rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 The Gains From Agricultural Tariff Liberalization

|Country|Aggregate<br>Gains|St.Dev.<br>Gains|Consumption<br>Efects|Income<br>Efects|Pro-Poor<br>Bias|Gains<br>Poor|Gains<br>Rich|
|---|---|---|---|---|---|---|---|
|Developing world|2.50|1.01|4.64|-2.15|-0.44|2.20|2.64|
|Armenia|3.12|0.45|3.70|-0.58|-0.87|2.79|3.66|
|Azerbaijan|1.73|0.83|4.37|-2.64|1.64|2.59|0.95|
|Bangladesh|0.64|0.94|4.87|-4.23|-2.15|-0.37|1.78|
|Benin|1.50|1.81|4.25|-2.75|-4.75|-0.78|3.97|
|Bhutan|6.53|1.56|10.61|-4.08|1.80|6.51|4.71|
|Bolivia|3.56|1.11|5.01|-1.45|-0.00|2.70|2.70|
|Burkina Faso|0.72|0.64|3.79|-3.08|-0.09|0.73|0.83|
|Burundi|-3.23|2.68|7.82|-11.05|-4.80|-4.59|0.21|
|Cambodia|0.17|1.25|4.95|-4.79|0.11|0.47|0.36|
|Cameroon|6.25|1.02|9.60|-3.35|-1.38|5.42|6.80|
|Central African Republic|3.05|0.99|7.29|-4.24|1.45|3.57|2.13|
|Comoros|0.46|0.64|1.59|-1.13|-1.41|-0.35|1.06|
|Cˆote d’Ivoire|1.66|1.53|4.62|-2.97|-3.75|-0.76|2.99|
|Ecuador|5.05|0.97|6.98|-1.93|2.35|5.90|3.56|
|Egypt, Arab Rep.|3.20|0.63|4.95|-1.75|-1.31|2.43|3.74|
|Ethiopia|0.41|0.73|3.68|-3.26|-0.20|0.63|0.83|
|Gambia, The|4.51|2.38|6.08|-1.57|-5.72|1.07|6.79|
|Georgia|1.49|0.27|2.16|-0.68|-0.27|1.25|1.53|
|Ghana|-0.50|0.52|1.19|-1.69|-0.84|-1.02|-0.19|
|Guatemala|2.82|0.33|3.78|-0.96|-0.19|2.57|2.76|
|Guinea|2.56|0.85|4.93|-2.38|-1.63|1.82|3.45|
|Guinea-Bissau|1.47|2.06|5.89|-4.42|-4.62|-1.69|2.93|
|Indonesia|2.88|0.38|3.13|-0.25|0.63|3.05|2.42|
|Iraq|1.33|0.25|1.79|-0.46|0.57|1.48|0.91|
|Jordan|6.55|1.06|6.87|-0.32|2.64|7.58|4.94|
|Kenya|2.83|1.25|7.64|-4.82|-2.24|1.61|3.85|
|Kyrgyz Republic|1.10|0.30|1.82|-0.72|-0.21|1.01|1.22|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of country’s own agricultural import tariffs.

29
brief review of some of the recent literature on these subjects, focusing primarily on services that are inputs into production.

The plan of the paper is as follows. Section 1 briefly discusses the role of services in economic growth. Section 2 presents some stylized facts regarding global trends in trade and foreign direct investment (FDI) in services, and the policies affecting trade. Section 3 reviews a number of channels through which openness to trade in services may increase productivity of an economy and summarizes the findings of some of the empirical literature that focuses on these channels, at the level of the economy as a whole, industries and the firm. Section 4 discusses a number of the policy implications suggested by the extant research. Section 5 concludes.

