rafmacalaba/datause-encoder-data huggingface dataset:
and collated by the World Bank.<sup>2</sup> These data include various measures of inequality and poverty, as well as a number of other useful distributional indicators, such as decile or (in some cases) percentile shares of the income or consumption distribution. We restrict our analysis to observations after 1980 and for which GDP and other basic socioeconomic variables are available. This reduces the sample to about 165 countries between 1980 and 2018 and includes all economic ranges, from low to high income.<sup>3</sup> The information from PovcalNet is combined with the World Development Indicators (WDI). This is the primary World Bank collection of development indicators compiled from officially recognized international sources.

There are more than 1,900 Gini and other inequality measures observations in our data set, most of which are calculated from direct access to household surveys. In the analysis, the Gini index—an indicator typically not sensitive to the tails of the income distribution—is accompanied by a measure that is more tail-sensitive, the D9/D5 ratio. A bigger gap between income and consumption inequality detected by this ratio is interpreted as a validation of our hypothesis that consumption-based measures tend to capture less accurately the information from the upper tails of the distributions.

In section 4, household-level data on income and consumption are taken from several sources. For a first group of SSA countries—Ghana, Kenya, Nigeria, and Uganda—we use household income data obtained from the Rural Income Generating Activities (RIGA) project, a collaborative effort of the FAO, the World Bank, and American University.<sup>4</sup> The RIGA database is composed of a series of constructed variables about rural and urban income-generating activities created from the original consumption data sources. In particular, we focus on the household-level income aggregate dataset (RIGA-H), which provides data on different income sources, such as crop and livestock production, household enterprises, wage employment, transfers, and nonlabor earnings .

For the countries just noted, data on households’ consumption expenditure come instead from the original budget surveys compiled by national statistical bureaus and the World Bank, which can be easily linked

> 2 World Bank, “PovcalNet: An Online Analysis Tool for Global Poverty Monitoring,” database. <u>http://iresearch.worldbank.org/PovcalNet.</u>

> 3 We follow the most recent (2020) World Bank classification that uses the Atlas method to estimate the size of economies in terms of gross national income (GNI) per capita: low income = below US$1,036; lower-middle income = between US$1,036 and US$4,045; upper-middle income = between US$4,046 and US$12,535; and high income = above US$12,535.

> 4 The microdata are available to users upon request. For more see FAO, “The RIGA Database.” <u>http://www.fao.org/economic/riga/riga-database/en.</u>

5
29 Tablo 6. Demographic Trends In World Bank Regions and Country Departments

||Popula|tion (milli|ons)|Stationary|Year||
|---|---|---|---|---|---|---|
|||||population|when||
|Region and<br>couLntry<br>department|1965|2100|Station-<br>ary|asXof<br>1965<br>population|popula-<br>tion<br>douiles|Year<br>when<br>NRR=I|
|World total|4,843|10.560|10,837|224|2052|2045|
|Borrommrs and non-borrowers|||||||
|Sub-Saharan Africa|457|2,168|2,264|474|2009|2045|
|**AsIa**andOceania|2,573|4,997|5,115|199|(a)|2035|
|Europe, Middle East,|||||||
|and North Africa|1,147|2,190|2,233|203|2160|2045|
|Aerica|667|1,205|1,224|184|(a)|2030|
|Borrowers only|||||||
|Afrfca<br>Region|415|2,035|2,125|513|2008|2040|
|1Occidental and Central|40|187|<br>195|496|2008|2040|
|2 Eastern|106|520|544|515|2008|2040|
|3South-Central<br>and|||||||
|Indian Ocean|52|251|262|500|2009|2040|
|4 Western|119|593|619|519|2007|2040|
|S Sdielian|36|173|182|504|2011|2040|
|6Southern|62|310|323|525|2008|2040|
|**Asia**Region|2,382|4.756|4,843|203|2107|2035|
|IOther Swuth|134|423|439|327|2019|2035|
|2Southeaut|274|626|641|235|2036|2035|
|3 China|1,040|<br>1.695|<br>1,695|<br>163|(a)|2000|
|4Indio|765|1,655|<br>1,698|<br>222|2048|2010|
|5 Indonesia<br>and Pacific|168|358|<br>370|220|2052|2035|
|Ehk Region|476|1,425|<br>1,466|<br>308|2022|2045|
|1PakIstanand Turkey|147|519|<br>535|365|2016|2045|
|2Norn<br>Africa|55|172|177|322|2016|2025|
|3 NiddleEast|170|609|629|370|2013|2040|
|4Europ|105|125|125|120|(a)|2020|
|**LACRegion**|388|879|898|232|2038|2025|
|1Br ail|136|287|306|226|2044|2015|
|2 Contral America|104|263|267|258|2027|2025|
|3Caribbean and Other|70|159|165|234|2035|2030|
|4 Tmperate and Tropical|78|158|160|205|2087|2015|

Note: World Bank regions and country departments are defined In Table 14.

