rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5209

# **Abstract**

Many investments in infrastructure are built on the belief that they will ineluctably lead to poverty reduction and income generation. This has entailed massive aid-financed projects in roads in developing countries. However, the lack of robust evaluations and a comprehensive theoretical framework could raise questions about current strategies in Sub-Saharan Africa. Using the second Cameroonian national household survey (Enquête Camerounaise Auprès des Ménages II, 2001) and the Cameroon case study, this paper demonstrates that investing uniformly in tarred roads in Africa is likely to have a much lower impact on poverty than expected. Isolation from a tarred road is found to have no direct impact on consumption expenditures in Cameroon. The only impact is an indirect one in the access to labor activities. This paper reasserts the fact that access to roads is only one factor contributing to poverty reduction (and not necessarily the most important in many cases). Considering that increase in non-farming activities is the main driver for poverty reduction in rural Africa, the results contribute to the idea that emphasis on road investments should be given to locations where nonfarming activities could be developed, which does mean that the last mile in rural areas probably should not be a road.

This paper—a product of the Transport Unit, Africa Region—is part of a larger effort in the department to assess the impact of rural roads on development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at graballand@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
# **1. Introduction**

Sustainable development goals (SDGs) represent a significant broadening of the view that development is just about financial poverty reduction towards a more wholistic vision covering a range of priorities including gender equality and quality of life. (UNDP 2017). These priorities have become a focus for international development work and speak directly to the human centered, multi-dimensional vision of economic and social development argued for by Sen (1999), Nussbaum (2000), Martinetti (1994), Kanbur and Basu (2009) and many others who, from a theoretical perspective, emphasize the importance of empowerment, inclusion, and capability expansion in the process of economic growth, which others have stressed (see for instance Klugman et al (2014), Langer et al (2015), Team and Doss (2011)) must also be gender sensitive. Women’s self-help groups in India provide an interesting and concrete example of an intervention that is both well aligned with theoretical ideas about development as a process of capability expansion and contributes to policy priorities of gender empowerment such as SDG 5. There has been some rigorous research on self-help groups but as they continue to evolve in their conception and design, it is important to update the evaluation picture: this paper offers such an update.

One of the earliest studies of self-help groups in India is a paper based on field work conducted in in 2001-03 by Garikipati (2008) who found that loans procured by women were diverted to general household purposes though later work has tended to report less pessimistic findings. Swain and Varghese (2009), for example, found evidence that longer SHG membership and NGO training were both positively associated with the creation of new assets. Subsequently, studies have looked at empowerment in different ways including social, political and psychological terms and reported on a growing 2
incorporates both the direct effect of aging and the correlated income effect: for the reasons given above, the former is believed to dominate.

The figure shown in Box 4.7 also used the fitted (linear) regression line produced by the relationship between population aging and pension spending. The original regression sample, it should be noted, is based on a sample of country/year observations which do not include post-transition period data for the transition socialist economies. The pre and post-1990 data points shown in Box Figure 4.7 are intended to illustrate two points: First, the international fitted line can be used as a yardstick with which to compare the pension spending levels of these demographically diverse countries. Second, the posttransition points demonstrate the inelasticity of pension spending to dramatic declines in GDP. While some of the data have been revised (see for example, Ukraine), the pattern of inelasticity seems robust.

In generating Figure 1I.10, health spending data were taken from Murray, Govindaraj, and Chellaraj (1993) which documented the sources for the 1993 World Development Report, "Investing in Health", to obtain data on national health spending in a large group of countries. The sample is smaller than the one used in the previous two figures because complete and accurate data was available for only sixty-six countries for which pension spending data was also available. In some cases, countries were eliminated by the author because only partial data on health spending were available or the data quality appeared poor. In many cases, the available year for the health spending ratio was different from the year for the pension spending ratio; however, all data for health and pension spending pertained to the 1982-1992 period.

