rafmacalaba/datause-encoder-data huggingface dataset:
helps explain the rapidly increasing earnings inequality associated with rising rewards for educational qualifications at a time of tremendous schooling expansion. Therefore, for almost all developed countries, returns are higher at the top of the earnings distribution. It could be a high income phenomenon, since the returns are also higher at the top in Singapore (Patrinos et al. 2009) and for whites in South Africa (Mwabu and Schultz 1996); but then this is not the case for Korea (Lee and Lee 2002) or for other groups in South Africa (Mwabu and Schultz 1996).

<!-- Start of picture text --> Figure 4: 9th - 1st Decile of Male Return Differences from Quantile<br>Regressions Across Various European Countries and the USA<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>Country<br>9th - 1st decile<br><!-- End of picture text --> Source: Martins and Pereira (2004) In the United States, the returns for university graduates are higher at the higher quantiles. However, for high school graduates returns are lower at higher quantiles in 1972 and 1979; but this pattern is reversed in the post-1985 period, during which an increase in wage differentials by education has been documented (Buchinsky 1998). Lemieux (2006b) presents evidence from 12
# **4. The Colombian Labor Market: Stylized Facts**

Tables 1 and 2 report key stylized facts of the Colombian labor market. Unemployment rates estimated from the FSLS rounds reach 10.9 and 11.7 percent in 2008 and 2009 respectively, very similar to the official unemployment rates for the comparable 13 main metropolitan areas: 10.9 and 12.3 percent respectively (DANE 2010, p. 9). In 2008, labor market participation reached 54.4 percent, 66.3 percent among males and 44.5 percent among females in the working age (12 and older). By occupation, 60.3 percent (60.7 percent) of males (females) were salaried, 38.8 percent (37.1 percent) self-employed and 0.9 percent (2.2 percent) unpaid workers. By economic sector, some 51.8 percent of workers were occupied in services, followed by 40 percent in retail, manufactures and other sectors combined. About 27 percent of workers pertained to SISBEN 1 and 2.<sup>4</sup> Only 38.8 percent of occupied workers were in the formal sector (defined in this paper as workers with pension and health insurance coverage).

The composition of the labor market does not change much in 2009 except for a marked decrease in the participation rate down to 51.1 percent, clearly associated with the crisis. Those most affected are males, although these changes do not substantially alter the existing gender gap in Colombia. Self-employment increases for both males and females, while construction and services drop their share in total occupation. In 2009, more workers pertain to SISBEN 1 and 2 categories than the previous year. Most interestingly, a significant proportion of Colombian workers report voluntarily choosing their employment category: 44

> 4 SISBEN is a means-tested targeting mechanism, categorizing households by socioeconomic condition based on a set of personal and household features and assets. The index goes from 1 to 6, where 1 is the poorest and 6 the richest. Social programs in Colombia typically consider eligible beneficiaries to individuals and/or households pertaining to SISBEN 1 and/or 2.

