung (2004) compared the earnings of qualified teachers who choose to teach with the opportunity wage for those who do not teach. They find that the rate of return on career choice for teachers has been declining for both men and women over the past 25 years although teaching is still relatively well paid for women.

There are also a few studies on the earnings effect on the aggregated teacher labor market. Thomas (1975) uses UK time series data between 1962 and 1970, and finds significant salary effects suggesting a 1 percent fall in relative starting salaries will induce a 2-5 percent fall in the relative supply of male graduates entering teaching and similar effects for average salaries of teachers. Effects for female graduates are up to twice as big. Zabalza, Turnbull and Williams (1979) also used UK time series, and estimated that the elasticity of graduate new entrants with respect to wage equals 2.4-3.9 for men and 0.3-1.8 for women; and that with respect to relative starting wages, it is 3.4 for men and 2.8 for women. Court et al. (1995) updated the time-series study in the UK, extending the study period to 1986-1992. The salary effect is still strong, suggesting that a 1 percent fall in relative starting salaries will induce a 4 percent fall in the relative supply of graduates entering teaching.

One recent study outside the US and UK is by Ortega (2006). Using Venezuelan data, his results suggest that the teacher wage premium and wage dispersion have little effect on the quality mix of applicants to teaching. Most students’ preference for teaching 4
<!-- Start of picture text --> Dry Bulk Fleet Composition<br>re<br>60%<br>50%<br>40% Size Category<br>2 —® Handysize<br>3 30% —® Handymax<br>“ —® Panamax<br>20% ~® = Capesize<br>40% a mn<br>0%<br>1995 2000 2005 2010 «2015 382020 1995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
Voraberger (2003) and Breuss and Steininger (1998) use a CGE model of the Austrian economy to compare the relative macroeconomic and GHG impacts of policies that support the production heat, electricity, and/or fuels from biomass. They find that the impact on gross domestic product (GDP) and employment differs widely for different biomass technologies. But the more interesting conclusion that arises from their analysis is that a combination of policies like recycling revenues from taxation of fuel as a subsidy for biomass technologies has better impact than a policy based purely on either subsidies or taxes. Reilly and Paltsev (2007) use a model called MIT Emissions Prediction and Policy Analysis (EPPA) model to simulate the demand for land necessary for achieving different stabilization levels of GHG concentrations in the coming decades. Their main conclusion is that large increase in domestic biofuel production would result in the United States becoming a net importer of food as opposed to an importer of oil.

2. **Models that emphasize international trade** : A second set of models focus on the impacts of trade liberalization in agriculture and its impact on the production and prices of agricultural commodities. Elobid and Tokgoz (2006) simulate the impact of the removal of trade tariffs and federal tax credit in the United States on production, consumption, and trade using a multimarket international model for ethanol. Their model is calibrated on 2005 market data. They find that with the removal of trade distortions, the world ethanol price increases as demand for ethanol, and therefore imports, increases in the United States. Brazil would benefit in a big way from the removal of the U.S. duties. As more sugarcane is diverted toward the production of ethanol, the price of raw sugar increases. But the marginal effect of removal of the federal tax credit for refiners blending ethanol is a reduction in the refiners’ demand for ethanol, prompting a reduction in imports and a corresponding decline in the world ethanol price. Thus, the net effect of the removal of both the tariff and the tax credit is to temper the increase in imports and the increase in demand for ethanol. Gohin and Moschini (2007) perform a numerical simulation of the EU biofuel directive using a farm-detailed 50
# **Appendix Table 3: Definition and Sources of variables used in Regression Analysis**

