rafmacalaba/datause-encoder-data huggingface dataset:
# _4.b) Povertv decomposition bv socio-economic group in each localitv_

Having investigated how the economy of each of the three locality contributed to the poverty reduction in Ghana, one would like to know how different socio-economic groups contributed to poverty changes in each locality. The same exercise above, therefore, is applied to different localities separately -- with decomposition of the locality-specific percentage change in _P._ into the components for three socio-economic groups.

For Accra, Table 5a shows that the overall poverty incidence increased by 166 percent. There was a large contraction of the formal sector in the capital, compensated for by an increase in the informal sector. The mean expenditure decreased and the distribution of expenditure at the lower end also worsened substantially in both subsectors -- much more so in the formal sector. It is this deterioration of the distribution which accounted for much of the decrease in living standards for the poor, and the increase in the incidence of poverty in Accra noticed earlier (see Figure 14). The comparison from 1989 and 1992 shows that there is a slight increase in mean expenditure per capita in both Urban Formal and Urban Informal groups, however, poverty incidence increased due to the worsening in inequality.

In Other Cities poverty incidence has decreased by 17 percent during 1988 to 1992. The larger part (95 percent) of it comes from an improvement in expenditure distribution at the lower end and an increase in mean expenditure for the urban informal group. Again there is a shrinking Urban formal sector but a growing urban informal sector (see Figure 15). The comparison between 1989 and 1992 in most part is consistent with the above result except that it shows that inequality in formal sector has worsened, instead of improved.

For Rural Areas, during 1988 and 1992 poverty incidence has decreased by 19 percent, the largest decrease among all three localities. Much of this large part of decrease (67 percent) came from a 12 percent increase in mean expenditures at the farming sector and an improvement in expenditure distribution at the lower end in the informal sector (see Figure 16). In spite of an improvement in expenditure at the farming sector it lost some population over this period (see next paragraph for an explanation). The Informal sector in the rural areas absorbed the labor that was shed by the farming sector. Fortunately for rural welfare, the informal sector in this area performed better than in Accra, registering both an increase in mean expenditure and an improvement in the inequality. Evidently labor was "attracted" to the rural informal sector because of growing incomes. The results from 1989 and 1992 comparison is consistent with these results.

15
urban population shares.<sup>36</sup> Below, we briefly assess how, compared to the WUP data, these estimates change our impression of LAC’s level of urbanization _vis-à-vis_ other regions of the world. Using the AI and cluster urban population share estimates, we then re-examine the basic empirical relationships between urbanization, development and structural transformation that were discussed in Section 1 with a view to addressing the question of whether LAC countries depart significantly from these relationships. Because the _WorldPop_ data are only available for a much more limited sample of countries, we focus on the results obtained using the _LandScan 2012_ and _GHS-Pop_ data. In doing so, we concentrate on a global sample of 146 countries – including 25 LAC countries – for which we have complete data.<sup>37</sup>

# _4.1. Re-assessing LAC’s level of urbanization relative to the rest of the world_

Figure 2 shows levels of urbanization for both the world and major regions, including LAC, as estimated using both the WUP data and the different permutations of methods (AI and cluster) and input population data sets ( _LandScan_ and _GHSPop_ ).<sup>38</sup> The WUP data show LAC to be far more urbanized than the world overall. Hence, while, based on national definitions of urban areas, the share of the population living in urban areas in LAC in 2014 is estimated to be 80 percent, for the world as a whole it is only estimated to be 53 percent. Furthermore, LAC also appears to be the most urbanized region in the world after North America. However, for the various consistent sets of estimates of urban population shares, the picture is very different. In particular, LAC’s level of urbanization appears much closer to that for the world overall. They also show LAC to be less urbanized than the Middle East and North Africa region in all six measures, less urbanized than South Asia in three measures, less urbanized than Europe and Central Asia in two measures, and East Asia and the Pacific in one measure. In four of the six measures, LAC appears as the fourth most urbanized among seven regions.

> 36 Annex 3 examines the extent to which the regional and global maps of urban areas generated as a side-product of the analysis correlate with each other.  It shows that there is a generally high level of agreement between the maps generated using the AI and cluster methods and different gridded population data sets.  Interestingly, the main differences are driven less by the method adopted (AI versus cluster) and more by the choice of population layer.

