rafmacalaba/datause-encoder-data huggingface dataset:
ENIGH 2006 from 20,900 households and 83,600 individuals; and the HBS 2006 from 8,600 households and 34,900 individuals. We restrict the samples to children aged 15, for which we have 7,626 observations in the PNAD 2006; 22,600 in the SUSENAS 2005; 1,921 in the ENIGH 2006; and 683 in the HBS 2006. Although some children in boarding schools and other institutions are likely to be out of the sample frame, those samples should otherwise be representative for the total population of 15 year-olds.

In these four countries, these are the staple surveys for assessing the distribution of household income and, in some cases, consumption expenditures. But they also collect information on other topics, including labor supply, education and migration. We use information on parents' characteristics for estimating the total population of 15 year-olds in groups defined by similar gender, mother's education and father's occupation. The classification of the family background variable can be made comparable with the ones in the PISA by appropriate aggregation of coding categories. Parental characteristics are missing for orphans, children who do not live with their parents, or whose parents did not report their education. For instance, the information on mother's education is missing for about 15.0% of 15 year-olds in the PNAD 2006, 8.7% in the SUSENAS 2005, 11.9% in the ENIGH 2006, and 3.8% in the HBS 2006. When comparing the two surveyed populations, children with missing parental background information in the household surveys are not dropped, but associated with those with the same information missing in the PISA survey.

# **3. Measuring Inequality in Educational Achievement**

Measures of inequality in educational achievement are based on distributions of standardized test scores ( _yij_ ), constructed from the IRT-adjusted scores ( _xij_ ) by means of a transformation such as equation (2). In the case of PISA, the transformation is given by (2) exactly, with , and . That operation involves both a translation of the original distribution (by the difference between the new arbitrary mean and the original mean, re-scaled) and a rescaling (by the ratio of the new to the original standard deviations).

In the field of inequality measurement it is usual to impose axioms, or desirable properties, that individual indices should respect. Three common such axioms are: (i) _symmetry_ : which requires that the measure be insensitive to any permutation of the _y_ vector; (ii) _continuity_ in any individual income; (iii) and the _transfer principle_ : which requires that the measure should rise (strong axiom) or at least not fall (weak axiom) as a result of any sequence of mean-preserving spreads.

In addition, inequality indices often satisfy _either_ one of two invariance axioms: (iv-a): _scale invariance_ : which requires that the index be insensitive to any re-scaling of the _y_ vector: , where _y_ is the vector of interest, and λ is a positive scalar.

8
can use the analysis developed in this paper to guide some back-of-the-envelope calculations. First, the change in prices—prices have continued to rise since end-2007 through the first quarter of 2008, to yield even larger price "shocks" then those shown in Table 5. Second, these price changes were adjusted to allow for the decline in the dollar relative to the currencies of our sample countries, and for inflationary effects. Third, we reduced the price changes to adjust for barriers to transmission of changes between domestic and international prices, using 66 percent as a “guesstimate” of the average value of this parameter<sup>8</sup> for lowincome countries.

With these assumptions, we can assess the impacts on poverty in each sample country taking into account both increases in commodity prices and estimated impacts on unskilled wage rates relative to other prices. The simple average of the estimated effects on national poverty rates (US$1/day) in this nine-country sample is an increase of 4.5 percentage points. Applying this average result to all low-income countries translates into an increase in the poverty headcount of 105 million people (out of the low-income population of 2.3 billion). Alternatively, as the rate of poverty reduction has averaged 0.68 percent annually since 1984, a 4.5 percent increase in the poverty headcount corresponds to a loss of almost seven years of poverty reduction.

While any such back-of-the envelope calculation must be treated with great caution, there seem to be good reasons to be concerned about the potentially adverse impacts of large changes in world food prices for poverty in poor countries. The high shares of staple foods in the expenditures of poor people increase their vulnerability to food price rises, while the limited share of output marketed by small, subsistence farmers reduces their benefits. There 8 While the evidence on the value of this parameter is limited, Dawe (2008) found an average of 51 percent transmission from world to domestic prices between 2003(Q4) and 2007(Q4) for a sample of Asian countries, including countries such as India and the Philippines, where policies have typically sought to insulate domestic prices from world market price shocks.

