rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Correlation Matrix, Panel of 76 Countries, 2003-2005**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|ENTRY (1)||||||||
|ENTRY PER CAPITA (2)|***<br>0.2728|||||||
|BUSINESS DENSITY (3)|0.574<sup>***</sup>|0.8731<sup>***</sup>||||||
|ENTRY PROCEDURES (4)|***<br>-0.2425|***<br>-0.5545|***<br>-0.5505|||||
|RIGIDITY OF EMPLOYMENT (5)|***<br>-0.2566|**<br>-0.1457|***<br>-0.2573|0.3153<sup>***</sup>||||
|GOVERNANCE (6)|0.3721<sup>***</sup>|0.5286<sup>***</sup>|0.5431<sup>***</sup>|***<br>-0.6322|***<br>-0.2811|||
|DOMESTIC CREDIT (% GDP) (7)|0.1297<sup>*</sup>|0.3988<sup>***</sup>|0.3472<sup>***</sup>|**<br>-0.5107|***<br>-0.3475|0.7031<sup>***</sup>||
|GDP PER CAPITA (8)|0.3096<sup>***</sup>|0.5504<sup>***</sup>|0.4753<sup>***</sup>|***<br>-0.5349|**<br>-0.1633|0.8173<sup>***</sup>|0.7066<sup>***</sup>|

Note: Asterisks, *, **, and ***, indicate significance at 10%, 5%, and 1%, respectively. Source: World Bank Group Entrepreneurship Database and World Bank (2007).

We use two different estimation methods: random-effects GLS and population-averaged Generalized Estimating Equations (GEE). In the latter a year trend was added as a control. In fixed-effects specifications (not shown) most of the variation in the sample was accounted for by the country dummies, thus providing no additional insight into the determinants of new firm registrations.

Table 3 presents the regression results based on the panel data. We find that entry rates are significantly related to better governance, even after controlling for GDP per capita. This finding is robust to the estimation method used, i.e. GLS or GEE. This suggests that government corruption and enforcement is the driving force in the decision of entrepreneurs to join the formal sector. Next, we find that entry per capita is significantly related to the number of entry procedures, access to finance, and economic development. This measure of new firm formation is independent of the previous history of entrepreneurship; hence, it is not surprising that GDP per capita turns out to be an important predictor. Finally, we find that business density is strongly and significantly related to lower barriers to entry and better governance (in the GEE model). These findings spotlight the importance of the business environment in formal private sector development and growth.<sup>22</sup>

> 22 We also added the corporate tax rate and the GDP growth rate to assess the impact of taxation and the business cycle, respectively. These two variables were not significant in any of the models. We also added to the models in Table 3 the interaction between GDP per capita and governance. This term reached significance, with a positive sign, only in model (5). However, the main effect of governance continued to be insignificant. This evidence seems to indicate that good governance is especially conducive to higher density in high-income countries.

25
differential is contained in Coelho, A.M. and Corseuil, C.H. “Diferenciais Salariais no Brasil: Um Breve Panorama”, _Discussion Paper_ , No. 898, IPEA, Rio de Janeiro (August 2002).

> 7 Psacharopoulos, G. and Tzannatos, Z. (eds.) _Case Studies on Women’s Employment and Pay in Latin America_ , (Washington: World Bank) (1992).

8 Stelcner, M., Smith, J. B., Breslaw, J. A. and Monette, G. (1992) “Labor Force Behavior and Earnings of Brazilian Women and Men, 1980”, in Psacharopoulos, G. and Tzannatos, Z. (eds.) _Case Studies on Women’s Employment and Pay in Latin America_ (Washington: World Bank) (1992): 39-88.

9 Tiefenthaler, J. “Female Labor Force Participation and Wage Determination in Brazil, 1989” in Psacharopoulos, G. and Tzannatos, Z. (eds.) _Case Studies on Women’s Employment and Pay in Latin America_ (Washington: World Bank) (1992): 89-118.

10 Loureiro, P.R.A. and Carneiro, F.G. “Discriminação no Mercado de Trabalho: Uma Análise dos Setores Rural e Urbano no Brasil”, _Brazilian Journal of Applied Economics_ , 5 (2001): 519-545. Using a different data set Kassouf also finds similar results. See: Kassouf, A.L. “Retornos à Escolaridade e ao Treinamento nos Setores Urbano e Rural”, _Revista de Economia e Sociologia Rural_ , 35 (1997): 59-76.

