rafmacalaba/datause-encoder-data huggingface dataset:
|Medium Size Cities and Sinaloa State Water SupplyProject(03)|
|---|
|Medium Size Cities and Sinaloa State Water SupplyProject(03)|
|Medium Size Cities and Sinaloa State Water SupplyProject(03)|
|Medium Size Cities and Sinaloa State Water SupplyProject(03)|
|Agricultural Credit Project(08)|
|Agricultural Credit Project(08)|
|Agricultural Credit Project(08)|
|HighwaySector Project(02)|
|HighwaySector Project(02)|
|HighwaySector Project(02)|
|HighwaySector Project(02)|
|HighwaySector Project(02)|
|HighwaySector Project(02)|
|HighwaySector Project(02)|
|Lazaro Cardenas Industrial Port Project|
|Lazaro Cardenas Industrial Port Project|
|Lazaro Cardenas Industrial Port Project|
|Lazaro Cardenas Industrial Port Project|
|Lazaro Cardenas Industrial Port Project|
|Lazaro Cardenas Industrial Port Project|
|Lazaro Cardenas Industrial Port Project|
|Lazaro Cardenas Industrial Port Project|
|Lazaro Cardenas Industrial Port Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Chiapas Agricultural Development Project|
|Small and Medium Scale MiningDevelopment Project(02)|
|Small and Medium Scale MiningDevelopment Project(02)|
|Small and Medium Scale MiningDevelopment Project(02)|
|Small and Medium Scale MiningDevelopment Project(02)|
|Small and Medium Scale MiningDevelopment Project(02)|
|Small and Medium Scale MiningDevelopment Project(02)|
|Small and Medium Scale MiningDevelopment Project(02)|
|Small and Medium Scale MiningDevelopment Project(02)|
|Technical TrainingProject(02)- CONALEP Project(02)|
|Technical TrainingProject(02)- CONALEP Project(02)|
|Technical TrainingProject(02)- CONALEP Project(02)|
|Technical TrainingProject(02)- CONALEP Project(02)|
|Technical TrainingProject(02)- CONALEP Project(02)|
|Technical TrainingProject(02)- CONALEP Project(02)|
# Children's Work and Schooling: Does Gender Matter? Evidence from the Peru LSMS Panel Data

# I. Introduction

The labor and school outcomes of children have received increasing attention recently, especially with the emergence of the problem of child labor. According to the ILO, about one in seven of the world's children participate in labor activities, with significant regional differences.' Notwithstanding the regional differences, certain regularities emerge from the empirical literature on the subject. Most of children's "work" is in family-based enterprises-agriculture or non-farm business.<sup>2</sup> This work rises with age, household size, number of siblings and in general, poverty. Parents take school quality into account when sending their children to school. The decision to send children to school is weighed against the opportunity cost which includes direct school costs (which may or may not be important) and foregone earnings from child labor.

In the empirica **l** iterature on child labor and schooling, there is a tendency to narrow the discussion and analysis of the determinants of children's activities to two non-leisure activitiesmarket labor and schooling.4 However, it is widely known that work at home constitutes a large part of children's work-especially that of girls.<sup>5</sup> Few studies go beyond presenting simple summary statistics and study the opportunities and constraints that affect the work of boys and ' In the Americas, child labor force participation rates are 13% in South America, 8% in the Caribbean and 10% in Central America. See Grootaert and Kanbur (1995) for details. 2 A regional exception is South Asia where work for wages constitutes a significant portion of child labor. 3 See for example the evidence in Grootaert and Patrinos (1999). 4 Market labor typically includes both work for wages and work in a production process in the household that results in marketable output. Only children who are "economically active" are classified as child laborers (see Basu, 1998).

5 See for instance Grootaert and Patrinos (1999).
25 Rapawy, Stephen and W. Ward Kingkade, "Estimates and Projections<sup>of the Labor</sup> Force and Civilian Employment In The USSR: 1950 to 2000"<sup>(mimeo, Center</sup> for International Research, U.S. Bureau of the Census, September 1988).