20
# 2.3 _Other explanatory variables_

Reverse causality from corruption that varies at the firm-level to the size of the country which varies at the country-level is highly unlikely. A more serious problem with our results for the relationship between corruption and country size (henceforth, main results) is the omitted variable bias problem. To guard against this problem, we control for several potential determinants of corruption that may also vary systematically between small and large countries. These controls are motivated by the existing studies on the determinants of corruption. They can be grouped into micro or firm-level and macro or country-level determinants.

Firm-level studies suggest that the level of corruption experienced by a firm depends on its ability to pay bribes, outside options available, bargaining power vis-à-vis public officials, visibility/exposure to public officials, and the degree of interaction with the various government agencies. These determinants of corruption are proxied by various firm characteristics in the literature which motivates our choice of the firm-level controls. The data source for all firm-level controls used below is ES.

First, industry choice affects firms’ ability pay bribes, outside options available to the firm, its bargaining power against public officials, and the degree of its interaction with the government officials. Industry choice affects firms’ position in the value-added chain and level of competition. This may in turn affect the perceived ability to pay bribes (see for example, Rand and Tarp 2012). Industry-specific requirements also affect the sunk cost component of physical capital and therefore a firm’s outside options and bargaining power with the public officials. Industries with greater reliance on publicly provided physical infrastructure (electricity, water, et cetera) and institutions (courts, police, et cetera) are likely to have lower bargaining power and more 10
model-based estimates are generated using a parametric bootstrap under the assumption that the model holds, and much remains to be learned about how model misspecification affects MSE estimates. The CV for each area is defined as the ratio of the square root of the estimated mean squared error to the mean estimated poverty rate, a definition sometimes referred to as CV(RMSE). We report the average CV across areas. CVs are an important indicator of uncertainty because in many cases national statistics offices will not publish statistics when CVs exceed a maximum threshold.<sup>21</sup> Comparisons of the average CV between the model-based and direct estimates should be interpreted with caution, because CVs of the direct estimates are not defined for areas with no poor households in the survey, leading the set of areas included in the average to differ for direct estimates.

- **Correlation and Root Mean Squared Error.** These measure the accuracy of the prediction. The correlation represents the simple correlation between the small area estimates and the actual headcount poverty rates calculated from the full census. The Root Mean Square Error (RMSE) is equal to the square root for the average squared difference between the estimates and the actual census poverty rates

The root mean squared error in this context is a function of the predicted poverty rates and is therefore a measure of accuracy. In contrast, the average mean squared error described above is a measure of precision or uncertainty.

- **Average Coverage rate.** The average coverage rate represents the share of areas for which the estimated 95 percent confidence interval contains the actual census poverty rate, and are a standard metric used to assess the performance of estimators in the literature.<sup>22</sup> The upper and lower bounds of the confidence interval are determined by multiplying the square root of the estimated mean squared error by 1.96 and adding and subtracting it from the point estimate.

# IV. Main Results

## A. Fay-Herriot area model

Table 5 presents the coefficient estimates from the Fay-Herriot area level model, which is used as a reference point for comparison with the direct estimates and the unit level model.<sup>23</sup> The area-level predictor variables were selected using the lasso plugin method, to prevent overfitting the model to the

> 21 For example, the US Census Bureau considers serious data quality issues related to sampling error to occur when the estimated coefficients of variation (CV) for the majority of the key estimates are larger than 30 percent. 22 See, for example, Tarozzi and Deaton (2009), Demombynes et al (2007), Elbers, Lanjouw, and Leite (2008), Pratesi and Salvati (2007), Hidiroglu and Yu (2016), among many others.