11 Leme, M.C.S. and Wajnman, S. “Tendências de Coorte nos Diferenciais de Rendimento por Sexo” in Henriques, R. (ed.) _Desigualdade e Pobreza no Brasil_ , IPEA, Rio de Janeiro (2000). A similar conclusion is drawn by Barros, R.P. de, Ramos, L. and Santos, E. “Gender Differences in the Brazilian Labour Market”, in Schultz, P.T., _Investments in Women’s Human Capital_ (Chicago: Chicago University Press) (1995).

18
|**P-value**<br>**(1) vs (2)**<br>0.16<br>0.06<br>0.22<br>0.18<br>0.56<br>0.71<br>0.26<br>0.18<br>0.02<br>0.98<br>0.27<br>0.01<br>status in all the independent covariates<br>(5)<br>**Opinion on Taxes**<br>0.009<br>(0.038)<br>0.000<br>2916<br>the mean and standard deviation of the<br>_∗_signifcant at the 1%, _∗∗_signifcant at|
|---|
|**Table (C.1)**<br>Testing Balance Between Groups (Second Wave)<br>**Means by Treatment Assigment**<br>**Priming Group**<br>**Control Group**<br>**Overall Sample**<br>**(1)**<br>**(2)**<br>**Mean**<br>**St. Deviation**<br>Male [=1]<br>0.52<br>0.55<br>0.54<br>0.5<br>Age<br>42.8<br>43.98<br>43.41<br>14.14<br>Ed: Secondary or More<br>0.98<br>0.98<br>0.98<br>0.14<br>Economic Strata: 1<br>0.09<br>0.08<br>0.08<br>0.28<br>Economic Strata: 2<br>0.33<br>0.31<br>0.32<br>0.47<br>Economic Strata: 3<br>0.39<br>0.39<br>0.39<br>0.49<br>Economic Strata: 4<br>0.13<br>0.15<br>0.14<br>0.35<br>Economic Strata: 5<br>0.05<br>0.04<br>0.04<br>0.21<br>Economic Strata: 6<br>0.01<br>0.03<br>0.02<br>0.15<br>Religious [Yes =1]<br>0.82<br>0.82<br>0.82<br>0.39<br>Ideology (1 Left - 10 Right)<br>5.34<br>5.25<br>5.3<br>1.9<br>Joint orthogonality test p-value<br>Sample Size<br>1,012<br>1,070<br>2,082<br>_Notes:_ The joint orthogonality test is a joint signifcance test of all the covariates from a multinomial logit of the treatment<br>listed in the table<br>**Table (C.2)**<br>Priming Effects of Altruism and Attitudes Towards Migrants (Second Wave)<br>Dep. Variable<br>(1)<br>(2)<br>(3)<br>(4)<br>**Altruism**<br>**Policy Altruism**<br>**Opinion on Effort**<br>**Opinion on Economy**<br>Treatment (Priming = 1)<br>-0.034<br>0.008<br>-0.037<br>-0.007<br>(0.038)<br>(0.037)<br>(0.037)<br>(0.037)<br>R-squared<br>0.000<br>0.000<br>0.000<br>0.000<br>Observations<br>2710<br>2892<br>2876<br>2874<br>_Notes:_ The dependent variable_Yi_ represents the outcome for individual_i_as measured in the survey and standardized using<br>control group. The variable_Treatment (Priming=1)_is the assigned treatment status to COVID priming of the individual. _∗∗_<br>the 5%, _∗_signifcant at the 10%. Standard errors are reported in parentheses.|

29
# **Section III: The profitability and productivity of male and female‐owned firms**

In this section, we investigate the differences in size and firm‐level performance between firms owned by women crossovers and non‐crossovers, as well as between crossover firms and those owned by men operating in the same sectors. We use the KASSIDA data set (discussed above) to document these differences and examine to what extent the differences are explained by production inputs and the scale of the operation.

