rafmacalaba/datause-encoder-data huggingface dataset:
one-third of the rate seven years earlier. The reduction in extreme poverty was even more dramatic in relative terms, as the rate dropped to around 0.5 percent, less than one-sixth of where it stood seven years earlier.

The observed poverty reduction after 2003-04 was accompanied by strong income growth of the bottom 40 percent of the population, the World Bank’s indicator of shared prosperity. Between 2003 and 2011, using the official income aggregate, the real per capita income of the bottom 40 percent grew by more than seven percent annually, while mean income growth was less than six percent. In particular, during the first part of the decade, 2003 to 2008, real income per capita of the bottom 40 percent grew at an annualized rate of 7 percent, while the mean income growth rate was about 6.1 percent. During the second half of the decade, from 2008 to 2011, the less well off in Uruguay benefitted more from growth than the average person by even more, with real income per capita of the bottom 40 percent growing at 7.7 percent, while the mean growth rate was about 5.1 percent. This trend coincides with the expansion of safety nets during the second part of the decade.

Labor market trends have been similar to poverty trends, as labor income is the main component of household income. Unemployment grew during most of the 1990s, reaching a maximum level of 16.9 percent of the labor force in 2002. This problem was compounded by the fact that, among those who managed to retain employment during the crisis, labor informality (defined as jobs with no social security protection) also increased, from just below 40 percent in the early 1990s to a maximum of 42.7 percent in 2004. The situation began to improve significantly only after 2004. Unemployment fell rapidly and averaged 6.1 percent in 2012, slightly higher than that of 2011 (6.0 percent), while informality also declined as a consequence of better macroeconomic conditions and enhanced enforcement of social security collection.

**Figure 6: Unemployment rates, 2006-2012**

<!-- Start of picture text --> 16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>2006 2007 2008 2009 2010 2011 2012<br>Total Males Females<br><!-- End of picture text --> Source: INE

**Figure 7: Sectoral employment shares, 2006-2012**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2006 2007 2008 2009 2010 2011 2012<br>Primary sector Manufacturing etc.<br>Commerce Other services<br><!-- End of picture text --> Source: INE 6
_Notes:_ Figure shows the density (y-axis) of monthly sales per worker (in logs) in formal micro firms (blue dotted line) and informal (red line) businesses in Zambia in 2019 (Aberra et al., 2022).

Adoption of digital technologies varies by firm type as shown in Table 1. In general, micro firms report higher shares of ICT adoption than informal businesses. One exception is the use of tablets, where only 3.8% of micro firms report using tablets in contrast to 8.91% of informal businesses. This suggests potential substitution effects between computers and tablets. The use of websites is the only digital technology that is captured in all three surveys. Not surprisingly and in line with other findings (Jolevski and Islam, 2019), formal non-micro firms have the highest share of websites created. Given the relatively higher level of digital adoption among formal firms (regardless of their size), it is plausible for us to assume that the spillover effect on digital adoption occurs from formal to informal sectors in Zambia. This is further confirmed by the fact that 94% of informal businesses sell to individuals instead of firms in Zambia.

**Table 1: Digital Variables by Survey Type (in %)**

||Forma<br>5+Employees|l<br>Micro (1-4)|Informal|
|---|---|---|---|
|Sample Size|600|97|914|
|Use of Computers||28.25|5.36|
|Use of Tablets||3.80|8.91|
|Use of Cell-/Smartphone||76.33|58.26|
|Website/Social Media|57.14|40.69|5.51|
|Mobile Money (MM)||50.64|42.71|

_Notes_ : All variables, except the number of firms, are in % of all firms. Sampling weights applied. The number of firms may vary for some variables. Note that there are additional digital variables for formal firms. “Formal” covers businesses included in in the World Bank Enterprise Survey (WBES). “Micro” covers businesses included in the World Bank Micro Enterprise Survey (WBMES), and “Informal” covers businesses included in the World Bank Informal Sector Enterprise Survey (WBISES).