Murray, Govindaraj and Chellaraj (1993) present a variety of regressions which estimate the income elasticity of health spending over a wide cross-section of countries. They also present a survey of the literature on the income elasticity of health spending. Surprisingly, the influence of demographic factors is ignored. Several studies on the demographic determinants of government expenditures have observed a strong correlation between demographic aging and health spending. Other studies show that, not surprisingly, health spending per person tends to rise dramatically by age cohort (eg., Vukovich 1991, Smeeding et.al, 1988). While an extensive analysis of the demographic determinants of public health spending is beyond the scope of this study, the limited evidence suggests that demographic factors may have an independent effect on health spending ratios. The relationship shown in Figure 1.10 begins to illustrate this independent effect.

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education, industry, or other observable characteristics. Firm-specific match quality that arises from heterogeneous firms is one possible source of the inequality that is not explained by observed workers characteristics in the census or household survey data.

# V. Conclusions

Wage inequality in Mexico remains high, but it has been falling since the mid-1990s. The neoclassical models (Heckscher-Ohlin and Stolper-Samuelson theorem) focus on the relative price of skillintensive goods, which rose in Mexico after Mexico joined the GATT but reversed direction after NAFTA went into effect. Inequality follows a similar path. The neoclassical models, however, fail to explain the residual inequality that remains after industry and demographic characteristics (e.g. age and education) are considered. In other words, while falling inequality is consistent with the predictions of neoclassical trade models, recent research has suggested that the neoclassical models that assume homogenous firms miss much of the story.

This paper contributes to this literature in three ways. We first develop a simple model of withinsector heterogeneity to generate predictions of the changes in inequality that might be due to changes in the relative price of tradeable goods. We add firm-level heterogeneity to a simple Salter-Swan model and simulate the changes in within-sector inequality that could emerge even among homogeneous workers.

Our second contribution is to combine micro-data from the Mexican population census and household surveys to evaluate the importance of inequality that is not due to observable characteristics. Consistent with the literature, we find strong and consistent evidence that suggests that age, gender, education, and industry explain a small fraction of total inequality.

Our third contribution is to compare the predicted changes in inequality with actual changes in inequality in Mexico using household survey data. We find that the fall in inequality that occurs since NAFTA is strongly consistent with our model’s predictions of falling inequality that are driving by falling relative prices of tradable goods.

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where ln ~~�~~ G <u>c</u> c � is the natural logarithm of emissions per GDP for industry s and country c, c is the share of informal employment in that industry and country and EEEE c are the control variables. Given the ss<sup>EE</sup> potentially large differences across industries, Equation (8) is also estimated separately as a cross-section xx across countries for a given industry. As mentioned in the model section, implies that emissions per worker are larger in the informal sector than in the formal sector and implies the opposite. If cannot be rejected, this implies αα> 0 that the emissions per worker are the same in both sectors. As the formal sector might produce very αα< 0 αα= 0 different goods from the informal sector, it cannot be determined whether the production of a specific good generates more emissions in either sector. Also, only if it is found that , there is a direct implication for the informality based on GDP. αα> 0

# **4. Data and Descriptive Statistics**

The study uses data from two different sources: the International Income Distribution Data Set (I2D2) for informal employment shares and the Global Trade Analysis Project (GTAP) for other sector indicators: value added, capital per worker, share of value added in total GDP, and the share of imported intermediate goods in the total intermediate goods (Aguiar et al., 2019). The I2D2 data set compiled by the World Bank is a global harmonized labor force survey database that provides comparable labor market indicators such as employment status, wages, and hours worked at both household and individual levels across countries and time. It contains 600 surveys for 120 countries collected at different points of time. For many countries, especially low-income and fragile or conflict-affected states, it has data only for a single year. The GTAP 10 database combines detailed data on bilateral trade, transport, and protection characterizing economic linkages among regions, together with individual country input-output (I-O) databases which account for inter-sectoral linkages within regions for the most recent year available (2014).