The first place to start with this examination is the profit of different firms. Women will have incentives in crossing over if it is a profitable endeavor. Looking within female‐owned firms, Figure 1 shows the distribution of the logarithm of monthly profits for crossovers versus non‐crossovers. As per the literature on the importance of sectors in explaining gender gaps (Klapper and Parker 2011; Bardasi, Sabarwal and Terrell 2011), many of the women who cross over make higher profits than women who do not.

**Figure 1: Distribution of (log) monthly profits for women crossovers and non‐crossovers – Kassida data set**

<!-- Start of picture text --> Profit Distribution by Crossover<br>.4<br>.3<br>.2<br>.1<br>0<br>8 10 12 14 16<br>logmthprofit<br>Crossover(f)<br>Non-crossover(f)<br>kernel = epanechnikov, bandwidth = 0.4290<br>Density<br><!-- End of picture text --> In Figure 2, we examine the relative profits of female crossover firms versus male‐owned firms in the same sectors. This figure shows distributions of profit that are a lot more similar than those in Figure 8
higher average values for household size and all age categories of household composition (with the exception of number of household members above the age of 60) when compared to their non-NE counterparts. On the other hand, the opposite is true concerning the comparisons of these characteristics across pension recipient and non-recipient households.

Figure 1 shows 2002 household NE and old-age pension participation rates by per capita consumption deciles. We observe the poverty-targeting nature of the NE program as the percentage of program recipient households declines significantly with consumption level. While 32.3, 28.2 and 18.7 percent of the households participate in NE in the 1<sup>st</sup> , 2<sup>nd</sup> , and 3<sup>rd</sup> deciles, respectively, the participation rates in the 8<sup>th</sup> , 9<sup>th</sup> and 10<sup>th</sup> decile are 6.8, 1.7 and 3.8 percent, respectively. Conversely, household participation rates in old-age pension across consumption deciles are much more uniform, ranging from 36.7 percent in the 1st decile to 45.9 percent in the 10<sup>th</sup> decile.

# **5. Empirical Methodology:**

In order to compute unbiased estimates of the impact of a program on a set of outcomes, the researcher must estimate the counterfactual, i.e. what would have happened if the program had never been implemented. The estimation of the counterfactual is achieved by creating a control group, which is subsequently compared with a treatment group. The members of the control group should be identical to their counterparts in the treatment group in observed and unobserved characteristics, except for the fact that they do not participate in the program. For large enough samples, random assignment of an intervention would generate a control group that would be ex ante statistically indistinguishable from the treatment group. Control groups would then serve as sound counterfactuals, free from selection bias issues that often plague impact 16
Migration-Development Nexus: A Gravity Model Approach.” MPRA Paper 19227, University Library of Munich, Germany.

Levy, M. B., and W. J. Wadycki. 1974. Education and the Decision to Migrate: An Econometric Analysis of Migration in Venezuela.” _Econometrica_ 42 (2): 377–88. McFadden, D. 1974. “Conditional Logit Analysis of Qualitative Choice Behavior.” In P. Zarembka, ed., _Frontiers of Econometrics_ . New York, NY: Academic Press.

Menezes-Filho, N. A., and M.-A. Muendler. 2011. “Labor Reallocation in Response to Trade Reform.” NBER Working Papers 17372, National Bureau of Economic Research, Inc.

Moretti, E. 2011. “Local Labor Markets.” In O. Ashenfelter and D. Card, eds., _Handbook of Labor Economics_ . North Holland: Elsevier.

Morten, M., and J. Oliveira. 2014. “Migration, Roads and Labor Market Integration: Evidence from a Planned Capital City.” mimeo.

Muendler, M.-A. 2007. “Balassa (1965) Comparative Advantage by Sector of Industry, Brazil 1986–2001.” mimeo.

———. 2008. “Trade and Workforce Changeover in Brazil.” In Bender, Lane, Shaw, Andersson, and von Wachter, eds., _The Analysis of Firms and Employees: Quantitative and Qualitative Approaches_ . Chicago and London: The University of Chicago Press.

Muriel, B., and C. Terra. 2009. “Sources of Comparative Advantage in Brazil.” _Review of Development Economics_ 13 (1): 15–27.

Ortega, F., and G. Peri. 2013. “The Effect of Income and Immigration Policies on International Migration.” _Migration Studies_ 1 (1): 47–74.