**Table 2: Summary Statistics**

||Micro|Informal|
|---|---|---|
|Distance of Informal to Closest Formal Firm (Baseline)||0.96|
|Distance of Informal to Closest ES Firm||1.43|
|Distance of Informal to Closest Formal Firm (Enumerated)||0.68|
|Distance of Informal to Closest Formal Firm with Website||1.81|
|Number of Informal Businesses Within Square||12.49|
|Num. of People Who Worked Last Month|2.23|2.04|
|Years in Operation|5.25|3.30|
|Age of Main Owner|35.60|37.72|

7
Benoit, E., 1978, “Growth and defense in developing countries.” _Economic Development and Cultural Change_ 26(January): 271-280.

Benos, Nikos, 2009, “Fiscal Policy and Economic Growth: Empirical Evidence from EU Countries,” unpublished, University of Ioannina (September 2009).

Biswas, B. and R. Ram (1986) “Military expenditures and economic growth in less developed countries: an augmented model and further evidence.” _Economic Development and Cultural Change_ 34(January): 361-372.

Bleaney, Michael, Norman Gemmell, and Richard Kneller, 2001, “Testing the Endogenous Growth Model: Public Expenditure, Taxation, and Growth over the Long Run,” _Canadian Journal of Economics_ , Vol. 34, pp.36-57.

Bose, N., Haque M.E., and Osborn, D. R., 2007, “Public Expenditure and Economic Growth: A disaggregated analysis for developing countries,” _The Manchester School_ , 75, 533-56.

Colombier, C., 2009, “Growth effects of fiscal policies: an application of robust modified M-estimator,” _Applied Economics_ , 41, 899–912.

Easterly, William and Sergio Rebelo, 1993, “Fiscal Policy and Economic Growth: An Emperical Investment,” Journal of Monetary Economics, vol. 32, no: 3, pp. 417-58.

Folster, Stefan and Magnus Henrekson, 2001, “Growth Effects of Government Expenditure and Taxation in Rich Countries,” _European Economic Review_ , vol. 45(8), 1501-20.

Ghosh, Sugata, and Andros Gregoriou, 2008, “The Composition of Government Spending and Growth: Is Current or Capital Spending Better?,” _Oxford Economic Papers_ , 60 (June), 484-516.

Glomm, Gerhard and B. Ravikumar, 1997, “Productive Government Expendituers and Long-run Growth,” _Journal of Economic Dynamics and Control_ , vol. 21, No.1, 183-204.

Grossman, Philip J., 1990, “Government and Growth: Cross-sectional Evidenece,” _Public Choice_ Vol.65: 217-227.

Grossman, Philip J., 1988, “Government and economic growth: A nonlinear relationship,” _Public Choice_ vol. 56(2): 193-200.

Gupta, Sanjeev, Benedict Clements, Emanuele Baldacci, and Carlos Mulas-Granados, 2005, “Expenditure Composition, Fiscal Adjustment, and Growth in Low-income Countries,” _Journal of International Money and Finance,_ 24 , 441-463 23
card. The need for a specific voter ID can undermine citizens’ ability to exercise their voting rights. Online voter registration is only possible in India, and online voting is not permitted in any of the surveyed countries.

# **3.2.1. Legal requirements**

Economies around the world have adopted different electoral systems to fit their political frameworks. Voting is compulsory in nearly 13% of the world’s economies. In 85% of the economies, casting a ballot during elections is not mandatory, while in 2% of the economies there are no elections at all<sup>12</sup> (International IDEA 2017). In all four economies included in this report, voting is not mandatory and therefore there are no sanctions for nonvoters. In Chile, India, and Vietnam, although voting is voluntary by law, it is compulsory for citizens to be on the voter registry list. The Constitution of Ghana, on the other hand, states that citizens are entitled to be registered as a voter, which does not constitute an obligation to be included on the registry.

