rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7282

# **Abstract**

The contribution of women to labor in African agriculture is regularly quoted in the range of 60 to 80 percent. Using individual-disaggregated, plot-level labor input data from nationally representative household surveys across six SubSaharan African countries, this study estimates the average female labor share in crop production at 40 percent. It is slightly above 50 percent in Malawi, Tanzania, and Uganda, and substantially lower in Nigeria (37 percent), Ethiopia (29 percent), and Niger (24 percent). There are no systematic differences across crops and activities, but female labor shares tend to be higher in households where women own a larger share of the land and when they are more educated. Controlling for the gender and knowledge profile of the respondents does not meaningfully change the predicted female labor shares. The findings question prevailing assertions regarding substantial gains in aggregate crop output as a result of increasing female agricultural productivity.

This paper is a product of the “Agriculture in Africa—Telling Facts from Myths” project managed by the Office of the Chief Economist , Africa Region of the World Bank, in collaboration with the Surveys and Methods Unit, Development Economics Department of the World Bank, the African Development Bank, the Alliance for a Green Revolution in Africa, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, and the University of Rome Tor Vergata. 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 of the paper may be contacted at: apalacioslopez@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 4 Persistence of price shocks, 1900-1987

|Commodity|Persi|stence|Longest Lag years|
|---|---|---|---|
||Autocoar<br>measure|Deaton<br>PER20||
|Rice<br>|0.11<br>|0.18<br>|9|
|Palmoil<br>|0.13<br>|0.13|5|
|Coffee<br>|0.38|0.17|11|
|Bananas|0.45|0.59|10|
|Wheat|0.46|0.24|10|
|Sugar<br>|0.52<br>|0.11<br>|6|
|Cocoa|0.65|0.29|2|
|Tea<br>|0.72|0.37|2|
|Beef|1.0||0|
|Maize|1.1|0.19|10|
|Lamb|1.30||4|
|Average|0.61||6.28|
|lmber|0.1||8|
|wool|0.35||2|
|Jute|0.4|0.19|5|
|Hides<br>|0.43<br>||2|
|Cotton<br>|0.67|0.39|3|
|Tobacco|0.73||4|
|Rubber|1.0||0|
|Average|_0.51_||3.43|
|oil|0.51||11|
|Silver<br>|0.65<br>||8|
|Tm|0.65|0.43|c|
|Lead|0.73||3|
|Aluminium|0.93||5|
|Zinc|1.0||0|
|Copper<br>|1.0<br>|0.31|0|
|Coal<br>|1.0<br>||0|
|Average|0.81||4.0|

Source: Cuddington and Urzda (1987), Deaton and Laroque (1989, Table 2) Notes: Annual data. The first measure is the sum of the statistically significant autocorrelation coefficients, as calculated by Cuddington and Urzia and explained in the text. Deaton and Laroque's measure of persistence is PER20, given in from Table 5 below, and explained therein. The longest lag is the highest order stadsdcally significant lag.

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_Table 9. Coefficients of Class-Level Variables Estimated in Individual Multi-Level_<sup>_Regressions on English_</sup> _and Mathematics Achievement_ (Standard Errors)

|||_Percen_|_Engl_<br>_t Varia_|_ish_<br>_nce Ex_|_plained_|||_Perce_|_Mathe_<br>_nt VaH_|_matics_<br>_ance Ex_|_plained_||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_Variable_|_Coeffi_|_cient_|_Sch._|_Cl._|<br>_Stud._|_Tot._|_Coeffic_|_ien_|_Sch._|<br>_Cl._|_Stud._|_Tot._|
|Female teacher<br>(0inMale)|.56|(.16)|12|5|-|6|.66 (.1|9)|16|2|-|4|
|Teacher's Age|.02|(.01)|10||-|4|.03(.0|1)|22|-|-|4|
|Log Teachers' Age|1.52|(.73)|10|-||4|2.42 (|.87)|23|||4|
|Teacher Oualificatione<br>(Reference-Standard<br>Trained)|-||8|||3|-||28|-|-|7|
|Form 4 + 2 years|.24|(.24)-|||-||.14(.|45)'|||||
|Form 2 + 2-3 years|-.07|(.13)|||-|-|-.28(|.28)'|-|-|-|-|
|Untrained|*.41|(.14)||-|||-1.41 (|.32)||-|||
|StandardTrained<br>(0 - Untrained & Non-<br>Std. Qual.)|.40|(.17)|6|2|-|3|.59(.|21)|14|-||3|
|Teacher Experience|.014|(.008)|||||.019|(.01)'|15|-||3|
|CiassSize|.03|(.019)|-|-|-|-|.026|(.016)'|-||||
|LogClass Size|2.36|(1 2)'|2|3|-|1|1.94|(I1C7)'|4|||I|
|PlanningTime|-. 03|(.02)'|-|-|-||-.018|(.023)'|||||
|Marking Time|-.003|k.01)'|-|-|-|-|.003|(.02)'|||||
|Instruction Time|.0002|(.0008)-|-|-||-|.004|(.002)|14|-|-|3|
|Log Instruction Time|.44|(.78)'|||-|-|1.80|(.55)|1||-|4|
|Class Activities|-.03|(.04)'|-|-||-|.04|(.06)'|-||-||
|Tex, Available|1.86|(.33)|40||-|16|2.21|(.32)|57||-|13|
|Log Texts|2.45|(.46)|38|-|-|15|2.92|(.44)|53|-|-|12|
|Supervised Study|04|(.04)'|-|-|-|-|.13|(.06)|2|3||-|
|Teaching Load|-.0006|(.02)'|-|-|-|-|-.005|(.02)'|-||||
|Game Time|.02|(.04)|-|-|-|-|002|(.05)'|||||

