rafmacalaba/datause-encoder-data huggingface dataset:
-5 -

acts e use of the retrospective study results, in order to Improve the quality of future lending.

A central theme that recurs throughout the analysis Is the need for greater ficlency In the power sector. The Issue Is not that power Is unimportant to modern economies, os that this capital Intensive sector will not continue to merit significant resources In the future, but rather that developments over the past few years have highlighted the need to Improve efficiency In the production and use of electricity. Thus, both governments and donors would be well advised to more carefully scrutinize continued requests for resource flows to the sector, and to actively search for methods of Increasing technical, financial and managerial effectiveness.

The results of this study confirm that the developing countries have made significant gains in terms of access to, and per capita consumption of electricity over the last few decades. Other evidence also suggests that In many countries the power sector tends to be better organized and perform better than other sectors of the economy. However, during periods of high growth, power utilities have had to weather oil price Increases and high Inflation and have been hampered In their efforts to attain financial targets because governments have been slow In responding to changing conditions and In granting tartff Increases. The study confirms that both the Bank and Its borrowers should take early steps to arrest deteriorating trends, given that power Investments absorb as much as half of all public Investments In some countries, and often are the cause of severe debt-related and macroeconomic stresses. While Bank staff have frequently diagnos-d such problems In the past, recommended remedial measures have been less effective because of over-riding political priorities.

The evidence available tends to confirm that the soclal compact on which power utilities are based and th9 assumptions under which they operate within developing economies need to be rescrutinized. Over the past decade, the decline In sector performance has been paralleled by a shift towards large monolithic government-controlled electric utilities. This
# _4.9 Cost-Effectiveness_

Table 8 compares the program’s impact to its costs, including administrative costs. Columns 1-4 show the cost-effectiveness analysis for the third round, i.e. two years after treatment began and columns 5-8 show the cost effectiveness analysis for the second round, i.e. one year after treatment began. The estimates used in the cost-effectiveness calculations are taken from Table 3 for the education outcomes, from Table 4 for the health outcomes, and from Table 5 for the household level outcomes. Given the costs of the program and the estimated program impacts, we estimate the benefits for spending $100 on each transfer scheme, and we limit the analysis to program impact estimates that are statistically significant at most at the 10 percent level.

The first two columns compare how cost-effective are transfers to mothers versus transfers to fathers in raising education and health outcomes and household level economic wellbeing indicators. Each $100 spent on transfers to mothers add about 0.40 years of student participation and 0.72 years to cumulated years of education completed. In contrast, $100 spent on transfers to fathers add only about 0.29 years of student participation but have no statistically significant effect on cumulated years of education. The effects on child labor are not statistically significant for transfers to mothers nor for transfers to fathers. Taken together, these estimates imply that transfers to mothers are more cost-effective than transfers to fathers in improving education outcomes.

The results on health outcomes and household level outcomes are rather mixed. Transfers to mothers are more cost effective in improving the C-reactive protein measure, whereas transfers to fathers are more cost-effective in reducing the probability of a child being ill in the 30 days preceding the survey. For household level outcomes, transfers to mothers and to fathers are equally cost-effective in improving perceived own social status, but estimates clearly show that transfers to fathers are more cost effective than transfers to mothers in increasing livestock ownership.

Columns 3 and 4 contrast conditional cash transfers with unconditional cash transfers. Overall, conditional cash transfers are shown to be more cost-effective than unconditional cash transfers for all outcomes for which the point estimates are statically significant at the 10 percent

> the number of children 7-15 years old in the household. We find that the results are consistent: transfers to mothers have a greater effect on enrollment than transfers to fathers, and conditional cash transfers have a greater effect on enrollment than unconditional cash transfers.

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Policy Research Working Paper 8631

# **Abstract**

A growing body of work has shown that the quality of national institutions that enforce written contracts plays an important role in shaping a country’s comparative advantage. Using highly disaggregated bilateral and unique harmonized firm-level trade data across a large number of countries, this paper contributes to this literature by providing a comprehensive analysis of the mechanisms through which institutional frictions affect the pattern of aggregate trade flow, distinguishing the effects on the intensive and extensive margins. The analysis finds that contractual friction distorts countries’ trade pattern beyond its effect on domestic production structure, by deterring the probability of exporting (the extensive margin) and export sales after entry (the intensive margin), particularly in industries that rely more heavily on relationship-specific inputs (more vulnerable to holdup problems). The analysis also finds that contractual frictions matter more for the intensive margin than the extensive margin of exporting. In addition, better contracting institutions increase the probability of survival of new export products in more contract-intensive industries. These results have important policy implications for developing countries that seek to boost export growth but many of which suffer from poor contracting institutions.