> 37 In addition to these 146 countries, estimates of urban population shares were generated for a further 65 countries using either the AI and/or cluster methods.  The urban population share estimates for these countries are reported in Table A4.2 in Annex 4.

> 38 Figure 2 has been constructed using the 146-country sample for which we have complete data.  However, it also looks very similar when instead constructed using the data for all countries for which we are able to generate estimates of urbanization levels using the AI and cluster methods.

12
**<u>Table 4: Significant predictors of how households rank the four vignettes</u>**

||Vigne|tte||
|---|---|---|---|
|1(poorest)|2|3|4(richest)|
|**Tajikistan**<br>||||
|Household size (+)|Special secondary schooling (+)|Log Consumption p.c . (+)|Log Consumption p.c. (+)|
|Basic education (-)|Number of employed (+)|Uzbek (+)|Uzbeck (+)|
|Services sector occupation (+)|Small holding (-)|Primary schooling (+)|Number of employed (+)|
|Small holding (+)|Sogd (+)|General secondary (+)|Agriculture sector (-)|
|Khatlon urban (-)|Khatlon (+)|Number of employed (+)|Small and med. holding (-)|
|Gbao rural (-)|Gbao urban (-)|Small holding (-)|Sogd (-)|
||Gbao rural (+)|Sogd rural (-)|Khatlon urban (+)|
|||Khatlon urban (+)|Khatlon rural (-)|
|||RRP rural (-);Gbao (+)|RRP (-);Gbao rural (+)|
|Pseudo R<sup>2</sup> 0.022|0.018|0.016|0.019|
|**Guatemala**||||
|Female respondent (+)|HH owns land (+)|HH owns land (+)|HH owns land (+)|
|Marital Status Union (-)|Female Headed Household (-)|Marital Status Union (-)|Years of Education (-)<br>|
|Marital Status Married (-)|Respondent is HH head (+)|Divorce/Separated (-)|Household migrant to Latin<br>America (-)|
|Divorce/Separated (-)|Female respondent (+)|Santa Ana Huista (-)|Cuilco (-)|
|Widowed (-)|Marital Status Union (-)|Jacaltenango (-)|Santa Ana Huista (-)|
|HH migrant to Latin America<br>(-)|Marital Status Married (-)||Jacaltenango (-)|
|Internal migrant (-)|Divorce/Separated (-)|||
|Migrant to USA (+)|Widowed (-)|||
|Santa Ana Huista (-)|Migrant to USA (+)|||
||Cuilco (+); Jacaltenango (-)|||
|Pseudo R<sup>2</sup>0.05|0.03|0.025|0.045|
# **1. Introduction**

Development monitoring and policy evaluation efforts have traditionally put a high weight on progress against poverty, defined in terms of household consumption or income as measured in surveys.<sup>2</sup> It is well recognized that household consumption or income aggregates need to be normalized for cost-of-living differences, including differences in household size, although there are differing views on how the normalization should be done. It has also come to be recognized that neither income nor consumption (even with seemingly appropriate normalizations) can be considered a sufficient statistic for welfare. Standard survey methods allow a fairly complete accounting of the market goods consumed (including own-farm products). However, there are important non-market goods that are typically excluded, such as access to public services for health care or schooling. And household aggregate income or consumption reveals nothing directly about distribution within the household.

There have been attempts to define development indices that allow for these ―nonincome‖ factors. An early example was Morris’s (1979) ―Physical Quality of Life Index‖, which is an average of literacy, infant mortality and life expectancy. A now famous example is the ―Human Development Index‖ (HDI), which aggregates life expectancy and education, along with income.<sup>3</sup> A recent example is the ―Multidimensional Poverty Index‖ (MPI) of Alkire and Santos (2010). This combines 10 components, two for health (malnutrition, and child mortality), two for education (years of schooling and school enrollment), and six for ―living standards‖ (including both access to services and proxies for household wealth).<sup>4</sup> All such composite indices confront the problem of setting the trade-offs (relative weights) across their dimensions. This is essentially _ad hoc_ in all these measures.<sup>5</sup> And there are objections that can be raised. For example, Ravallion (2012a) points out that the HDI puts a

> 2 For example, since the mid-1990s the World Bank—the largest international development agency—has deemed poverty reduction to be its ―overarching goal.‖ The first of the United Nation’s Millennium Development Goals is to halve the 1990s income or consumption poverty rate for the developing world by 2015.