_Notes:_ 'Not sigai ficant (p > _.05)_ b Chi-square p=.0177 for contrasts for English; p=.0002 for Mathematics. Measured from grand mean except where noted.

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deterministic fashion unlike the EM algorithm. Thus, coefficients are drawn from the parameter posterior distribution rather than chosen by likelihood maximization. Hence, the iterative process is a Monte‐Carlo Markov Chain (MCMC) in the parameter space with convergence to the stationary distribution that averages over the missing data. The distribution for the missing data stabilizes at the exact distribution to be drawn from to retrieve model estimates averaging over the missing value distribution. The DA algorithm usually converges considerably faster than using standard EM algorithms: The performance of the estimation technique was assessed based on an _ex post_ simulation using the NBHS 2009 data and mimicking the Rapid Consumption methodology by masking consumption of items that were not administered to households. The results of the simulation were compared with the estimates using the full consumption from NBHS 2009 as reference. The simulation results distinguish between different levels of aggregation to estimate consumption.<sup>26</sup> The methodology generally does not perform well at the household level (HH) but improves considerably already at the enumeration area level (EA) where the average of 12 households is estimated. At the national aggregation level, the Rapid Consumption methodology slightly over‐estimates poverty by 1.6 percent. Assessing the standard poverty measures including poverty headcount (FGT0), poverty depth (FGT1) and poverty severity (FGT2), the simulation results show that the Rapid Consumption methodology retrieves almost unbiased estimates. Generally, the estimates are robust as suggested by the low standard errors.<sup>27</sup> The assumption that the imputed components of consumption follow a joint normal distribution might provide an explanation as to why poverty is slightly overestimated. This would be due to the imputed means of consumption of the imputed items being slightly lower than the actual means since their true distributions are generally skewed to the right. This possibility was explored by assuming a non‐parametric error term in the imputation procedure through the use of chained equations, which performed almost indistinguishably as well as the multivariate‐normal approximation.

> 26 The performance of the estimation techniques is presented using the relative bias (mean of the error distribution) and the relative standard error. The relative error is defined as the percentage difference of the estimated consumption and the reference consumption (based on the full consumption module, averaged over all imputations). The relative bias is the average of the relative error. The relative standard error is the standard deviation of the relative error. The simulation is run over different household‐module assignments while ensuring that each optional module is assigned equally often to a household per enumeration. The relative bias and the relative standard error are reported across all simulations.

> 27 These standard errors are estimated empirically using a bootstrap approach taking into account intra‐cluster correlation within enumeration areas.

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CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18: Consumption of infrastructure services in Côte d’Ivoire is highly differentiated by budget**

- a. Mode of water supply, by income quintile

<!-- Start of picture text --> piped water standposts<br>wells/boreholes income quintile surface water<br>b. Prevalence of connection to power grid among population, by income quintile<br>income quintile<br>% of population<br>% of population<br><!-- End of picture text -->

- b. Prevalence of connection to power grid among population, by income quintile

Legend: Q1 – first budget quintile, Q2 – second budget quintile, etc. Source: Banerjee and others 2009.