Redding, S., and P. K. Schott. 2003. “Distance, Skill Deepening and Development: Will Peripheral Countries Ever Get Rich?” _Journal of Development Economics_ 72 (2): 515–41.

Redding, S., and A. Venables. 2004. “Economic Geography and International Inequality.” _Journal of International Economics_ 62 (1): 53–82.

Wagner, J. 2012. “International Trade and Firm Performance: A Survey of 34
At the start of the project, some 2500 families in the area lived in precarious stilt houses ( _palafitas_ ) informally constructed over the inlet. Conditions were quite dangerous, particularly for children who would frequently fall into the water, in some cases leading to death.

The Ribeira Azul Program combines physical interventions with investments to improve the social and economic conditions of the area’s population. This includes housing and infrastructure improvements (roads, water, sanitation, public lighting), and programs in health care, child nutrition, education, training, and employment generation through cooperatives. Community participation has been a fundamental aspect of the program. The project has been implemented by CONDER (Urban Development Company of the State of Bahia), and AVSI (Association of Volunteers for International Service), the Italian NGO implementing the social interventions working in partnership with a local NGO, CDM (cooperação para o desenvolvimento e morada humana).

# **A. PSIA Approach and Methodology**

The PSIA work focused on answering four key issues which would have implications for scaling up. These include:

- _What has worked well with the integrated urban upgrading approach and what has not?_

- _How cost effective were the interventions?_

- _With the multi-sectoral approach, what are the institutional arrangements, how have they worked?  What are the lessons learned?_

- _What are the main sustainability issues and how will these impact on the poor over time_ ?

Several methodological approaches were used for carrying out the PSIA, combining quantitative and qualitative tools. The area of study was the Novos Alagados II neighborhood in the Ribeira Azul area, chosen because here the full range of physical, economic, and social interventions have taken place.

For the first issue of analysis, what has worked well and what has not, an extensive qualitative study was carried out in Novos Alagados II involving focus group discussions and in-depth interviews. Focus groups were organized around the themes of housing, education, health, employment/cooperatives, and institutional actors. Cross cutting themes that were discussed include specific issues of children and youth, leisure, violence, knowledge of the program and perceptions of the integrated approach.

The cost effectiveness analysis used available data on costs, physical interventions, and number of beneficiaries provided by the government agency CONDER (Urban Development Company of the State of Bahia) and AVSI, CONDER’s partner in implementing the social interventions. A unit cost was calculated for 12 indicators. While assessing the relative value of each cost can be somewhat subjective, the data 2
# **Acknowledgements**

This working paper is based on the report “Measuring Financial Performance in Infrastructure” (World Bank, 2006). The report draws upon information from a variety of sources including publicly available information and information available to the World Bank as a result of studies commissioned by the Bank, undertaken by Bank staff or otherwise provided to the Bank. The World Bank has used the most up to date data that was available to it in preparing this report.

The report was prepared by a team from the Infrastructure and Energy Unit of the Europe and Central Asia Region of the World Bank under the guidance and supervision of Lee Travers (Sector Manager ECSIE). Task team members comprised: Jane Ebinger, Alexander Danilenko, Maka Lomaia, Subramaniam Janakiram, Soumya Chattopadhyay, Sanjay Sinha, Cem Alkan, Alfiya Mirzagalyamova, Josephine Kida and Larisa Marquez.

The report benefited from review, comments and input from: Paul Amos, Michel Audige, Alex Bakalian, Ani Balabanyan, Cecilia Briceno-Garmendia, Bojan Borojevic, Jean-Charles Crochet, Mark Davis, Istvan Dobozi, Anca Dumitrescu, Ben Eijbergen, Lev Freinkman, Katherina Gassner, Sandu Ghidirim, Ellen Hamilton, Richard Hamilton, Marat Iskakov, Serdar Japbarov, Peter Johansen, Peter Kelly, David Kennedy, Henri Kerali, Iftikhar Khalil, Anupam Khanna, Sunja Kim, Elena Klochan, Martha Lawrence, Astrid Manroth, Farid Mamedov, Joseph Melitauri, Yuri Miroshnichenko, Celestin Monga, James Moose, Ana Otilia Nutu, Eric Petersen, Silvia Poghinu, Taras Pushak, Cesar Quieroz, Lulin Radulov, Marianne Fay, Peter Roberts, Gevorg Sargsyan, Raghuveer Sharma, George Tharakan, Peter Thomson, Cordula Thum, Gleb Zinoviev, and many others who spent time making the review possible.