This paper is a product of the Development Research 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 hmaemir@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
# **Market Imperfections Exacerbate the Gender Gap: The Case of Malawi**

Amparo Palacios-López<sup>1</sup> World Bank and Ramón López University of Chile

**Keywords:** Gender, Agriculture, Productivity, Decomposition Methods, sub-Saharan Africa, Malawi.

**JEL Classification:** C21, J16, Q12.

1 Corresponding author (apalacioslopez@worldbank.org). The authors would like to thank Vivian Hoffmann, Jorge Holzer, Asif Islam, Talip Kilic, Patricio Korzeniewicz, Howard Leathers, Kenneth Leonard, Kabir Malik, Michael Settels, and the seminar participants at the Agricultural and Resource Economics Department of the University of Maryland for their comments on earlier versions of the paper.
# **1. Setting the stage**

In Rawls’s _Law of Peoples_ individuals from various countries meet to organize a contractual arrangement regulating their relations in a metaphor similar to the one for the citizens of the same nation from his _Theory of Justice_ . There are differences though since the global gathering is between representatives of each nation (people) rather than between all world individuals. And the outcome is different too, in two important respects. Rawls rejects the application of the global difference principle in favor of fairly limited aid to the “burdened peoples” that are hampered by poverty from achieving a “decent” society, and assumes that migration takes place only in response to egregious violation of human rights, famine, and political and religious oppression. In other words, global redistribution is minimal and with a clear cut-off point,<sup>2</sup> and economically-driven migration is not approved.<sup>3</sup> Thus, peoples are basically separated entities.

We shall take Rawls’s assumptions as a fair representation of the existing world situation. Indeed, they are. First, in 2004, aid from rich to poor nations amounted to onequarter of 1 percent of rich nations’ gross domestic income.<sup>4</sup> At the same time, these nations were spending between 3 and 8 percent of GDI for domestic welfare payments. Obviously, domestic and foreign poor are not treated equally: one “domestic poor” is worth, on average, about 100,000 “foreign poor” (Milanovic, 2006). Similarly, using an optimal taxation framework, Kopczuk, Slemrod and Yitzhaki (2005), calculate that the implicit weight US policy-makers place on a foreign poor person is 1/2000 of the implicit weight they assign to an American poor person.

Second, in 2002, total migration from poor to rich countries was 4.3 million people which represented a tiny percentage (less than 1/10 of 1 percent) of more than 5

> 2 Not having open-ended international transfers was one of key points explicitly stressed by Rawls (1999, p. 106 and p. 118).

> 3 See Rawls (1999, p. 39 and p. 74).

> 4  See <u>http://www.oecd.org/dataoecd/17/39/23664717.gif, accessed February 9, 2007. This includes only</u> Development Assistance Committee (DAC) members (basically, the “old” OECD countries).

2
18 with a corresponding increase in the share of the urban population (this is the trend observed over the last five years). Using the fact that the overall population for Bangladesh is expected to grow by 1.5 percent per year over the next four years, and by 1.2 percent thereafter, we compute accordingly the growth in per capita GDP over time in the two sectors. What is still missing is an estimate of the change in the average propensity to consume, for which we need to use the World Bank's RMSM-X model.

From the RMSM-X model, given the limited availability of foreign financing, we find that private consumption as a share of GDP is expected to decline nationally by four percentage points to help finance investments (in the model, aggregate savings are measured residually as the difference between aggregate gross investments and the current account deficit). For simplicity, we assume that this drop in average propensity to consume affects all households in a similar way, in both urban and rural areas. Using the elasticities of poverty to growth computed with household data in the previous section, one finds the poverty forecasts given in Table 7 by sector and nationally using sectoral population shares (Table 7 uses the upper poverty lines; the trends obtained with the lower poverty lines are similar).

According to Table 7, poverty will decrease in both urban and rural areas, but more so in urban areas due to higher growth there (which more than compensates for higher urban population growth and rising inequality). Nationally, the headcount index with the upper poverty lines would be in 2008 at 29.05 percent, versus 53.08 percent in 1995-96. The reductions in the poverty gap and squared poverty gaps are even larger (proportionately to their 1995-96 level), indicating that growth would not leave the worst off behind even though inequality may be expected to rise over time (at least in urban areas).