> 23 In particular, examining the Fay-Herriot model allows us to quantify the potential gains in efficiency and predictive accuracy from including sub-area level predictors in these contexts.

19
_26_ _Price Support at Any Price?_ The JOB PROMOTION FOR FARMERS experiment results in a 55 % reduction of the number of farmers (almost 4% yearly, a number broadly in line with the experience of EU countries that have enjoyed the fastest reduction of the farm employment in the period 1965-90, Poczta 1994). Farm employment in 2010 is about 1/3 lower than in the base run. The share of farmers in total employment falls to a reasonable 10% level. The MORE PENSIONERS experiment gives a much smaller, but also significant result of decreasing the number of farmers by 10% (compared with the base run).

Thanks to the reduction of the number of fanners, the average farm area grows to 9.5 ha (MORE PENSIONERS) or even 13.4 ha (JOB PROMOTION FOR FARMERS) - quite satisfactory results by current EU standards. Let us remember that the price support system allowed only for an increase of the farm area to 7.8 ha (the base run area was 8.6 ha).

Both scenarios increase significantly the labor productivity in agriculture (by 10% in the MORE PENSIONERS experiment, by more than 50% in (the JOB PROMOTION FOR FARMERS experiment). In the second experiment, the agricultural sector seems to find its equilibrium position, at which no artificial support is needed to allow for a reasonable incorne relation level. The gap in labor productivity between the agriculture and the non agricultural sectors shrinks to a level, at which agriculture can successfully compete with other sectors in generating income per employee (results of the MORE PENSIONERS experiment, although much more modest, are encouraging as well).

Table 4.3 gives an overview of macroeconomic effects generated by the experiments.

**Tab.4.3 Alternatives for price supporting Simulation experiments: MACROECONOMIC IMPACT**

||Fast pat<br>r -du<br>(EU pr. I y 2|h/output<br>ction<br>000, quotas)|More pe|nsioners|Jobs prom<br>farm|otion for<br>ers|
|---|---|---|---|---|---|---|
||Index<br>2010<br>(base9 100|growth<br>rate<br>(aver.)<br>1991-2010|Index<br>2010<br>(base= 100<br>_ _ _ _ _ _ _ _ _ _<br>_ _ _ _<br>_ _ _ _|growth<br>rate<br>(aver.)<br>1991-2010<br>_ _ _ _ _<br>_ _ _ _|Index<br>2010<br>(base= 100<br>as9190(a-<br>_ _ _ _ _<br>_ _ _ _|growth<br>rate<br>(aver)<br>20 10|
|**Gross Domestic**<br>**Product**|**97.1**|**4.2%**|101.7|**_4.5%_**|**105.2**|**4.6%**|
|Fixed Capital Formation|89.1|6.4%|105.0|7.3%|113.8|7.8%|
|Personal Consumption|98.8|3.7%|100.6|3.8%|104.7|4.0%|
|_of which:_<br>Food products|95.5|1.4%|100.2|1.7%|101.6|1.7%|
|Other goods & services|99.8|4.5%|100.8|4.6%|105.7|4.9%|

_Source: Model POLAGR, simulation Jan.1995_
<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>-2<br>-3<br>1997-2005 2006-2011 2012-2019<br>GS Capital Mame Labor Mm Human capital Ge TPP GDP Growth<br><!-- End of picture text --> <!-- Start of picture text --> a<br>6<br>4<br>2<br>a<br>-2<br>4<br>-6<br>“8<br>PPSho ohFFELPSe eth oly LEfoeEIEgh LSSRSPE APE PEnNABE meABE APE BT PT oP<br>Ge Capital Gam Labor Mm Human Capital Mmm TPP GDP Growth<br><!-- End of picture text -->
regions may use which funds and for which objectives. Each Member State or region then prepares regional support plans taking into account the EC’s thematic guidelines. These plans are presented to the EC where there is a discussion between the EC and the Member States. Once the two parties agree, the EC advances part of the granted funds to the Member States. Only then the details of the projects themselves are fleshed out by the regions and the Member States. The approach is clearly top down from the EC to the Member State to the region. In many cases, especially in regions with weak or non-existent governments, the key decisions are made by the EC and the member state.