(a) Population will not double with projected rates.

M~ .
# **2010:M6**

Source: Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010. Shaded area marks period of recession.

Our interest is in the coefficients related to the dummy variables for the various quarters; more specifically, we would like to know whether there was a significant change in expenditure per capita between 2007 and 2008, 2008 and 2009, and 2009 and 2010. The results based on the estimates in Table 1 are summarized in Table 2. They show that real per capita consumption was lower in 2008 than in 2007 (p-value = 0.00, and -1,058 is more negative than -291), significantly higher in 2009 than in 2008, and significantly lower again in 2010.

In other words, _real expenditure per capita in Thailand was, on average, higher during the main recession year (2009) than in either the previous year or the subsequent year_ , after holding other influences constant. The main explanation is relatively straightforward: prices fell sharply during the recession, which boosted purchasing power, while households were able to smooth their consumption relative to their incomes; we discuss these effects more fully below.

For each section of the population of interest – for instance, urban dwellers, or poor households – we did similar computations, first estimating regressions based on the sample for the group in question, and then testing for changes in expenditure per capita over time. Tables 3-5 report the results; for all cases where there was a significant change in per capita expenditure from one year to the next (at the 5% level), the numbers are shown in bold face; for the cases were real expenditure was lower in 2009 than in 2008 the cells are highlighted in yellow.

The experience of Greater Bangkok was out of synch with the rest of the Thailand. In all of the regions except for Bangkok, real expenditure per capita fell in 2008, rose in 2009 relative to 2008, and then fell again in 2010 (Table 3), and this was also true of rural areas in general. In Bangkok the pattern was reversed, with a large increase in real spending in 2008, a drop in 2009 (although not to the levels of 2007), and a rebound in 2010. The rise in spending in Bangkok in 2008 was not, however, mirrored in urban Thailand overall.

Poor households (defined as those in the bottom 40% of the expenditure per capita distribution) and the very poor (in the bottom fifth) saw their real expenditure levels fall in 2008, recover ground in 2009, and fall again in 2010; the remaining households (“non-poor”) also saw a rise in Losers and Winners in Recession: Thailand 2008-09 15
_Table 5_ - Price elasticity by consumption decile

|**Consu**<br>||**In Natura**||**In**|**gredients**||**P**|**rocessed**||**Ultra**|**-processed**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**mption**|||||||||||||
|**Decile**|Low-<br>bound|Medium|Upper-<br>bound|Low-<br>bound|Medium|Upper-<br>bound|Low-<br>bound|Medium|Upper-<br>bound|Low-<br>bound|Medium|Upper-<br>bound|
|**1**|-3.18|-3.19|-3.20|-2.16|-2.17|-2.17|-1.62|-1.62|-1.62|-2.92|-2.92|-2.93|
|**2**|-2.69|-2.70|-2.71|-1.76|-1.77|-1.77|-1.27|-1.27|-1.28|-2.50|-2.51|-2.51|
|**3**|-2.48|-2.49|-2.50|-1.54|-1.54|-1.55|-1.12|-1.13|-1.13|-2.22|-2.23|-2.23|
|**4**|-2.28|-2.29|-2.30|-1.31|-1.31|-1.31|-0.99|-0.99|-0.99|-1.98|-1.99|-2.00|
|**5**|-2.09|-2.10|-2.11|-1.16|-1.16|-1.17|-0.85|-0.85|-0.86|-1.84|-1.85|-1.86|
|**6**|-1.97|-1.98|-1.99|-1.06|-1.06|-1.06|-0.75|-0.75|-0.75|-1.67|-1.68|-1.68|
|**7**|-1.79|-1.80|-1.81|-1.01|-1.01|-1.01|-0.59|-0.60|-0.60|-1.46|-1.46|-1.47|
|**8**|-1.69|-1.70|-1.71|-0.85|-0.85|-0.86|-0.47|-0.47|-0.47|-1.29|-1.30|-1.31|
|**9**|-1.52|-1.53|-1.54|-0.73|-0.74|-0.74|-0.34|-0.35|-0.35|-1.18|-1.19|-1.20|
|**10**|-1.29|-1.30|-1.30|-0.60|-0.60|-0.61|-0.17|-0.17|-0.17|-0.92|-0.93|-0.94|

Source: Estimates based on POF 2017/2018. Lower-bound and Upper-bound are estimated using a 95% confidence level. Estimates assume a price shock of 20%.

