rafmacalaba/datause-encoder-data huggingface dataset:
- 10 -

without having to choose appropriate exchange rates. Third, savings rates tend to be stationary, whereas absolute saving flows grow over time, so that, by using rates, spurious correlation with time-trended explanation variables can be minimized.

In addition to the level of education, two other explanatory variables have been introduced in the savings function. First, the GDP per capita growth rate has been included because the relationship between the two has been extensively emphasized in the economic literature. There are different channels through which savings and growth are related, but the effect of a variation in economic growth is generally expected to be positive on savings rates.<sup>10</sup> The life-cycle hypothesis argues that because productivity growth makes the young richer than the old, the young will be saving more than the old is dissaving and, therefore, leads to a positive impact on aggregate private savings. An additional argument is that consumers desire to hold a fixed target wealth/income ratio, then if income is growing faster, wealth must grow faster. To make wealth grow faster, it is necessary to save more. Finally, it is often argued that investment opportunities are greater in a growing environment, leading to a higher propensity to save.

Second, demographic factors play an important role in the life-cycle models as both young and old people are expected to save less than middle-age working people (see Deaton and Paxson (1992) for a review). The correlation between the dependency ratio, defined as the percentage of the population lesser than 15 years and older than 65 years to total population, is expected to be negative, though empirical results are often ambiguous in most existing studies.

The data has been drawn from the World Bank's data base for the period 19601990. The annual data has been expressed as five-year average in order to capture the long-term trend. For each of the 74 countries in our sample, the gross domestic product per capita and the gross domestic saving have been defined in nominal terms and in local currency. The real GDP growth rate has been calculated using as a basis the year 1987. The dependency rate ratio was extracted from the data base of the International Economic Department from the World Bank.

10. For an opposite view, see the recent survey by Caroll and Weil (1994).
-5-

(b) Women's heavy workload, which cuts into the time available to meet agents.

(c) Sociocultural and religious factors that inhibit male EAs' communications with women farmers.

(d) The EAs' perception that women have physiological limitations for farming.

(e) The EAs' perception that women are less able to understand most extension messages because of their generally lower level of education (Sigman 1989).

# Case 3: **MALAWI:** UONENS **AGRICULTIJAL WRK AND** ACCESS TO **EXTENSICO.**

Agra-economic survey data show that women farmers in M-lawi do 50-70X of the agriculturat operationsin the smatlhotder sector, either atone or with their husbands and families.. They cuttivate food and cash crops in gardens they manage personalty or with their husbands. Accordinrg to the National Soopte Survey of Agricutture data, men recelve most of the extension visits and training as wet as such services as credit, and technical informat,on. (Ministry of Agriculture, Goverrment of Matawi 1983.)

# 3. Constraints Women Farmers Face: Barriers to Access

1.14 Women farmers generally have more difficulty than men operating effectively in factor markets, if they can get access at all. As a result, most women have less access to and higher effective costs for information, technology, inputs and credit. Their productivity is thereby depressed (Schultz 1988). The explanations underlying these barriers to access relate to child-bearing, time, mobility, education, and an array of socio-cultural characteristics. To design extension services so they effectively help women farmers, it is essential to understand the nature of the special constraints women face and tne implications of these constraints for extension.

# (a) Land Availability and Tenure.

1.15 Land availability and tenure is a problem for women because they tend to have less secure tenure, land which is more fragmented, or simply smaller plots. Women's relatively less favorable access to land can be a strong disincentive to adopting new techniques or investing in the land. For their part, extension agents may be reluctant to work with distant plots. In developing extension messages, agents should keep the following points in mind:

(i) Plots allocated to African women are freguently far from their villages and far from other plots the woman cultivates.

1.16 Women's labor productivity is reduced and their time management
<!-- Start of picture text --> 50<br>48.2 49.1<br>48<br>oo”<br>- 2 Aly<br>=a5 46 -* —_s—._-= ~.y . * a i<br>a845.4 ..<br>= 44 . “’ 43.4<br>o —- Unweighted : a<br>—— Population. weighted. \ mS<br>42 ae<br> a<br>41.0<br>40<br>1993 1997 2001 2005 2009 2013<br><!-- End of picture text -->
Appendix I.A.9 Historical Demographic Data, Costa Rica 1892-1973

|**Year**|1892|1927|1950|1963|1973|
|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|23.00|21.92|18.87|15.60|14.85|
|Population aged 20 to 59/<br>population_over_60 years old|11.00|_10.15_|8.82|7.34|6.92|
|Percentage of population<br>over 60 years old|4.07|4.26|4.75|5.09|5.56|
|Percentage of population<br>under 15 years old|41.46|41.49|42.88|47.64|44.09|
|Women over 60 years old/<br>Men over 60 years old|1.00|1.00|0.90|1.06|1.00|

