rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4** : Heterogeneity in Effect of Life Expectancy at Birth on Earnings

|||**Males**|||**Females**||
|---|---|---|---|---|---|---|
||I|II|III|I|II|III|
|Age|0.103***<br>[0.018]|0.084***<br>[0.018]||0.069***<br>[0.011]|0.054***<br>[0.007]||
|Age-squared*(1/100)|-0.10***<br>[0.000]|-0.10***<br>[0.000]||-0.10***<br>[0.000]|-0.10***<br>[0.000]||
|% Urban|1.252***|0.947***|0.892***|0.907***|0.933***|0.949***|
||[0.475]|[0.201]|[0.154]|[0.121]|[0.130]|[0.151]|
|% Male|||||||
|Life expectancy at birth|**_0.011_**<br>**_[0.008]_**|**_0.010_**<br>**_[0.007]_**|**_0.012**_**<br>**_[0.006]_**|**_0.009**_**<br>**_[0.004]_**|**_0.007**_**<br>**_[0.003]_**|**_0.009***_**<br>**_[0.003]_**|
|Parents’ life expectancy||0.002|0.008*||0.001|0.004*|
|||[0.004]|[0.004]||[0.003]|[0.002]|
|% Married||0.472**<br>[0.180]|0.578***<br>[0.144]||0.429***<br>[0.125]|0.434***<br>[0.074]|
|Cohort size||-12.837***|-12.065***||-9.243|-10.247|
|||[0.643]|[1.059]||[7.188]|[7.816]|
|Birth-year Fixed Effects|||YES|||YES|
|<br>Constant|3.796***|4.350***|5.671***|4.596***|4.905***|5.708***|
||[0.893]|[0.980]|[0.562]|[0.370]|[0.334]|[0.324]|
|N|4204|3878|3878|4149|3822|3822|
|Adjusted R-square|0.997|0.998|0.998|0.997|0.998|0.998|
|||**Urban**|||**Rural**||
|Age|0.096***<br>[0.012]|0.083***<br>[0.010]||0.069***<br>[0.013]|0.054***<br>[0.015]||
|Age-squared*(1/100)|-0.10***<br>[0.000]|-0.10***<br>[0.000]||-0.10***<br>[0.000]|-0.10***<br>[0.000]||
|% Urban|||||||
|% Male|0.495***|0.377***|0.103|0.193|0.124|0.074|
||[0.091]|[0.074]|[0.091]|[0.140]|[0.133]|[0.147]|
|Life expectancy at birth|**_0.012***_**|**_0.011***_**|**_0.010***_**|**_0.002_**|**_0.003_**|**_0.007_**|
||**_[0.004]_**|**_[0.003]_**|**_[0.003]_**|**_[0.008]_**|**_[0.009]_**|**_[0.007]_**|
|Parents’ life expectancy||0.002|0.005||0.007|0.011**|
|||[0.002]|[0.003]||[0.006]|[0.005]|
|% Married||<br>0.294***|<br>0.501***||<br>0.534**|<br>0.592***|
|||[0.089]|[0.095]||[0.237]|[0.124]|
|Cohort size||-7.169*|-5.264||-7.316***|-6.624***|
|||[4.065]|[3.542]||[1.637]|[2.369]|
|Constant|4.337***|<br>4.653***|<br>6.396***|5.706***|<br>5.380***|<br>5.988***|
||[0.543]|[0.473]|[0.336]|[0.900]|[1.086]|[0.642]|
|N|4200|3874|3874|3959|3657|3657|
|Adjusted R-square|0.998|0.999|0.998|0.996|0.996|0.996|

Note: Standard errors in brackets. Significance levels: * p<0.05, ** p<0.01, *** p<0.001.

42
declined fourteen-fold between 1961-73 and 1980-87. flOI During 1980-88, the average productivity of new investment was only 7 percent, as shown in Chart 2.

The main causes for the low, and declining efficiency of capital in Sub-Saharan Africa can be traced to distortions in the following policy areas: poor public sector management including bad public investments; inadequate or inappropriate financial intermediation; price distortions will led to less productive resource allocation; and inadequate institutional and human capital development. Sustainable long-term growth will require decisive policy reform in all these areas to reverse the declining trend of capital efficiency and to achieve the levels required to reach the output growth targets.