|**Variable**|**Definition**|**Source**|
|---|---|---|
|SME Loans|Total SME loans for a bank expressed as a percentage of total loans|UAB and World Bank SME Survey|
|Investment Loans|Total SME loans for investment purposes expressed as percentage of total SME loans|UAB and World Bank SME Survey|
|Log Total Loans|Logarithm of total loans for a bank|UAB and World Bank SME Survey<br>and Bankscope Database|
|GCC Dummy|Dummy variable that takes value 1 if a bank is in GCC region and 0 otherwise|World Bank|
|State Ownership Dummy|Dummy that takes value 1 if a bank is owned by government and 0 otherwise|UAB and World Bank SME Survey|
|Log Number of Branches|Logarithm of total number of branches for a bank|UAB and World Bank SME Survey<br>and Bankscope Database|
|Separate Unit for SME Clients|Dummy variable that takes value 1 if a bank has a separate unit for SMEs and 0 otherwise|UAB and World Bank SME Survey|
|Maximum Coverage Registry<br>or Bureau|Highest coverage ratio of either the public registry or the private bureau and includes the<br>number of individuals and firms listed with information on their borrowing history from past<br>fiveyears|World Bank‟s Doing Business|
|Legal Rights Index|The degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders<br>and thus facilitate lending. The index ranges from 0 to 10, with higher scores indicating better<br>protection|World Bank‟s Doing Business|
|Time to Register Property|Number of days that property lawyers, notaries or registry officials indicate is necessary to<br>complete aprocedure.|World Bank‟s Doing Business|
|Time to Enforce a Contract|Number of days counted from the moment the plaintiff decides to file the lawsuit in court until<br>payment. This includes both the days when actions takeplace and the waiting periods between|World Bank‟s Doing Business|
|Recovery rate for Closing a<br>Business|Recorded as cents on the dollar recouped by creditors through reorganization, liquidation or<br>debt enforcement(foreclosure) proceedings|World Bank‟s Doing Business|
|Time to Close a Business|Number of years for creditors to recover their credit. The period of time measured is from the<br>company‟s default until thepayment of some or all of the moneyowed to the bank.|World Bank‟s Doing Business|
|Partial Credit Guarantee|Outstandingcreditguarantees expressed as apercentage of GDP|Saadani,Arvai,and Rocha(2010)|
|Median Coverage of PCG<br>Scheme|Median share of loan guaranteed|Saadani, Arvai, and Rocha (2010)|
## **Table 7: Structure of Employment**

|||**Base**|**Reform**|**Reform,**<br>**Prices**|**Reform,**<br>**Prices &**<br>**Phase**<br>**out**|
|---|---|---|---|---|---|
||2013/14|2024/25|2024/25|2024/25|2024/25|
|||% To|tal Employ|ment||
|**Energy Sector**|1.8%|1.5%|1.5%|1.2%|1.2%|
|**Agriculture**|18.3%|18.2%|17.8%|17.5%|17.1%|
|**Other Mining and**|13.0%|12.4%|12.7%|12.8%|13.0%|
|**Manufacturing**||||||
|**Water and Sanitation**|0.8%|0.8%|0.7%|0.7%|0.7%|
|**Construction**|11.0%|11.9%|12.8%|14.6%|17.1%|
|**Transportation**|7.3%|7.5%|7.0%|6.8%|6.2%|
|**Other Services**|47.9%|47.7%|47.6%|46.3%|44.7%|

_Source:_ Authors estimates

## **Table 8: Employment Growth**

||**Base**<br>Annu<br>|**Reform**<br>al % Change 2<br>|**Reform,**<br>**Prices**<br>013/14 - 2024<br>|**Reform,**<br>**Prices &**<br>**Phase out**<br>/25<br>|
|---|---|---|---|---|
|**Energy Sector**|0.3%|0.1%|-1.2%|-1.5%|
|**Agriculture**|1.8%|1.8%|1.7%|1.7%|
|**Other Mining and Manufacturing**|1.7%|1.9%|2.1%|2.3%|
|**Water and Sanitation**|1.5%|1.0%|1.0%|0.7%|
|**Construction**|2.7%|3.5%|4.9%|6.5%|
|**Transportation**|2.0%|1.7%|1.6%|0.8%|
|**Other Services**|2.1%|2.0%|1.9%|1.7%|

_Source:_ Authors estimates

# 6.  Conclusions

The results presented here demonstrate the extent of the effects that the July 2014 reforms can be expected to have on the Egyptian economy in the short and the long-term. An advantage of undertaking this analysis with a dynamic CGE model is that it captures in a consistent way the direct and indirect effects of the current and possible future reforms, international energy price movements, and the introduction of offsetting transfers for the poor.