Ratios calculated using data found in Mitchell, B. R. 1983 "Intemational Historical Statistics:<sup>The Americas and Australasia"</sup>

# Appendix I.A.11 Historical Demographic Data, Egypt 1917-1960

|Year|1917|1927|1937|1947|1960|
|---|---|---|---|---|---|
|Population aged15 to64/<br>population over 65 years old|-|-|15.65|18.98|15.47|
|Population aged 20 to59/<br>population over 60 years old|5.92|6.94|7.20|7.63|7.06|
|Percentage of population<br>over 60 years old|7.45|6.57|6.38|6.01|6.07|
|Percentage of population<br>under 15 years old|28.04|38.68|39.20|38.07|42.76|
|Women over 60 years old/||||||
|Men over 60 years old|1.13|1.16|1.18|1.20|1.13|

Ratios calculated using data found in Mitchell, B. R. 1982 "International Historical Statistics: Asia<sup>and Africa".</sup>
POLICY RESEARCH WORKING PAPER 2491

# Summary findings

There are concerns that trade reform and globalization will increase the uncertainty that the average worker, especially the relatively unskilled worker, faces. The increased competitiveness of product markets and greater access to foreign inputs, the argument goes, will lead to more elastic demand for workers. This may have adverse consequences for both labor market volatility and wage dispersion.

Fajnzylber and Maloney argue that while the case that trade liberalization should increase own-wage elasticities may be broadly compelling for competitive importcompeting industries, it is less so for imperfectly competitive, nontradable, or export industries. They test the hypothesis using establishment-level panel data from three countries with periods of liberalization.

The data provide only mixed support for the idea that trade liberalization has an impact on own-wage elasticities. No consistent patterns emerge.

If globalization is making the lives of workers more insecure, it is probably working through some other mechanism.

This paper-a product of the Poverty Reduction and Economic Management<sup>Sector Unit, Latin America and the Caribbean</sup> Region-is part of a larger effort in the region to study the impact of liberalization<sup>on labor market risk. Copies of the paper</sup> are available free from the World Bank, 1818 H Street NW, Washington,<sup>DC 20433. Please contact Tania Gomez, room</sup> 18-102, telephone 202-473-2127, fax 202-522-0054, email address<sup>tgomez@worldbank.org. Policy Research Working</sup> Papers are also posted on the Web at www.worldbank.org/research/workingpapers.<sup>William Maloney may be contacted</sup> at wmaloney@worldbank.org. November 2000. (43<sup>pages)</sup> _The Policy Research Working Paper Series disseminates the findings of work_<sup>_in progress to encourage the exchange of ideas about_</sup> _development issues. An objective of the series is to get the findings out quickly,_<sup>_even if the presentations are less than fully polisbed. The_</sup> _papers carry the names of the authors and should be cited accordingly. The findings,_<sup>_interpretations, and conclusions expressed in this_</sup> _paper are entirely those of the autbors. They do not necessarily represent the_<sup>_view of the World Bank, its Executive Directors, or the_</sup> _countries they represent._ Produced by the Policy Research Dissemination Center
89 Table 16 Sectors in which MERCOSUR Common External Tariff exceeds or falls below the average by 3 percentage points or more Difference from average common external tariff (percentage points) Sector ISIC Description