8
|P077856|2005|2011|3/7/2011|13 Closed|India|
|---|---|---|---|---|---|
|P077856|2005|2012|10/16/2011|14 Closed|India|
|P077856|2005|2012|6/21/2012|15 Closed|India|
|P077856|2005|2021|7/23/2020|16 Closed|India|
|P077896|2002|2002||0 Closed|Afghanistan|
|P077896|2002|2003|4/8/2003|2 Closed|Afghanistan|
|P077896|2002|2004|3/17/2004|5 Closed|Afghanistan|
|P077896|2002|2005|3/16/2005|7 Closed|Afghanistan|
|P077896|2002|2006|9/13/2005|8 Closed|Afghanistan|
|P077896|2002|2006|3/10/2006|9 Closed|Afghanistan|
|P077896|2002|2007|8/8/2006|10 Closed|Afghanistan|
|P077903|2006|2006||0 Closed|Gambia,The|
|P077903|2006|2007|10/5/2006|1 Closed|Gambia,The|
|P077903|2006|2007|6/15/2007|2 Closed|Gambia,The|
|P077903|2006|2008|12/27/2007|3 Closed|Gambia,The|
|P077903|2006|2008|6/26/2008|4 Closed|Gambia,The|
|P077903|2006|2009|12/30/2008|5 Closed|Gambia,The|
|P077903|2006|2009|6/26/2009|6 Closed|Gambia,The|
|P077903|2006|2010|12/14/2009|7 Closed|Gambia,The|
|P077903|2006|2010|6/30/2010|8 Closed|Gambia,The|
|P077903|2006|2011|3/18/2011|9 Closed|Gambia,The|
|P077903|2006|2012|11/20/2011|10 Closed|Gambia,The|
|P077903|2006|2012|6/16/2012|11 Closed|Gambia,The|
|P077903|2006|2013|12/29/2012|12 Closed|Gambia,The|
|P077903|2006|2013|6/24/2013|13 Closed|Gambia,The|
|P077903|2006|2014|12/20/2013|14 Closed|Gambia,The|
|P077977|2005|2005|6/16/2005|2 Closed|India|
|P077977|2005|2006|9/20/2005|3 Closed|India|
|P077977|2005|2006|3/27/2006|4 Closed|India|
|P077977|2005|2007|9/27/2006|5 Closed|India|
|P077977|2005|2007|3/30/2007|6 Closed|India|
|P077977|2005|2008|9/26/2007|7 Closed|India|
|P077977|2005|2008|1/3/2008|8 Closed|India|
|P077977|2005|2009|7/8/2008|9 Closed|India|
|P077977|2005|2009|1/19/2009|10 Closed|India|
|P077977|2005|2009|5/19/2009|11 Closed|India|
|P077977|2005|2010|11/23/2009|12 Closed|India|
|P077977|2005|2010|5/28/2010|13 Closed|India|
|P077977|2005|2011|12/11/2010|14 Closed|India|
|P077977|2005|2011|5/31/2011|15 Closed|India|
|P077977|2005|2012|1/1/2012|16 Closed|India|
|P077977|2005|2012|4/14/2012|17 Closed|India|
|P078034|2005|2005|6/22/2005|1 Closed|East Asia and Pacific|
|P078034|2005|2006|12/27/2005|2 Closed|East Asia and Pacific|
|P078034|2005|2007|2/13/2007|3 Closed|East Asia and Pacific|
|P078034|2005|2008|3/6/2008|4 Closed|East Asia and Pacific|
|P078034|2005|2009|1/28/2009|5 Closed|East Asia and Pacific|
depreciation), then the project’s marginal productivity must be at least 50 percent for the given tax rate to yield enough revenues.<sup>11</sup> Such high productivity is more likely to arise in situations in which the initial endowment of public capital is low (relative to that of other productive assets)— specifically, when public capital services are substantially under‐provided, so that the marginal product of capital exceeds its user cost by a wide margin. Empirically, the international evidence is consistent with the view that the marginal productivity of infrastructure capital is higher in developing countries, especially poorer ones, than in industrial countries.<sup>12</sup> Even if public sector projects fall in the intermediate area of having high returns for the economy as a whole, but insufficient returns for public finances to improve public sector solvency, it may still be sub‐optimal to cut such projects during fiscal austerity. Ideally, one would devise a marginal revenue collection scheme that allowed the public sector to capture the returns, and hence eliminate the wedge between economy‐wide returns and public sector returns. After all, the business of the public sector is precisely to provide public goods that yield a high return for the economy as a whole.

In addition, public investment projects are more likely to exhibit higher marginal productivity _ex‐post_ if the government’s _ex‐ante_ project evaluation capabilities are sufficiently strong, resulting in the selection of high‐return projects and the rejection of low‐return ones. This, however, is far from assured in practice. In Latin America, for example, Chile features thorough project evaluation procedures (Fontaine 1997), but it represents the exception rather than the rule. In contrast, Costa Rica lacks both _ex‐ante_ and _ex‐post_ formal evaluation procedures (Bolaños 2005).

Hence, the extent to which public investment pays for itself through increased tax collection depends not only on the kinds of investment projects under consideration, but also on country‐specific public capital endowments and institutional factors.

> 11 See Servén (2006) for the analytics underlying this and other similar calculations.

> 12 See Calderón and Servén (2007).