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strictly green jobs are mid-level or elementary occupations. High-level strictly green occupations – defined as those with a 1-digit ISCO code of 1-3 – are 65 percent of the sample but high-skilled green jobs (jobs are the number of people working in an occupation) are only 55 percent of the sample (Figure 1). This mismatch is due to a larger number of South Africans working in mid-level (ISCO 1-digit code of 4-8) or elementary (ISCO 1-digit code of 9) strictly green occupations. For example, an estimated 61,000 work as garbage collectors (elementary occupation) and 201,000 are employed as motor vehicle and mechanics fitters (mid-level occupation). This is in contrast with only about 3,600 employed as biologists and related professionals and about 3,000 geologists and geophysicists, all classified as high-level occupations. That said, a much higher proportion of strictly green jobs are in high-level occupations (55 percent) as compared to the share of high-level occupations in the general labor market (24 percent) (Figure 1). The greater share of high-level green occupations reflects findings from similar studies in the global north (Consoli et al. 2016; Vona et al. 2019; Bowen, Kuralbayeva, and Tipoe 2018).

The greenest South African occupations are disproportionately classified as elementary or mid-level. Of the 20 strictly greenest occupations, 85 percent of the jobs are in mid-level or elementary occupations (1-digit codes 5,6,7,8, and 9 in Table 3), though they only represent 8 of the top 20 occupations with the highest strictly green GTI scores (Table 3).

Figure 1: Green jobs and all jobs, by skill intensity of the occupation <!-- Start of picture text --> Green strict Green broad All employed<br>100 100 100<br>55 12 24<br>62<br>80 80 80<br>47<br>60 60 60<br>40 37 40 40<br>29<br>20 20 25 20<br>9<br>0 0 0<br>Elementary Mid-level High-level<br>Percent<br><!-- End of picture text --> Source: authors’ calculations using PALMS. Note: strictly green jobs are those with at least one task on the strictly green task list. Broadly green jobs are those with at least one task that is on the broad green task lists (which includes the strictly green tasks).

Broadly green jobs are more prevalent in mid-level and elementary occupations. Once the definition of green tasks is expanded to include broad green tasks, 88 percent of jobs are in mid-level and elementary occupations. For example, if we allow that occupations under code 7 – such as extraction and building trades (code 71), metal machinery and related trades (code 72) and precision, handicraft and craft trades (code 73) – may adopt green technologies, they 13
statistically significant rate. There is no effect of household size on the attainment of boys. In rural areas, sibling rank significantly affects the attainment of girls but not of boys. The education of the oldest prime-age female in the household significantly increases the grade-forage for both boys and girls.

While grade-for-age measures educational attainment over a long period, it is only an indirect measure of the stock of time use. A closer measure of time use is the time children allocate to school. Since this information is not available for 1997, I present the results of probit regression on decision to attend school full-time based on cross section (1994 panel households only) in columns 3 and 4. Here the decision to attend school full-time is positively associated with age-it increases with age but at a decreasing rate. The estimated coefficients of other regressors are largely insignificant, suggesting that unobserved heterogeneity may be clouding the estimated standard errors. I therefore do not discuss these results in detail here but provide them for illustrative purposes only.

# _Housework_

Estimation results of the determinants of time allocation to housework are presented in table 3. Here, there are some effects that are common to boys and girls, and some that are stronger for girls than for boys. Among the common effects, age increases the housework time of both boys and girls but at a decreasing rate. Household size and the age structure of females in the household also affect boy-girl time in housework in a similar manner. Children in bigger households tend to do less housework, presumably because of economies of scale in housework. The presence o:f prime-age females in the household lowers the housework time of both boys and girls. Birth order significantly increases the time allocation to housework for both boys and girls, suggesting that older siblings engage more in housework than do younger ones. Changes in cluster level female unemployment also affect the housework time of both boys and girls. In 20
Policy Research Working Paper 4990

# **Abstract**

A large literature studies the effects of trade policy changes on developing-country exports on household incomes, and recent contributions have increasingly addressed the effects of administered protection, such as anti-dumping duties. In 2003 the United States imposed anti-dumping tariffs on imports of catfish from Vietnam ranging from 37 to 64 percent. As a result, Vietnamese exports of catfish to the U.S. market declined sharply, thus providing a unique opportunity to study the effects of U.S. trade policy changes on Vietnamese families. Using data on Vietnamese households, the authors study the responses of catfish producers in the Mekong delta of Vietnam between 2002 and 2004. The evidence suggests that the rate of growth of income of households that depended on catfish sales was significantly affected. In addition, the anti-dumping duties triggered significant exit from catfish farming. Households adjusted by moving out of catfish aquaculture and into wage labor markets and agriculture, but not into other aquaculture activities. Finally, the evidence also suggests that households found it difficult to change their catfish production levels, and that performance in aquaculture affects other household economic activities.