|A. High tariffs||B. Low tariffs||
|---|---|---|---|
|3117 Bakery products|3.5|3111 Slaughtering, preserving meat|-4.0|
|3119 Sugar, choc. Confectionery|3.0|3115 Vegetable, animal oils & fats<br>|-3.8|
|3131 Distilling spirits<br>|6.8<br>|3116 Grain mill products<br>|-3.5<br>|
|3132 Wine industries|4.7|3232 Tanneries|-3.8|
|3134 Soft drinks|6.6|3311 Sawmills|-3.5|
|3140 Tobacco|5.4|3319 Wood & cork products nec|-3.5|
|3211 Spinning, weaving textiles|3.0|3420 Printing, publishing|-3.4|
|3213 Made-up textiles exc. clothing|6.0|3511 Basic industrial chemicals|-5.8|
|3213 Knitting mills|4.8|3512 Fertilizers, pesticides|-8.8|
|3214 Carpets, rugs|5.5|3522 Drugs, medicines|-6.5|
|3215 Cordage, rope|4.8|3530 Petroleum refineries|-10.5|
|3220 Wearing apparel exc. footwear|6.7|3540 Products of petroleum, coal|-9.5|
|3319 Furniture|4.7|3692 Cement, lime, plaster|-9.5|
|3523 Soaps, cosmetics|3.5|3699 Non-metalic mineral prod. nec|-3.8|
|3560 Plastic products|4.0|3720 Non-ferrous metals|-5.0|
|3610 Pottery, china|3.8|3845 Aircraft|-11.5|
|3811 Cutlery, hardware|4.2|||
|3812 Metal furniture|4.1|||
|3833 Electrical appliances|5.2|||
|3843 Motor vehicles|5.1|||
|3849 Other transport equipment|6.8|||
|3853 Watches, clocks|5.6|||
|3902 Musical instruments|3.6|||
|3903 Sporting goods|6.8|||
|3909 Manufactures nec|4.5|||

Source: Olarreaga, Soloaga and Winters (1999) The macroeconomic cycles in Argentina and Brazil have indeed dominated regional trade, more than structural incentives from the Mercosur. When Brazil’s currency became seriously overvalued following the introduction of the Real Plan in 1994, Argentine exports to Brazil rose rapidly. After Brazil’s deep devaluation in early 1999, Argentine imports from Brazil surged. There were numerous and recurrent interventions to counter these shocks, such as Argentine export subsidies by 2000 following the Brazilian devaluation, and ongoing friction on such issues as whether safeguard protection could be applied to partner imports in light of the sharp exchange rate swings.
Central Staistical Bureau of Latvia (2003b), Labour Force Surveys: main indicators in 1996-2001.

Central Staistical Bureau of Latvia (2004), Labour Force Surveys: main indicators in 2003.

Ehrenberg, Ronald G. and Robert S. Smith (2003). Modern Labor Economics, Theory and Public Policy. 8th ed., Addison Wesley.

Hazans, Mihails (2002), “Age-Earnings Profiles and Human Capital in Transition: Evidence from the Baltic States”. http://www.eurofaculty.lv/papers/DownloadsEconomics/mihails_hazans.pdf Hazans, Mihails, John Earle, and Raul Eamets (2002), "Labour Markets in the Baltic States", Background paper for the OECD (2003) publication “Labour Market and Social Policies in the Baltic Countries”.

Hazans, Mihails (2003a), “Commuting in the Baltic States: Patterns, Determinants, and Gains” ZEI (Center for European Integration Studies, Univ. of Bonn) Working Paper No. B02-2003 (available at www.zei.de) Hazans, Mihails (2003b), “Determinants of inter-regional migration in the Baltic Countries,” _ZEI_ (Center for European Integration Studies, Univ. of Bonn) Working Paper No. B17-2003. (available at www.zei.de) Hazans, Mihails (2003c), “Potential emigration of Latvian labour force after joining the EU and its impact on Latvian labour market”, _<u>http://www.gdnet.org/cf/search/display.cfm?search=ALLKB&act=DOC&docnum=DOC14353</u>_ Hazans, Mihails (2003d), “Returns to Education in the Baltic Countries,” paper presented to the conference „ _Human Development in EU Accession Countries_ , Riga, 1-2 June 2003.” (http://www.biceps.org/documents/Hazans_RETURNSTOEDUCATION.pdf).

Hazans, Mihails (2004a), “Does Commuting Reduce Wage Disparities?”, _Growth and Change,_ Volume 35 Number 3, Special Issue on Commuting, pp. 360-390.

Hazans, Mihails (2004b) “Looking for the Workforce: The Elderly, Discouraged Workers, Minorities and Students in the Labour Market”, In: AccessLab, Report on Workpackage 4, Human Capital, Spatial Mobility, and Lock-In – The Experience of Candidate countries. http://accesslab.wifo.ac.at/ Hellerstein, Judith K., David Neumark, and Kenneth R. Troske (1999), "Wages, Productivity, and Worker Characteristics: Evidence from Plant-Level Production Functions and Wage Equations," Journal of Labor Economics, Vol. 17(3), July, 409-46.

