rafmacalaba/datause-encoder-data huggingface dataset:
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 11. Benchmarking Benin’s power infrastructure**

|**Indicator**|**Unit**|**Low-income**<br>**countries**|**Benin**<br>**2009**|**Middle-Income**<br>**countries**|
|---|---|---|---|---|
|Access (national)|% of population|33|25|50|
|Access (urban)|% of population|71|53|100|
|Access (rural)|% of population|4|2|32|
|Installed generation capacity|MW per million<br>people|20|14|799|
|Electricity consumption|KWH/capita/year|107|72|4,479|
|Power outages|Days/year|10|24|6|
|Value lost due to outages|% sales|6.5|7.5|1.6|
|Firms that indicate that power in a major constraint|% firms|52|70**|31|
|Reliance on own generators|% firms|41|27|18|
|Collection rate*|% of billing|92|96|91|
|Cost-recovery ratio*|% of operating cost|89|72|85|
|Revenue per unit*|U.S. cents per KWh|14|14|13|
|System losses*|% generation|24|21|20|
|Total hidden costs*|% revenue|69|12|0|
|Effective power tariff|U.S. cents per KWh|**Benin**|**Predominantly**<br>**thermal**|**Other developing**<br>**regions**|
|Residential|at 100 kWh|18|14.5||
|Commercial|at 100 KWh||18.8|5.0 – 10.0|
|Industrial|at 50,000 kWh|11|14.2||

_Source_ : Low-income country figures as of 2005 from the power AICD database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data. For Benin, national access data were taken from World Bank (2009a) and are based on estimates in 2006. Electricity consumption was taken from the CIA Factbook and is for 2007. Power outages were taken from World Bank (2009a) and are for 2009. Value lost to outages was taken from Enterprise Surveys 2009. Firms that indicate that power is a constraint and firms that own generators taken from Enterprise surveys 2004. Collection rate is taken from World Bank 2009a and is for 2009. System losses are for 2009 and taken from World Bank 2009a. Tariff information is for 2009 and is from World Bank 2009a.

* Represents information only for SBEE and does not include information for CEB; ** As of 2004

The power distribution utility in Benin, SBEE, is relatively efficient. Its system losses are modest relative to African peers, and its collection rates are relatively high. Total system losses are estimated at 21 percent, with 17 percent from distribution. While these losses are high relative to the 10 percent international benchmark, system losses are lower than in comparable low-income countries and even with the level of middle-income countries in Sub Saharan Africa (table 11). In other West African countries losses are higher: Togo has been burdened with transmission losses of around 30 percent (2004), Côte d’Ivoire around 23 percent (2008) (Tallapragada and others 2009). SBEE has been able to collect 96 percent of its billing, above the average for low-income peers (table 11).

# **Challenges**

Important differences in access to electricity remain between urban and rural populations. Whereas 53 percent of urban dwellers have access to electricity, only 2 percent of rural residents do. Lack of adequate and reliable supply of electricity has forced more than 60 percent of the population to rely on biomass as 27
Policy Research Working Paper 6858

# **Abstract**

This paper provides an overview of the impact of onceand-for-all changes in commodity prices and other prices on household welfare. It begins with a collection of stylized facts related to commodities based on household survey data from Latin America and Africa. The data uncover strong commodity dependence in both continents: households typically allocate a large fraction of their budget to commodities and they often depend on commodities to earn their income. This income and expenditure dependency suggests sizable impacts and adjustments following commodity-price shocks. The paper explores these effects with a review of the literature. It studies consumption and income responses, labormarket responses, and spillovers across sectors. It ends up providing evidence on the relative magnitudes of various mechanisms through which commodity prices affect household (and national) welfare in developing economies.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 dlederman@worldbank.org and guido.porto@depeco.econo.unlp.edu.ar.

_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
2.<sup>4</sup> Subject to data availability, _xj_ can include household variables such as the household head’s age, sex, education, ethnicity, religion, language (i.e., which can represent household tastes), occupation, and household assets or incomes. Occupation-related characteristics can generally include whether the household head works, the share of household members that work, the type of work that household members participate in, as well as context-specific variables such as the share of female household members that participate in the labor force, or some variables at the region level. Other community or regional variables can also be added since these can help control for different labor market conditions.

