rafmacalaba/datause-encoder-data huggingface dataset:
The country with the lowest fiscal-regulation score is Haiti.<sup>12</sup> On average, labor regulation is highest in Latin America and the Caribbean, followed by the Middle East and Northern Africa. OECD countries do not have high labor regulation in average, but they do exhibit large dispersion: over the entire sample (all regions combined), Portugal has the highest labor regulation score while the United States has the lowest. In the remaining areas OECD countries always occupy the bottom of the distribution. For instance, The Netherlands reaches the lowest score in financial regulation, Finland in bankruptcy and trade, Iceland in contract enforcement, and Canada in entry regulation. The harshest regulatory environments are in Sierra Leone (contract enforcement), Haiti (entry), Syria and Iran (financial markets), Tunisia and Papua New Guinea (trade), and The Philippines (bankruptcy).

Table 2 shows simple correlations between the regulation indices. The strongly positive correlations among all but the fiscal burden and labor indices suggest that regulation policy comes in “packages.” Judging from these correlations, we can distinguish three regulation categories: fiscal burden, labor, and “product market,” where the latter is a composite of the entry, trade, financial markets, bankruptcy, and contract enforcement indices.<sup>13</sup> We obtain the product market index by averaging the scores of the five components; we also compute an “overall” regulation index by averaging the scores of all seven components. We choose to give equal weights to all components despite the strong correlation among the first five because we don’t have any priors about the importance of labor market or fiscal regulation relative to the others.

The arrangement of the regulation indices into these three groups receives additional statistical support from factor analysis. The first three principal components obtained from factor analysis of the seven basic indices explain, respectively, 53%, 15%, and 12% of their overall variance. These principal components have a close one-to-one match with the indices resulting from the three categories. For the first principal component, the five “product market” regulation indices receive high and similar loadings, and not so the labor and fiscal regulation indices. For the second principal component, the

> 12 Keep in mind that the rank of a country is relative to the sample, therefore changing the composition of the sample will most likely alter the ranking as well.

> 13 The term “product market regulations” is taken from Nicoletti et al. (2000).

10
# **_Power asymmetries_**

To reflect the economic and welfare asymmetry discussed above, we use annual country-level GDP data (state level data) from the GGDC&CB (2005) dataset, and Population Action International (2004) data to calculate GDP and GDP per capita for each of the basin riparians.<sup>iv</sup> The ratio between the values of the riparians is the basis for the power asymmetry in the basin. The former is a measure of overall power ( _Economic power_ ) while the latter is a measure of wealth ( _Welfare power_ ). The two variables were constructed by dividing the value of the wealthier, or the more economically powerful riparian by the value of the less powerful riparian. Therefore, the value is always greater or equal to 1; the higher the value, the greater the power asymmetry. In our analysis we use only the variable _Economic power_ per the justification provided in the theory section.

# **_Geography_**

We use the 14 geographical configurations identified in Dinar S., (2008). These configurations were re-categorized into three groups, capturing the rivers that fall under the ‘through-border’ geography—or the most asymmetric of the river geographies—and the rivers that fall under the ‘border-creator’ geography—or the most symmetric of the river geographies. The remaining rivers that fall under the other configurations were included under ‘other’ geography, whereby this category served as the benchmark. The reasons for this regrouping are as follows: (1) the distorted distribution of the 14 categories doesn’t allow the estimated regression model to be fully ranked, and (2) we are mostly interested in the impact of the two extreme geographies and their ability to explain interactions between riparian states. In fact, all the other geographies have some combination of spatial asymmetry and symmetry so ranking them would be quite impossible.