> 3 The index has been published since 1990 in the UNDP’s _Human Development Reports_ ; see, for example, UNDP (2010). 4

> For further discussion of the various approaches to measuring multidimensional poverty see Tsui (2002), Bourguignon and Chakravarty (2003), Alkire and Foster (2007), Wagle (2008) and Ravallion (2011).

> 5 Furthermore, the weights are rarely explicit in the space of the postulated core dimensions of welfare. Instead, the weights are only made explicit in a derived space of pre-defined functions of those dimensions. Thus indices such as the HDI and MPI lack transparency about their assumed trade-offs. For further discussion of this point see Ravallion (2011).

2
Policy Research Working Paper 10515

# **Abstract**

Exposure to fine particulate pollution (PM2.5) increases mortality and morbidity and reduces human capital formation and worker productivity. As a consequence, high levels of particulate pollution may adversely affect economic activity. Using a novel dataset of changes in the annual gross domestic product of Indian districts, this paper investigates the impact of changes in the level of ambient PM2.5 on district-level gross domestic product. Using daily temperature inversions as an instrument for pollution exposure, this paper finds that higher levels of particulate pollution reduce gross domestic product. The effect is non-trivial— the median annual increase in the level of PM2.5 reduces year-to-year changes in gross domestic product by 0.56 percentage points.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at abehrer@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
When drought shocks occur, cash transfers contribute to an intensification of production through a substantial increase in the number of fields in which fertilizers are applied (+0.157, a 42% increase relative to control). We also find that cash transfers allow households exposed to shocks to keep selling part of their production and to sustain revenues from these harvest sales. The interaction effect between cash transfers and shocks for the value of crop sales is significant and of large magnitude (+15,287 CFA), an increase of 57% relative to control (+13.4 percentage points or 29% for the likelihood of selling part of the harvest). Cash transfers mitigate the reduction of the value of crop sales induced by drought shocks (-26,420 CFA), so that we cannot reject the null that crop sales are unaffected by shocks (p-value=0.167). While we do not observe a significant effect on yields, the interaction term is positive, and yield estimates are notoriously noisy. As such, the positive impacts on harvest sales is suggestive of an increase in the total value of agricultural production (considering the full portfolio of crops). Overall, these results highlight a first channel through which cash transfers facilitate income smoothing: households eligible for cash transfers hit by shocks early in the crop cycle intensify production, and as such are able to maintain revenues from crop sales.<sup>36</sup> Another potential pathway could be that cash transfers may have allowed beneficiary households to sell their harvest later in the season when prices are higher (as in Delavallade and Godlonton, 2020), though we do not have direct evidence for that channel.

Off-farm household enterprises constitute a second mechanism through which cash transfers can facilitate income smoothing when shocks occur. As for agriculture, results on household enterprises reveal heterogeneity in impact by exposure to climatic shocks (Table 7). On the one hand, cash transfers do not affect the likelihood of operating an off-farm household enterprise or the number of household enterprises operated by households not affected by shocks. On the other hand, drought shocks reduce the likelihood that households operate off-farm enterprises and profits from these enterprises. Interestingly, when climatic shocks occur, cash transfer beneficiaries are more likely to keep operating a household enterprise (by 14.8 percentage points,

> 36 Table A8 documents cash transfer program impacts on land ownership and usage (matched-DID specification, equation 2). There is no evidence of impacts on this dimension, which is not surprising given frictions in land markets in Niger. The areas rented, rented out, the number of fields or the likelihood of small infrastructure investment do not seem to be affected by cash transfers, low rainfall, or the interaction between cash transfers and drought. Cash transfer beneficiaries may cultivate slightly less land when hit by a shock, which is consistent with an intensification of farming activities on the areas that are cultivated, while low-return plots affected by shocks may be abandoned.