Figure 2 depicts the medium bound elasticity differences between the four food categories, and their increasing magnitude among lower consumption deciles. The level of progressiveness of potential pricing policies for different food categories can be observed through the ratio between the elasticity in the bottom and the top consumption deciles. This ratio is 3.14 for ultra-processed and 2.46 for in-natura foods. This indicates that, for example, the impact on households at the bottom of the distribution of a taxation of ultra-processed would be proportionally larger than a subsidy on in-natura foods, despite the price elasticity of in natura foods being higher in absolute terms.

_Figure 2_ - Price elasticity by food type <!-- Start of picture text --> 0.00<br>-0.50<br>-1.00<br>-1.50<br>-2.00<br>-2.50<br>-3.00<br>-3.50<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>In Natura Ingredients Processed Ultra-processed<br>Source: Estimates based on POF 2017/2018.<br>Price elasticity<br><!-- End of picture text --> 15
33 TablO 9. Anmuul Population Increase and Growth **Rate** by Geographic Region and IncOne Group, 1985-2100

||AnnUal po|pulatio|n inCr|ease (m|illions)||Annt|Ual grow|th rate|(perce|nt)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Region|1985-90|2000|2025|2U50|2100||1985-90|2000|2025|2050|2100|
|World|87.6|90.6|74.4|40.5|6.9||1.73|1.46|0.90|0.42|0.07|
|Less developed(a)|80.8|86.4|_n.7_|40.7|6.9||2.09|1.75|1.05|0.49|0.08|
|More developed(e)|6.8|4.6|1.2|-0.5|0.1||0.57|0.36|0.09|-0.04|0.01|
|Africa|18.5|23.9|25.0|18.4|2.3||3.06|2.74|1.66|0.90|0.10|
|East Africa|7.9|10.3|11.2|8.2|1.0||3.15|2.83|1.74|0.93|0.10|
|West Africa|7.1|9.6|10.4|7.9|1.0||3.22|2.93|1.78|0.97|0.10|
|North Africa|3.6|4.1|3.5|2.3|0.4||2.66|2.24|1.27|0.64|0.09|
|America|10.8|10.0|6.8|2.6|0.7||1.55|1.21|0.66|0.23|0.06|
|Latin America and Caribb|ean 8.6|8.7|6.6|2.8|0.7||2.04|1.64|0.91|0.34|0.08|
|Northern America|2.2|1.3|0.4|-0.2|0.0||0.80|0.45|0.11|-0.07|0.00|
|Asia|54.4|53.8|40.4|19.4|3.6||1.84|1.47|0.84|0.35|0.06|
|EastandSouthOaSt Aaia|26.0|26.4|17.0|5.0|1.4||1.52|1.29|0.67|0.18|0.05|
|South Asia|24.9|25.1|21.4|11.9|2.4||2.22|1.75|1.06|0.49|0.09|
|Southwest Asia|3.4|3.8|3.3|2.0|0.3|4|2.73|2.26|1.26|0.62|0.09|
|Europe and U.S.S.R.|3.7|2.7|1.0|0.0|0.0||0.48|0.33|0.11|0.00|0.00|
|Oceania|0.4|0.3|0.2|0.1|0.0||1.41|0.98|0.51|0.17|0.04|
|Income groUP(b)||||||||||||
|Low|51.2|53.6|45.7|25.6|4.7||1.99|1.65|1.02|0.48|0.08|
|Lower-middte|17.2|19.3|16.9|10.0|1.8||2.38|2.03|1.20|0.57|0.09|
|Upper-middle|11.4|11.8|8.9|4.4|1.0||1.84|1.54|0.86|0.37|0.08|
|High(oil exporters)|0.7|0.9|0.7|0.5|0.1||3.27|2.94|1.35|0.76|0.09|
|IndUStrial market|3.6|2.0|-0.4|-1.4|0.0||0.48|0.26|-0.05|-0.18|0.00|
|Nonraportin9nOnmem,er|<br>3.5|3.1|2.2|1.2|0.2||0.93|0.74|0.46|0.24|0.05|