10
|Title|PPR Workirs Paper Series<br>Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS216 Price and Quality Effects of<br>Verm--Revisited: A Case Study of<br>Korean Footwear Exports|Jaime de Melo<br>L. Alan Winters|June 1989|M. Ameal<br>61466|
|WPS217 Public Debt, North and South|Helmut Reisen|||
|WPS218 Public Finance, Trade and<br>Development: The Chilean Experience|Vittorio Corbo|July 1989|A. Oropesa<br>61758|
|WPS219 Rural Credit in Developing Countries|Avishay Braverman<br>J. Luis Guasch|June 1989|C. Spooner<br>37570|
|WPS220 Capacity Building for Policy Analysis|Samuel Paul<br>David Steedman<br>Francis X. Sutton|July 1989|E. Madrona<br>61712|
|WPS221 How Does Uncertainty About the Real<br>Exchange Rate Affect Exports?|Ricardo J. Caballero<br>Vittorio Corbo|June 1989|A. Oropesa<br>61758|
|WPS222 Why Stabilisation Policies in Zambia<br>Did Not Succeed|Christopher Colclough|||
|WPS223 Overvalued and Undervalued Exchange<br>Rates in An Equilibrium Optimizing<br>Model|Jose Saul Lizondo|||
|WPS224 The Economics of the Government<br>Budget Constraint|Stanley Fischer|May 1989|S. Fischer<br>33774|
|WPS225Targeting Assistance to the Poor.<br>Using Household Survey Data|Paul Glewwe<br>oussamaKanaan|June 1989|B. Rosa<br>33751|
|WPS226 Inflation and the Costs of<br>Stabilization: Historical Cases,<br>Recent Experiences and Policy Lessons|Andres Solimano|||
|WPS227 Institutional Reforms in Sector<br>Adjustment Operations|Samuel Paul|||
|WPS228 Economic Performance of Developing<br>Countries in the 1980s|Robert Lynn<br>F. Desmond McCarthy|||
|WPS229 The Demographic Transition and Saving<br>for Life-Cycle Motives in Developing<br>Countries|Steven B. Webb<br>Heidi Zia|||
|WPS230 Unemployment, Migration and Wages<br>in Turkey, 1962-1985|Bent Hansen|July 1989|J. Timmins<br>39248|
**Table 13: Agricultural Export Shares by Level of Processing, 1990/91- 2006/07 (in % of World Trade)**

||Develop|ingExports|toDCs|Developi|ngExp toI|ndus Co.|Industr|ial Exports t|oDCs|Industrial|Exports toI|ndus Co.|W|orldTotal E|xports|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Product|1990-91|2000-01|2006-07|1990-91|2000-01|2006-07|1990-91|2000-01|2006-07|1990-91|2000-01|2006-07|1990-91|2000-01|2006-07|
|**Tropical Products**||||||||||||||||
|Raw|1.5|2.7|2.6|4.7|3.1|2.8|1.9|1.9|1.7|3.1|1.9|1.6|11.2|9.7|8.8|
|Final|0.1|0.4|0.5|0.4|0.4|0.5|0.5|0.4|0.5|1.5|1.8|1.9|2.5|2.9|3.4|
|Subtotal|1.6|3.1|3.2|5.1|3.5|3.3|2.4|2.3|2.1|4.6|3.7|3.6|13.7|12.6|12.2|
|**Temperate Products**||||||||||||||||
|Raw|0.8|2.2|3.3|1.9|1.4|1.8|3.7|4.6|4.1|9.3|7.3|6.3|15.8|15.6|15.5|
|Final|1.3|3.5|4.5|2.2|2.1|2.8|3.4|3.0|2.8|8.0|8.0|8.5|14.9|16.5|18.5|
|Subtotal|2.2|5.7|7.8|4.2|3.5|4.6|7.0|7.7|6.9|17.3|15.2|14.8|30.7|32.1|34.1|
|**Seafood, Fruits & Veg**||||||||||||||||
|Raw|1.1|2.4|2.9|7.5|7.3|6.5|1.7|1.7|1.8|9.8|7.9|7.2|20.2|19.3|18.4|
|Final|0.2|0.6|0.8|2.6|2.4|2.4|0.6|0.4|0.5|2.7|2.5|2.4|6.1|5.8|6.0|
|Subtotal|1.3|3.0|3.7|10.1|9.7|8.9|2.3|2.1|2.3|12.5|10.3|9.5|26.2|25.1|24.4|
|**Other Processed Food**||||||||||||||||
|Raw|0.1|0.3|0.3|0.6|0.4|0.3|0.1|0.2|0.1|0.5|0.3|0.1|1.3|1.2|0.8|
|Final|0.2|1.2|1.5|0.5|1.1|1.3|1.8|2.2|2.2|6.6|7.5|7.9|9.1|12.1|12.9|
|Subtotal|0.3|1.5|1.8|1.1|1.5|1.6|2.0|2.4|2.3|7.1|7.9|8.1|10.4|13.3|13.7|
|**Other Agric. Products**||||||||||||||||
|Raw|1.4|2.1|2.8|3.5|2.8|2.9|2.1|1.8|1.7|7.5|5.9|4.6|14.5|12.6|12.0|
|Final|0.2|0.6|0.6|0.4|0.5|0.5|0.8|1.0|1.1|3.1|2.2|1.4|4.5|4.2|3.6|
|Subtotal|1.6|2.7|3.4|3.9|3.3|3.3|2.9|2.8|2.8|10.6|8.1|6.1|18.9|16.9|15.6|
|**All Agricultures**||||||||||||||||
|Raw|4.9|9.7|11.9|18.4|15.1|14.3|9.5|10.2|9.5|30.2|23.3|19.9|63.0|58.4|55.5|
|Final|2.0|6.2|7.9|6.1|6.4|7.5|7.1|7.0|6.9|21.9|22.0|22.2|37.0|41.6|44.5|
|Subtotal|6.9|16.0|19.8|24.5|21.5|21.7|16.6|17.2|16.4|52.0|45.3|42.1|100.0|100.0|100.0|