# **_Treaty data_**

The treaty dataset is retrieved from several depositories and includes 226 country dyad observations.<sup>v</sup> Eighty-six of the corresponding rivers are not governed by treaties while 140 are, providing a diverse pool of observations to examine the hypotheses. Three hundred and eleven treaties were identified and analyzed for their content. Of these, 40 provide only periodical re17
explanatory power.<sup>18</sup> On average the Gini coefficient explains about 15 percent of the variability of the perceptions about unfairness, as measured by the R-squared. On the other hand, the absolute indicators of inequality correlate negatively with the unfairness perceptions, and the explanatory power of such indicators is lower than of the relative indicators. It is interesting to note that indicators often mentioned in the mass media, such as the ratio between the richest 90% and the poorest 10%, exhibit low explanatory power, although this may be due to mismeasurement of the top incomes. The results of the high correlation between unfairness perceptions and income inequality seem to be driven by the population that perceives inequality as very unfair (columns 2, 5, and 8), rather than just unfair (columns 3, 6, and 9), as the correlations in the latter are close to zero for almost all indicators.

These results are consistent with experimental evidence from Amiel and Cowell (1992, 1999) who show that support for the scale-invariance axiom was greater than for translation invariance, reflecting greater support for relative inequality indicators. Moreover, the results are also consistent with graphical evidence that shows decreasing relative inequality, but rising absolute inequality during the 2000s in LA (Figure 4 and Figure 7, respectively). Since unfairness perceptions also declined over time, the relative indicators do a better job of tracing such evolution.

> 18 The results are very similar if we exclude the observations with income equal to zero. For example, pooling all the data and excluding individuals with zero income changes the correlation of the Gini with the share of the population that perceives income distribution as either unfair or very unfair from 0.39 to 0.40.

13
Africa (2.9%) than in the other two regions, but only in LAC were these higher levels of budget allocation sustained for all sub-regions."4 <!-- Start of picture text --> Figure 2: Loan Commitments to Research<br>Components as a Percent of Project<br>j Loan Commitments, 1982-1989 (percent)<br>Percent<br>4-j<br>3 .- .<br>2'-<br>- A,nucl *-Trend1<br>01982 1983 1984 1985 1988 1987 1988 1989<br>Year<br><!-- End of picture text --> _Table 8. Loan Commitments to research components as a percent of Bank Project loans, by vear, 1982-1989_

|Year|Total<br>loan<br>commitments<br>(US$ Millions)|Loan<br>commitments<br>to research<br>components<br>(US$ Millions)|Percent of<br>total<br>loan<br>commitment<br>(%)|Percent of<br>total loan<br>commitment<br>weighted by<br>project size|
|---|---|---|---|---|
|1982|479|1?.73.7||5.2|
|1983|476|14.4|3.0|2.7|
|1984|562|11.6|2.0|2.8|
|1985|531|13.4|2.5|3.1|
|1986|607|7.4|1.2|1.4|
|1987|403|13.8|3.4|4.3|
|1988|604|9.6|1.6|1.9|
|1989|797|10.6|1.3|2.4|

**14 See Annex 3 Table 5** for breakdown by aubregion.

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It is possible that firms that have multinational activity may choose to locate in countries with good governance which could create a selection bias with our results. To check for this we controlled for two dummy variables indicating whether a firm operates in more than one country and if any of its owners is a foreigner. Regression results with these controls are roughly similar to the ones reported in columns 5 and 6 of Table 2 where we have include one more variable to highlight a relatively neglected area in the literature. This variable is firm’s perception of how easy it is to get information on various rules and regulations (1-6 scale, higher values imply information available more easily). These controls do not change our results much from above. Information availability is associated with a better perception of government helpfulness (significant at less than 1% level) while multinational presence shows no significant effect.