Public spending decisions strongly affect the efficiency of overall investments. The role of the government is particularly large in Sub-Saharan African countries -- total public expenditures were more than 27 percent of GNP in 1986 (compared to 19 percent in low-income countries outside Africa). Although no estimate of the return on total public investments is available, Sub-Saharan Africa: From Crisis to Sustainable Growth presents examples of poorly justified and highly inefficient public investments. There has been duplication and waste, and several investments have been overdimensioned. In addition to the lack of operation of market mechanisms or alternative objective criteria to evaluate public investment projects, the rapid increase in foreign financing in the 1980s may have tended to relax the rigor of investment decisions. Investments in new facilities and equipment have also been preferred over maintenance of existing ones, resulting in a marked deterioration of existing capital stock, thereby reducing overall productivity.

J/ Based on the estimate from Sub-Saharan Africa: From Crisis to Sustainable Growth, which assumes a depreciation rate of 10 percent of GDP.

28
In contrast, the other categories provided by SENAI are aimed at training a new generation of practitioners of a trade or profession. Apprenticeship consists of on-the-job training and often some accompanying study (classroom work and reading) to learn a trade or profession. They typically last 2 years and target the youth. Habilitation courses are also long duration courses (1,100 to 1,700 hours) targeted at high school students or graduates. They provide minimum qualifications for certain mid-level technical jobs (e.g. industrial automation), and seek to complement traditional education while the student is at school or unemployed. Qualification courses target older, low-skill workers and aim at preparing them for a new trade or profession. This category includes job retraining programs for workers who need new qualifications due to technological or organizational change. Generally it reflects changes in occupation rather than an upward movement in the same field. Courses are basic-level and duration varies between 160 and 500 hours. Initiation courses have similar objectives as qualification courses but are shorter duration (40 to 80 hours) and specialized on a certain function. For example, there is a qualification course for automotive painter job (180 hours) which includes three modules: automotive refinish (120 hours), colorimetry (40 hours) and polishing and crystallization (20 hours); and there are initiation courses for a car polisher job (40 hours) and automotive paint preparation job (80 hours).

# **2.2 Summary statistics**

Our empirical analysis uses data for the years 2009 to 2012, a period characterized by high real exchange rate volatility. Panel A of Figure 1 depicts the evolution of the real effective exchange rate. We observe that the Brazilian currency observed a marked effective real appreciation until July 2011, when it started to move in the opposite direction. Panel B displays the evolution of the Brazilian currency (the _real_ ) relative to the the euro, the US dollar and the renminbi. We see that while the overall trend was broadly similar across these three currencies, the magnitude of relative changes varied considerably.

Table 1 reports summary statistics on the attributes of manufacturing producers over the sample period, discriminating firms according to export status. We observe that the 7
WPS3656

# World Bank Lending and Financial Sector Development

Robert Cull Laurie Effron<sup>•</sup>

## **Abstract**

Using a new database of World Bank loans to support financial sector development, we investigate whether countries that received such loans experienced more rapid growth on standard indicators of financial development than countries that did not. We account for self-selection with treatment effects regressions, and we also use propensity score matching techniques. Our results indicate that borrowing countries had significantly more rapid growth in M2/GDP than non-borrowers, and swifter reductions in interest rate spreads and cash holdings (as a share of M2). Borrowers also had higher private credit growth rates than non-borrowers in treatment effects regressions, but not in standard panel regressions with fixed country effects. On the whole, however, the results indicate significant advantages for borrowers over non-borrowers in terms of financial development.