Ca) "Wore developed" cO0Wrises EurOPe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. "LeSS devOloped" cOaprises the rest of the world. Cb) Criteria for income groups are given in World Bank 1988. See listing In Table 15 of this paper.
38 84 log(pi) =at+06, ¢ 1 +62 ¢ i +63 ¢ (+ X0 +) M, +) We + tie i=1 t=1 t = ~~— |———————_~~ } « 100 ~~m~~ <u>(Fos:</u> ~~pop + Refugees)~~ *

wmy =% + wmy +2 wmy +43 my + agyr2006 + asyr2007 + agt + azhungry + e; vit = at >, dBa t a(kmto Kakuma,,) + yroadyy + Oy + Ue + eit v t
WPS4249 one percentage point increase in the remittances to GDP ratio is estimated to lead to reductions in poverty that vary between 0.08 percent for poorer countries to 1.12 percent for richer countries, with an average estimated reduction of 0.37 percent.<sup>20</sup>

# **4. MICRO-ECONOMETRIC EMPIRICAL STRATEGY**

# _(a) Inequality Decompositions_

As argued in the seminal paper by Stark, Taylor and Yitzhaki (1986), the impact of migration and international remittances on income inequality depends on the magnitude of remittances in relation to income from other sources and upon the position of remittancereceiving households in the distribution of income. In their perspective, the uncertainty, net expected benefits and the distributional impact of migration “investments” depend, at any point in time, on past migration flows.

In particular, when migration is incipient, its costs and related uncertainty are likely to be high, so that migrants can be expected to come from among the better off. As a result, at this point remittances would tend to have an un-equalizing effect. Over time, however, the uncertainty and costs involving migration are likely to diminish with the spread of information and contacts across a wider range of households, which may lead to increases in migration among the worse off, with potentially favorable effects on income inequality.

To illustrate their hypothesis, Stark, Taylor and Yitzhaki (1986) proposed a decomposition of income inequality that allows estimating the effects of discrete and marginal changes in remittances on the distribution of household income. We use this as a first approach to uncovering the link between remittances and inequality on the basis of household survey data from 10 Latin American countries.<sup>21</sup> It must be noted, however, that this approach entails the assumption that remittances operate as exogenous transfers by migrants, so that the implicit counterfactual scenario is that of changes in remittances without changes in migration. In other words, initially we do not consider the fact that migration also entails losses of income associated with the migrants’ absence from their communities.

Following Stark, Taylor and Yitzhaki (1986), let _yk_ represent the different _k_ components of household income (i.e., non-remittances and remittances income). The Gini coefficient for any particular income source k can be written as:

> 20 This is the estimated poverty effect of one percentage point increase in the remittances to GDP ratio in a country where that ratio is 4.9 percent and the Gini Coefficient is 0.5, using the average poverty elasticity from all alternative choices of instrumental variables reported in table 3.

> 21 This type of decomposition has been used, among others, by Rodriguez (1998) and by Barham and Boucher (1998).

12
Moreover, as with vocational training, a key concern is that any gains to those receiving the vouchers come at the expense of others in the economy who would have otherwise been hired. Groh et al. (2016a) find suggestive evidence of this in Jordan. When they examine impacts by region, and look at longer‐term time trends, they find a lasting impact of the subsidy on employment in the less populated labor markets outside of Amman. But the control group ends up with a lower employment rate than other cohorts of graduates had received in recent years, and direct survey evidence suggests that they were competing directly with the treatment group for some jobs. The result is that wage subsidies do not seem likely to have increased aggregate employment in this case.

An alternative to giving subsidies to workers has been to give the subsidies to firms, to encourage them to hire more workers. De Mel et al. (2016) test the impact of wage subsidies given to microenterprises to encourage them to hire workers. They find 24 percent of firms use the subsidy to hire a worker, resulting in an increase in employment while the subsidy is in effect. But the dynamics then look reasonably similar to those in Figure 1, with much of this impact disappearing as soon as the subsidy is removed, and no long‐term impact after two years.

A final use of subsidies is to use them to help prevent liquidity‐constrained firms from shedding workers during a temporary shock. This type of policy was another common response to the global financial crisis. The idea was that firms suffering a temporary demand shock and/or liquidity shock may fire workers who they would later want to hire back. A subsidy may prevent them from firing these workers in the first place, and hasten the recovery of these firms if hiring and firing is costly. Bruhn (2016) evaluates a wage subsidy program Mexico used during the global financial crisis, using difference‐in‐difference analysis to compare the employment trajectories of durable manufacturers in industries eligible for the program to those in industries ineligible for the program. She finds employment to be 6 to 13 percent higher in the affected industries during the program, and to grow faster after the crisis, suggesting the program helped firms to recover more quickly from the shock.