Large energy subsidies have introduced major distortions to the Egyptian economy that have led to significant misallocations of resources and reduced productivity, impeding economic 18
**Maddala G.S.** : “Limited Dependent and Qualitative Variables in Econometrics”, _Econometric Society Monographs_ , Cambridge University Press 1983

**Martínez Hermes, Medina Carlos and Roberto Steiner** : “Violence, Inequality, Poverty and Drug Prohibition in Colombia”, CEDE, Universidad de Los Andes, June 2001

**McLanahan Sara** : “Family Structure and Stress: A Longitudinal Comparison of TwoParent and Female-Headed Families”, _Journal of Marriage and the Family_ , Vol. 45, No. 2 (May, 1983) , pp. 347-357

**Martínez Hermes and Carlos Medina** : “Violence and Drug Prohibition in Colombia”, Crisis States Programme, DESTIN, LSE, _Working Paper No. 32_ , August 2003

**McMillan John, Johnson Simon, and Christoher Woodruff** . "Property Rights And Finance," American Economic Review, v92(5,Dec), 1335-1356, 2002

**Ministerio de Defensa Nacional** , República de Colombia: “Homicide and Arms Control in Colombia”, _Serie 2001 No.2_ , Bogota July 2001

**Moser Caroline** : “Colombia: Essays on Conflict, Peace and Development”, World Bank, 1999

**Moser C and C McIlwaine:** “La Violencia y la Exclusión en Colombia – según la Percepción de Comunidades Urbanas Pobres”, _World Bank_ , 2000

**Pinto Maria, Vergara Andrés and Yilberto Lahuerta** : “Costos Generados por la Violencia Armada en Colombia 1999-2003”, Archivos de Economía, Departamento Nacional de Planeación, 2005

**Poterba James** (ed.): “International Comparisons of Household Saving”, _University of Chicago Press_ , 1994

**Red Cross, International Committee (CICR):** “Informe de Actividades CICR”, Colombia, 2004

**Rosenbaum Paul and Donald Rubin** : “The Role of the Propensity Score in Observational Studies for Causal Effects”, _Biometrika Vol. 70, No. 1_ , pp. 41-55, 1983

**Ross Stephen, Westerfield Randolph and Jeffrey Jaffe** : “Corporate Finance”, Fourth Edition, Irwin, 1996

**Rubio Mauricio** : “Criminalidad Urbana en Colombia”. _Documento de Trabajo No. 2, Paz Pública_ , CEDE Uniandes, 1997b

**Rubio Mauricio** : “La Violencia en Colombia: Dimensionamiento y Políticas de Control”, _Latin American Research Network Working Paper_ , IADB, 1998

**Rubio Mauricio** : “Los Costos de la Violencia en América Latina”, _Programa de Salud y Desarrollo_ , IADB, 1999a

**Rubio Mauricio** : “Crimen e Impunidad: Precisiones sobre la Violencia.” Bogotá, Tercer Mundo Editores y CEDE, 1999b.

46
6 <!-- Start of picture text --> Figure 1<br><!-- End of picture text --> CHILE: MINIMUM WAGE INDICES <!-- Start of picture text -->
1.6  -r<br>1.7<br>1.21<br>1.3X 1.1  -'<br>0.9<br>0.6<br>0.7<br>0.6<br>0.5<br>0.4-<br>0.3<br>1969 1970 1971 1972 1973 1974 1975 1976  1977 1976 1979 1960 1981 196 1963 1964 1965<br>0  WIN/W  +  WMIN/CPI  O  WMIN/MPI<br><!-- End of picture text -->

period will very much depend upon other<sup>(unrevealed)</sup> macroeconomic aspects determining the effectiveness of a certain MW level. Moreover, selectivity bias affecting OLS estimates could make the<sup>comparisons between fitted and actual</sup> wages very unreliable, particularly if the bias does not remain constant throughout time.

# IV. The Model and the Hypotheses

In this paper we apply a correction<sup>for selectivity bias to estimate an</sup> earnings function for several different<sup>years. The basic idea is the same</sup> underlying the Linneman-Castaned **a**<sup>pproach, but our test does not rely upon any</sup> specific judgment about an initial year<sup>serving as a reference for comparisons.</sup>
and adapt the Exit/Voice/Loyalty theoretical framework as a game between cross-border traders and border officials to better understand the theory of change and generate testable hypotheses. Section 4 details the experimental design, describes the data, lays out the econometric approach, and presents the limitations of the study. Section 5 assesses the impact of the training on traders’ knowledge, border-crossing time and intensity, corruption, and GBV. Section 6 analyzes the degree to which the training effects are driven by the traders’ use of voice or by lower bribe demand from border officials. We conclude with a set of policy implications from these results in Section 7.