For additional robustness, we included the following in the list of controls above (data sources in brackets): level of corruption (International Country Risk Guide, 2000), a dummy for exporting firms, industry and type of ownership fixed effects (WBES), number of procedures required to register a property, an index of the difficulty of firing workers and an index of investor protection (World Bank, Doing Business, 2003-04), a dummy indicating if a country has a public registry or a private credit bureau (Djankov et al 2007), incidence of bribe payment (WBES), percentage of firm’s senior management’s time spent with government officials (WBES). With all these controls, the estimated effect of _English_ equaled 6.3 percentage points (p-value of .017) for the government being “very helpful” and -8.1 percentage points for “very unhelpful” (p-value of .007).<sup>2</sup>

> 2 Our main results in the paper do not change much when we control for the degree of legal formalism as measured by Djankov et al (2003a) and age of the firm. However, these controls cause the sample size to decline from 48 countries above to 42 countries (legal formalism) and 31 countries (age of the firm).

6
considered countries is 10 years and 3 months, with a variation between countries of at most 5 months.

While virtually all children in developed countries are enrolled in fourth grade, this is not necessarily the case in developing countries. If a substantial part of the potential student population of a country is not enrolled in school in fourth grade, this might introduce sample selection bias relative to full-enrollment countries. The most detailed data on this are Filmer’s (2003) enrollment profiles based on national survey data, which include Colombian data for 2000. The data show that while 98.5% of Colombian children have been enrolled in first grade, this declines to 91.6% in fourth grade. While the rich and middle class, as well as the urban population in general, come relatively close to universal enrollment at fourth grade, this is not true for the poorest 40% of the Colombian population, or for the rural population. Even though more than 97% of the poor and of the rural population in Colombia enroll in first grade, early drop-out rates among them are very large. At the age of 15-19, only 83.4% of the poorest 40% of the Colombian student population, and only 81.4% of the rural Colombian population, report that they were enrolled in fourth grade (Filmer 2003; cf. Pritchett 2004). This selective non-attendance at the primary-school level may somewhat distort the Latin American samples of students tested by PIRLS, although the enrollment data show that the effect can be expected to be limited.

The PIRLS sampling procedure ensured that a representative sample of the target population was tested in each country. PIRLS used a two-stage sampling method, sampling in the first stage schools and in the second stage intact class(es) of the school. This let to a sample size of 3,300 students in Argentina and 5,131 students in Colombia. In the comparison countries we analyze, the sample size ranges from 2,494 to 7,633 students.

The performance tests were paper and pencil tests, with the assessment lasting a total of 80 minutes for each student. The PIRLS tests were constructed to test a range of relevant skills and competencies that reflected how well the reading literacy of students was developed. Each subject was tested using a broad sample of tasks with differing levels of difficulty in order to represent a coherent and comprehensive indicator of the continuum of students’ abilities. The test items included multiple-choice items and questions requiring the students to construct their own responses. Using Item Response Theory (IRT), PIRLS mapped performance on a scale with an international mean of 500 test-score points and an international standard deviation of 100 test-score points.

6
TABLE 6.13

|Sm<br>l!Oa|g<br>(I!vc<br>uand<br> <br>iofin**FW**D<br>(Valu<br>Based<br>in D<br>|Wood Indstq<br>epatO<br>es in pareat<br>on Value-A<br>omestic Price<br>|.<br>**OIS**Egin<br>Vw -ble<br>fi<br>hes<br>re amstand<br>dded<br>s _<br>|of Augmtd<br>g_ofC _<br>Pri<br>rd aro)<br>Bas<br>|**Co**<br>ce Vahe Add<br>ed on Value<br>in World Pri<br>|<br>-Added<br>ces<br>|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|Intaenpt<br>|1-553<br>(0.761)|1.156<br>(0.671)|1.l82<br>(0.441)|1.871<br>(0.805)|1.459<br>(0.704)|2.092<br>(0.468)|
|Traoe Variables:<br>Effective Rate of<br>Protection<br>|4.061<br>(0.062)|4.122<br>(0.052)|4.126'<br>(0.049)|4.541<br>(0.066)|.061?<br>(0.055)|-0.6110<br>(0.052)|
|% Exports<br>|4.712<br>(0.567)|4.026<br>(0.516)|0.123<br>(0.498)|-1.040e<br>(0500)|4297<br>(0.541)|4.103<br>(0529)|
|InPuts:|||||||
|In (SeneZaese Skilled<br>and Unskilled)|0.48S<br>(0.139)|||0.487<br>(0.147)|||
|In (Other African<br>Skilled and Unskilled)|4026E-3<br>f0.13E-3)|||4029E-3<br>(0.14E-3)|||
Table <u>5: regression results lagged model</u> Dependent variable: change in CPIA rating 20112014 Variable of interest: change in social <u>policy conditions 2006-2010</u> AGGREGATED ANALYSIS CPIA social inclusion 0.00349* (.00183) SUB-COMPONENT ANALYSIS CPIA gender equality .0267241 (.0264928) CPIA building human resources -.0028954 (.0050412) CPIA equity of public resources use .0118641 (.0161222) CPIA social protection .0202407 (.0140276) CPIA environmental sustainability .0220407** (.010993) <u>Note: Results from estimating a lagged model with the change in CPIA C scores during 2011-2014</u> as dependent variable and the accumulation of ‘social policy’ prior actions during 2006-2010 as variable of interest. Only coefficient estimates and robust standard errors of prior actions variable are reported. * Significance at 10%; ** significance at 5%; *** significance at 1%.