# **1. Services and Growth**

Economic theory postulates that aggregate growth is a function of increases in the quantity and productivity of capital and labor inputs, with long run (steady state) growth being driven by technological progress. Growth theory accords no special role to services activities, with the exception of financial services. The seminal work here is Goldsmith (1969), which stressed the role of financial services in channeling investment funds to their most productive uses, thereby promoting growth of output and incomes.<sup>3</sup> Subsequent work has shown that financial services can affect growth through enhanced capital accumulation and/or technical innovation. In a survey of the relevant literature, Levine (1997) identifies five major functions that financial systems perform in reducing transactions costs and improving the allocation of real resources: facilitating the trading of risk, allocating capital to productive uses, monitoring managers, mobilizing savings through the use of innovative financial instruments and easing the exchange of goods and services.<sup>4</sup> Intuitively, other services activities also have a powerful influence on growth. Low cost and high quality telecommunications will generate economy-wide benefits, as the communications network is a transport mechanism for information services and other

> 3 Goldsmith used the ratio of the value of financial intermediary assets to GNP to gauge financial performance, and found a “rough parallelism” between economic growth and financial development. 4 Calderon and Liu (2003) confirm the direction of causation between financial development and economic growth, using data on 109 countries for 1960-1994.

2
calculation we include the gross of tax returns to private capital and the rates of return to domestic savings (rs) and foreign savings (rf) (see Table 6.1). Despite foreign savings forming a relatively small share of total savings, they have a high price elasticity, and hence, form a relatively larger share of capital funds supporting marginal investments. The shares of investments and domestic and foreign savings over GDP in percentage terms have been calculated from the national accounts<sup>14</sup> .

**Table 6.1 Investment, savings, price elasticities, and weights for the EOCK estimation, 2003–2011**

|_Weights f_|_or the comput_<br>Private<br>GFKF|_ation of the_|_EOCK_|Elasticities|||Weights (f1–f<br>Inv.|3)<br>DS|FS|
|---|---|---|---|---|---|---|---|---|---|
||+ inventory<br>investment/<br>GDP|Domestic<br>savings/<br>GDP|Foreign<br>savings/<br>GDP|**η**|**Eds**|**Efs**|Investment|Domestic<br>savings|Foreign<br>savings|
|||||Investment|Dom. sav.|For. sav.|**f1**|**f2**|**f3**|
|2003|19.1%|22.9%|1.1%|−1|0.3|3|64.9%|23.4%|11.7%|
|2004|20.5%|24.9%|0.9%|−1|0.3|3|66.9%|24.3%|8.7%|
|2005|19.8%|24.4%|1.0%|−1|0.3|3|65.6%|24.2%|10.2%|
|2006|21.8%|26.2%|0.8%|−1|0.3|3|68.2%|24.5%|7.2%|
|2007|21.9%|26.5%|1.4%|−1|0.3|3|64.4%|23.4%|12.2%|
|2008|21.3%|26.9%|1.8%|−1|0.3|3|61.4%|23.3%|15.3%|
|2009|17.7%|23.8%|0.8%|−1|0.3|3|64.6%|26.1%|9.3%|
|2010|18.0%|23.9%|0.2%|−1|0.3|3|69.5%|27.6%|2.8%|
|2011|19.8%|25.1%|0.9%|−1|0.3|3|66.3%|25.1%|8.6%|
|Average|<br>**20.0%**|**24.9%**|**1.0%**||||**65.8%**|**24.7%**|**9.6%**|

_Source:_ Authors’ calculations using SHCP data.

**In addition, for the calculation of the EOCK below, we report the estimated rates on investment, the net of tax real rates on domestic savings, as well as the marginal opportunity cost of foreign savings.** For domestic savings, the economic opportunity cost of savings (rs) is taken as the return to saving reduced by the taxes on the income earned on saving at an effective tax rate of 10 percent. For the marginal opportunity cost of foreign savings in Table 6.2, we use the formula: MCFS = rf * (1−ts) * (1+k/Efs), where rf is the weighted average interest rate on savings in real terms (from Table 5); ts is the tax rate on savings; k is the share of foreign capital that is responsive to changes in interest rates; and Efs is the price elasticity of supply of foreign savings. We adopt the following values for our estimation: ts = 10 percent; k = 0.90; and Efs = 3.0.

> 13 The authors reviewed the literature on estimates of the price elasticities of investments and savings used in the analysis of the EOCK for other countries. These are documented in the World Bank report “Mexico: Estimation of the Economic Opportunity Cost of Capital for Public Investment Projects.”

