rafmacalaba/datause-encoder-data huggingface dataset:
customers. Although growing in popularity worldwide, providing a prepaid option is a relatively new practice in Africa, and its impact is still not well understood. But for those countries for which data are available, roughly 60 percent have introduced the use of prepayment meters (figure 3). Indeed, Lesotho, Namibia, Mozambique, and South Africa already offer this option to a majority of residential customers.

<!-- Start of picture text --> Figure 3. Prepaid services<br>a. Frequency distribution of countries adopting b. Time trend in percentage of customers using<br>prepaid meters prepaid meters<br>2000 2001 2002 2003 2004 2005<br>10<br>9 Tanzania 4 4<br>8 Benin 0 0 6 6 6 7<br>7<br>6 Burkina Faso 10 10 10 10 10 10<br>5 Malawi 2 4 6 8 10 14<br>4<br>Ghana 5 4 4 10 28<br>3<br>2 Rwanda 48 65<br>10 Mozambique 80 73<br>0 1-20 21-50 51-90 91-100 no data Namibia 93 98<br>Lesotho 100 100 100 100<br>percentage of customers with prepaid<br>meters South Africa 100<br>number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Indicators Database

# **b. Nonresidential tariffs**

Nonresidential tariffs can be classified in two groups: commercial and industrial (Table 4). Linear tariffs are the most common regime for nonresidential customers in Africa (figure 4). About 60 percent of commercial customers and over 50 percent of industrial customers are billed based on linear tariffs. A more detailed description on commercial and industrial tariff structures practiced in Africa can be found in Annexes 3 and 4.

Pricing for nonresidential customers is typically more complex than for residential customers. It is usually structured as a three-part tariff including a monthly fixed charge (defined by characteristics of the network), a demand charge (defined by the level of peak demand served in kilovolts or kilowatts), and a volume charge (defined by the energy served and reflected in the definition of the blocks). In addition, volume charges may be differentiated by time-of-use (TOU). In fact, only a handful of countries (Benin, Cape Verde, Rwanda, and Mali) apply simple linear tariffs to their nonresidential customers without making use of any of these additional features.

Fixed charges are somewhat more prevalent among non-residential customers, than was the case for residential customers. They are practiced by 18-19 countries in the sample.

10
33 community groups, and/or charity (Deaton and Paxson, 1992). Also, there is at least the potential for private purchase of insurance by younger workers, against likely events threatening consumption pattems when they are old, such as nursing home insurance. (It should be noted that adverse selection and moral hazard problems seem sufficiently severe that these instruments have not developed very widely even In the United States.) In any event, the equity motivation for old age benefit plans requires that an individual or group identify a living standard against which older peoples' resources are compared, and provide a means to achieve affordable insurance. Policymakers often disagree over the proper equity standard, making it difficult to compare retirement systems in some cases. In addition it Is widely held that young workers underestimate their future needs, either as a result of poor information or systematic myopia, and as a result should be forced to purchase more insurance for patemalistic reasons." Before leaving the discussion over public versus private provision of retirement income, it must be noted that retirement systems are often criticized for two other reasons as well: because of administrative inefficiencies, and because of inadequate Integration. Administrative inefficiencies in public systems were recently highlighted in a study of several Latin American public pension funds; that report concluded that most public plans reported negative rates of return during the decade of the 1980s, and devoted excessive funds to bureaucratic management (Mesa-Lago 1989, 1990). This problem is not limited to Latin American public plans; a recent study of U.S. private pensions found that rates of return eamed were below market rates eamed by diversified mutual fund portfolios (McCarthy and Tumer 1989). It has also been reported that record-keeping is problematic in many Eastem European and Latin American pension systems, particularly when they, lack modern computers and computer software to keep track of contributions and assets.<sup>2</sup> " Similar bookkeeping difficulties in Eastem Europe make it virtually impossible to compute govemmental liability for long-term benefit promises. This is obviously a matter of much concem to the govemments and to lending institutions, since retirement system promises are a form of national debt which affects a country's economic health. Data collection and processing problems are not limited to public plans; indeed, computerized record-keeping and analysis of privately sponsored pensions has been possible in only the last half decade in the United States.
than other ASEAN countries. Education spending accounted for nearly 3 percent of GDP in 2019, of which primary education absorbed more than half of overall spending. Cambodia’s constitution mandates public schools provide 9 years of free basic education. Health spending was only 1.7 percent of GDP in 2019, only modestly increasing since 2009 from 1.2 percent. Best performers in ASEAN spend nearly twice the amount as a share of GDP. For example, between 2009 and 2019 Malaysia and Vietnam spent, on average, 5 percent of GDP on education. Thailand and Vietnam spent, on average, more than 2 percent of GDP on health. Cambodia, together with Myanmar and Lao PDR, lag among ASEAN countries respectively. Cambodia lags on some human capital indicators due to weaker education attainment, skill utilization, and child nutrition outcomes.<sup>10</sup> Cambodia needs to catchup and align its education and health outcomes with best performers within ASEAN and the world. As the CEQ analysis demonstrates, increased education and health spending to match top performing ASEAN countries could help Cambodia redistribute wealth and support low-income households.