The following linear model is typically employed in empirical studies to project household consumption on household and other characteristics (x�) where υ�� is a cluster random effects, ε� is the idiosyncratic error term, and y� is household consumption typically modeled in log form. Note that we suppress the subscript that indexes households to make the notation less cluttered.<sup>5</sup> For convenience, we also refer to the survey that we are interested in imputing poverty estimates for as the target survey, and the survey that we can estimate Equation (1) on as the base survey. The former survey is usually more recent (or offers more disaggregated information, as in the case of a census) and has no consumption data, while the latter is usually older and has consumption data.

> 4 More generally, _j_ can indicate any type of relevant surveys that collect household data sufficiently relevant for imputation purposes such as labor force surveys or demographic and health surveys.

> 5 Conditional on household characteristics, the cluster random effects and the error terms are usually assumed � � uncorrelated with each other and to follow a normal distribution such that υ��|x�~N�0, σ��� and ε�|x�~N�0, σ���. While the normal distribution assumption results in the standard linear random effects model that is more convenient for mathematical manipulations and computation, it is not necessary for this type of model. As can be seen later, we can remove this assumption and use the empirical distribution of the error terms instead, albeit at the cost of somewhat more computing time.

6
planning services (Rosenzweig and Schultz 1989). This is especially so in Ethiopia where injectable contraceptives are the main method. Injectable contraceptives are ideal for women without education because they do not require any user action except the visit to a family planning clinic every 3 months.<sup>16</sup> In addition to the expected larger effect of family planning for women with no education, the age profile of fertility and the effect of other factors on fertility are likely to be different across education groups. Rather than assuming the appropriate specification given such interactions across education groups, the main sample is restricted to women with no formal education who have ever been married or lived together with a man. Among the original sample, 65 percent of women never attended school. Table 2 shows the descriptive statistics for this sample.<sup>17</sup> The main dependent variable is the number of children a woman had given birth to at the time of the survey (children ever born) which averages just over 4. The large number of births reflects the high fertility rate in Ethiopia, especially considering that the average age of the women in the sample is just over 28 years.<sup>18</sup> Age is captured using five-year age groups with the excluded category being aged 15-19. With the high population growth rate in Ethiopia younger cohorts are larger than older cohorts, but the percentage that have married or lived with a partner is smaller for young women compared to older women explaining the lower percentages of the two youngest age groups (15-19 and 20-24) in the sample. Just over half of the women are Orthodox Christian, a quarter are Muslim, and the remaining women are mainly other Christian. The remaining variables are the same community characteristics as in Table 1 used for the first stage.

Because there is no information in our survey data on migration of women the definition of access to family planning implicitly assume that a woman has spent her entire life in the area where she was found during the survey. This does not seem to be a problematic assumption. Data from the 2005 National Labour Force Survey show that 70 percent of women 15 to 45 have always lived in their current location and that another 15 percent have resided there for 10 years or more, presumably a move associated with marriage and the onset of the women’s entrance into family formation.

> 16This also makes them attractive for women who do not want to reveal to their partner that they are using contraceptives (Ashraf et al. 2010).

> 17The descriptive statistics for the full sample is available on request.

> 18For comparison the equivalent number for Guatemala is 2.8 and Guatemala has one of the highest total fertility rates in Latin America (P ̈ortner 2008). See also World Bank (2010) for TFR for other countries.

11
Table 5: Terms-of-Trade in Interstate Trade, 1994 (1990=100)

||Hypothetical<br>changes in moving<br>to world prices<br>(1)|Actual<br>changes<br>(2)|
|---|---|---|
|Armenia|68.3|75.6|
|Azerbaijan|73.9|88.8|
|Belarus|80.1|86.2|
|Estonia|68.2|83.2|
|Georgia|55.2|64.3|
|Kazakhstan|98.5|94.8|
|Kyrgyzstan|87.3|81.1|
|Latvia|75.7|80.0|
|Lithuania|65.2|77.8|
|Moldova|46.6|56.2|
|Russia|137.6|118.9|
|Tajikistan|75.3|77.1|
|Turkmenistan|134.7|139.5|
|Ukraine|86.2|93.0|
|Uzbekistan|91.0|90.9|

Source: Column 1: Tarr, 1994. Column 2: Dikhanov, 1995.
and at savings banks that are members of World Savings Bank Institute (from Peachey and Roe, 2006) to produce the most comprehensive, though admittedly still rough, accounts-based estimates of access to date. While this represents a step forward, the accounts-based approach provides little information about the account holders themselves, and thus about the nature of financial exclusion in a given country.