> 14 Data on Mexican national accounts were kindly provided by SHCP.

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Policy Research Working Paper 5414

# **Abstract**

Child labor statistics are critical for assessing the extent and nature of child labor activities in developing countries. In practice, widespread variation exists in how child labor is measured. Questionnaire modules vary across countries and within countries over time along several dimensions, including respondent type and the structure of the questionnaire. Little is known about the effect of these differences on child labor statistics. This paper presents the results from a randomized survey experiment in Tanzania focusing on two survey aspects: different questionnaire design to classify children work and proxy response versus self-reporting. Use of a short module compared with a more detailed questionnaire has a statistically significant effect, especially on child labor force participation rates, and, to a lesser extent, on working hours. Proxy reports do not differ significantly from a child’s self-report. Further analysis demonstrates that survey design choices affect the coefficient estimates of some determinants of child labor in a child labor supply equation. The results suggest that low-cost changes to questionnaire design to clarify the concept of work for respondents can improve the data collected.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to improve household survey methods. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at kbeegle@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
combined cycle gas turbine (CC), oil or gas steam turbine (ST), diesel generator (DG), hydro (hydro), nuclear (nuclear), coal-fueled steam turbine (coal), wind farm (wind), solar photovoltaic (PV) and concentrated solar power (CSP).

# **4.1. Existing and Planned Generation Capacity**

Table 1 presents electricity generation capacities installed by 2018 in each economy by type of generation technology. As of 2018, the MENA region has almost 300 GW of electricity generation capacity, of which two-thirds is natural gas-based generation (gas turbine and combined cycle). The higher share of natural gas-based generation capacity reflects the fact that natural gas is locally available and gas-based generation is the cheapest source to produce electricity.

**<u>Table 1. Installed Capacity in MENA Countries as of 2018 (MW)</u>**

|**Economy**|**GT**|**CC**|**ST**|**DG**|**Hydro**|**Coal**|**Wind**|**PV**|**CSP**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|
|Algeria|6,907|10,368|2,435|-|276|-|10|432|150|**20,579**|
|Bahrain|3,096|700|125|-|-|-|1|10|-|**3,932**|
|Egypt, Arab<br>Rep.|12,647|7,845|14,799|-|2,832|-|747|50|20|**38,939**|
|Iraq|4,952|15,079|5,995|1,769|2,524|-|-|-|-|**30,320**|
|Jordan|2,837|188|242|810|-|-|287|460|-|**4,824**|
|Kuwait|6,496|2,925|9,354|-|-|-|10|10|50|**18,845**|
|Lebanon|930|140|1,694|-|253|-|-|-|-|**3,017**|
|Libya|3,995|4,638|1,190|-|-|-|28|-|-|**9,851**|
|Morocco|834|1,230|600|292|1,770|2,895|1,018|-|180|**8,819**|
|Oman|7,099|2,352|-|149|-|-|-|-|-|**9,600**|
|West Bank<br>and Gaza|1,140|-|-|15|-|-|-|-|-|**1,155**|
|Qatar|7,470|4,408|-|-|-|-|-|-|-|**11,878**|
|Saudi Arabia|21,436|31,841|31,226|270|-|-|-|-|-|**84,773**|
|Sudan|458|220|906|535|2,250|110|-|-|-|**4,478**|
|Syrian Arab<br>Republic|2,800|967|3,324|-|1,571|-|-|-|-|**8,662**|
|Tunisia|2,559|2,218|1,080|-|62|-|208|10|-|**6,138**|
|United Arab<br>Emirates|21,644|6,944|2,460|31|-|-|-|373|125|**31,577**|
|Yemen,Rep.|-|407|495|590|-|-|-|-|-|**1,492**|
|Total|**107,300**|**92,470**|**75,925**|**4,461**|**11,539**|**3,005**|**2,309**|**1,345**|**525**|**298,878**|

Note: CC = Combined Cycle; GT = Gas Turbine; ST = Steam Turbine; DG = Diesel Generator; Hydro = Hydroelectric; PV = Solar Photovoltaic; CSP = Concentrated Solar Power; UAE = United Arab Emirates Source: World Bank (2020).