Table 2: Central Government Spending, 2019

|**Type**|**Amount**<br>**(billion KHR)**|**Percent**<br>**of GDP**|**Included in**<br>**CEQ**<br>**Analysis?**|
|---|---|---|---|
|**Central Government Expenditure**|27,317|24.8||
|**Social Benefits**|2,310|2.1||
|Social security|898|0.8|Partial|
|Social assistance to citizens|1,219|1.1|Partial|
|Social assistance to social and cultural entities|138|0.1|No|
|Other social benefits|56|0.1|No|
|**Health**(MoH spending)|1,924|1.7|Yes|
|**Education**(MoEYS spending)|3,679|3.3|Yes|
|**Other Spending**|19,404|17.6|No|

Source: MEF, MoH, MoEYS.

Social protection spending consisting of social assistance and social security amounted to about KHR 2,255 billion, or about 2 percent of GDP, in 2019. About half of Cambodia’s social protection spending was devoted to social security, primarily for former civil servants as pensions. The other half, almost 1 percent, was devoted to social assistance (Figure 3). Social assistance spending in 2019, in particular on cash transfers, was very low in Cambodia albeit better than in some developing countries in East Asia and the Pacific.<sup>11</sup> Cash transfer spending was minimal in 2019 despite the full national roll out of the Conditional Cash Transfer for pregnant women and children below 2 years old. However, during the COVID-19 pandemic Cambodia’s social assistance spending increased significantly.

> 10 World Bank (2020).

> 11 The National Social Protection Policy Framework (2016–2025) has two main pillars: social assistance and social security. The social assistance pillar includes emergency response, human capital development, vocational training, welfare for vulnerable people. The social security pillar includes pension, health insurance, employment injury insurance, unemployment insurance, disability insurance.

8
sources such as retained earnings or loans from family and friends, and external financial sources such as equity, local commercial banks, foreign owned commercial banks, trade credit (supplier or customer credit), investment funds or special development financing, informal sources such as a moneylender or an informal bank and other sources. The sum of these proportions adds up to 100 percent.

We adopt two different categorizations of the various sources of financing. In the first categorization, we have the following six groups : **Bank Financing** that includes Local commercial banks and Foreign commercial banks; **Informal Finance** that includes financing from informal sources such as a money lender or an informal bank; **Operations Finance** that includes Trade Credit **; Equity Finance; Investment Funds** that includes Investment funds or Special Development Financing or other State Services and **Internal Finance** that includes Internal Funds or Retained Earnings, Loans from Family and Friends and the Other category.

In the second categorization, we adopt Allen et al. (2006)’s classification of financing sources into two groups: **Bank Finance** , that includes Local commercial banks and Foreign commercial banks and **Self Fund Raising** that includes all other sources such as retained earnings, informal sources, loans from family and friends, trade credit, investment funds, equity and the other category.<sup>12</sup> One limitation of our survey data is that financing patterns are given in terms of proportions of financing, not as debt to asset ratios, as is common in the previous literature.

> 12 Allen et al. (2006) consider two additional financing sources that we do not have information on in our survey: the state budget and foreign investment. This is unlikely to influence our results on bank finance and informal finance since we have only 116 firms in our sample that have more than 50% foreign ownership and as Allen et al. themselves mention, the state budget contributes to only 10% of state owned companies’ total funding.

