rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4.5. Comparison between Ghana and Tanzania**

As mentioned earlier, this study follows the design of a survey experiment conducted in the Mara region of Tanzania in 2014 (Arthi et al 2018). The main difference in design of the two experiments is related to the way information was captured for the recall group. In the Tanzania case, the recall households were interviewed only once at the end of the season. In Ghana, the recall households were visited at the beginning of the agricultural season for the baseline survey, which collected an initial listing of plots and household workers, and again at the end of the season. The Tanzania approach allows for the comparison of the treatment group with what would be a ‘typical’ cross-sectional household survey that is conducted at the end of the agricultural season without any information from the start or during the season. Conversely, the Ghana approach resembles a panel survey and enables us to control for differences in baseline characteristics and disentangle the possible sources of recall bias by allowing more rigorous estimates of listing bias.

**Table 11** summarizes the results of both studies. Columns 3 and 4 replicate **Table 2** , and columns 1 and 2 are the equivalent numbers for Tanzania. Weekly visit households in Tanzania report 39.5 hours of farm labor per person per plot, which is significantly less than in Ghana (106.4 hours). However, this difference can be easily explained by differences in plot size, with plots being much smaller in Tanzania (0.33 ha) than in Ghana (1.19). What is much more striking is the large difference in (unconditional) recall bias – 18 percent in Ghana, versus 207 percent in Tanzania. What would explain such large differences in the magnitude of recall bias?

**Table 11: Farm labor per person per plot, Ghana and Tanzania**

||**Tanzania**<br>**Weekly**<br>*
Distances between the values of cardinally measurable variables are meaningful; whereas ordinal variables merely consist of ordered categories. 1 When numeral scales are assigned to these categories according to their order, the implied cardinal distances are hard to interpret. Moreover, poverty orderings are inconveniently sensitive to changes in the choice of these scales. Let us consider a simple example with a five-category ordinal variable, where the four worst categories reflect deprivations. Suppose the population distributions across the five ordered categories in two countries are _A_ = (0 _._ 1 _,_ 0 _._ 3 _,_ 0 _._ 2 _,_ 0 _._ 2 _,_ 0 _._ 2) and _B_ = (0 _._ 2 _,_ 0 _._ 1 _,_ 0 _._ 1 _,_ 0 _._ 2 _,_ 0 _._ 4), respectively. If we use the numeral scales of (1 _,_ 2 _,_ 3 _,_ 4 _,_ 5) for the five categories and use the well-known poverty gap measure with the numeral scale of the fourth category (i.e., the second best category) as the poverty line, then the former society (A) reflects higher poverty than the latter (B).<sup>2</sup> However, if we simply change the numeral scales to (1 _,_ 5 _,_ 6 _,_ 7 _,_ 8), then the poverty ordering reverses.

Yet, the practice of using ordinal variables has been on the rise, in both developed and developing countries alike, due to the recent surge of interest in studying deprivation in non-monetary indicators, which are often ordinal in nature (e.g. access to basic facilities

> 1Based on the classification of measurement scales by Stevens (1946), whenever numeral scales are assigned to different ordered categories of an ordinal variable according to the orders or ranks
It may be the entire maturity of the loan or bond guaranteed or a shorter period for fiscal planning.

> 25 In Moody’s default frequency tables, an entity defaulting in year one is no longer represented in the sample of entities in year two (i.e. it can only default once). However, in the context of South Africa, entities may experience a credit event in year one (and the government steps in to make a periodic debt service payment) and may or may not experience a credit event in year two (see Chapter 2.a. for a discussion of the definition of a credit event).

> 26 Consistent with its definition of a credit event.

18
# **TABLE OF CONTENTS**

|1. INTRODUCTION ...............................................|1|
|---|---|
|2. NATIONAL CONSUMER SURVEY ...............................................|3|
|3. THE MEASUREMENT OF WELFARE AND POVERTY .........................|......................<br>4|
|_3.1 Household Expenditure ..............................................._|_4_|
|_3.2 Price Index .._|_5_|
|_3.3 Poverty Lines in Nigeria ..............................................._|_6_|
|_3.4 Basic Needs Approach to Poverty ................................................_|_7_|
|_3.5 Poverty Line
problems for men, chronic gastro-intestinal (CGI) problems (particularly for men), chest pains, wearing glasses (for men) and “other problems” (men and women). Amongst the diseases recorded, the strongest predictors of overall _SAH_ are heart attacks and high blood pressure.