Romer, Paul M., "Crazy Explanation for the Productivity<sup>Slowdown," NBER</sup> Macroeconomics Annual (Cambridge, Mass: MIT Press, 1987).

, "Increasing Returns and New Developments in the Theory of Gr '7th" in The 6th International Svmposium in Economic Theorv and Econometrics<sup>ed. by</sup> William Barnet (Cambridge, Mass: Cambridge University<sup>Press, 1991).</sup> Schroeder, Gertrude, "USSR: Toward the Service<sup>Economy at a Snail's Pace,"</sup> Gorbachev's Economic Plans, Vol. 2, Study Papers (Washington: US Congress, Joint Economic Committee, November 23, 1987) 240-260.

, "Consumption in the USSR and the US: A Western Perspective," (mf,imeo, 1990a).

_ "Soviet Consumption in the 1980s: A Tale of Woe," (mimeo, Charlotteville, Virginia: University of Virginia, June 1990b).

Summers, Robert and Heston, Paul, "A New Set of International<sup>Comparisons of</sup> Real Product and Price Levels Estimates for 130 Countries, 1950-1985,"<sup>The</sup> Review of Income and Wealth 34 (March 1988) 1-25.

Tretyakova, Albina, "USSR: Gross National Product<sup>Accounts by Republic, 1987"</sup> (mimeo, Center for International Research, U.S. Bureau<sup>of the Census,</sup> February 1992) U.S. Congress, Subcommittee on Technology and Natinal<sup>Security of the Joint</sup> Economic Committee, "Eastern Europe: Struggling To Stay<sup>on the Reform</sup> Track" (mimeo, Embargoed until 1400 & June 1992).
- 3 -

Unreliable electricity supply is also a crucial constraint in Africa. The electrification rate is estimated at 30.5 percent in the region; 580 million of people still live without access to power.<sup>1</sup> Firms may have better access than households. However, the quality of the electricity supply remains poor. Many firms are experiencing frequent power outages, and therefore, firms are highly dependent on own generators, adding to more operating costs. Back-up systems are generally costly. For example, the average cost of a back-up power system is three times higher than the cost of grid electricity in Nigeria (Adenikinju, 2003).

The current paper aims at examining the impacts of improving the quality of public infrastructure services in five East African countries: Burundi, Kenya, Rwanda, Tanzania and Uganda, where the industrial sector, especially manufacturing, has been weak in recent years. While the countries achieved relatively high GDP growth of 5–8 percent, the sectoral contribution of manufacturing was less than 0.5 percent of GDP (Figure 1). Burundi and Rwanda experienced negative growth in the manufacturing sector in recent years. By contrast, the service sector and the construction industry contributed significantly to economic growth in the region.

The paper recasts light on the impacts of the quantity and quality of infrastructure services on firm production. The data come from the 2005 Business Environment and Enterprise Performance Survey (BEEPS), which was conducted by the European Bank for Reconstruction and Development and the World Bank. The remaining sections are organized as follows: Section II provides an overview of infrastructure qualities measured by the BEEPS database. Section III establishes our empirical model and Section IV describes the data. Section V presents our main estimation results and policy implications. Then Section VI concludes.

1 International Energy Agency World Energy Outlook Electricity Access Database.
the econometric analysis of section 3 are at quarterly frequency, PRI<sup>2</sup> is transformed to quarterly frequency by taking period averages. All data are from the IMF (source and line numbers listed in the appendix), including debt and asset flows from which the stocks of external liabilities and assets have been cumulated (with the appropriate valuation adjustment<sup>4</sup> ).