12
Table 2: Local Government Administrative Structure and Size : Frequency Distribution of Countries

|Name<br>n|N<br>|Min <br>|Max <br>|Distribution<br>al|Countries,<br>e—£$§|Coun<br>|tries,<br>wn||
|---|---|---|---|---|---|---|---|---|
|noeO|a|1|4|.<br> ; s-|<br>|tier-47countries|Iran(<br> Bang<br>Chin<br>car(4|4}<br>ladesh(4)<br>a/4) Mada<br>)|zas-|
|||||.|East-Timor<br>(1.1|Soma|lia<br>(0.9|mln)|
|;<br>LG<br><br>|||||th)<br>Eq.<br>Guinea<br>|Con<br>|o<br>DR<br>|(0.6<br>|
||||||||||
|popu-<br>lation|177||nln|-<br>a|<br>th) Cyprus (1.6 th)<br>Switzerland<br>(2.7<br>th)|mln) <br>mln) <br>min}|Burundi <br> Indonesi|(0.5<br>a (0.5|
|||||.|Czech<br>Rep<br>(0.01|Lybi|a<br>(70.4.|tsk)|
||||||tsk)<br>France<br>(0.01|Bots|wana|(43.9|
|LG<br>aver-<br>||0.01<br>|70.4<br>||tsk) Lebanon (0.01<br><br><br>|tsk) <br>|Somalia <br>|(35.4<br>|
|agearea|177|tok|tek||tsk)<br>India<br>(0.01<br><br>|tsk) <br>|Namibia <br>|(13.7<br>|
|||||Sree|tsk)<br>Phillipines|tsk)|Congo|DR|
||||||(0.01tsk)|(11.6|tsk)||

Source: Authors’ calculations based upon data sources reported in Annex Table Al. Note Units of measurement: th - thousand people, min - million people, fsk - thousand square kilometers. Distribution of 6G avernge population is only for countries with this indicator lower than 200 thousand people (87% of the sample). Distribution of LG averge aree is only for countries with this indicator lower than 4 thousand sq. kilometers (92% of the sample}
# Table A.3: Determinants <u>of Log Annual Remittances (OLS)</u>

||Wife's|Report|Mean of M<br>Wife's|igrant's and<br>Report|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Reported earnings ratio|0.761***<br>(0.25)|0.631*<br>(0.34)|0.714***<br>(0.20)|0.626***<br>(0.21)|
|Migrant's annual earnings (log)|0.892***|0.819***|0.819***|0.811***|
||(0.21)|(0.21)|(0.19)|(0.18)|
|Hhold has other members working abroad (indicator)|-0.14|-0.12|-0.417***|-0.459***|
||(0.23)|(0.24)|(0.16)|(0.16)|
|Hhold's annual medical expenditure as a share of migrant's income|-0.109|-0.172|0.379|0.365|
||(0.61)|(0.61)|(0.45)|(0.45)|
|Household's monthly income in India (log)|-0.002|-0.053|0.022|-0.002|
||(0.02)|(0.05)|(0.01)|(0.03)|
|Median monthly income of Gulf migrants (log)|0.551*|0.453|0.671***|0.693***|
||(0.31)|(0.36)|(0.21)|(0.25)|
|Additional controls|Yes|Yes|Yes|Yes|
|R-squared|0.19|0.25|0.38|0.41|
|Observations|108|108|108|108|

*** denotes statistical significance at the 1% level, ** at the 5% level and * at the 10% level. p p y ( ) g reports (columns 3 & 4). Additional controls consist of the wife's years of education and indicators for whether a wife is employed, unaware of her husband's expenses, having disagreements over remittance uses and if the migrant spends on temptation goods. All regression includes a constant term. Bootstrapped standard errors (with 500 replications) are displayed in parenthesis, clustered at the district level.

_Source:_ Authors’ analysis based on data from QSKF.

41
much of the growth could have come outside of the initial specialization industry, just as we observed broader marginal productivity spillovers. But it is not clear why this would be the case, and it hints that very interesting patterns may exist when jointly studying districts at the regional level. For example, it is known that Indian government policy prior to the deregulations promoted industrial placement in less developed locations in the name of distributional equality. These patterns are perhaps showing how this initial, artificial placement unwound itself, with a general movement of regional industry towards a focal point (somehow determined). Our current framework treats each district as a separate entity, and answering these types of questions will necessitate modeling the spatial distances between districts of various types. Such an extension would also allow us to use plant age data contained in our surveys to measure the degree to which maturing industries move from diversified districts to specialized locations (e.g., Duranton and Puga, 2001).