Jurajda, Štepán (2000), “Gender Wage Gap and Segregation in Late Transition”, _Davidson Institute Working Paper_ No. 306.

Kunze, Astrid (2000), “The Determination of Wages and the Gender Wage Gap: A Survey”, _IZA Discussion Paper_ No. 193.

Laxton, Valerie (2001) “Occupational Segregation and gender wage gap in Eastern Europe”. Paper presented to workshop “Labour Markets, Work and Welfare during the Transition and Integration Processes, Riga, 23-30 March 2001”.

46
# 4.2. Unemployment costs

Unemployment expenditures are directly related to the high-wage hypothesis of this paper. It is paid by the Unemployment Fund, Fundusz Pracy (FP), which also collects the contributions. It totaled around 2 percent of GDP in 1991-94. Registered unemployment rose from zero to 2.8 million between 1989 and 1993. The labor force remained roughly constant throughout 1989-1994, while the number of pensioners rose by 2 million (see table 9 in section 8); thus new entries exactly matched retirement. Self-employment rose by 0.5 million, compensating for part of the 3.3 million employment decline. People between 15 and 24 1 ears old represent more than 40 percent of the unemployed and the unemployment rate of the age group 18-24 was 32 percent in 1994, compared with 14 percent for the whole active population (GUS, Aktwynosc zawodowa i bezrobocie w Polsce, March 1995).

Given that many of the workers affected by economic restructuring were absorbed by the pension system, and given the large share of young unemployed, also in the low-unemployment vojvodships (see GUS: Bezrobocie rejestrowane w Polsce, quarterly), a lot of good evidence would have to be provided to reject the hypothesis that insider wage-setting cost at least half of Table 7: Other Social Expenditure

||1989|1990|1991|1992 1|1993|1994|
|---|---|---|---|---|---|---|
||||percentage|of GDP|||
|Social insurance-farmers|1.0|1.3|1.9|2.1|2.1|2.4|
|Unemployment fund|0.1|0.6|1.6|2.2|2.0|2.1|
|||pe|rcent of ZUS|expenditur|e||
|Family allowances in ZUS|16.7|16.0|14.6|15.3|15.4|15.8|

Source: Budgetary data and GUS Rocznik 1994, p. 227, 1991, p. 239.

23
<!-- Start of picture text --> Age-specific fertility by religion, allindia (2000)<br>OA<br>0.15<br>0.05 = =.<br>ie)<br>15-19 20-24 25-29 20-34 35-39 AO0-A4A A5-A9<br>Age<br>— Hindu = [Vuslim —_ - Christian — - - Cther<br><!-- End of picture text -->
**Table 1: Labor force participation and distribution across activities of rural men and women aged 16 to 70, from 1997 to 2006**

|||1997|2000|2004|2006|
|---|---|---|---|---|---|
|**Rural men:**||||||
|Working|%|89.0|87.8|82.4|82.1|
||age|38.2|38.7|40.5|40.8|
||education|7.4|7.8|7.8|7.9|
|Migrants|% of labor force|7.8|11.6|19.0|24.5|
||age|27.8|28.4|30.2|30.8|
||education|7.9|8.4|8.2|8.1|
|Agriculture|% of labor force|76.6|70.7|60.9|55.3|
||age|38.9|39.7|43.6|45.2|
||education|7.3|7.7|7.7|7.9|
|Local wage|% of labor force|32.3|34.1|23.5|23.5|
||age|36.2|36.2|40.1|41.5|
||education|8.8|8.9|9.1|9.2|
|Family business|% of labor force|13.3|13.3|18.6|13.5|
||age|38.5|39.3|42.5|44.4|
||education|8.2|8.3|8.0|7.9|
|Obs. Number||3582|4026|4023|4273|
|**Rural women:**||||||
|Working|%|85.9|82.1|75.1|73.4|
||age|38.0|38.8|40.8|41.1|
||education|5.6|6.2|6.1|6.4|
|Migrants|% of labor force|5.6|8.2|11.6|14.9|
||age|23.1|23.6|25.1|26.9|
||education|7.8|8.4|7.7|7.8|
|Agriculture|% of labor force|76.0|68.4|61.3|56.4|
||age|38.5|39.9|43.6|44.7|
||education|5.5|6.0|5.8|6.0|
|Local wage|% of labor force|21.7|22.3|15.0|15.8|
||age|32.1|32.6|36.8|38.3|
||education|8.4|8.6|8.7|8.8|
|Family business|% of labor force|9.8|9.5|13.4|11.5|
||age|38.3|38.3|41.8|43.4|
||education|6.6|7.2|6.7|7.1|
|Obs. Number||3445|3891|3849|4012|

Note: CHNS rural sample of men and women aged 16 to 70 years.