4
<mark>Lastly, in addition to the aforementioned, our analysis holds implications for enhancing the design of future longitudinal surveys to produce more and better data for addressing critical gaps in climate migration research. Desired improvements concern the collection of relevant contextual information regarding the ‘migrants’ in order to accurately classify different types of migration and unravel more clearly their drivers and impacts. This includes details about the nature and duration of migration episodes, as well as the characteristics, history, and purposes of migrant individuals. By gathering such additional information and carefully tracking migrant individuals over time, we could create migration variables that go beyond merely measuring the presence or absence of migrants within a household and provide more nuanced evidence to better inform targeted policies and programs.</mark> The rest of the paper is arranged as follows. Section 2 outlines the conceptual framework. Section 3 presents data sources and descriptive statistics, while Section 4 describes the methodology. Section 5 illustrates the results of the empirical analysis and Section 6 concludes.

# **2. Conceptual framework**

Our conceptual framework is placed at the crossroads of the NELM and poverty traps literatures. The distinctive features of the NELM theory, in contrast to the neoclassical counterfactual, lie in its consideration of migration decisions as household-driven rather than individual-oriented in the presence of incompleteness in insurance and credit markets (Stark & Bloom, 1985). Moreover, the objective function is not limited to maximizing expected income, but also includes minimizing risk exposure (Millock, 2015). Within this framework, where formal risk management tools are absent, households manage risk by strategically allocating family workers to diverse and uncorrelated (i.e., geographically dispersed) labor markets with the aim of diversifying income streams (e.g., via remittances). In the context of rational behavior models, households facing higher levels of risk assess the expected remittances against the marginal contribution of each family member to local agricultural productivity under stress. This establishes a connection between migration and agricultural productivity, the direction of which is context-specific and depends—in addition to other determinants such as factor endowments, technology, etc.—on the relative scarcity or abundance of agricultural workers. The poverty traps literature, on the other hand, delves into the self-reinforcing mechanisms whereby households risk being pushed below 6
Policy Research Working Paper 7700

# **Abstract**

The high level of exports and their product and market concentration exposes the Democratic Republic of Congo to the economic fluctuations of the country’s trade partners. This paper uses the United Nations Conference on Trade and Development trade data set to analyze the Democratic Republic of Congo’s export patterns for the period 1960–2014. The data confirm that the country’s exports remain highly concentrated. The product concentration on minerals is high and reaches exceptional levels. The geographic concentration is also high, while there is a shift in destinations. Hence, EU27, traditionally the main market destination for the Democratic Republic of Congo, lost its importance to China for most of the past decade. This trend continued to increase in the past few years from 2010 to 2013. The clear prevalence of commodity products within the Democratic Republic of Congo’s exports and the higher exposure to the Chinese economic cycle are sources of vulnerability. The empirical analysis indicates that the country’s exports appear to be significantly sensitive to foreign demand fluctuations. This exposure increases the volatility of the country’s macroeconomic framework to exogenous shocks, with negative consequences on growth in gross domestic product and on external balances. The analysis concludes that increasing the Democratic Republic of Congo’s resilience requires product and market diversification of exports. This diversification requires improvements in the investment climate and business environment, with emphasis on skills and infrastructure.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. 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 cbouhabib@worldbank.org and ekebede@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. Tractors Used in Agricultural Production ('000)

||1985|1986|1987|1988|1989|1990|
|---|---|---|---|---|---|---|
|Bulgaria|55|54|54|54|n/a|n/a|
|Czechoslovakia|137|139|140|141|n/a|n/a|
|GDR|158|162|165|168|n/a|n/a|
|Hungary|55|54|54|53|52|50|
|Poland|924|989|1,044|1,101|1,153|n/a|
|Romania|184|194|184|183|n/a|n/a|
|USSR|2,798|2,854|2,735|2,692|2,689|2,630|
|World|24,504|25,284|25,535|25,865|n/a|n/a|

n/a - Not available.

_Source:_ Agriculture _and Food Processing, 1990,_ Hungarian Mlnistry of AgrLculture, Budapest.