## JEL Classifications: F33, G21, O16

Keywords: International Financial Institutions, Development, Banking

## World Bank Policy Research Working Paper 3656, July 2005

_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 thank Thorsten Beck, Gerard Caprio, Stijn Claessens, George Clarke, Charles Goodheart, Greg Ingram, James Hanson, Patrick Honohan, Daniel Lederman, David McKenzie, Caglar Ozden, Kyle Peters, and Colin Xu for helpful comments, and Maria del Pilar Casal, Sarojini Hirshleifer, and Gulmira Karaguishiyeva for excellent research assistance.  Authors are World Bank staff members. <u>Contact information: Robert Cull, World Bank, 1818 H Street, N.W., Washington D.C., 20433. Phone: (202)473-</u> 6365. Fax: (202)522-1155. Email: rcull@worldbank.org.
|Eastern Dedicated Freight Corridor-3|Sanjeev Deorao Moholkar|
|---|---|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|KZ SKILLS AND JOBS PROJECT|Ayesha Y. Vawda|
|Bangladesh Weather and Climate Services Regional Project|Arati Belle|
|Bangladesh Weather and Climate Services Regional Project|Arati Belle|
|Bangladesh Weather and Climate Services Regional Project|Arati Belle|
|Bangladesh Weather and Climate Services Regional Project|Arati Belle|
|Bangladesh Weather and Climate Services Regional Project|Arati Belle|
|Bangladesh Weather and Climate Services Regional Project|Arati Belle|
|Bangladesh Weather and Climate Services Regional Project|Arati Belle|
|Bangladesh Weather and Climate Services Regional Project|Arati Belle|
|Assam Citizen-Centric Service DeliveryProject|Mohan Nagarajan|
|Assam Citizen-Centric Service DeliveryProject|Mohan Nagarajan|
|Assam Citizen-Centric Service DeliveryProject|Mohan Nagarajan|
|Assam Citizen-Centric Service DeliveryProject|Mohan Nagarajan|
|Assam Citizen-Centric Service DeliveryProject|Mohan Nagarajan|
|Youth Inclusion|Michelle Paola Rebosio Calderon|
|Youth Inclusion|Michelle Paola Rebosio Calderon|
|Youth Inclusion|Michelle Paola Rebosio Calderon|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|Local Economyand Infrastructure Development Project|Tafadzwa Irvine Dube|
|AG Roadmapto improve water & energyservices|Marco Antonio Aguero|
|AG Roadmapto improve water & energyservices|Marco Antonio Aguero|
|AG Roadmapto improve water & energyservices|Marco Antonio Aguero|
|ADDITIONAL FINANCING - MSIP(EU-IPA)|Tatyana Shadrunova|
|ADDITIONAL FINANCING - MSIP(EU-IPA)|Tatyana Shadrunova|
|ADDITIONAL FINANCING - MSIP(EU-IPA)|Tatyana Shadrunova|
|ADDITIONAL FINANCING - MSIP(EU-IPA)|Tatyana Shadrunova|
in 2018). The pace of public debt increase was slower for resource poor countries (18 percentage points of GDP) and minerals and metals abundant countries (15 percentage points).

**Figure 4. Public Debt in Sub-Saharan Africa, by resource abundance** _(% GDP)_

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Resource Poor Minerals & Metals Oil Rich Oil Rich excl.<br>Rich NGA<br>2007 2013 2018<br><!-- End of picture text --> _Source: The figure reports GDP-weighted averages by region. IMF – World Economic Outlook Database, October 2018_ Fast borrowers had among the highest debt levels in 2018.<sup>5</sup> **Figure 5** and **Figure 6** add more granularity to the broader debt trends over the past five years. The figures show the 75th percentile of countries with the highest debt levels in 2018, as well as the sharpest increases in public debt between 2013 and 2018, respectively. Eight of the twelve fast-borrowing countries over the past five years can be found in the 75<sup>th</sup> percentile of debt-to-GDP in 2018. This implies that these eight countries (Angola, Cabo Verde, Congo Rep., Mauritania, Mozambique, Sierra Leone, Sudan, and Zimbabwe) not only exhibit the largest levels of indebtedness but also their debt levels grew at the fastest pace. Additionally, seven of the 12 fastest-borrowing countries are commodity dependent (of which 4 are oil abundant countries). Yet, 40 percent of the fastest borrowers are not commodity exporters, such as Sudan, Mozambique, and Cabo Verde.

> 5 Fast borrowers are defined as those countries in the upper quartile of the distribution of the change in public debt between 2013 and 2018, measured in percentage points of GDP.