Second, we would like to evaluate the longitudinal changes highlighted in this study in terms of the impact of specific infrastructure projects (e.g., the Golden Quadrangle project) or trade liberalizations. These factors have been shown to play an important role for Indian manufacturing (e.g., Goldberg et al., 2010a,b), but we do not know their role in terms of the specialization and diversity of India’s districts. For example, highly tradable goods may have also been the goods that could have formed the basis for the most specialized districts before India’s reforms. Tracing out the economic geography of these economic shocks is important, and the rich longitudinal data for India provide a unique laboratory for doing so in a developing economy. Likewise, Ellison et al. (2010) consider coagglomeration and the inter-linkages of industries within a local area. It would be interesting to quantify what happens to related industries located in specialized or diversified districts when these dramatic changes occur.

Finally, manufacturing is a natural starting point, especially given its study in many countries. India, however, has relied extensively on services growth for much of its economic development, and it is critically important in this context for us to learn about how this sector’s patterns are similar or different to those in manufacturing (e.g., does services growth link to specialized districts for services). This extension would also allow us to compare India more closely with the work on Colombia by Duranton (2013b), who finds some interesting differences 23
domestic endowments or of relevant policies. Fortunately most of the relevant variables are now covered by established datasets of indicators based on statistics or, very often, surveys. To meet the expectations of policy makers to understand the differences of dynamics by industry, the analysis had to be disaggregated at the sector level. This provides a “meso-economic” description of how differences in factor endowment may be more conducive of certain products or exports.

The insight that trade outcomes can reveal information on sources of competitiveness or comparative advantage goes back half a century, when Balassa introduced the concept of revealed comparative advantage. This paper follows this tradition. It proposes that the structure of the global export matrix can be essentially explained by a few latent factors of endowments that determine comparative advantage. Thus the position of the problem is “inverse”: country and product factor variables are deduced from the export outcome data, in contrast to the traditional econometric approach whereby export outcomes are regressed against actual endowment variables.

A nonlinear dimensionality reduction procedure (exponential-PCA or E-PCA) analyzes and produces the few latent factors. Although based on off-the-shelf mathematical tools and intensive computations, the main outputs consist in country specific information of practical value, including the latent composition of trade and latent comparative advantage ratios. Ultimately the paper proposes a scientific solution to the old problem of estimating the potential for diversification of countries based on existing data and trade theory.

The remainder of the paper is organized as follows.

The first part of the paper (section 2) introduces the key concepts and surveys the literature.

The second part of the paper develops an alternative concept of product space (section 3) rooted in neoclassical trade theory (section 4). It introduces the factor supply elasticity, which is a measure of the productivity gains brought about by changes in the supply of factors across countries. The estimation of the model requires non-linear techniques -Poisson Pseudo Maximum Likelihood (PLM)-, in contrast to previous implementations, and corresponds to a non-linear projection of the product space onto a one-dimensional manifold (section 5). Implementation to trade data and various factors of competitiveness is commented on (section 6).

Finally, the third part of the paper proposes to push the model further by applying a consistent procedure of dimensionality reduction to the product space to identify its dimensions. It combines non-linear estimation (Poisson PLM) with principal component analysis (E-PCA) (section 7). Country and product coordinates are estimated through this non-linear “projection”. The significance of the principal axes in this reduction and their relationship with known factors of competitiveness are investigated (section 8), along with comparisons of latent against revealed comparative advantage by country (section 9).

The research dataset is available at www.worldbank.org/trade -> Data 3
all available DHS surveys to make a large dataset where each observation is an individual child. We merge to the child level data a new dataset on population density at the level of DHS sub-national regions (hereafter regions). For each of the over 1,800 regions, we manually matched the region to publicly available, published demographic data for the closest available year to the year of the survey. Appendix table 8 lists all of the countries and years in the international sample as well as the source of the region level data on population density.

**Independent variable.** Our independent variable is the interaction of the log of population density at the region level with local prevalence of open defecation near a child. We estimate local prevalence of open defecation near a child by estimating the fraction of the households in a child’s primary sampling unit (PSU)<sup>4</sup> that defecate in the open rather than using a toilet or latrine. We do this by computing the fraction of households in each PSU in the sample that report open defecation.<sup>5</sup> This is a local (neighborhood-level) measure of exposure to open defecation, and not merely a property of the child’s own household (Montgomery and Hewett, 2005). To isolate and emphasize the negative externality of neighbors’ open defecation, we also control for whether a child’s own household defecates in the open in all of the regressions we present.