# **4. Conclusion**

In this study, we analyze the association of World Bank lending with the quality of social policies, as measured by the Bank’s Country Policy and Institutional Assessment (CPIA) ratings.

Results from estimating a cross-sectional 2SLS model indicate that social policy lending has a significantly positive effect on the quality of social policies: one additional social policy condition increases the CPIA social inclusion score by .009 points on average. Alternatively, it takes 30 social policy conditions on average to increase the CPIA cluster C score by 1 standard deviation.

When disaggregating the analysis by CPIA C components, we find that prior actions targeting _social protection and environmental sustainability_ have a positive effect, with 2SLS coefficient estimates equal to .073 and .050 respectively. These findings suggest that the right kind of conditionality is able to improve social policies, therefore providing an 15
# **Appendix 1: The Surveys**

This Appendix describes the two main sources of information used in this paper: the Integrated Household Survey and the Tobacco Clubs Survey. The household survey is the main source of data for our analysis. The Tobacco Clubs Survey has a more modest role as complementary data to support some of the claims in the empirical analysis. It is becoming increasingly common, especially in research on institutions in Africa, to supplement a major data set with pieces of evidence from small-scale surveys or interviews with farmers. See Udry (2003) for a discussion of different research strategies and data collection efforts in Africa.

## **The Malawi Integrated Household Survey (IHS)**

The sample design of the IHS is a two-stage stratified sampling procedure from a sample frame of Enumerator Areas (EA) from the 1998 Population Census. The survey covers the three geographical regions (north, center and south), dividing the country into rural and urban areas. The urban stratum includes the four major urban areas (Lilongwe, Blantyre, Mzuzu, and the Municipality of Zomba); all other areas are considered as rural. The total sample is 20 households per each of the 564 EA selected, resulting in 11,280 households surveyed.

The Integrated Household Survey (IHS) consists of two questionnaires: a household questionnaire, and a community questionnaire. The community questionnaires include typical questions on socio-economic conditions at the village level. The household questionnaire includes several modules: A) Household identification; B) Household roster (size, demographics; marital status); C) Education (schooling attainments); D) Health (recent illness or injury, chronic disease, treatments); E) Time use and labor (household work, employment); F) Security and safety (crime, crime protection); G) Housing (dwelling characteristics); H) Consumption (over the last three days); I) Consumption (during last week); J) Consumption (non-food items during last month); K) Consumption (non-food items during last three months); L) Consumption (non-food items during last year); M) Consumption (durables); N) General Agriculture (general); O) Agriculture (rainfed cultivation): P) Agriculture (rainfed crop sales); Q) Tobacco (club membership, land allocation, input use, yields, sales); R) Agriculture (dry-season cultivation); S) Agriculture (dry-season crop sales); T) Agriculture (tree crops); U) Agriculture (livestock); V) Household enterprises; W) Other income; X) Gifts; Y) Social safety nets; Z) Credit; AA) Subjective assessments of well-being; AB) Recent shocks; AC) Deaths; AD) Child anthropometry. The main ingredient for our analysis is module Q) on tobacco farming.