The I2D2 database is used to compute informal employment shares in total employment in each of nine sectors, comprising agriculture, mining, manufacturing, transport and communication, finance, construction, trade, utilities, and other services. According to the operational definition used by the International Labor Organization, informal workers include all (i) contributing family workers, (ii) informal employers, own-account workers, members of cooperatives if they own an informal economic unit, and (iii) employees whose employer has no contribution to social security and who are not entitled to and do 9
# **Empirical Results**

# **_Impact of Port Efficiency on Maritime Transport Costs_**

Figure 2 displays the results of an output-oriented intertemporal DEA. In 2000 the average efficiency score for the 35 ports in the dataset was 0.37 (on a 0–1 scale). By 2007, the end of the period of analysis, average port efficiency had risen to 0.49. Average scores for the period as a whole indicate that ports in East Asia and Pacific (0.42), excluding China and Singapore, and South Asia (0.43) were much more efficient than ports in Africa (0.35), although there is room for improvement in all regions.

**Figure 2 Technical efficiency of ports, by region, 2000–07**

<!-- Start of picture text -->
1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Africa East Asia and Pacific South Asia<br>2000 2001 2002 2003 2004 2005 2006 2007<br>Efficiency score<br><!-- End of picture text -->

_Note_ : Efficiency scores range from 0 (most inefficient) to 1 (most efficient).

Port efficiency measures from the time-varying relationship between the use of port assets and port throughput are considered in the estimation of the determinants of maritime transport costs (equation 5). Table 5 presents the estimation of the pricing formula that links the cost of transporting goods from origin to the destination country to a marginal cost (columns 1 and 2). Column 3 adds the markup term. We control for possible biases that might emerge from endogeneity or misspecification. We use port throughput, terminal area, berth length, and the number of cranes as instruments and the standard Hausman test for endogeneity, the Hansen _J_ - statistic for overidentification of instruments, and the identification test of Craig-Donald. All the tests favor ordinary least squares over an instrumental variables estimation (results available upon request). Alternating or combining instruments does not improve the overall test statistics, primarily the model fit.

Censoring observations and omitting outliers leaves 5,906 observations. We include only countries and commodities with observations available for the entire sample period (2000–07). The within-transformation is used to estimate equation (5). The fixed-effects model controls for 14
**Table 6: Interactions with industry variables: extensive and intensive margins**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Pro|bability to exp|ort||Log of export||
|Water|-0.0535***|0.0305|-0.00727|-0.356***|-0.111|-0.161|
||(0.0131)|(0.0217)|(0.0167)|(0.110)|(0.269)|(0.173)|
|Water*GVC|-0.0192*<br>(0.0104)|||-0.597***<br>(0.0864)|||
|Water*Rauch classification||-0.0518***<br>(0.00839)|||-0.171<br>(0.124)||
|Water*differentiated|||-0.0630***<br>(0.0104)|||-0.316**<br>(0.133)|
|Tariff (log)|-0.552***|-0.666***|-0.645***|-3.608***|-3.640***|-3.685***|
||(0.0540)|(0.0558)|(0.0556)|(0.208)|(0.211)|(0.212)|
|=1 if parts & components|0.101***<br>(0.00127)|||0.0583***<br>(0.0108)|||
|Rauch classification||0.0614***<br>(0.00101)|||-0.186***<br>(0.0142)||
|=1 if differentiated|||0.0719***<br>(0.00113)|||-0.190***<br>(0.0150)|
|Observations|9,767,065|9,243,789|9,243,789|2,945,477|2,776,219|2,776,219|
|R-squared|0.380|0.381|0.380|0.340|0.342|0.342|
|Importer-Industry-2dig FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Industry-2dig FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

Robust SE in brackets clustered by importer-product. *** p<0.01, ** p<0.05, * p<0.1 15
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" of surveys from fifteen of these countries (Table 1).8 The pooled sample includes households on four continents in both low- and middle-income countries. The fifteen surveys were administered between 1988 and 1997.