25
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sectors as follows (in brackets are the number of original industries that are combined in the broad sector):

1. <u>Agriculture</u>

2. <u>Fishing</u> 3. <u>Mining and quarrying</u> 4. <u>Manufacturing (8): Food & beverages; Textiles & wearing apparel; Wood & paper;</u> Petroleum, chemical & non-metallic mineral products; Metal products; Electrical & machinery; Transport equipment; Other manufacturing 5. <u>Utilities and construction (3): Recycling; Electricity, gas & water; construction</u> 6. <u>Trade (4): Maintenance & repair; Wholesale trade; Retail trade; Hotels & restaurants</u> 7. <u>Business services (3): Transport; Post & telecommunications; Financial intermediation</u> & business activities 8. <u>Social and household services (4): Public administration; Education, health & other</u> services; Private households; Others 9. <u>Re-export and re-import</u> In addition, there are six components of final demand in the full table.<sup>12</sup> In our analysis, we do not distinguish between final demand that comes from consumers or that represents capital formation or inventories. Hence, we aggregate all six components together into a single final demand vector.

# <u>Countries</u>

To make the country-detail more manageable, we also have to aggregate here. Given that we are interested in sub-Saharan countries, it is natural to collapse all trade-flows with countries outside of the region into the following groups: 4. <u>OAF (Other African countries): South Africa and North African countries, i.e.</u> Morocco incl. Western Sahara, Algeria, Tunisia, Libya, Egypt.

5. <u>EUC (EU28): the 28 EU countries</u> 6. <u>USA (United States)</u> 7. <u>CHN (China): including Hong Kong and Macau</u> 8. <u>ROW (rest of the world): there already is a ROW category in the original Eora</u> database, but we enlarge this group, adding all countries not in sub-Saharan Africa, nor in regions 4 to 7.

In addition, we distill the information of the full, integrated MRIO table into a series of country-specific (and year-specific) input-output tables, one for each sub-Saharan country. Breaking-up the information by country is a more efficient way of maintaining some regional detail than working with a single IO table that includes all 45 sub-Saharan countries with the above 5 regions. Given that a single IO table for the 45 countries of interest and the 5 regions listed above would still contain more than 200,000 coefficients, it would still be very difficult to distill useful insights from it. Moreover, bilateral trading relationships between many individual countries in the region are extremely limited.

> 12 The 6 final demand components are final consumption by households, non-profit institutions, and government, as well as gross fixed capital formation, changes in inventories, and the net change in valuables.

20
# **2. Labor market performance 1997-2010**

# _Changing patterns: recent data show an increase in labor force participation rates_

The evidence shows that in recent years there was a reversal of the decline in the labor force participation found in studies of the Labor Force Surveys that covered the period 1997-2007 (ILO, 2009; Phan, 2009). The steady decline in the labor force participation for both men and women, which these studies found, was broken. That decline was largely explained by the youth staying on longer in formal education and by older workers exiting the labor force at earlier ages. In contracts, labor force participation increased for both sexes in the latter period 2007-2009.

Importantly, two age groups recently increased their participation significantly: the 15-19 year olds (from 37.3 percent in 2007 to 43.8 percent in 2009) and the over 50 (from 55.6 percent in 2007 to 58.9 percent in 2009). This evolution was true for both men and women, but 15-19 years old women increased their participation more than their male counterparts (from 36.4 percent to 43.6 percent for women, against 38.1 percent to 43.9 percent for men).

Although the contemporaneous local inflationary crisis of 2008 and the global financial crisis may have contributed to this evolution, such a descriptive analysis does not prove a causal relationship between the two. One possible reason for the fact that the youth and older workers changed most their participation may be that these age categories were the most flexible in terms of their labor force participation choice. It is slightly worrying that some youth decided to abandon their studies early, presumably to help their family, therefore potentially diminishing their lifetime income. It will be interesting to see whether labor force participation rates return to previous levels, and whether individuals who left school return to formal education.

_A high participation rate and employment-to-population ratio_ As it has been noted before (e.g. Phan, 2009; ILO, 2010a), labor force participation is high in Vietnam; it is toward the high-end of the range of the participation rates of other lower-middle-income countries (Table 1). The participation rate of women is lower than for men in all countries, but the participation of women in the labor force in Vietnam has 5