10
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Box 1. The hidden costs of utilities**

A monetary value can be attributed to observable operational inefficiencies—mispricing, unaccounted-for losses, and undercollection of bills, to mention three of the most conspicuous—by using the opportunity costs of these inefficiencies: tariffs for uncollected bills and production costs for mispricing and unaccounted-for losses. These costs are considered hidden since they are not explicitly captured by the financial flows of the operator. Hidden costs are calculated by comparing a specific inefficiency against the value of that operational parameter in a wellfunctioning utility (or the respective engineering norm) and multiplying the difference by the opportunity costs of the operational inefficiency.

_Source:_ Briceño-Garmendia, Smits, and Foster (2009)

**Figure 12. The hidden costs of the urban water utility SDE**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>-<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Under-collection of billings Under pricing Distributional losses<br>Percentage of revenues<br><!-- End of picture text --> _Source:_ Derived from Banerjee and others (2008b).

The leading contributor to the hidden costs in Senegal’s water sector is the misalignment of tariffs and costs, which is commonly seen as the main source of inefficiency in other African utilities. Fortunately, SDE has been able to progressively align tariffs with costs; nowadays, tariffs recover not only operational costs but even the capital investment in its entirety! Costs due to losses or unaccountedfor water are minimal. Not surprisingly, Senegal’s SDE is among the most efficient of comparable utilities (figure 13).

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Policy Research Working Paper 7927

# **Abstract**

Labor market dynamics have played a significant role in the remarkable social gains experienced across Latin America over the recent past. Assessing the quality of employment, beyond the perspective of income, to include other fundamental aspects of jobs—such as whether jobs are secure, provide benefits, or allow human capital accumulation— can shed light on the sustainability of these achievements. This is particularly pertinent given the region’s current economic slowdown. Using harmonized data for 17 countries in Latin America, this paper connects the role of job quality with the recent process of inclusive growth across the region, and particularly with how individuals worked their way out of poverty. The paper first proposes a multi-dimensional measure for job quality, and then uses this measure to compare job quality across countries and over time. The paper also studies some of the correlates of job quality and the relationship between job quality and poverty. The main finding is that job quality across the region began to increase since 2004, coupled with the favorable terms of trade brought to the region by the commodities super cycle of the 2000s. The best predictors of job quality are age, gender, education, formal employment, and union membership. Both health and retirement benefits are the dimensions of job quality that are best correlated with not living in poverty.

This paper is a product of the Poverty and Equity Global Practice Group. 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 at pbrummund@cba.ua.edu and crodriguezc@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
We carry out the data analysis using decadal growth rates. We estimate the following equation, which is essentially a lagged difference version of the levels regression equation (1) and includes initial income to control for convergence effects: where Xc is a set of control variables from equation 1 ( _t=time period ; k= number of lags_ ). Equation (3) amounts to regressing growth on initial income and changes in the set of explanatory variables.

Ideally, we would like to estimate the growth regressions over three decades, as in Dollar and Kraay (2002). However, to do this we would need the one-quarter (or one-third) most regulated economies in each period, which is not available. As a result, we use only average growth the 1990s and continue to use the regulation data from Doing Business. This approach is valid, assuming that regulations are slow to change and have been roughly constant since 1990. We add the regulation index, a dummy variable for the most heavily regulated countries, and an interactive term between trade and the regulation dummy. Upon differencing over time, given the assumption of slow changes in regulations over time and a time invariant dummy variable, we end up with only the interactive term between the change in trade over time and the dummy variable as an additional explanatory variable to the decadal growth regressions.<sup>14</sup> We extend the decadal panel dataset of Dollar and Kraay (2002), omitting the countries for which no data are available on regulations, legal origin and institutional measures, and considering only the 1990s. We end up with a maximum of 98 observations over the 1990s. The list of countries is provided in the Appendix, Table _A2_ .

> 14 To the extent that the worst regulated countries are slow to grow, as a robustness check, we tried including the regulation dummy in the regression, it was very close to zero and never significant (not reported).