We allow for regime-specific heteroskedastic errors and base model selection on the Schwarz statistics. Our results appear generally robust to lag length. We find regime switching in the mean for all 15 countries.<sup>5</sup> The regime dates associated with both policy intervention indices are reported in Appendix 1. For most countries, PRI<sup>1</sup> and PRI<sup>2</sup> identify similar regime switching points. Compared to PRI<sup>1</sup> , regimes based on PRI<sup>2</sup> appear more persistent. In particular for the high-income countries in our sample (Australia, Germany, Japan and New Zealand), PRI<sup>1</sup> generates policy cycles of shorter duration and more frequent regime changes. The higher volatility of PRI<sup>1</sup> is explained by the fact that the difference in the mean of high and low intervention states is much less pronounced and in the case of Japan only borderline significant. Based on PRI<sup>1</sup> , regime classification is far more clear-cut if countries go through periods of high versus low intervention (developing countries) as compared to low versus some intervention (non-developing countries). It is therefore not surprising that PRI<sup>2</sup> , scaled by Japan’s policy response index, is much less volatile for both developing and non-developing countries, as small fluctuations in the degree of intervention are filtered out.

> 4 See Kraay _et al._ , 2000. 5 Model specifications for country-specific MS-AR models are available from the authors upon request.

11
Middleton, Alan (2007), “Globalization, Free Trade, and the Social Impact of the Decline of Informal Production: The Case of Artisans in Quito, Ecuador,” _World Development_ 35(11): 1904-1928 Moyo, Busani (2011), “Do Water Cuts Affect Productivity? Case Study of African Manufacturing Firms,” _Water SA_ 37(3): 349-356 Rand, John and Nina Torm (2012), “The Benefits of Formalization: Evidence from Vietnamese Manufacturing SMEs,” _World Development_ 40(5): 983-998.

Reynaud, Arnaud (2003), “An Econometric Estimation of Industrial Water Demand in France,” _Environmental and Resource Economics_ 25: 212-232 Rodrik, Dani (2016), “Premature Deindustrialization,” _Journal of Economic Growth_ 21(1):1-33 Safavian, Mehnaz, and Siddarth Sharma (2007) “When Do Creditor Rights Work?” _Journal of Comparative Economics_ 35: 484-508 Schneider, Friedrich, Andreas Buehn, and Claudio E. Montenegro (2010), “New Estimates for the Shadow Economies All Over the World” _International Economic Journal_ 24(4): 443-461 Smiley, Sarah L. (2016), “Water Availability and Reliability in Dar es Salaam, Tanzania,” _Journal of Development Studies_ 52(9): 1320-1334 Stel, Andre v., David J. Story, A. Roy Thurik (2007) “ The Effect of Business Regulations on Nascent and Young Business Entrepreneurship” _Small Business Economics_ 28(2-3): 171-186 Thomas, Duncan (1992) “Prices, Infrastructure, Household Characteristics and Child Height” _Journal of Development Economics_ 29: 301-331 World Bank. (2016). _High and Dry: Climate Change, Water, and the Economy_ . Washington, DC: World Bank.

World Bank. (2017). _Uncharted Waters: The New Economics of Water Scarcity and Variability_ . Washington, DC: World Bank.

23
**Policy Re"aerch**

**Human Reowurces Operations|** WPS 1013

This paper -a joint product of the Latin America and the Caribbean Country Department II and the Industry Development Division, Industry and Energy Department- is part of a broader study of Mexico's labor market being carried out by the Human Resources Operations Division of the Latin America and the Caribbean Country Department II. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Debbie Young, room R2-050, extension 30932 (November 1992, 40 pages).

Revenga, Riboud, and Tan evaluated how Mexico's Labor Retraining Program (PROBECAT) affected unemployed and displaced workers. As part of the World Banksupported Manpower Training Project (loan 2876-ME, 1987), PROBECAT has provided short-tenn vocational training to more than 250,000 unemployed people. Their evaluation was based on new longitudinal data on PROBECAT trainees developed for this purpose, and includes data on a control grup of uneimpkIyed people who did not join PROBECAT. Their main findings were as follows:

* Training increases the monthly eanrings of male trainees, but this effect varies systematically depending on the person's level of schooling attainment.

* The monetary benefits of training outweigh the costs of the PROBECAT program for certain groups of trainees. For male trainees over 25 with prior work experience, the benefits outweigh the costs of training within _three mont/s_ of starting work. For all other males except those with no prior work experience, the benefits outweigh the costs within _one year._

O On average, the trainees found jobs more quickly than the control group. But training does not shorten the term of unemployment for those without work experience.