# **3.2.2. Non-discrimination provisions**

The Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), adopted in 1979 by the United Nations General Assembly, mandates that women should not be discriminated based on their marital status. Such practice impairs women from exercising their rights and freedoms in the political, economic, civil, and social spheres. _Living Life_ attempted to capture variations on procedures and requirements based on gender. The data found that in Ghana, Chile, and Vietnam voter registration provisions are equal for women and for men regardless of their marital status. However, in India, married women must state the name of their husbands when applying to register to vote. Unmarried women should indicate the names of their mothers and fathers when registering to vote. This is not required for married or unmarried men. This provision is established in India’s 1960 Registration of Electors Rules.

# **3.2.3. Voter registration procedures and documents required**

In Chile and Vietnam, the voters list is generated automatically based on a pre-existing civil registry. From a public administration point of view, this type of voter registration system enables the production of more reliable and up-to-date voters lists with higher coverage since they account for all citizens that are already registered in a civil registry database. However, such a register can be very costly to establish and maintain. Automatic registration cuts back on most procedures that a person would have to go through to register to vote. It also cuts the time needed to perform data entry and to compile voter lists (ACE 2013).

When voter registration is directly drawn from a pre-existing civil registry, citizens only need to go through one procedure when they are registered in the public civil registry for the first time. In Chile, this happens when an individual is registered at birth and obtains his NID. Once registered at birth, the individual is

> 12 https://www.idea.int/data-tools/data/voter-turnout/compulsory-voting.

15 | P a g e
15 model. Yet, we include two “environmental” variables based on the data available. The first variable is the wage rate per employee in USD as a proxy for GNI at utility level as reflected in LN_WAGERATE. Another variable we use is the population served per connection (LN_POP_CONN). In general, the population per connection differs widely by region and is linked to urbanization and utilities’ connection policies. In Africa, for instance, population per connection is very high due to the high cost of connection and the accepted practice of sharing connections. In Eastern Europe and Central Asia, it is still rather commonplace that apartments building are outfitted with only one connection instead of individual household connections – although this pattern is changing. In Latin America and the Caribbean, the number of population per connection is mostly linked to household size. It is assumed that the relationship between population served per connection and water losses is positive as more people served per connection essentially increases the stress on the water system.

# **5. Estimation Results**

The analysis was conducted using cross-sectional data from utilities covering 69 countries. This sample is smaller than the available sample, as we cleaned the data set for outliers taken out the 1 percent at either tail of the distribution. Given the nature of the dependent variable, we used a model with fixed effects in which the water losses as measured by LN_NRW_KM and controlled for heteroskedasticity. Table 5 shows the results of the panel data regression with fixed effects. Estimated coefficients are almost all significant and have the expected sign as can be seen in Table 5. It also shows that the fixed effects variable captured by the country effects is very significant. The overall fit of the model is satisfactory, with a R square adjusted of 0.61. The errors in the model are not correlated with the regressors in the fixed effects model. About 40 percent of the variance in the model is due to differences across panels (or countries), showing that the characteristics of the country have a major effect on water losses – and hence the environment in which the utility operates plays key role in explaining water losses.

## **Insert Table 5: Estimation Results of Fixed Effects Model**
performance consistent with what is expected given its level of economic development. In particular, Egypt performs as well as or better than other countries of similar per capita GDP regarding the following infrastructure indicators. For transportation: road network length, paved roads, quality of railroads, and quality of air transport. For telecommunication: main telephone lines, telephone faults, and waiting list of mainline installation. For power generation and water: access to electricity, quality of electricity supply, access to improved water source, and access to sanitation facilities.