The assumptions outlined above, as well as many others which have not been mentionedl<sup>0</sup> , could be challenged. For example, to the extent that the necessary increase in aggregate saving to finance ' The are many such assumptions scattered throughout the RMSM-X model. Here are a few. Inflation is assumed to remain stable between 3.5 and 5.5 percent, reflecting the Government's efforts to control monetary growth in order to avoid pressure on prices and the balance of payments. The budget deficit is also projected to be remain stable within the 5 to 6 percent range. While the Govermment is not likely to raise the revenue-GDP ratio by 0.5
diminished significantly in 1992. Education becomes the most important factor in 1992, but only explain about 6 percent of variation. The gender of household head explains little between-group inequality. The explanatory power of socio-economic group also declined between 1988-1992.

Table 7. Ghana: Static Expenditure Per-single-person Inequality Decomposition (RB), GLSS1(1988) and GLSS3(1992)

|Variable|_E(O)_|_1988_<br>_E(J)_|_E(2)_|_E(O)_|_1992_<br>_E(1)_|_E(2)_|
|---|---|---|---|---|---|---|
|Sex of household head|1.5|1.4|1.1|0.4|0:4|0.3|
|Socioeconomic group**a)**|3.8|3.9|3.3|2.6|2.5|1.9|
|Educationfb)|7.6|8.1|7.2|6.1|6.3|5.3|
|Administrative regionc)|12.1|12.4|10.4|4.1|3.8|2.9|
|Locality|5.9|6.4|5.7|1.2|1:1|0.9|
|All '|29.0|30.5|30.2|20.8|20.5|17.4|

a) Socioeconomic groups are defined as 'Farming', 'Nonfarm', 'Wage-formal', 'Urban-informal' and 'Other', the group is determined by the largest share of household income source.

**b)** Education is defined as 'No education', 'some primary', 'completion of primary', 'some middle',

'completion of middle', 'some secondary', 'completion of secondary and 'higher than secondary'.

c) Including ten administrative regions.

d) The partition by 'all' superimposes all previous partitions simultaneously, excluding the Locality. It is a gauge of joint explanatory power of all partitions.

## _5. b) Econometric analysis in decomposing income inequalitv_

The above analyses are all based on non-parametric methodologies. An alternative way to analyze the determinants of income inequality is to use multivariate regression analysis.<sup>9</sup> Specifically, let Y be expenditure per capita, and Z be variables that can explain the expenditure per capita, then

# _(4) yi =Ia. jZ,, + uj_

where i=1 to n, indicating n observations, a s are the parameters to be estimated, ui is an error term, assumed to be normally distributed with mean zero and standard deviation a, and j=1 to m, indicating m independent variables .

Since <!-- Start of picture text --> ni<br>E cov[a.,Z.,1Y, y<br>(5) J=- ESj 100,<br>J= 1<br><!-- End of picture text --> This section is based on the methodology developed by Gary S. Fields and Gyeongjoon Yoo, 1995.

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52 Table A2.21: Number of households using LPG and estimated average quantity purchased per month by purchasing households in Mexico

|**Quintile**|**Number**<br>**Rural**|**of user households**<br>**Urban**|**(percent)**<br>**Total**|**Kg purchase**<br>**Rural**|**d per household**<br>**Urban**|**(per capita)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|533,300 (32)|1,341,636 (77)|1,874,936 (56)|22.7 (4.4)|23.5 (4.3)|23.3 (4.4)|
|**2**|600,786(55)|2,514,017(88)|3,114,803(79)|24.1 (5.9)|25.5 (5.6)|25.2 (5.7)|
|**3**|587,654 (66)|3,222,272 (91)|3,809,926 (86)|25.1 (7.1)|26.3 (6.7)|26.1 (6.8)|
|**4**|431,773(78)|4,003,896(91)|4,435,669(90)|27.8 (9.3)|29.5 (8.4)|29.3 (8.5)|
|**5**|278,910 (90)|4,913,026 (84)|5,191,936 (84)|30.5 (14.5)|37.0 (13.6)|36.7 (13.7)|
|**All**|2,432,423(54)|15,994,847(87)|18,427,270(80)|25.2 (7.3)|30.0 (8.9)|29.3 (8.6)|

_Source:_ Authors’ calculations. _Note_ : See notes for Table A2.3. User households exceeded purchasing households by 4.8 million.

The expenditure share of LPG for purchasing households was high for the lowest quintiles and higher for rural than urban households (Table A2.22). The expenditure shares averaged across all households under-estimate the actual shares because millions of user households appear to have reported no purchase during the survey period.