According to Ederveen (2003), the administration and evaluation of the projects are largely in the hands of the regional or national authorities. Earlier the evaluation was rather ad hoc, but nowadays, the Member State or region is subject to rather strict guidelines from the EC. The responsible regional or national authorities keep the EC informed of progress and provide reports to show that the funds are being used in accordance with established guidelines. The EC examines the control systems and distributes the remainder of the contribution from the Structural Fund. According to EC (2004a), the EC analyzes the development of the monitoring indicators and the assessment studies and organizes exchanges on particular themes. It also informs the authorities responsible for the programs of any new Community priorities that have an impact on regional development.

# **4 Summary of convergence/divergence and economic performance of regions**

In this section, we survey the rich literature on the EU regional catch-up process and analyze the evidence to see which of the hypotheses we laid out above is best supported by the evidence. First, we look at studies on cross-national European convergence. While admittedly our main focus is on regional performance, some elements of the cross-national process of convergence are similar to that of regional convergence and would help shed some light on our hypotheses, especially the market vs. interventionist policies question. Second, we look at the evidence on the cross-regional question. Third, we look at some individual country studies to examine what implications they hold for our hypotheses. The one caveat is that we do not assess the different methodologies followed in the papers we look at. We assume that the peer review process has 15
**Table 1. Place and Timing of Landline phone Installation**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dependent Variable:|H|ad landlinep|hone in 19|93|Timin|gof landline|phone insta|llation|
|lnincome_91|0.238***|0.256***|0.233**|0.233**|-0.968**|-1.027**|-0.838*|-0.841*|
||(0.082)|(0.094)|(0.102)|(0.103)|(0.384)|(0.486)|(0.453)|(0.458)|
|lnpopulation_91|0.036|0.014|0.023|0.028|-0.165|-0.082|-0.179|-0.162|
||(0.141)|(0.150)|(0.155)|(0.155)|(0.642)|(0.709)|(0.709)|(0.718)|
|poor_91||0.089|0.040|0.033||-0.382|-0.099|-0.123|
|||(0.184)|(0.178)|(0.181)||(0.786)|(0.731)|(0.757)|
|remote_91||-0.005|-0.043|-0.044||0.096|-0.043|-0.045|
|||(0.153)|(0.176)|(0.177)||(0.727)|(0.885)|(0.896)|
|% arable_91|||-0.213|-0.223|||1.052|1.020|
||||(0.197)|(0.204)|||(0.907)|(0.920)|
|mountains|||-0.096|-0.102|||1.066|1.046|
||||(0.176)|(0.177)|||(0.895)|(0.900)|
|migrate_91||||-0.402||||-1.320|
|||||(1.087)||||(4.061)|
|Observations|57|57|57|57|57|57|57|57|
|R-squared|0.270|0.274|0.286|0.287|0.236|0.240|0.271|0.271|

Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1 30
<mark>Burkina Faso shoulder virtually all the care work in households, a recent survey</mark><sup>13</sup> <mark>provides quantitative precision: 96% of women reported that they do most or all the cooking in their households, while 80% reported that they do most or all the childcare. In addition, women’s agency in decisions on spending money is limited: only 66% of surveyed women report being able to fully decide how to spend their own income, and 60% indicate not fully participating in decisions on how to spend household money even on major household purchases.</mark> Women’s economic participation is also restricted by their limited ability to fully control their health and reproductive lives. For example, while Burkina Faso has succeeded in reducing maternal mortality more than some peer countries (Mali, Niger, Uganda) and the mortality ratio per 100,000 live births (320) is lower than the regional average (425), it remains well above the global mean (160), and mortality reduction in Burkina Faso has progressed at a slower pace than in aspirational peer countries (Ethiopia, Rwanda). Nationally representative data shows that in 75% of households, the husband is the main decisionmaker over the wife’s health, while female genital mutilation is highly pervasive across income groups—ranging from 77% of women having been mutilated in the first income quintile, to 69% in the fifth income quintile (DHS 2010). This is despite female genital mutilation having been legally banned since 1996, showing the importance of addressing customary norms beyond legal changes. The UN’s most recent data from 2015 shows that the situation has not improved, with 68% of women aged 15-29 having suffered genital mutilation (UN 2019).