We also tested two extended versions of these regressions. In the first, we included controls for socio-economic variables that could influence the structure of reporting biases in _SAH_ or pick up omitted health attributes. We added controls for household expenditures, personal incomes, education, workforce participation, demographics and regional effects. The results are also given in Table 3. The controls do not add much to the explained variance. However, a number of the controls were highly significant, notably education and the regional effects; we return to discuss these later. We find that the coefficients on the specific health attributes tend to increase when we add the controls. This is most dramatic for the cluster of variables, “had surgery last year” through to “other problems” (Table 3). Adding the controls makes the coefficient on past surgery more negative, and it becomes significant at the 1% level. The controls have a similar effect on the coefficients on “wearing a hearing aid” which only diminishes _SAH_ significantly when the controls are added. By contrast, the coefficients on “feeling chest pain” and wearing glasses (for men) become insignificant when the controls are added. The overall pattern is suggestive of adaptive behavior, whereby people with worse objective health status tend to adapt by overstating their subjective health. Thus the resulting negative correlation between objective health and the error term in the _SAH_ regressions without controls tend to bias downward the regression coefficients.

The second extension added health attributes from 2000 using the panel, to test for lagged effects consistent with adaptation. (Recall that we cannot do this for the physical activity data, which were only asked of pensioners in 2000.) This means a lower sample size, with 2,600
Purchasing Power Parity (PPP) conversion from the World Bank.<sup>6</sup> In addition to labor productivity, the survey provides information on several firm and manager characteristics, including exporting and multinational status, management practices, firms’ sector and region, and managers’ education and experience in the sector (see Table B1 for summary statistics).

We convert some of firm and manager characteristics into three indexes, namely the _managerial quality index_ , the _management human capital index_ , and the _innovation and skill index_ . First, the managerial quality index aggregates information on firms’ managerial

> 6Due to confidentiality, information on labor productivity is not available for Chile and Poland in the FAT survey. Also, in the case of Burkina Faso, only 12 out of 80 firms with FTM provided information on both sales and intermediate costs. Therefore, our productivity analysis focuses on a smaller sub-sample excluding these three economies.

7
# **1 Introduction**

Malnutrition remains a major public health concern in most developing countries. The World Health Organization (2002) estimates that about 3.7 million deaths among young children worldwide were related to malnutrition in 2000. Similarly, Pelletier et al. (1994) estimate that about one-half of childhood deaths in developing countries are caused by undernutrition. Malnutrition has also been associated with mortality and morbidity in later life, delayed mental development, decreased cognitive and behavioral functioning throughout childhood and adolescence and poorer performance in school (de Onis et al., 2000; Galler and Barrett, 2001; Glewwe et al., 2001; Shariff et al., 2000).

In Cambodia, almost half of the children under five are malnourished as measured by the height-for-age or weight-for-age indicator (National Institute of Statistics et al., 2001). Given the grave consequences of malnutrition, the issue deserves serious attention. However, as with many other developing countries, the resources available for improving children’s nutritional status are severely limited in Cambodia. Hence, efforts must be made to allocate the available resources in an efficient manner. In particular, geographic targeting, or targeting according to locational information, is often easy to administer and implement, and can be quite effective when malnourished children are concentrated in certain locations. However, the formulation of an effective geographic targeting policy requires knowledge of the location of malnourished children, information which is often not readily available. The Cambodia Demographic and Health Survey (CDHS) 2000 data provide information on child nutritional status at the level of 17 strata, a level that is more aggregated than the provincial level.<sup>1</sup> Hence, this information is too aggregated to be useful for formulating targeting policies.

Such informational constraints are among the central issues concerning the formulation of targeting policies (Ravallion and Chao, 1989; Kanbur, 1987). This study aims to overcome

> 1In Cambodia, there are four administrative divisions, which are province, district, commune
2

# **1) Introduction.**

Measured by the Gini coefficient for the distribution of household income per capita, inequality in Brazil rose from 0.574 in 1981 to 0.625 in 1989. After that five-point (or 9%) increase during the 1980s, Brazil’s inequality was the second highest in the world, narrowly behind Sierra Leone’s Gini of 0.629. From its peak of 0.625 in 1989, Brazil’s Gini fell by six points, or roughly ten percent, to 0.564 in 2004. These are not insubstantial changes. According to World Bank (2005), the 2004 number would place Brazil as the tenth most unequal country in the world, behind Bolivia, Botswana, the Central African Republic, Guatemala, Haiti, Lesotho, Namibia, South Africa, and Zimbabwe.<sup>1</sup> While Brazil’s inequality level continues to be very high by international standards, its inequality has been less stable than is sometimes argued.<sup>2</sup> Large changes had also occurred in earlier periods, with the Gini coefficient for the distribution of (adjusted) household per capita incomes as measured by the decennial Censuses, rising from around 0.500 in 1960 to 0.565 in 1970 (see Bonelli and Sedlacek, 1989). Inequality rose further between 1970 and 1976, reached a peak on that year, and then fell from 1977 to 1981. See Bonelli and Sedlacek (1989), Hoffman (1989) and Ramos (1993).