Table A2.22: Shares of total household expenditure on LPG for all households and for purchasing households in Mexico (%)

|**Quintile**||**All households**||**Pur**|**chasing househ**|**olds**|
|---|---|---|---|---|---|---|
||**Rural**|**Urban**|**Total**|**Rural**|**Urban**|**Total**|
|**1**|2.0|3.7|2.8|7.5|6.9|7.1|
|**2**|2.9|3.7|3.5|5.9|5.6|5.6|
|**3**|2.9|3.1|3.1|5.1|4.7|4.8|
|**4**|2.6|2.7|2.7|4.6|4.1|4.1|
|**5**|2.4|1.9|1.9|3.9|3.1|3.2|
|**All**|2.5|2.8|2.7|5.6|4.4|4.6|

_Source:_ Authors’ calculations.

# **Peru (ENAHO 2009)**

The data are taken from the 2009 National Household Survey on Living Conditions and Poverty (ENAHO) administered by the National Institute of Statistics and Information (INEI) in Peru. The fieldwork for the survey was carried out between January 2009 and December 2009.

The value of all non-purchased items was about 33 percent of total purchased expenditure, while non-purchased food accounted for about 53 percent of all non-purchased items. These ratios tended to decline at the upper quintile levels. All food expenditures were deflated using adjustment factors provided in the survey data set.

The survey asked households to report the primary energy source for cooking. LPG was the primary cooking fuel for more than half of the households surveyed, followed by firewood (Figure A2.6). Although 84 percent of households was connected to electricity, as in other countries, very few households cooked primarily with electricity.
degree of selection present in school-based testing. However, current household-based data is limited and not yet comparable across a significant number of countries.

A second limitation regards data availability. While this is the largest learning outcomes database to date, data are still sparse for some countries. This introduces bias if data availability is correlated with education quality or progress. For example, if countries that perform worse have data only in later years (because they were later to introduce assessments), their average score will be likely biased upwards, as the test scores will reflect more recent testing, not stronger performance. Since we provide year-by-year scores this can be accounted for.

Relatedly, when averaging data across subjects, levels and over time, there is a possibility that averages reflect the availability of data rather than learning gains. For example, let’s examine a case where a country has a score of 500 in 2000 in math and jumps to 550 in 2005. If this country added reading in 2005 and scored 450, the average score across subjects in 2005 would be 500, reflecting no learning progress since average scores would be 500 in both years. However, an apples-to-apples comparison in math shows learning grains from 500 to 550. To address this issue, we construct disaggregated measures by subject and schooling levels as well as aggregated ones. This enables analyses at each level considering the trade-offs.

A point of emphasis is that while learning measures human capital better than prior proxies, such as enrollment, learning does not capture the concept of human capital in its totality. Moreover, assessments do not capture only cognitive skills. For example, recent evidence suggests test scores pick up as differential effort as well as cognitive ability (Gneezy, List, Livingston, Sadoff, Qin, and Xu 2017). We use learning outcomes in this paper with these caveats in mind.

# II. DESCRIPTIVE STATISTICS SUPPLEMENT

Several statistics demonstrate the coverage and detail of the database. Supplement Table III presents country-subject-level observations by year. The data are spread over time, slightly weighted towards recent years since countries are increasingly participating in assessments. A related feature of the data is a large influx of data in particular testing years. This is more prevalent for developing regions which participate in sporadic assessment.

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from the regression are interpreted as increasing or decreasing the odds of _nonparticipation_ (or observing a zero). As this may be counter-intuitive, we reverse the signs on the estimated coefficients and re-interpret the results in terms of the odds of _participation_ in Table 4.

A unitary increase in age or household size of the respondent leads to a decrease in likelihood of participation by 9.5% and 3.2%, respectively, whereas an increase in one year of education increases the odds of participation by 75%. Income only marginally impacts trip demand with increases by $1 USD leading to an increase in participation of 0.12%. This relative insensitivity to income changes is a common finding among recreational demand studies. If the respondent lives in Yerevan, the likelihood of participation is decreased by an overwhelming 137%. This may be owing to the fact that in the household sample, over 80% of the sampled househols are from Yerevan, the capital city. For the trip count equation, a one unit increase in travel costs or education decreases the number of trips by 1.5% and 6.6%, respectively. Thus, although travel costs are not a significant determinant in the decision to recreate, they do impact the number of trips a person decides to take. Also, a person’s education appears be important both decisions, but in opposite directions. Those with higher education tend to participate more often, but as one frequents the site more often this effect diminishes. Greater household size also works in opposite directions for the participation and quantity decisions. A one unit change in household size decreases participation by 3.2% but for those who do go, it increases the number of trips by 10.2%. Upon closer inspection of the data, it was found that households with more children were associated with higher trip frequencies. The impact of income on trip frequency was found to be negligible.

14