This multi-country LSMS data set is unique in five important respects. First, it enables us to look at multiple infrastructure services for the same household. Second, because the LSMS surveys are primarily designed to measure households' economic well-being (i.e., living standard), the data set arguably contains the best information available on household expenditures, consumption, and income available anywhere for multiple developing countries. This enables us to clearly identify the poorest households in our sample and their use of infrastructure services. Third, the LSMS surveys generally utilize similar survey administration protocols, quality-control procedures, and survey questions across countries. Fourth, the LSMS surveys have been implemented in many developing countries; this enables us to construct a global perspective on infrastructure coverage and the poor that is not possible with a survey in a single country. It is important to emphasize, however, that the households in our sample from these fifteen countries are not in any sense a random sample of households in the developing world. Fifth, some LSMS households surveys were accompanied by community surveys that gathered information about the availability of infrastructure (and other) services in the areas where sample households live. The community surveys enable us to distinguish between (1) households that do not have infrastructure services and could not have such services because they do not have access in their neighborhoods; and (2) households that do not have infrastructure services, but do have access and could have chosen to have such services if they had the resources and desire to do so.

The fifteen LSMS surveys in the multi-country data set include roughly similar questions, but the answer categories and exact question wording are often different from country to country. For 8 These fifteen LSMS surveys were chosen because the data and supporting documentation were readily available and because these surveys contain all or most of the infrastructure and household consumption variables of interest. When more 4
and 7 in Cˆote d’Ivoire, which are much larger than the corresponding values in India (5.0), China (4.9) and the U.S. (3.3). The results offers a _prima facie_ evidence that resources are severely misallocated in our sample countries. A plausible explanation for our findings is that policies and institutions in our sample countries may prevent the more productive firms from eliminating the less productive ones.

<!-- Start of picture text --> Log TFPQ Log TFPR<br>−10 −5 0 5 −5 0 5<br>Cote d’Ivoire Ethiopia Cote d’Ivoire Ethiopia<br>Ghana Kenya Ghana Kenya<br>.4 .8<br>.3 .6<br>.2 .4<br>.1 .2<br>0 0<br><!-- End of picture text --> Figure 3: Distribution of TFPR and TFPQ Table 2: Dispersion of TFPR and TFPQ

||Cˆote d|’Ivoire|Ke|nya|Gha|na|Ethi|opia|Ind|ia|Ch|ina|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||TFPR|TFPQ|TFPR|TFPQ|TFPR|TFPQ|TFPR|TFPQ|TFPR|TFPQ|TFPR|TFPQ|
||2003-12|2003-12|2010|2010|2003|2003|2011|2011|1994|1994|2005|2005|
|S.D|0.65|1.24|1.52|2.41|0.95|1.75|0.78|1.30|0.67|1.23|0.63|0.95|
|75–25|0.88|1.74|1.99|3.34|1.43|2.61|1.26|1.94|0.81|1.60|0.82|1.28|
|90–10|1.99|3.25|3.94|5.67|2.89|4.47|2.56|3.67|1.60|3.11|1.59|2.44|
|Cov (TFPQ,TFPR)|0.7|0|0.|85|0.6|9|0.|74|||||
|Reg.Coef|0.4|2|0.|52|0.4|4|0.|53|||||
|N|4146|4146|757|757|1151|1151|4012|4012|41,006|41,006|211,304|211,304|

Note: Log(TFPR) and Log(TFPQ) are demeaned by industry-specific average. Industries are weighted by their value-added shares. The statistics for Cˆote d’Ivoire are calculated by taking the average for the years 2003-2012. The statistics for India and China are taken from Hsieh and Klenow (2009). We compute these statistics for India using the Prowess database and obtain similar values as in Hsieh and Klenow (2009).