# **2 Context and intervention**

## **2.1 Context**

Small-scale, informal cross-border trade – sometimes misperceived as illegal smuggling in parts of Africa (Golub, 2015) – provides opportunities for gains from trade and reduced price dispersion across neighboring countries in the Great Lakes Region of Africa.<sup>1</sup> Yet, cross-border traders face demands for bribes which pose a threat to their small-scale trading businesses in the Democratic Republic of Congo, which is ranked 161st out of 180 countries in the Corruption Perceptions Index of 2018 according to Transparency International and well-known as a fragile and war-affected zone, particularly in its eastern provinces.<sup>2</sup> Similarly, the World Bank’s Doing Business rankings place DRC 188th out of 190 countries in terms of the time and cost to trade across borders, while Rwanda’s more favorable institutional environment is ranked 88th (World Bank, 2018).

Demands for bribes also coincide with gender-based violence (GBV) during border crossings given the gender dimensions of cross-border trade. This is particularly important in DRC, where sexual violence is prevalent and the large majority of cross-border traders are women and most border officials are men. According to the most recent Demographic and Health Survey data from DRC, 28% of women aged 15-49 in North Kivu and 35% of those in South Kivu have experienced sexual

> 1In 2011, the DRC Government issued a decree officially banning the collection of taxes on the import of foodstuffs (including vegetables and fruits) and addressing the perceptions of cross-border trade as being illegal (Government of the DRC, 2011). While most of the traders engaged in cross-border trade are not smugglers, some smuggling of banned products (e.g., charcoal from Rwanda) does indeed occur along the border (Titeca and Kimanuka, 2012).

> 2This study took place during a period of heightened tension between the DRC and Rwanda against the backdrop of the “March 23” rebel group’s armed conflict with the DRC Government (Rono, 2013), with temporary curfews and border closures also occurring.

5
Policy Research Working Paper 7640

# **Abstract**

A growing literature aiming at explaining differences across firms in productivity and access to global export markets has focused on the internal organization of firms. This paper contributes to this literature by evaluating the impact of a program that focuses on enhancing competitiveness of small and medium enterprises in Brazil by providing coaching and consulting on management and production practices. Specifically, the paper tests whether the program induces treated firms to reorganize knowledge by adding more layers of different skills and competencies to their workforces. Using a unique firm-level dataset, the number of layers of the firms are compared before and after the program. The impact of the program is identified by relying on an instrumental variable approach, exploiting the quasi-experiment roll-out of its implementation, which was carried out at different times across Brazilian regions. The analysis finds that the program had an effect and that this effect is heterogeneous. The program is particularly effective in promoting the reorganization of firms with initially fewer layers. The results confirm another finding of the literature, namely that in re-organized firms inequality of wages increases, as firms pay higher wages in added higher layers than in pre-existing ones. Finally, these results are used to discuss how the change in firms’ organization is positively correlated with export performance.

This paper is a product of the Development Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at marciocruz@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 6. Labor Supply Response to Health Shocks**

||(1)<br>Saw Any<br>Clients|(2)<br># of Clients|(3)<br># of Regular<br>Clients|(4)<br># of Casual<br>Clients|(5)<br>Sex Work<br>Income|(6)<br>Other<br>Income|(7)<br>Total<br>Income|
|---|---|---|---|---|---|---|---|
|Mean of Dependent Variable^|0.837|1.624|0.600|1.011|720.361|88.831|809.192|
|**Panel A. Household Sickness**||||||||
|Somebody in Household|0.026|0.077|0.002|0.073|49.503|4.473|53.976|
|(other than respondent) Sick|(0.013)**|(0.035)**|(0.019)|(0.029)**|(20.034)**|(5.910)|(20.227)***|
||[0.031]|[0.047]|[0.003]|[0.072]|[0.069]|[0.050]|[0.067]|
|Respondent Sick|-0.082|-0.169|-0.045|-0.122|-105.308|-2.230|-107.537|
||(0.018)***|(0.044)***|(0.023)**|(0.031)***|(22.612)***|(4.845)|(22.752)***|
||[-0.098]|[-0.104]|[-0.075]|[-0.121]|[-0.146]|[-0.025]|[-0.133]|
|Observations|12293|12293|12293|12293|12293|12293|12293|
|Number of women|192|192|192|192|192|192|192|
|**Panel B. Sexually Transmitted I**|**nfection(STI)**|||||||
|STI|-0.226|-0.417|-0.077|-0.350|-155.124|-29.210|-184.333|
||(0.049)***|(0.117)***|(0.062)|(0.084)***|(61.423)**|(8.763)***|(64.838)***|
||[-0.270]|[-0.257]|[-0.128]|[-0.346]|[-0.215]|[-0.329]|[-0.228]|
|First Day After STI|0.059|0.274|0.115|0.155|229.805|-25.734|204.072|
||(0.042)|(0.090)***|(0.056)**|(0.087)*|(88.362)**|(13.271)*|(89.040)**|
||[0.070]|[0.169]|[0.192]|[0.153]|[0.319]|[-0.290]|[0.252]|
|Observations|10393|10393|10393|10393|10393|10393|10393|
|Number of women|192|192|192|192|192|192|192|