19
fall below the extreme poverty line. Given Djibouti’s already high rates of poverty incidence, the diagram illustrates the detrimental impact of a nominal devaluation exchange on the poor in the absence of social transfers that would offset the real income loss experienced by households hurt by the currency devaluation.

**Figure 9: Poverty Impact after Nominal Exchange Rate Devaluation**

<!-- Start of picture text --> Extreme<br>Poverty Line National<br>Poverty Line<br>20000 80000 300000 1000000<br>density: Adult-equivalent per-capita household expenditure, 1996<br>density after 30% nominal devaluation and 60% pass-through<br>density after 60% nominal devaluation and 60% pass-through<br>density after 100% nominal devaluation and 60% pass-through<br>Source: Authors' calculation based on EDAMS 1996<br><!-- End of picture text --> The negative poverty impact of a currency devaluation varies according to the location of the household, the gender of the head of the household, the income group to which the household belongs, and to the household’s main source of labor income: • **_Rural households would experience larger declines in extreme poverty relative to urban households_** .

• **_17.5 percent of households in the lowest consumption quartile would fall below the extreme poverty line and about 20.2 percent of middle-income households in the 3_**<sup>**_rd_**</sup> **_quartile will drop below the national poverty line._** Looking by income groups, 20.4 percent of middleincome households in the 3<sup>rd</sup> quartile where commerce as the main wage income sources fall below the national poverty line, followed by 18.4 percent of households in the same income group whose main source of wage income is in public administration.

• **_The highest increase in extreme poverty would be experienced by households whose main wage source is from commerce (6.5 percentage points) followed by public administration (6.1 percentage points)_** .Tables A.2b and A.2d of the appendix provide the outcomes on headcount poverty and the poverty gap by households whose main source<sup>32</sup> of wage income is derived from the four largest sectors: public administration, services (excluding financial), commerce and transport and telecommunications. The incidence of extreme poverty is predicted to rise the most, by 7.6 percentage points for households whose main wage income source is from services. In terms of the national poverty line, the highest increase is from households whose main wage source is from commerce (6.5 percentage points) followed by public administration (6.1 percentage points).

> 32 The sector which is considered the main source of wage income contributes over 90 percent to the overall wage income for a household.

20
the percent increase in column 4 - the share that did not care about the targeting of humanitarian assistance as long as they themselves received assistance- was especially large. Essentially nobody in the mobile phone survey admitted to having this preference. But a non-negligible share - 9.9 percent- admitted to it in the internet survey, which represents a 990 percent increase. Thus, even in the case where the percentage point difference between the surveys was one of the smallest among sensitive questions, the percent increase was the largest.

Third, the difference between the modalities in the sensitive questions was significantly larger than the difference in non-sensitive questions. The average difference between the modalities for sensitive questions in columns (1)-(4) was 3.4 times greater than the average of the absolute difference in columns (5)-(8)<sup>23</sup> ; the largest percentage point difference in the sensitive questions (column 1) was over twice as large as the largest difference for the non-sensitive questions (column 6); the smallest difference in the sensitive questions was still 9.9 percentage points (columns 3 and 4) while the smallest difference for the non-sensitive questions was essentially zero (columns 5 and 8); and the differences in the sensitive questions were all statistically significant at the 1 percent level, while two of the four differences in the non-sensitive questions were not statistically different from zero at conventional significance levels and were very low in magnitude.<sup>24</sup> And fourth, despite the strong average difference reported across sensitive questions in Table 4, the results also illustrate that the difference between modalities was not equal across the sensitive questions. The largest difference between the modalities was in the question that was potentially sensitive due to social desirability bias and the possibility of the respondent’s safety being threatened- the question asking respondents to name the party most responsible for the violence they witnessed. The point estimate was almost twice as large as the sensitive question with the second largest difference between the modalities- the question on whether violence could ever be morally justified; and the 95 percent confidence interval of the estimate of the difference in naming the party

> 23The average of columns (1)-(4) was 16.2 percentage points; the average of the absolute difference in columns (5)-(8) was 4.75 percentage points.

> 24The larger difference in sensitive than non-sensitive questions between the modalities survives more formal comparisons as well. See Appendix 1.

18