A more satisfying, but costlier, approach is to interview users and potential users of financial services. These demand-side efforts to measure outreach are based on surveys of individuals and households. Broadly speaking, there are two approaches: (1) stand-alone surveys that focus only on access to financial services, which tend to be relatively expensive but produce rich data sets and a detailed portrait of access, and (2) a small module of questions on financial usage and access that is embedded within a larger survey that is designed to cover another topic (e.g., surveys of household expenditures or labor market participation) or multiple topics (as in the Livings Standards Measurement Surveys (hereafter, ‘LSMS’)). The marginal cost of the modules is much lower than that of stand-alone surveys, but they yield data that are much less rich.

To date, neither approach has produced comparable financial usage data at regular intervals that could be used to monitor the situation in a given country over time, or to compare outreach across a large number of countries. Because the stand-alone surveys are costly, they tend not to be repeated at regular intervals, and when a stand-alone financial survey is eventually repeated, there is no guarantee that the sampling frame and questions will be the same as in the previous survey, or that the same organization will deploy the survey. Because the modules of financial questions are placed within a survey designed for a different purpose, they tend not to be given high priority, and 4
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Tablc 12: O&M Expcnditurc and Rcquircmcnls (in Million Currciit Rupecs)

|Fiscal Ycar|1988|1989|1990|1991|1992|
|---|---|---|---|---|---|
|Rcquiremcnts|1704|1875|2161|2408|2617|
|Expcnditure|1704|1513|1617|1718|1985|
|ShortfalI(/)|0|-19|-25|-29|**_-L4_**|

Sourcc: World Bank (1994) Rural infrastructure deficiencies have also been widely documented. Witness the road density in Pakistani Punjab, which is just one half of the road density in Indian Punjab. Road maintenance expenditure has been seriously neglected; it was Rs 1.3 billion in 1990-91, far short of the Rs 8 billion required for proper maintenance of the road network. It is also clear that education spending has been seriously neglected.

# Public Enterprises in Agriculture

Although the expenditure on public enterprises is not included in the buadget, theii losses are met mostly by borrowing from banks guaranteed by the government, and sometimes by direct support. Guaranteed borrowing is effectively part of the fiscal deficit and crowds out the private sector from the credit market. Public enterprises often have an undesirable financial structure, with little equity and heavy reliance on short-term public debt.

There are many public institutions in Pakistan for implementing governnent interventions in agricultural markets, including output market price control, control of input supplies, and external trade of agriculture output. A host of public sector corporations were set up, for reasons such as providing cheap food to urban consumers and guarding the farmer's against private 'exploitation'. Notable among these agencies are the Punjab Seed Corporation (PSC), the Sind Agricultural Services Organization (SASO), Punjab Agricultural Development and Supplies Corporation (PADSC) Pakistan Agricultural Services and Storage Corporation (PASSCO), Agriculture Development Authority (ADA), Cotton Export Corporation (CEC), and Rice Export Corporation of Pakistan (RECP). All are inefficient, with a high cost of operation compared to the private sector. For example, a study in 1989 showed that the private rice mills operated at 40 percent less cost than the RECP. Table 13 summnarizes the operating profit and losses of four major agriculture sector corporations in Pakistan.

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depends on the interaction of these factors. We tested this relationship using data from a nationally-representative household survey for Nepal. We compared the observed rates of MWP of women living in households that had sent migrants with the rates simulated under a counterfactual scenario of no migration. To construct these counterfactuals we estimated a model of household male migration and female MWP decisions, identifying observed and unobserved differences in the returns to characteristics based on migration status.