**Dependent variables.** Our dependent variables are infant mortality and height-for-age. Infant mortality is a child level indicator, which we define for all live births that occurred at least one year before the date of the survey and no more than ten years before the date of the survey. Infant mortality is coded as 0 if the child survived her first year of life, and 1,000 if the child died within the first year. This scaling of the indicator by 1,000 makes our infant mortality estimates consistent with published population-level infant mortality rate (IMR) statistics. The second dependent variable is a child’s height-for-age _z_ -score.<sup>6</sup> A

> 4 DHS surveys use two-stage random sampling. First, a PSU, which is either a rural village or a small set of urban blocks, is selected; second, households within the PSU are randomly selected.

> 5Because the fraction of households in a PSU that defecates in the open is estimated from a sample, and not from data on every household in the neighborhood, this is a noisy measure of the true fraction of households in a child’s local area who defecate in the open; this random measurement error will attenuate our coefficients, so any sanitation gradient we uncover may be a lower bound.

> 6Following standard practice using these WHO _z_ -scores, we omit any child beyond _±_ 6.

9
Given these two assumptions, we propose the following proposition that lays out the estimation framework.<sup>13</sup>

# **Proposition 1: Imputation framework**

_Given Assumptions 1 and 2, the poverty rate based on data in survey 2 can be predicted using data in survey 1. In particular, let P(.) be the poverty function and_<sup>_y_</sup> 12<sup>_be defined as_</sup> β 1<sup>'</sup> _x_ 2 + μ 1 + ε 1 _, we have_

# **Corollary 1.1**

_Let_<sup>βˆ</sup> 1<sup>_,_μˆ</sup> 1<sup>,</sup><sup>_and_εˆ</sup> 1<sup>_represent the estimated parameters obtained from equation (1) and let_</sup> ˆ 1 ˆ ' ~ˆ ~ˆ ~ˆ ~ˆ _y_ ,2 _s_ = β 1 _x_ 2 + μ ,1 _s_ + ε ,1 _s , where_ μ ,1 _s and_ ε ,1 _s represent the s_<sup>_th_</sup> _random draw from their estimated distributions. The poverty rate_<sup>_P_</sup> 2<sup>_in period 2 and its variance can be estimated as_</sup>

# **Corollary 1.2**

_Instead of Assumption 2, assume the traditional but more restrictive assumption that the consumption model parameters in equation 1 remain the same in period 2 (that is,_ β 1 ≡ β 2 _,_ μ 1 ≡ μ 2 _, and_ ε 1 ≡ ε 2 _). Given Assumption 1 and this stricter assumption, we have_ 1 _W_ ( _y_ 2 ) = _W_ ( _y_ 2 ) (7) _where W(.) is a general one-to-one mapping welfare function, which includes the poverty function P(.) as a special case._

# **Proof** .

Appendix 1.

Some remarks about Proposition 1 and its corollaries may be useful. First, the simulation of the error terms for households in survey 2 is mandatory rather than a matter of choice since we

> former type of changes is set to zero under the stricter assumption but allowed to occur with our more general assumption.

> 13 Note that in situations where Assumption 1 fails (e.g., one survey is representative of the whole population while the other survey specially targets a population segment such as elderly people), survey imputation may still be feasible conditional on the fact that Assumption 2 holds. In such cases, Assumption 2 essentially boils down to implying that the estimated parameters for equation (1) with the appropriate adjustments (say, by including the dummy variables for different population groups) apply to the population group targeted by the other survey.

16
# **List of Tables**

Table 1: Household interview status during the last visit

||Observations|Frequency|
|---|---|---|
|Interviewed (in main survey phase)|4,534|90.68|
|Tracked (and interviewd)|48|0.96|
|Refused|47|0.94|
|Not found|100|2.00|
|Dead|84|1.68|
|Moved away (not tracked)|48|0.96|
|Crisis area|139|2.78|
|Total|5,000|100|
|Total number of migrants|329|6.6|

Source: Authors’ estimations based on GHS Data 26