This paper—a product of the a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to explore adjustments to trade policy in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gporto@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
5

**Figure 2:  The Sample of Firms Studied**

<mark>The sample included all FECUs filed with the SVS in 2001, 2002 and 2003. The 2003 sample includes 476 firms. Their breakdown by size of firms, sectors and access to capital markets is illustrated below.</mark> <!-- Start of picture text --> 476 firms. Their breakdown by size of firms, sectors and access to capital markets is illustrated below.<br>Sample breakdown by size of cies Sample breakdown by sector Agriculture<br>11% 6% 6%2%8% M iningM anufacturing<br>Mega 17%<br>11%<br>Industry<br>Large<br>43% 12% Utilities<br>Medium 5%<br>Construction<br>14% Small 1% Commerce<br>Micro Real estate<br>Services<br>21% 43%<br>Listed Vs non listed firms Listed firm breakdown by access to<br>foreign capital markets<br>Firms with access<br>5% to foreign capital<br>markets:<br>38%<br>Firms with access<br>Listed firms (in to local capital<br>Chile or abroad): 36% markets:<br>62% 53% Firms without<br>Non listed firms access to local<br>capital markets:<br>Source: FECUs<br><!-- End of picture text --> The data reveal that Chilean firms tend to be conservatively managed, but profitability has been depressed for several years because of a lack of cost restructuring. As shown in Figure 3, the balance sheet structure of the average Chilean firm is conservative: the average leverage ratio of the sample firms was 1.3x over 2001-2003, and only 0.4x when only long-term financial liabilities (bank loans and bonds) are included (including the portion falling due within a year). As a result, the average interest coverage ratio was a healthy 227x. Even when the portion of long-term debt maturing within a year is added to interest charges, the ratio remained a healthy 145x. The current ratio of Chilean firms was also high, at 59x. By contrast, the average return on equity (RoE) of Chilean firms was 6 percent, compared to an estimated cost of capital of 10 percent on average over the same period. This is partly due to insufficient cost restructuring in light of the lower sale volumes resulting from the regional crisis (the average cost to income ratio of Chilean firms was 123 percent over the period).<sup>3</sup> 3 Leverage is defined as the ratio of total liabilities / total shareholders’ funds; interest coverage is defined as the ratio of EBITDA (Earnings before Interest, Taxes, Depreciation and Amortization) to interest charges; the current ratio is defined as the ratio of current assets to current liabilities; return on equity is defined as the ratio of net
Columns 2, 4, and 6 add pre-program child and household characteristics and state, year, and grade fixed effects. These controls do not meaningfully impact our estimates and the differences between the two treatment types are statically significant at the 10 percent level in all subjects. In addition, these results are not driven by the outcomes in specific years (see Figure 2) or grades (see Figure 3), although the negative impacts of cash transfers are larger for students in higher grades (5th and 6th grades, compared 3rd and 4th grades).

We next explore program impacts by the age at which children first received the program. A large literature has shown that nutrition interventions are the most impactful between conception and the second birthday (Pollitt et al., 1995), and children in our sample ranged from in-utero to six years of age at the time that transfers began. Figure 4 plots the treatment effects by age at the follow-up survey. For children younger than two, we find positive point estimates, but correspondingly large confidence intervals that prevent us from rejecting the null hypothesis of no impact.

We also test whether there is any treatment heterogeneity along two dimensions that are particularly relevant for southern Mexico: household expenditure and ethnicity. Table 6 contains estimates from models that interact treatment indicators with an indicator for “poor” households, defined as those with below median expenditure per capita. While coefficients and comparisons between them are imprecise, the negative coefficients on the interaction terms suggest that poor households experience larger declines across the three subjects for both cash and in-kind transfers.

Interacting treatment with an indicator for an indigenous household (defined as at least one member speaking an indigenous language), we find the negative impacts of PAL are even greater among the indigenous population: Table 7 shows the impact of cash and inkind transfers on test scores are between 0.137 to 0.288 standard deviations lower among the indigenous compared to the non-indigenous students. Among non-indigenous students, in-kind transfers have no impact on learning outcomes and cash transfers have a negative (albeit insignificant) impact.

Finally, we estimate treatment effects across the test score distribution with quantile regressions. Figure 5 shows that for neither the cash nor the in-kind treatment is there significant evidence of treatment across the outcomes’ distribution.

# **5 Mechanisms**

To help understand the mechanisms through which the PAL transfers affected student learning, we adopt a simple learning production function (e.g. Glewwe and Miguel, 2007).

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