6
# Figure III: Payment of Tax Liabilities

## A: Income Tax

Average Payment Share of Net Liability, Income Tax <!-- Start of picture text --> 2006 2008 2010 2012 2014<br>Year<br>Self-Employed, cut 04-2015 Corporations, cut 04-2015<br>Self-Employed, cut 04-2013 Corporations, cut 04-2013<br>Self-Employed, cut 04-2011 Corporations, cut 04-2011<br>1<br>.95<br>.9<br>.85<br>Average Payment Share<br>.8<br><!-- End of picture text -->

## B: Sales Tax

<!-- Start of picture text --> Average Payment Share of Net Liability, Sales Tax<br>2009m1 2010m7 2012m1 2013m7 2015m1<br>Month<br>Self-Employed, cut 04-2015 Corporations, cut 04-2015<br>Self-Employed, cut 04-2013 Corporations, cut 04-2013<br>Self-Employed, cut 04-2011 Corporations, cut 04-2011<br>1.05<br>1<br>.95<br>.9<br>.85<br>.8<br>Average Payment Share<br>.75<br><!-- End of picture text --> Notes: This gure shows the average of the payment share, dened as the total payment made by the taxpayer for a specic tax period, divided by the nal tax liability to be paid for that period. The average is an unweighted average across all taxpayers with an positive nal liability for each scal period. The nal tax liability is net of any deduction made for tax withheld, and the payment data does not include tax withheld. The income tax data includes all declarations led and payments made by June 2015. The sales tax data includes all declarations led and payments made by October 2015. The blue series correspond to the self-employed and the red series corresponds to corporations. We show the series for three dierent cuts in the paymen data, taking into account all payments made before the cut date.

40
# **6. Conclusions**

The recent literature on decentralization of anti-poverty programs in developing countries has highlighted the need for quantitative empirical analysis of specific programs, with particular attention to any evidence relating to whether programs might be diverted from their original mandate, as a result of the influence of local elites. In this paper, we have analyzed a unique combination of data sets from the Ecuadorian Social Investment Fund (FISE), during 1993-1996. The combination of a detailed, parish-level poverty map, full program administrative data on project implementation, and provincelevel electoral results has allowed us to investigate the relationship between local income distribution and project choice in an unprecedented way.

To help our understanding of the role played by elites in community-level decision-making, we presented a simple model of project choice between local public goods and excludable goods that are particularly demanded by the poor. The model was designed specifically for this case study, where a key distinction among a menu of projects - all of which may appear to be pro-poor - was between projects that would benefit the entire community (through increased provision of local public goods, such as schooling, health clinics or roads), and those that provided excludable private goods that the poor were in great need of, but which brought no direct benefit to the elite.<sup>37</sup> The main examples of such private goods were latrines, and latrine construction projects accounted for 13% of all FISE projects.

Under the assumption that influence over local decision-making processes is a non-decreasing function of income, the model generates two basic predictions: other things equal, latrine projects would be more frequently found in poorer villages, and in less unequal villages. Inequality would reduce the probability that a community received latrine projects, controlling for poverty and need, through an “elite capture” mechanism: decision-making power would be more concentrated in the hands of those to whom additional latrines would be of no use, reducing the chances that they would be provided.

Both predictions are borne out empirically. Controlling for infrastructure need and a set of geographic and demographic variables, the poverty headcount is associated with a

> 37 We acknowledge that building latrines for the poor may have positive health externalities for the nonpoor.  Whether the elite take this effect into consideration during the decision-making process is uncertain.

28
37 cont. Annex Table A1. Summary of Data Set on Poverty, Inequality, International Migration and Remittances