Lastly, while the gender gap in both gross primary and secondary school enrollment has reversed in recent years, more than half of Burkinabè women aged 15-24 are illiterate, and girls have lower competency in math, especially in late primary school (PASEC 2015). These gender gaps in harmonized test scores have not decreased since 2017. In conjunction with prevailing social norms and women’s lower access to market information, these challenges contribute to the fact that women in Burkina Faso concentrate in lower-paying sectors, as employees, as entrepreneurs and as farmers. Our calculations from the 2018/2019 EHCVM show a large degree of occupational segregation across sectors, with male-dominated industries being more lucrative. For example, the average monthly earnings in construction are 1,390,000 CFA, while the average earnings in the food sector are 262,000 CFA. Women also make up most family workers and are underrepresented both in middle and senior management. In agriculture, virtually all export crops are grown by men.

Three main policy priorities emerge from our findings. First, increasing women’s skills and engagement in higher-value sectors (in entrepreneurship, but also in agriculture). Second, increasing women’s access to

> 13 This survey was conducted as part of the endline for the World Bank’s ‘Travaux à Haute Intensité de Main d’œuvre’ (THIMO) project in Burkina Faso.

16
found that competition is at least as important as privatization and that the gains from privatization are significantly greater when the privatized firm faces more competition.<sup>8</sup> Although there is little evidence on the interaction between competition and privatization in the banking sector, the privatized banks have faced significant competition in most studies looking at bank privatization.

The situation in Uganda, with the privatization of a large public bank in a relatively small sector, is probably representative of many of the privatizations that have occurred in Sub-Saharan Africa in recent years.<sup>9</sup> Throughout the region, banking sectors tend to be highly concentrated (see Table 1), with few private banks having large rural networks.<sup>10</sup> Thus, the experience in Uganda is likely to be more representative of the experience in other countries in Sub-Saharan Africa than the experience in other countries that have been the focus of past studies.

We should note that that this analysis would not have been possible without the extraordinary effort of Bank of Uganda (BOU) staff in collecting, cleaning, and organizing the data. In addition, we are fortunate that branch-level financial and income statements are available for Stanbic. These data enable us to test whether performance improvements are greater in areas where Stanbic faces more competition. They also enable us to test whether geographic factors such as population density and income are linked to changes in Stanbic’s activities since privatization. This should provide a more detailed description of access to financial services for disadvantaged groups.

The rest of the paper is organized as follows. Section II provides background on the historical evolution of the Ugandan banking sector, UCB’s role in it, and UCB’s privatization process. Section III uses bank-level data to benchmark postprivatization profitability, portfolio quality, and cost management. Access issues are also touched upon by focusing on credit growth and portfolio composition. Section IV summarizes the

> 8 Megginson (2005b, Chapter 8) provides an extensive summary of this literature noting that “[t]aken as a whole, the results of these studies offer clear lessons for academics and policymakers alike.  Privatization, competition, and establishing an independent regulation all seem to improve telecom performance...” Megginson (2005b) also notes that most studies that have looked at privatization and competition have concluded that the reforms are complementary (Fink et al., 2003; Li and Xu, 2004; Wallsten, 2001). 9 Clarke, Cull, and Shirley (2005b) show that although state ownership remained fairly high in Sub-Saharan Africa at the beginning of the 1990s, it was one of the most active regions with respect to bank privatization in subsequent years.

> 10 Indeed, a recent study concluded that banking sectors are on average more concentrated in Africa than in any other region of the world (Honohan and Beck, 2007).

4