11
(Mungiu-Pippidi, 2015). In practical terms, limited access can be achieved if public officials steer contracts to a favored bidder by, for example, unjustified sole sourcing or direct contract awards; favoring a certain bidder by tailoring specifications or sharing inside information; or paying connected suppliers on time while delaying due payments to others (World Bank, 2009). Moreover, access might also be limited by colluding firms barring non-cartel member firms from access (e.g. through physically preventing them from submitting paper-based bids).

23. “Administrative efficiency” refers to the total transaction costs for completing a procurement process to society. It is approximated by the total administrative cost incurred by the government for achieving the predetermined outcome of public procurement, that is, the successful completion of the contract. We largely refrain from the full analysis of administrative costs for bidders due to a lack of sufficiently detailed time-series data (i.e. not considered in the micro-estimates but taken into account in the macro modeling).

24. The total direct savings achieved through an e-procurement intervention could hence be approximated by adding monetized values of the three outcome dimensions.

## FORMULA 1

Total savings= ∆Access+ ∆Economy+ ∆Efficiency (Govt. +Firms)

# 3.2 MECHANISMS

25. The main conceptual challenge of linking e-procurement to the above three outcomes is that e- procurement is a bundled intervention. Bangladesh’s e-procurement system, like most e-procurement interventions, bundles together a variety of changes which have a range of impact channels (Blum et al., 2018). We consider three main impact mechanisms as the most important for explaining the three outcomes of interest. While there is no simple mapping between mechanisms and outcomes, their relationship can be summarized as follows: 26. E-procurement can (i) make it more costly for public officials to erect intentional barriers to entry, by reducing their de facto discretion and by increasing the risk of detection, among others. It can (ii) reduce nonintentional barriers to entry by lowering the transaction costs for bidding firms. Both mechanisms are expected to directly affect access—and indirectly economy, as a more open and fair competition among bidders is expected to enhance value-for-money. E-procurement can (iii) increase administrative efficiency by automating the procurement process and removing information transfer costs for the government (e.g. postal costs). We discuss these mechanisms in greater depth below and set out hypotheses while also 9
more time in school, delaying their entry into the labor market. Overall, population aging has more than offset the effects of delayed entry into the labor market, and the share of workers with 20 or more years of experience increased by about 1.25 percentage points over the period. However, these aggregate trends hide substantial heterogeneity in two dimensions. The first dimension is across countries. Potential experience rose in Brazil, Chile, Costa Rica, Mexico, Panama, and Peru. In the rest of the countries, it remained constant or mildly declined. The second dimension is across schooling groups. In those countries, such as Argentina and Chile, where the expansion of secondary education took place earlier, it was primarily high school graduates who were aging. In Brazil and other countries that increased access to secondary education later, changes in the experience mix are more noticeable among the least educated. Fern ́andez and Messina (2018) show that changes in the relative labor supply of experienced workers have more bearing in explaining the experience premium among less educated workers than among highly skilled individuals in Argentina, Brazil, and Chile.

# **4. Lack of Polarization in Latin America**

The canonical model of the labor market, with two skill groups and factor-augmenting technology, has been quite successful in explaining the rising skill premium in the United States during the 1980s and 1990s (see Katz and Murphy 1992; Autor, Katz, and Krueger 1998). However, changes in the structure of occupations in the United States were only monotonically increasing in skill content during the 1980s (Acemoglu and Autor 2011). In the 1990s, the changes in the employment shares show signs of polarization. Relative employment growth was rapidly rising in the top occupations in terms of skill content, but also in the bottom, where jobs are intensive in manual, non-routine tasks. Meanwhile, the occupations in the middle of the skill distribution lost relative weight. Goos, Manning, and Salomons (2009, 2011) show that these trends are not unique to the United States. High-paying and lowpaying occupations expanded relative to middle-wage occupations in the 1990s and 2000s in 16 OECD countries.

Computers and information and communications technologies have certainly reached the most dynamic firms in developing countries (World Bank 2016). However, the relative abundance of low skill labor and different barriers to the diffusion of technology can slow down the process of technological change (Messina 2017). Are there signs of job polarization in Latin America?

An important challenge for the study of occupation polarization in the region is that most household surveys change the classification of occupations over time, limiting the possibility to make detailed comparisons over long periods. Figure 6 shows changes in the employment 12