31
that Standard 6 and 7 in primary school appear to be a time during which literacy actually erodes in the absence of the treatment, and it is in these grades that the largest treatment effects on literacy are seen. Among those in school at baseline, literacy is much higher and the only impacts are seen at the lowest grade levels (Standard 5 and 6) and thereafter literacy has achieved high enough levels that no upward treatment effects are detected. Put differently, the baseline dropouts return to grades at which literacy is increasing rapidly, whereas the baseline schoolgirls remain in school during grades at which literacy is already almost universal.

The impacts presented so far make use of self-reported enrollment and literacy. However, as part of this study, we also conducted an independent school survey that visited every school in Zomba attended by any of the core respondents in our study sample, and collected data on, _inter alia_ , each student’s attendance and their grade progression separately for each school term. We found the selfreported attendance data to be fairly accurate, and impacts estimated using data from the school survey are qualitatively very similar to those reported here.<sup>18</sup> Having shown strong treatment impacts on enrollment and relatively muted impacts on ‘learning’ (in the form of English literacy), both of which are based on self-reporting, we can now use the cross-sectional data from the Round 2 school survey to measure the extent to which the treatment improved the probability that a girl attended school regularly during all three terms in 2008 and whether she successfully completed her current grade – according to her teacher.<sup>19</sup> The attendance impact estimates presented in Table 4b confirm those presented above using self-reported attendance data. We also see a strongly significant 16 percentage point increase in grade completion among _baseline dropouts_ , but no statistically significant impact among baseline schoolgirls. If we compare these completion impacts to the attendance impacts, however, we see that the share of

> 18 For more on the relationship between self-reported attendance and the records from the school survey, see Baird, McIntosh, and Özler (2009b).

> 19 **“Attended school regularly”** is equal to “1” if the student’s teacher reported the student to have attended “more often than not” in each of the three school terms in 2008. **“Passed grade”** is equal to “1” if the teacher reported that the student made satisfactory progress to “pass this grade to continue to the next grade.” The school attendance reported by the student’s teacher is, in effect, a different variable than the self-reported attendance, and closer in spirit to the conditionality imposed by the program.

17
thermore, qualitative research finds that the classes were held infrequently, were generally of low quality, and were not taken seriously by participants, suggesting that the classes did not likely impart new knowledge on program recipients that would impact their food consumption decisions (Rodriguez Herrero, 2005). As shown below, our main empirical results are robust to separating the in-kind villages into the group with classes and the group without. Using data from the experimental sample, Cunha (2014) documents that both cash and inkind transfers led to equally sized increases in total consumption (food plus non-food) and food consumption between in-kind and cash communities. However, several of the in-kind food items were extra-marginal, as evidenced by greater increases in consumption of those goods in in-kind communities compared to cash communities. Some of these extra-marginal foods were nutrient rich, such as fortified powdered milk and vitamin enhanced corn flour, and thus children in in-kind communities were observed to consume more iron, zinc and vitamin C than children in cash communities.

# **3 Data and Sample**

## **3.1 Data**

Our data come from several sources: pre- and post-intervention surveys of individuals and households in PAL villages, student-level ENLACE test scores, student surveys from a subset of ENLACE test takers, and school-level data collected by the SEP.

In each of the experimental PAL villages, approximately 33 households were selected to be surveyed pre- and post-intervention. The pre-intervention survey was administered in the last quarter of 2003 and the first quarter of 2004, and the post-intervention follow-up survey was conducted in the final quarter of 2005.<sup>9</sup> The pre-intervention data allow us to confirm the randomization was successful, as well as segment the population by various socio-economic characteristics. From the post-intervention survey, we use data from a 24-hour food recall for children and a time allocation module for both children and adults. The food recall was completed by the survey respondent, usually the female household head, for all children aged 2 to 6 at the time of the follow-up survey and allows us to calculate the quantity of macro- and micro-nutrients consumed. For all children 12 and older, both pre-intervention and follow-up survey ask both primary and secondary

> 9To ensure respondents would not wrongly conclude that responses could affect their eligibility for aid, surveys were administered by Mexico’s National Institute of Public Health, a different agency than the one that administered PAL.

7