Note: All regressions are fixed effects regressions with controls for the month and for the day of the week. Clustered standard errors (at the individual level) in parentheses. All regressions include controls for the round of data collection. Sickness is an indicator variable equal to 1 if respondent reports a cough, fever, malaria, typhoid, diarrhea, cuts, burns, or other illnesses. ^Means of dependent variables are means when all shocks are equal to 0. * significant at 10%; ** significant at 5%; *** significant at 1%
# TABLE III

# KNOWLEDGE ON BILL AMOUNT AND PRICE DETERMINATION MECHANISM

|Last amount billed||
|---|---|
|Question|% Yes|
|Do you remember the amount of your last bill?|91.8|
|Price determination mechanism – Perceived knowledge||
|Question|% Yes|
|Do you know how the total amount of the bill is computed?|30.7|
|Price determination mechanism – Objective knowledge||
|Question<br>Correct Answer|% Correct Answer|
|How often does the company re-categorize consumers?<br>Every billing period|14.4|
|Re-categorization is calculated based on...<br>Last year’s consumption|38.9|
|What is the consumption level that divides categories R32 and R33<br>1500 m<sup>3</sup>|3.7|

_Notes_ : Results from a phone survey of 353 customers that had an annual accumulated consumption between 1480 and 1520 m<sup>3</sup> in the bill issued in May 2009. For the questions about objective knowledge, four alternatives were presented. In the question “How often the company re-categorize consumers?” the options were: a) every billing period, b) every two billing periods, c) every six billing periods, d) other. In the question “Re-categorization is calculated based on ...” the options were: a) difference in consumption between the current bill and the previous; b) last year’s consumption; c) last semester consumption; d) other. In the question “What is the consumption level that divides categories R32 and R33” the options were: a) 1000 m<sup>3</sup> ; b) 2000 m<sup>3</sup> ; c) 1,500 m<sup>3</sup> ; d) Does not know.

28
|EnergyEfficiencyGEF Project<br>Varadarajan Atur|
|---|
|EnergyEfficiencyGEF Project<br>Varadarajan Atur|
|EnergyEfficiencyGEF Project<br>Varadarajan Atur|
|EnergyEfficiencyGEF Project<br>Varadarajan Atur|
|EnergyEfficiencyGEF Project<br>Varadarajan Atur|
|EnergyEfficiencyGEF Project<br>Varadarajan Atur|
|EnergyEfficiencyGEF Project<br>Varadarajan Atur|
|EnergyEfficiencyGEF Project<br>Varadarajan Atur|
|Laos PDR FMAC<br>Kazi Mahbub-Al Matin|
|Laos PDR FMAC<br>Kazi Mahbub-Al Matin|
|Laos PDR FMAC<br>Kazi Mahbub-Al Matin|
|Mongolia Financial Sector Adjustment Credit<br>XiaofengHua|
|Mongolia Financial Sector Adjustment Credit<br>XiaofengHua|
|Mongolia Financial Sector Adjustment Credit<br>XiaofengHua|
|Mongolia Financial Sector Adjustment Credit<br>XiaofengHua|
|Mongolia Financial Sector Adjustment Credit<br>XiaofengHua|
|Mongolia Financial Sector Adjustment Credit<br>XiaofengHua|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|Environmental Protection and Sustainable Development of the Gua Douglas C. Olson|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|EnergyEfficiencyProject<br>Lucia Spinelli|
|MARINE ELECTRONIC HIGHWAY DEMONSTRATION PROJECT<br>Mustapha Benmaamar|
|MARINE ELECTRONIC HIGHWAY DEMONSTRATION PROJECT<br>Mustapha Benmaamar|
|MARINE ELECTRONIC HIGHWAY DEMONSTRATION PROJECT<br>Mustapha Benmaamar|