Then, the study examines the trends in infrastructure spending in Egypt from 1960 to 2007, disaggregated by sector of origin (public and private) and by two main sectors, transportation (including Suez Canal) and communications, and electricity. Relative to GDP, total infrastructure expenditure in Egypt fell after reaching its peak in the late 1980s, mainly due to a decline in public spending. Private investment took off in the mid-1980s, and its magnitude has been growing since then. In recent years, the rise in private investment is clear in transportation and telecommunications, with the private sector becoming a principal player in telecommunications since 2005. Rising private investment in the last two decades, however, has not entirely offset the decline in public spending.

In comparison with other developing countries, the share of public in total infrastructure spending in Egypt is larger than what would be expected according to the country’s income level. In contrast, by the same standard its private infrastructure spending is considerably smaller. The trends of infrastructure expenditure across countries also reveal declining public spending and a clear upward trend in private investment. Thus, not only Egypt but countries such as India, Indonesia, Pakistan, South Africa, and Turkey have experienced a composition shift from public to private infrastructure spending. In some cases, such as India and Turkey, private investment has increased sufficiently to offset the decline in public spending in infrastructure.

A large body of literature has found that improving infrastructure has a statistically significant and economically substantial positive effect on economic growth. Using data for 78 countries for the period 1960-2005, this study confirms the result on the beneficial growth impact of infrastructure in telecommunication, transport, and power generation. This impact is found to be larger if infrastructure development does not 49
children aged 10–18 years, who may have completed primary education at the time of the survey. When EDUCijs is the number of years of education completed, the sample comprises children aged 5–14 years, who are of primary school age at the time of the survey. Under the assumption that there is no correlation between the cumulative number of district casualties between 1996 and year _s_ and unobserved district-survey-varying factors, γ is the causal effect of a one-unit increase in CONFEXPijs on the rate of primary schooling completion (the number of years of education completed) by the 10- to 18-year-old (5- to 14-year old) group. The magnitude of γ can be directly compared to that of _β_ because both TOTCONFjt and CONFEXPijs are expressed in district casualties per 1,000 inhabitants.

Xijs is a set of controls that contains age at interview dummies and their interaction with the survey dummy in all specifications, thus allowing the educational attainment of each age group to vary independently between the two surveys.<sup>8</sup> These covariates are included to control for potential differences in the 2001–2006 change in the district-level age composition of the 10- to 18-year-old or 5- to 14-year-old group that, if correlated with conflict intensity, could bias the results. In some variants, Xijs also includes household characteristics at the time of the survey (rural location and education of the household head). These characteristics are only measured at the time of the survey and could potentially be caused by past conflict. However, finding similar results when these observable household characteristics are included would suggest that potential changes in the composition of households due to mortality or migration do not drive my findings.

To further reduce the potential for unobserved time-varying factors to bias the estimate of the effect of conflict exposure, in the main set of results, I report estimates of equation _(2R)_ in which the coefficients of the survey dummy, the age at survey intercepts, and their interactions are allowed to vary by development region. Here, the effect of conflict exposure is identified using the withindistrict change in conflict exposure at age _x_ between 2001 and 2006, net of 2001–2006 changes in educational attainment at age _x_ common to all districts in a given development region: 15
Table 3: Top 10 countries identified by principal components, 2004–2008 period average, by real and PPP-adjusted GDP growth rates<sup>*</sup>

|**Rank**|**Country**|**Real**|**Rank**|**Country**|**PPP**|
|---|---|---|---|---|---|
|||_without _|_migration_|||
|1|China|8.174|1|China|10.853|
|2|United States|5.492|2|United States|7.344|
|3|South Korea|0.805|3|Japan|1.796|
|4|United Kingdom|0.752|4|Germany|1.331|
|5|Japan|0.746|5|Russia|1.208|
|6|India|0.608|6|United Kingdom|0.992|
|7|Germany|0.563|7|South Korea|0.945|
|8|Russia|0.459|8|France|0.926|
|9|France|0.390|9|India|0.685|
|10|Singapore|0.340|10|Ireland|0.674|
|||_with m_|_igration_<sup>_†_</sup>|||
|1|China|8.187|1|China|10.381|
|2|United States|6.998|2|United States|9.190|
|3|Russia|2.260|3|Russia|3.358|
|4|India|1.806|4|India|1.808|
|5|United Kingdom|0.761|5|Japan|1.560|
|6|Germany|0.646|6|Germany|1.505|
|7|South Korea|0.630|7|United Kingdom|1.126|
|8|Japan|0.560|8|France|0.986|
|9|Canada|0.499|9|Spain|0.850|
|10|Saudi Arabia|0.493|10|Saudi Arabia|0.795|