## **The Tobacco Clubs Survey (TCS)**

The Tobacco Clubs Survey (TCS) was launched in the summer of 2007. Our objective was twofold: to gather qualitative information on the basis of personal interviews with farmers that belong to burley clubs, and to deployed a simple questionnaire with quantitative additional information. The TCS performs a supplementary role to the IHS (Udry, 2003).

37
and gives little indication of the true effect. On the other hand, PTAs may tend to create more trade than what would be expected from simple elimination of customs duties. This result could be driven by several reasons—for example, a plant’s inputs could be imported in low or zero MFN duty lines whereas the output could be exported in tariff lines with significant preference margins. Alternatively, a factory established to take advantage of trade preferences may also be an efficient producer of goods in lower tariff lines, therefore creating additional trade. The time dimension can also be important—the current export production structure in some PTA members may have been established at the time of higher average tariffs. Thus, even though the recent rounds of multilateral or unilateral liberalization have eroded preference margins, the fixed costs of establishing production facilities have already been met and exports remain competitive even in the absence of significant tariff preferences. Finally, other PTA provisions such as standards harmonization, customs cooperation, or investment liberalization may lead to increased trade in some products even if the margin of tariff preference for them is insignificant. Overall, our finding of a relatively small change in the PTA coefficient across specifications is probably driven by both sets of effects. In the following section, we attempt to assess the relative importance of exclusions and phase-in periods on the trade impact of PTAs by adding controls for these provisions to our model.

# **4.2 Application to exclusions and phase-in periods**

The starting point for our analysis is the model of preferential trade shown in Table 5. To account for the importance of sensitive lists, exclusions, and phase-in periods, we re-estimate this model with two additional variables that reflect the presence of exclusions and the length of phase-in schedules and simulate the trade impact of liberalizing these provisions. As the data on exclusions and phase-in periods for all 163 PTAs in the sample are not available, we will rely on a smaller subset of PTAs for which we do have this information.

In order to compile the required data, we turn to the Member Liberalization Index (MLI) developed by Adams et al. (2003). The authors calculate the index for 18 PTAs of which either Australia or one or more of its major trading partners are a member.<sup>40</sup> The aggregate MLI ranks each agree-

> 40These include the Andean Pact, EFTA, the EU, LAIA, MERCOSUR, NAFTA, SPARTECA, Australia-New

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Policy Research Working Paper 8360

# **Abstract**

This paper uses household surveys from 89 countries to look at gender differences in poverty in the developing world. In the absence of individual-level poverty data, the paper looks at what can we learn in terms of gender differences by looking at the available individual and household level information. The estimates are based on the same surveys and welfare measures as official World Bank poverty estimates. The paper focuses on the relationship between age, sex and poverty. And finds that, girls and women of reproductive age are more likely to live in poor households (below the international poverty line) than boys and men. It finds that 122 women between the ages of 25 and 34 live in poor households for every 100 men of the same age group. The analysis also examines the household profiles of the poor, seeking to go beyond headship definitions. Using a demographic household composition shows that nuclear family households of two married adults and children account for 41 percent of poor households, and are the most frequent household where poor women are found. Using an economic household composition classification, households with a male earner, children and a non-income earner spouse are the most frequent among the poor at 36 percent, and the more frequent household where poor women live. For individuals, as well as for households, the presence of children increases the household likelihood to be poor, and this has a specific impact on women, but does not fully explain the observed female poverty penalty.

This paper is a product of the Poverty and Equity Global Practice and the Gender Global Theme.. 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 may be contacted at amunozboudet@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