_Note_ : “Raw” includes both raw and intermediate goods, because their movements were highly correlated. _Source_ : Based on mirror data from UN COMTRADE Statistics.

21
<!-- Start of picture text --> v . .<br>DrQ India:IPV experienceP<br>a<br>a<br>é<br>><br>2<br>=<br>°<br>53<br>ve<br>a<br>c N<br>20<br>© 1950 1958 1960 1965 1970 1975 1980 1985 ©1990 1995<br>Year of birth<br>-— Quintile2 +—— Quintile 3<br>+— Quintile4 +——— Quintile 5<br>Generated by regressing ipv experience on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state<br><!-- End of picture text --> <!-- Start of picture text --> .<br>2 Bangladesh:IPV experience<br>oe<br>oc<br>&<br>vu<br>2s<br>a<br>5<br>g=<br>3<br>=<br>2~<br>2 en<br>5 1960 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile 2 t—— Quintile 3<br>-—— Quintile 4 -——— Quintile 5<br>Generated by regressing ipv experience on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> » . . .<br>an: Pakistan:IPV experience<br>oe<br>a.<br>is)<br>Be<br>be<br>a!<br>|<br>ia]<br>La<br>7<br>i 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile2 + Quintile 3<br>— Quintile 4 + +——— Quintile 5<br>Generated by regressing ipv experience on wealth, urban, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> .<br>an8 Nepal:IPV experience<br>O.<br>2°<br>‘s<br>5°<br>By<br>a<br>=<br>om<br>at<br>g<br>i1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 +—— Quintile 3<br>—— Quintile 4 +——— Quintile 5<br>Generated by regressing ipy experience on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
**Figure 2.** Time series of ownership concentration for stocks with high (solid) and low (dash) common ownership. Vertical solid lines represent each M&A deal.

<!-- Start of picture text --> 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1<br>High FCAP Low FCAP<br>.66<br>.64<br>.62<br>Ownership Concentration .6<br>.58<br><!-- End of picture text --> 34
22 premia coefficient estimates respectively. Each model was estimated for each year in the 1987-1999 interval for which data were available. The results in Table 3a are in line with existing analysis of the Brazilian labor market (see e.g. Ferreira and Barros, 1999). There are large and significant returns to education, and smaller and concave returns to experience. Measured with respect to zero years of schooling, returns to education fell consistently over the period. This decline was most pronounced for intermediate education categories (4-10 years of schooling). Returns to experience have also fallen. There is a substantial male wage premium, which has also been declining. In contrast, racial premia of both whites and Asians with respect to Afro-Brazilians have persisted or increased. Controlling for other observed characteristics, employers, the self-employed and formal employees all earn more than informally employed workers. Metropolitan and urban location premia vis-à-vis rural workers of identical characteristics have also fallen over the period, though they remain statistically significant. All specifications also include industry and interacted industry-skill indicators, the coefficients on which are reported in Tables 3b and 3c. These coefficients are then pooled and regressed on a vector of trade variables in the second stage.

Table 4a reports the first-stage results for the employment model in (2), as marginal effects of unit changes in each independent variable, with all other variables held at their mean values. These estimates are also mostly in line with expectations. Workers with more experience and education are less likely to be employed in agriculture or informally. In addition, those with higher education are also more likely to be employers, as are whites and Asians, and to work in the formal non-tradables sector, presumably often as professionals. We also see some evidence over time of more females entering the labor force. While men are more likely to be working, the male coefficient falls between 1988 and 1995 across most industries. The industry constants, which are pooled and used in the second stage, are summarized in Table 4b.