* Authors’ calculations, using data from the World Development Indicators. Index generated from first principal component of (1)–(5), variously excluding and including (4). Real GDP and PPP-adjusted indicate growth rates calculated from GDP data in real 2000 U.S. dollars and constant 2005 international purchasing power parity-adjusted dollars. _†_ Migration flows are calculated for 2005, the only year where data are available.

parallel in economic geography, where a small fraction of physical space typically accounts for a disproportionate share of economic activity. Thus, like would be the case for geographicallyconcentrated growth poles, global growth poles appear to follow a power law relationship (for cities, this relationship has been termed Zipf’s Law).<sup>13</sup>

> 13Annex Figure A.2 illustrates this power law relationship. This non-normality in the key dependent variable also poses a potential problem for the regressions to follow; a linear logarithmic transform was thus applied to mitigate this concern, and residual diagnostics are discussed.

15
(2) _YhdJ = a, + Xhd!p + 5Fd + ehdl,_ where 6 is the (margyinal) impact of program expenditure on the outcome. These two equations can be more compac.tly written as follows: (3) _Yhd, a, + Xhdp +_<sup>_6_</sup> _Fd l(tl ) + ehd,,_ where I(t-l) is a indicator function equal to 1 in year 1, and 0 otherwise.

The "naive" approach is to estimate equation (2) by ordinary least squares, using only data from time period 1. This will produce a consistent estimate of 6 only under the assump Lion that the expectation of ehdl conditional on _Xhd,_ is zero. This assumption is suspect, however, both because FONCODES is targeted to poor areas, and because it is demand-driven. If poorer districts, with worse educational outcomes, receive larger levels of _Fd,_ and if poverty is n(:t adequately controlled for in _Xhd,,_ then it is likely that the estimate of 6 will be biased down. The estimate could be negative even if the program actually improves outcomes (see, for exan, pie, the results in Pitt, Rosenzweig and Gibbons for Indonesia). To some extent, this problem can be countered by including district-level measures in _Xhdt,_ in particular the composite index of unmet basic needs which FONCODES uses to make allocations. It is still possible, however, that decisions to fund projects are based on more accurate information about local conditions, informnation that we do not possess. It is also possible that households in districts that care. more about education, or have greater local institutional capacity, are those that organize to apply for FONCODES funding. If institutional capacity and "tastes" for education are unobserved and positively correlated with better educational outcomes, the naive estimate of 6 may be bias,-d up.

If two cross-sections of data are available, one from time 0 and one fiom time 1, n .o re estimation strategies are possible. If the same districts and households are represented in l: o1:h cross-sections, then a fixed effects estimator is a good alternative. However, this has an important disadvantage for our analysis because fixed effects models can be estimated on only a subset of the data. The panel of households interviewed in both 1994 and 1997 consists of only 25% of all households interviewed, while the corresponding fraction of households living in districts which were included in both the 1994 and 1997 LSMS accounts for 71% of the sa:.)nple. Although the use of district (household) panel data would enable us to handle district (hou.;ehold) level heterogeneity, it comes at the cost of fewer observations and less precision.

An attractive alternative is to use a "difference-in-difference" estimator. Assume i lat the error tern in (3) takes the special form _ehd,= yFd + Shd,._ This is equivalent to a specificatioi with district-level fixed effects, with the added restriction that the fixed effects are proportional _;0_ the level of FONCODES funding received. We then estimate: 13