The results of our simulations show that the migration of male household members reduces women’s rates of MWP by 5.4 percentage points. The effect of male migration is strongest for women 25 to 35 years of age and for women with 11 or more years of education. The income effect of remittances from migrants and the substitutability of male and female time inputs in home production might explain the stronger impact of male migration on the MWP of women residing in landless households and in urban areas of Nepal. The MWP of women living in households with large landholdings is affected by male migration to a lesser degree, suggesting that in such households, males and females might complement each other in home production. We find evidence of substantial heterogeneity (based both on observable and unobservable characteristics) in the impact of male migration.

Work-related migration is already high in Nepal and will most likely continue rising in response to the economic incentives offered by neighboring countries. Our findings highlight the gender dimension of the impact of predominantly male work migration on the wellbeing of sending households. The effect of male migration on the work pattern of women left behind has important implications for the latter’s overall social status and might influence the outcomes of other household members, particularly children. Hence, strategies for economic development in Nepal should take into account such gender aspects of the migration dynamics.

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The second set of findings focuses on the magnitude and persistence of the parenthood penalty across various subgroups in Indonesia. These results offer important insights for policymakers seeking to target interventions effectively. First, we found that households with an extended family member living in the household experienced the fastest recovery from the birth penalty. For these households, the penalty became insignificant by the second year after childbirth. In contrast, for households without extended family members, the penalty persisted until year seven. Second, we observe significant differences in the magnitude and persistence of the birth penalty based on education level. Highly educated individuals faced larger penalties (approximately 30% in the first year) and for a longer period compared to their less educated counterparts (around 20% in the first year). Furthermore, we find that rural women experienced the highest birth penalty in year three, after which the trend began to reverse. In contrast, urban women saw this reversal begin in year four. These results suggest that expanding access to childcare could significantly reduce the persistence of the birth penalty, particularly for highly educated urban women, who experienced the largest and most persistent penalties. This subgroup represents an important target for policy interventions, where childcare provision could have the greatest impact.

The rest of the paper is structured as follows. Section 2 provides an overview of the Indonesia Family Life Survey (IFLS) data and the institutional context of the Indonesian labor market. Section 3 outlines our empirical strategy, including the DID methodology and the identification assumptions. Section 4 presents our main findings, including a detailed heterogeneity analysis and robustness checks. Finally, Section 5 discusses the policy implications of our findings and concludes with suggestions for future research.

# **2 Institutional Context, Data, and Descriptive Statistics**

## **2.1 The Indonesia Family Life Survey**

The Indonesian Family Life Survey (IFLS) is a longitudinal multitopic household survey that is representative of 83% of the population and covers 13 of the 27 provinces in Indonesia. Individuals were tracked through five survey waves spanning 21 years: 1993, 1997, 2000, 2007, and 5
39 28 to collective support mechanisms: none were university educated (though two had completed secopdary school); all 3 were craft-entrepreneurs; none were industry pioneers.

A second gap-filling role is related to function. While some areas appear well covered by private mechanisms, collective agencies, (e.g. NGOs, a local woodworking school, and the industry association ASNINDO) were important channels of information on how to dry wood to prevent warping or cracking, and on how to finish furniture to secure the 'look' preferred on export markets (Table 19).

# Aenuisitio ofTcnolocrical Canability andBuor S.ystems for Garment

While Indonesia's garment industry presently competes on the lower end of the quality spectrum, it is frequently argued that it needs to begin to move upmarket into garments with greater fashion content and more sophisticated design, and with the associated shorter turnaround time. The field survey confirmed that considerable technological improvement has -indeed been occurring. Fourteen of 31 firms -indicated that they have made .significant or major design improvements, with pribumi and non-pribumi not differing much in this regard. Seventeen of 31 claimed significant or major improvements in workplace organization. Nine firms cited significant or major improvements in equipment sophistication.

As with rattan, on the job learning emerges as the most important source of capability over the lifetime of garment firms (Table 20). One striking feature of the garment case is the negligible role of collective support provision. Whereas in rattan collective support played some, albeit modest