One concern that is typically voiced with respect to multinomial logit models such as (2) is that they assume that the odds ratios of any two possibilities (pj/pk) are independent of the number and nature of alternative outcomes. This is known as the independence of
# PRE Working Pager Series

|Be|AUZ||Contact<br>forf=|
|---|---|---|---|
|WPS689 Do Tax Policies Stimulate Investme<br>in Physical and Research<br>and<br>Development<br>Capital?|nt Anwar Shah<br>John Baffes|May 1991|A. Bhalla<br>37699|
|WPS690 The Terms-of-Trade<br>Effects from the<br>Elimination<br>of State Trading in Soviet<br>Hungarian<br>Trade|Gabor Oblath<br>- David Tarr|May 1991|1. Smith<br>37350|
|WPS691 Can Debt-Reduction<br>Policies Restore<br>Investment.and<br>Economic<br>Growth<br>in Highly Indebted<br>Countries? A<br>Macroeconomic<br>Framework<br>Applied<br>to Argentina|Jacques Morisset|May 1991|S. King-Watson<br>31047|
|WPS692 Health Financing in the Poor<br>Countries: Cost Recovery<br>or Cost<br>Reduction?|J. Brunet-Jailly|May 1991|0. Nadora<br>31091|
the same time, for households with no migrants, the unobservables promoting migration are positively correlated with woman’s employment ( _Corr_ ( μ , _v_ 0) = 0.256 ). Thus, higher values of μ are correlated with lower value of ν _1_ and with higher values of ν _0_ , so that the impact of migration on labor force participation of women is lower for the households with high μ (who are more likely to have a working migrant).

There are several qualifications to and possible caveats in our results. First, our results are obtained using the 2004 cross-sectional data. Without panel data we have no instruments to control for possible household- or community-level endogeneity. In this sense, our estimations of the impact of work-related migration are valid only to the extent that unobserved family and community characteristics are captured by the variables included in our empirical specification. Second, while the majority of women in our sample are wives of migrants, some are daughters and other relatives of migrants. The effect of male migration might be different depending on whether the migrants are husbands, fathers, brothers or some other relative of the women in the sending households. Our analysis fails to capture this heterogeneity. Finally, we focus only on the direct impact of male migration for work on the labor market behavior of women in sending households. At the same time, male migration for work may also affect the aggregate labor market conditions in the sending communities. Accounting for the general equilibrium consequences of work-related migration might reduce its estimated impact on the MWP of females left behind.

# **6. Conclusions**

This paper examines the extent to which male migration for work affects labor force participation by prime age females in Nepal. The theoretical model developed in this paper predicts that male migration for work might have two main effects on the MWP of the women in the sending households. First, the increase in household income due to remittances from the migrants might lead to a reduction in the rates of MWP by the women. Second, depending on the properties of the home production function, male migration can increase or decrease women’s productivity at home, thus having an ambiguous effect on their MWP. The overall effect of male migration on women’s MWP 18
Policy Research Working Paper 8618

# **Abstract**

To document the relative accuracy of methods for microdata collection on root and tuber crop production, an experiment was implemented in Malawi over a 12-month period, randomly assigning cassava-producing households to one of four approaches: daily diary-keeping, with semi-weekly supervision visits; daily diary-keeping, with semi-weekly supervisory phone calls; two six-month recall interviews, with six months in between; and a single 12-month recall interview. Lapses in diary-keeping can underestimate true production, albeit to a lesser degree compared to recall. And the comparisons between the diary variants and the variation in underestimation by recall period are unclear ex ante. The analysis reveals that compared to traditional diary-keeping, the household-level annual cassava production is 295 kilograms higher, on average, (and assumed as closer to the truth) under diary-keeping with phone calls. This effect corresponds to 28 percent of the average traditional diary-keeping production estimate. Although the difference between the estimates based on six-month recall and traditional diary-keeping is statistically insignificant, 12-month recall underestimates annual production, on average, by 516 kilograms and 221 kilograms, respectively, compared to diary-keeping with phone calls and traditional diary-keeping. While the recall-based approaches both underestimate true production, six-month recall does so to a lesser extent. The evidence additionally demonstrates likely gross overestimation in international and ministerial statistics on cassava yields in Malawi. For improved microdata on root and tuber crop production, the adoption of (i) diary-keeping with phone calls (particularly if deployed in a broader mobile phone–based survey) or (ii) six-month recall, as a second-best alternative, is recommended.

This paper is a product of the Development Data 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://www.worldbank.org/research. The authors may be contacted at hmoylan@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