* Male trainees are more likely to be employed three and six months after training than are the controls. Female trainees with work experience are more likely to be employed three, six, and twelve months after training than are the controls.

Men with no prior work experience spend the longest time job hunting after tmining (8 months, compared with the trainee mean of 4.4 months) and benefit less from training in terms of monthly earnings (128 thousand pesos compared with the average benefit of 152 thousand pesos). For this grup, the costs of training are offset only after 17 months of higher earnings.

* Male trainees are more likely to find employment in large firns than are comparable controls.

Policy ReseshsWorkino PaperrSeriesdisseminatestermdiofworkunderwayitheBanlAnobjecveoftheseriesPhe is to get these findings out quidldy. even if presentaions are less than fully polished. 'Me fimdings, interpretations. and conclusiDo ns dm fi papers dob not necessarily represent official Bank policy.

Produced by the Policy Research Dissen-ination Centcr
The labor force is aging in rural Mexico. In the last decade the average age increased and that is mainly due to out migration of the people in their 20’s and early 30’s. In the work force, men are slightly older than women; the average age reached 37.9 and 36.3 for men and women respectively in 2003. Moreover, on the job, men work around 24 percent more hours per week than women, reaching 40 hours weekly (Table 2.2).

# **Wages**

The average real wage in Mexico remained relatively low during the last decade, both in historical and international perspective. The Confederation of Mexican Workers (Confederación de Trabajadores Mexicanos--CTM) noted that the average worker's purchasing power in 2003 was only around 60 percent of its 1982 level.<sup>5</sup> Although the government increased the minimum wage by 21 percent during 1995, the cost of living rose by more than 50 percent as a result of the currency collapse. In September 1995, the minimum wage was sufficient to cover only 35 percent of workers' basic necessities, compared to 94 percent in December 1987. The government's anti-inflation APRE program called for the minimum wage to increase in line with projected inflation of 21 percent.

<!-- Start of picture text --> Figure 2.3: Rural Hourly Wage Distribution by Social<br>Security Condition, (2002 Pesos), Mexico, 2003<br>0 3 0 60 90 12 0<br>Hou rly W ag e<br>In form al Fo rm al<br>Source: ENE 2003, 2nd quarter.<br>.04<br>.03<br>.02<br>Density<br>.01<br>0<br><!-- End of picture text -->

> 5 Source: http://countrystudies.us/mexico.

11
**Table 6: Residual-Based ADF Test for Co-Integration on Key Variables in the ARDL Model**

Unit root tests for residuals *~~~~~~~~~~~~~ S r i: S* S* * * 4 * * S* * * * * * * * * * * * *I * * * * * * * * * * * * * *S Based on OLS regression of DLY on: INPT LY(-l) LLRT LI LT LFD 36 observations used for estimation from 1961 to 1996 Test Statistic LL AIC SBC HQC DF -4.3942 79.6475 78.6475 77.9305 78.4138 ADF(1) -4.5706 81.0263 79.0263 77.5923 78.5589 ADF(2) -3.7380 81.1517 78.1517 76.0007 77.4505 ADF(3) -4.2644 83.0405 79.0405 76.1726 78.1057 ADF(4) -3.3245 83.0510 78.0510 74.4660 76.8824 95% critical value for the Dickey-Fuller statistic = -5.2687 LL = Maximized log-likelihood AIC = Akaike Information Criterion SBC = Schwarz Bayesian Criterion HQC = Hannan-Quinn Criterion Results obtained using Microfit 4.0 by Pesaran and Pesaran (1997).

# V. **Summary and Conclusions**

Our study, though a single country case study, has demonstrated that an economywide approach to analyzing economic effects of land registration offers considerable improvements over current and past approaches which have been sector-focused. We have deployed contemporary analytical approaches, including the ECM, to deal with nonstationarity that is inherent in time series data. We have at the same time used classical techniques such as the ARDL to model high order lags that characterize the response of output and economic growth to changes in land registration.