In this paper, we describe the evolution of inequality and poverty in Brazil from 1981 to 2004, drawing on one the longest time-series of broadly comparable annual household surveys available anywhere in the developing world. Using standard decomposition techniques, we also seek to identify candidate determinants for both the levels and the changes that we observe in inequality and, to a lesser extent, poverty. The paper is mostly descriptive, and the purpose of the decomposition analyses
Most work in this literature focuses on the weak version of the pollution haven hypothesis. That is, it attempts to estimate the empirical relation between some measure of economic activity (such as trade or investment flows, plant location, or output) and a measure of the stringency of environmental regulations. The key question is whether more stringent environmental regulation has a negative effect on the relevant measure of economic activity. In one branch of the literature data on interjurisdictional differences in environmental policy is used. Researchers seek to determine whether tightening up environmental policy reduces the probability of a new plant locating in that jurisdiction, decreases inward foreign investment, increases the likelihood that a firm will move its production outside that jurisdiction, or whether net exports in affected sectors fall. In another branch of the literature, data for just one jurisdiction are used, and variations in the stringency of environmental regulation across sectors are exploited. For example, some studies look at the effects of US environmental policy on net trade flows across industries.

Much of the early work [see Jaffe et al. (1995) and Levinson (1996) for good surveys] found that environmental policy had little or no effect on trade and investment flows, and in some cases more stringent environmental policy was associated with increased competitiveness, a result that was sometimes taken as evidence in support of the Porter Hypothesis. A common explanation for the finding was that in most industries compliance with environmental regulations accounts for a small fraction of costs.

However, subsequent work has focused on strategies to deal with the endogeneity of environmental policy and unobserved heterogeneity. The concern is that there will often be an unobserved (or uncontrolled for) factor that tends to both make the jurisdiction an attractive location for polluting industry and which also results in increased pressure on regulators to tighten up environmental policy. Hence one might find a spurious positive correlation between the stringency of environmental policy and international competitiveness in the affected sector. Moreover, if governments are systematically less 10
widely (from insignificant amounts in Nepal, to 2.5 percent of GDP in Pakistan - see Figure 14). What is more, universal price subsidies for fossil fuels are typically untargeted and benefit wealthy households in absolute terms, which is associated with weak redistributive outcomes (see Clements et al., 2013; Abdallah et al., 2015; Coady et al., 2015).

> 45 Recent empirical work by Hille et al. (forthcoming) explores the employment effects of various policies such as a fossil fuel subsidy phaseout. They find that the net employment effects are small but positive. However, having the ex-ante knowledge of these quantitative effects can facilitate compensatory actions for workers. Vagliasindi (2013) also discusses in-depth experiences in various countries, including India, in eliminating fossil fuel subsidies.

> 46 South Asia is highly exposed to climate-related extreme events. According to the Global Climate Risk Index and the IMF Climate Change Dashboard, most economies in the region are ranked as the most climate-vulnerable globally (Germanwatch, 2021). The frequency and severity of climate impacts have intensified and are projected to worsen, due to gradual global warming - South Asia will see hotter weather, longer monsoon seasons, and increased droughts in upcoming decades (IPCC, 2018).

28
data from the previous year to set the SUA in the coming year. We do not find any states setting the SUA based on predictions of future utility expenses. We are also able to verify for nearly half of the states that update their base that they indeed use lagged regional CPI data on fuel and utilities to set the SUA.

Because shelter expenses account for a substantial amount of deductions and the SUA accounts for over 50% of shelter expenses, the SUA has a particularly strong effect on state-level variation in benefits per population. We plot the relationships between changes in synthetic SNAP benefits per population, SUA per population, energy usage per population and prices for the residential sector by different fuel types, and temperature by state during the Farm Bill in Appendix Figure O8. We focus on this period because the SUA only changes in October and not in April when the ARRA led to a rise in maximum benefits. We also focus on the heating and cooling SUA (HCSUA) because households predominantly use the HCSUA and the HCSUA has the largest value relative to other types of the SUA, such as telephone expenses. We see that changes in the SUA are highly positively correlated with changes in synthetic benefits per population and that these changes are also correlated with drops in temperature and rises in total energy usage from heating using sources such as electricity, LPG, and natural gas, and less so for changes in fuel prices. These figures also show variation in synthetic benefits per population that is not explained by the HCSUA, since maximum benefits are also changing during the Farm Bill. Therefore, we show our results are robust to controlling for a vector of energy usage and prices at the state-level in Table 2, because these variables could have an impact on retail prices directly. Residual policy variation should be driven by prediction errors by states in setting the SUA since most of them use lagged data, as well as idiosyncratic state-level differences in methodologies used to set the SUA. We show that results are nearly identical for states using either SUA
it exists, is a larger infusion of foreign and private investment, supplemented by corrective monetary policy, than has been seen so far.