|Country|Survey<br>Year|Region|Poverty<br>Headcount<br>($1/person/<br>day)|Poverty<br>Gap (%)|Squared<br>Poverty<br>Gap (%)|Gini Co-<br>efficient|Migration<br>as Share of<br>Country<br>Population|Official<br>Remittances<br>(million<br>dollars)|Official<br>Remittances as<br>Share of GDP<br>(constant 1995<br>dollars)|
|---|---|---|---|---|---|---|---|---|---|
|Tunisia|1990|Middle East, North<br>Africa|1.26|0.33|0.16|0.402|3.01|551|<br>3.71|
|Turkey|1987|Europe, Central Asia|1.49|0.36|0.17|0.435|4.18|2021|1.56|
|<br>Turkey|1994|<br>Europe, Central Asia|2.35|0.55|0.24|0.415|4.13|2627|1.66|
|<br>Turkmenistan|1988|<br>Europe, Central Asia|0|0|0|0.264|0|0|0|
|Turkmenistan|1993|<br>Europe, Central Asia|20.92|5.69|2.1|0.357|0|0|0|
|Uganda|1989|<br>Sub-Saharan Africa|39.17|14.99|7.57|0.443|0|0|0|
|<br>Uganda|1993|Sub-Saharan Africa|36.7|11.44|5|0.391|0|0|0|
|<br>Ukraine|1989|Europe, Central Asia|0|0|0|0.233|0|0|0|
|Ukraine|1992|<br>Europe, Central Asia|0.04|0.01|0.01|0.257|0|0|0|
|Ukraine|1996|<br>Europe, Central Asia|0|0|0|0.325|0|0|0|
|Uruguay|1989|<br>Latin America|1.1|0.47|0.4|0.423|0|0|0|
|<br>Uzbekistan|1988|Europe, Central Asia|0|0|0|0.249|0|0|0|
|Uzbekistan|1993|<br>Europe, Central Asia|3.29|0.46|0.11|0.332|0|0|0|
|Venezuela|1981|Latin America|6.3|1.08|0.25|0.556|0.08|0|0|
|Venezuela|1987|Latin America|6.6|1.04|0.22|0.534|0.14|0|0|
|Venezuela|1989|Latin America|8.49|1.77|0.49|0.557|0.19|0|0|
|Venezuela|1993|Latin America|2.66|0.57|0.22|0.416|0.29|0|0|
|Venezuela|1996|Latin America|14.69|5.62|3.17|0.487|0.36|0|0|
|Yemen|1992|Middle East, North<br>Africa|5.07|0.93|NA|0.394|0|1018|28.49|
|Yemen|1998|Middle East, North<br>Africa|10.7|2.42|0.85|0.344|0|1202|23.77|
|Zambia|1991|Sub-Saharan Africa|58.59|31.04|20.18|0.483|0|0|0|
|Zambia|1993|Sub-Saharan Africa|69.16|38.49|25.7|0.462|0|0|0|
|Zambia|1996|Sub-Saharan Africa|72.63|37.75|23.88|0.497|0|0|0|
|Zimbabwe|1991|Sub-Saharan Africa|35.95|11.39|4.56|0.568|0|0|0|

Notes: All poverty and inequality data from World Bank, Global Poverty Monitoring database. Migration data from U.S. Population Census and OECD, Trends in International Migration. Remittance data from IMF, Balance of Payments Statistics Yearbook.
# **Digital Technology Adoption and Jobs:**

**A Model of Firm Heterogeneity**<sup>**1**</sup>

Irene Brambilla<sup>2</sup> Universidad Nacional de La Plata and CONICET JEL codes: J24, J31 Keywords: ICT, jobs, labor demand, firm heterogeneity, rent-sharing, tasks

> 1 I thank Rita Almeida, Mark Dutz, Truman Packard, Daniel Lederman, Robert Willig and seminar participants at the World Bank’s Workshop on Digital Technology Adoption, Skills, Productivity and Jobs in Latin America, LACEA and AAEP for their comments and suggestions. The views expressed in this paper are those of the author.

> 2 Department of Economics, Universidad Nacional de La Plata. Email: Irene.brambilla@econo.unlp.edu.ar
for some firms to purchase, and credit constraints could prevent them from financing such an investment, difficulties in accessing local construction permits may discourage their investment in more resilient buildings. Even if the firm’s manager perfectly anticipates the adverse impact of climate change on her firm’s performance, she faces a more constrained adaptation menu. In this case, the observer is more likely to estimate a larger negative impact of rising temperatures on firm revenue. To the extent that the degree of credit or other local business environment constraints are correlated with firm performance within a given country, sector, and year, such market imperfections could bias our estimates. We evaluate the extent of the potential bias empirically and examine heterogeneity by measures of market imperfections to document how well firms in different contexts can adapt to rising temperatures.

# **4 Data**

## **4.1 World Bank Enterprise Survey data**

To investigate the economic costs of climate change, we rely on novel, geocoded firm-level data from the World Bank Enterprise Surveys (WBES). The data provide information from nearly 160,000 establishments that are harmonized across 134 countries and are collected about every five years in each country between 2010 to 2023. The WBES is designed to provide internationally comparable establishment-level data that are representative for the formal private sector economy for each country-year survey round. The surveys are stratified by firm size, broad sector (manufacturing versus services), and region. The WBES is the only available dataset that provides comparable firm-level data for a large set of countries, including many low- and middle-income countries. It allows us to estimate the impact of climate change based on microdata at a global scale rather than relying on temperature response functions estimated on a small subset of countries to predict marginal damages for other countries. The data are geocoded, making it possible to exploit rich variation in climate change across detailed locations within countries over time.

10