# **6.2 Calculating Counterfactual Productivity**

Next, we use our estimates to perform counterfactual liberalization experiments. Specifically, we assess the potential productivity gains associated with equalizing total factor revenue productivity (TFPR) across the existing set of firms in each 4-digit industry. The results of this liberalization experiment are reported in Table 3. The first column of Table 3 indicates that the potential TFP gains from better allocation of resources are much higher in Kenyan manufacturing sector compared to the corresponding values in Ethiopia, Ghana, and Cˆote d’Ivoire. More specifically, fully equalizing total factor revenue productivity (TFPR) across firms in each industry, could increase total productivity by 31.4 percent in Cˆote d’Ivoire, 66.6 percent in Ethiopia, 75.5 percent in Ghana and 162.6 percent in Kenya.<sup>12</sup>

> 12Note that the gains from reallocation increase with _σ_ . As Hsieh and Klenow (2009) point out, when _σ_ is larger, the TFPR gaps are closed more slowly in response to a reallocation of resources from low to high TFPR establishments, leading to a higher gains from reallocation.

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41 Table 15: Impact of gender of household head on electricity‐related variables

||Expen<br>dum|diture<br>my<sup>a</sup>|Electricity|dummy<sup>b</sup>|Grid d|ummy<sup>c</sup>|Log expe|nditure<sup>d</sup>|
|---|---|---|---|---|---|---|---|---|
||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|Angola|—|—|—|—|—|—|—|24|
|Botswana|—|0.07|—|0.09|—|0.08|—|—|
|Ethiopia|0.10|—|—|—|—|—|—|—|
|Ghana|‐0.04|0.11|‐0.02|0.11|‐0.02|0.11|‐10|‐10|
|Madagascar|0.06|—|0.09|—|0.08|—|—|—|
|Mali|—|—|—|—|0.09|0.10|—|—|
|Niger|—|—|—|—|0.12|—|—|—|
|Nigeria|—|0.14|—|0.10|—|0.17|—|—|
|Rwanda|—|—|—|—|—|—|14|—|
|São Tomé and Príncipe|0.08|—|—|—|n.d.|n.d.|—|—|
|Senegal|—|0.24|—|0.23|—|0.23|—|35|
|Sierra Leone|—|‐0.003|—|—|—|‐0.003|—|—|
|South Africa|0.10|—|n.a.|n.a.|0.02|—|—|—|
|Togo|‐0.06|—|‐0.07|—|‐0.06|—|—|—|
|Zambia|0.04|—|—|—|—|‐0.01|—|—|
|Median|0.06|0.09|‐0.02|0.11|0.05|0.10|NA|24|

_Source_ : World Bank staff calculations.

a. 1 if spending on electricity is positive, zero otherwise.

b. 1 for electricity (grid as well as off‐grid, such as solar and diesel generators) as the primary source of energy for lighting or cooking, 0 otherwise.

c. 1 for households connected to the grid or electricity (excluding off‐grid, such as solar and diesel generators) as the primary source of energy for lighting or cooking, 0 otherwise; see footnotes d and e and associated results in Table A.21 for more detail.

d. Logarithm of spending on electricity retaining all households with positive expenditure.

The results for the dummy represent the increase in the probability of the dependent variable being 1 as result of changing the gender dummy from 0 (male) to 1 (female). The results for log expenditure on electricity represent the percent increase in expenditure on electricity (not logarithm) as a result of changing the gender dummy from 0 to 1. — = statistically insignificant at 5; n.d. = no distinction made for different sources of electricity; n.a. = not available (data not collected); NA = not applicable.

The surveys in Malawi, Niger, and Nigeria enabled isolation of households who lived in communities connected to the grid. The probit regression for grid connection for households that could have connected was performed by taking these households as a single group as well as by separating urban from rural households. Statistically significant results were obtained when urban and rural households were combined only in Niger—female‐headed households were more likely to connect—and separating urban and rural households similarly returned statistically significant results only in urban Niger—again female‐headed households were more likely to connect.

Given the data limitations, these findings should be viewed only as preliminary, calling for more detailed investigation. A recent detailed analysis of the gender of the head of household in Africa since the 1990s suggests that the share of female‐headed households has been rising, while that poverty has been declining faster among female‐headed households than among their male counterparts (Milazzo and van de Walle 2015). These changing dynamics can inform future research and affect policy direction.