# 7.2 **An Agenda for Future Research**

The recent analytical work, including this paper, has set the stage for an in-depth inquiry into the dynamics of labor markets and poverty and their future prospects. The existing work clearly shows the need for us to understand the current trends in economic growth and their implications for employment generation and poverty reduction. We list a few such issues below for future research:

- We should be able to use the GLSS data sets to study both the distribution of earnings and the distribution of household incomes of public sector employees relative to other groups and the changes between 1987 and 1991. It might also be interesting to simulate the effects of the 1992 wage hike in the public sector on the distribution of earnings/income for 1991. The GLSS data set might be utilized to study the difference in earnings between the self-employed and the employees, and the change in the differential between 1987 and 1992.

- More detailed work on earnings inequality in selected socio-economic groups in urban and rural areas might be possible utilizing the GLSS for these years.

53
2019 drought being relatively less intense and the relative concentration of large commercial farms in the Copperbelt and Easter provinces, which were less affected by drought.

Figure 23. Cumulative change in weighted average log labor productivity, 2014-2021 (agriculture, forestry, and fishing; constant 2010 prices) <!-- Start of picture text --> 1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database <!-- Start of picture text --> Figure 24. Annual change in weighted average log<br>labor productivity, 2014-2021 (agriculture, forestry,<br>and fishing; constant 2010 prices)<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>-0.10<br>-0.20<br>-0.30<br>-0.40<br>-0.50<br>2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Structural transformation Entry* Exit*<br>Note: Averages are weighted by firms’ share of labor.<br>Source: World Bank and ZRA staff calculations based on CIT<br>database<br>Log-changes<br><!-- End of picture text -->

# 4.3.3. Productivity trends in mining

As with agriculture, forestry, and fishing, the mining and quarrying sector has also avoided the
# Trade, Transport, and Territorial Development<sup>*</sup>

KUNAL DASGUPTA<sup>†</sup> ARTI GROVER<sup>‡</sup>

## **JEL classification** : F11, F13, L25, O18, P23, P25, R12, R32

**Keywords** : Territorial development, trade cost, transport, international trade, manufacturing, firms.

> *We would like to thank Ritabrata Bose, Muskaan Chauhan and Arav Dalwani for excellent research assistance. The authors are grateful to the World Bank’s Research support budget (RSB) for funding support. Our research benefited greatly from thoughtful and constructive comments by Somik Lall, William Maloney and Denis Medvedev. We would also like to thank Petia Topalova for sharing her tariff data.

> †Indian Institute of Management Bangalore. E-mail: _kunal.dasgupta@iimb.ac.in_

> ‡The World Bank. E-mail: _agrover1@worldbank.org_
various fossil fuels on firm performance. The authors find that higher prices for all energy types are associated with higher energy efficiency. Cal`ı et al. (2023) evaluate energy prices’ direct and indirect impact on firms’ economic performance for eleven developing countries between 2002 and 2013. Using World Bank firm-level survey data, the authors find that higher energy prices do not necessarily hamper economic performance. They also report considerable heterogeneities, as energy-intensive firms report a smaller performance effect in response to a rise in energy prices than their less energy-intensive counterparts. Cal`ı et al. (2022) focus on medium- to large-sized manufacturing firms in two emerging economies (Mexico and Indonesia). They find that while surges in electricity price harm plants’ performance, increases in fuel prices yield positive outcomes for labor and total factor productivity and profits. The effects are particularly pronounced for capital-intensive manufacturing sectors. In the same vein, Amann et al. (2021) analyze the effects of energy price hikes on manufacturing firms in Oman. They show that increases in fossil fuel prices are causally linked to improvements in productivity and efficiency and lead to notable business upgrading. As Cal`ı et al. (2022), the Oman study only observes such innovation effects for fossil fuel price increases but not for electricity hikes, which are more detrimental to firm performance.

The extent to which energy inputs can serve as _substitutes_ depends on the country and policy context. Rentschler and Kornejew (2018) find that while electricity can be substituted away by a blend of other energy sources, it plays a minor role in replacing fossil-based energy sources. Amann et al. (2021) evaluate the substitutability of electricity and aggregated fossil fuels and find that Omani firms significantly increase their kWh consumption of electricity in response to rising fuel prices.

Firm-level evidence suggests that the effectiveness of the _absorption_ channels depends on firm characteristics and sectoral attributes. Particularly firms in energyintensive sectors respond more strongly to price hikes. Rentschler and Kornejew (2018) highlight that firm-level response patterns