Our empirical analysis using the ECM has generated three unambiguous findings. First, land titling is found to have statistically significant and positive long-run effects on financial development. Second, public expenditure on land registration --- an indicator of the quality of land registration services --- is found to have strongly significant and positive long run effects on economic growth. Third, land titling is found to have significantly negative effects on economic growth in the immediate period following its occurrence.

While we are able to explain the positive impact of land titling and quality of land registration services on financial development and economic growth, we are unable to explain the negative impact of land titling on economic growth. Our guess is that it may be due to the influences of price increases and speculation in land (often associated with land titling) which tend to deter investment in land and in land-intensive activities. In addition, it is possible that bankers and investors may take a wait and see attitude in transacting with new (and old) titles until the dust has cleared. Both the speculation in land and short term uncertainties in dealings with land titles could adversely impact economic growth in the short run. On the other hand, theory and supply response studies would indicate that the response of economic growth to land titling should occur with many lags which the ECM is unable to analyze; we have therefore used another model, 23
38 <u>Table 6: Survey ‘collections’ used in financial protection side of HEFPI database</u>

|Collection|<br>Title in full|<br>Type of collection|<br>Institution|<br>Geographic<br>focus|<br>Type of survey(s)|<br>Further information on data and access|
|---|---|---|---|---|---|---|
|CWIQ|Core Welfare<br>Indicators<br>Questionnaire|Multi-country<br>survey initiative<br>– somewhat<br>standardized|World Bank<br>originally, but<br>have been used by<br>other institutions<br>too|<br>Developing<br>world|Multipurpose survey|Some CWIQ surveys publicly accessible via<br>the World Bank Microdata Catalog.<sup>35</sup>More<br>CWIQ surveys can be accessed by World Bank<br>staff via the World Bank Microdata Library.<sup>36</sup><br>See also the Institute for Health Metric’s<br>Global Health Data Exchange (GHDx) entries<br>for CWIQ surveys.<sup>37</sup>|
|E123|Enquêtes 1-2-3|Multi-country<br>survey initiative<br>– somewhat<br>standardized|DIAL Research<br>Unit, France|Developing<br>world|Multipurpose survey|Details available (in French only) on the<br>Enquêtes 1-2-3 page of the DIAL website.<sup>38</sup>|
|EAPPOV|World Bank East<br>Asia & Pacific<br>harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>East Asia &<br>Pacific region|HBS, HIES,<br>multipurpose surveys|Accessible only to World Bank staff via World<br>Bank Microdata Library.|
|ECAPOV|World Bank Europe<br>and Central Asia<br>harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>Europe &<br>Central Asia<br>region|HBS, HIES,<br>multipurpose surveys|Accessible only to World Bank staff via World<br>Bank Microdata Library.|
|EUROSTAT-<br>HBS|Eurostat HBS Public<br>Use Files|Ex post<br>harmonized|European<br>Commission –<br>Eurostat|European<br>Union|HBS|Details available on the Eurostat microdata<br>website.<sup>39</sup>|
|HBS|Household Budget<br>Survey|National survey –<br>raw data|National<br>governments|Global|HBS|Survey-specific|
|HEIDE|World Bank<br>Household<br>Expenditure and<br>Income Data for|Ex post<br>harmonized|World Bank|World Bank’s<br>Europe &<br>Central Asia<br>region|HBS, HIES,<br>multipurpose surveys|Details of the HEIDE data set can be found in<br>the World Bank Microdata Catalog, and can<br>be found by searching for HEIDE in the study<br>description.|

> 35 The World Bank Microdata Catalog can be accessed at <u>http://microdata.worldbank.org/index.php/home</u> and is open access.

> 36 The World Bank Microdata Library can be accessed at <u>http://microdatalib.worldbank.org/index.php/home</u> and is accessible only to World Bank staff.

> 37 Search for CWIQ on the ‘series and systems’ page of the GHDx site at http://ghdx.healthdata.org/series_and_systems.

> 38 The relevant page is http://www.dial.ird.fr/enquetes-statistiques/enquetes-1-2-3.

39 The Eurostat microdata access website is http://ec.europa.eu/eurostat/web/microdata.