rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4: Expec**<br>|**ted Deaths fr**<br>I<br>|**om Ind**<br>I|**ustrial**<br>**E**<br>__||**Partic**<br>**xpecte**<br>**byPla**|**ulate Em**<br>**d Deat**<br>**nt Size**|**ission**<br>**hs**<br> <br>|**s in Brazil**<br>l_l|
|---|---|---|---|---|---|---|---|
|**Income**<br>**Decilea**|<br>**Total**<br>**Population**|**Pop.**<br>**Share**<br>**(%)**|<br>**Small**|**Med.**|**Large**|**Total**|**Death**<br>**Share**<br>**(%)**|
|**1**|<br>5,392,439|<br>4.8|<br>**1**|<br>0|<br>0|<br>**1**|<br>0.3|
|2|<br>6,281,428|<br>5.6|<br>0|<br>0|<br>1|<br>**1**|<br>0.3|
|3|<br>7,691,836|<br>6.9|<br>1|<br>0|<br>2|<br>3|<br>0.9|
|4|<br>5,197,602|<br>4.6|<br>0|<br>0|<br>0|<br>0|<br>0.0|
|_5_|4,995,732|4.5|1|<br>0|<br>0|<br>1|<br>0.3|
|6|<br>6,110,057|<br>5.5|<br>1|<br>0|<br>1|<br>2|<br>0.6|
|7|<br>7,908,071|<br>7.1|<br>3|<br>0|<br>2|<br>**_5_**|<br>1.5|
|8|<br>8,523,451|<br>7.6|<br>3|<br>0|<br>2|<br>5|<br>**_1.5_**|
|9|21,200,000|18.9|<br>4|<br>1|<br>191|<br>196|60.1|
|10|38,800,000|34.6|<br>23|<br>4|<br>85|<br>112|34.4|
|**Total**|112,100,616||37|<br>5|<br>284|<br>326|1|

a Income deciles are not population-weighted. See section 3.2.

These data indicate that industrial air pollution in Brazil is a problem for the relatively affluent, not the poor. Moreover, despite the greater pollution intensity of small enterprises, the mix of sector and plant scale in high-income areas is such that large plants create most of the problem.'

> 7 It should be emphasized that this study focuses on particulate air pollution from **the** manufacturing industry alone. 16
# **1. Introduction**

In Latin America, as in large parts of the developing world, aggregate indicators show that access to infrastructure services is still largely rationed, with both households and firms suffering from shortfalls across all major types of infrastructure (Estache and Fay 2009). This situation is likely to have been exacerbated over the last two decades by the rapid growth in the share of households attaining middle-income status and the growth in demand for infrastructure services they generated (Ferreira et al. 2013, Lakner and Milanovic 2015). However, despite its potential importance in designing infrastructure policy, there is no systematic information on how access to infrastructure varies across the income distribution.

This paper addresses this gap by documenting access to services and ownership of infrastructure-related durables in the water, energy, telecom, and transport areas, based on large scale harmonized household survey data from Latin America covering 14 countries over the 1992 to 2012 period. It provides a systematic disaggregation of access and ownership rates across the income distribution, allowing for an assessment of the situation of households at different levels of income and over time across the countries of the region.

For a number of indicators, the data reveal wide disparities across households at similar levels of income in different countries. We then econometrically derive country infrastructure premia, which show how much a household at a specific level of the income distribution benefits from simply being located in a given country, and assess the progressivity or regressivity of infrastructure access patterns across the income distribution. We then show that these premia are only partly explained by per capita GDP and are not systematically aligned with other country overall characteristics such as inequality, density, or urbanization, which means they are very likely to be for a large part policy driven.

Second, we compute the income elasticity of infrastructure access for our set of indicators, and use these to forecast the time that would be needed to close the remaining gap for households at different levels of the income distribution under a business as usual hypothesis. The general message is rather somber: without fundamental changes to the pace and efficiency of infrastructure delivery, universal access is in many cases still decades away. We also highlight an asset ownership “pecking order”, by which, as their income grows, poorer households tend to acquire first a fridge, then a washing machine, and finally a car.

2
# _Infrastructurefior Basic Services_

In developing countries the infrastructure for the provision of water and energy is poor or nonexistent. The acquisition of these services then has to be done by household members. In most settings the burden of provision of these services to the household falls largely on women and children. Since these are "outside chores" that are time-intensive, an obvious questions arises:

- Do "outside" chores constrain young children from allocating time to schooling? Are girls more likely to be affected by this than boys?

The analytical approach in the existing literature to assess the impacts of exogenous changes in these outside chores on time allocation is to use a variable that captures the "productivity of collection".<sup>2</sup> ' The data on Peru do not include this level of detail. All that can be constructed are dummy variables that indicate whether the household has access to in-house water or if it uses gas/electricity, firewood, coal or something else for energy. However, the crudeness of these variables is somewhat overcome by the fact that unlike previous work I am dealing with panel data where the results would control for unobserved heterogeneity.<sup>22</sup>

# _Demography and Life cycle_

The composition of a child's work inside and outside the household changes with age. Older girls are expected to spend more time on housework and older boys on income-generating activities. I include age and its square to capture these effects. The age composition of the female members in the household may also be indicative of the possibilities that are open to

> 20 Aggregating the unemployment rate to the cluster level does not completely get rid of the voluntary vs. involuntary quits problem. However it does tend to disconnect from the individual level by indicating that general changes in cluster level unemployment rates over time are indicative of changes in labor market situation.

21 This is typically kilograms collected per hour. See Kumar and Hotchkiss (1988) and Ilahi and Jafarey (1998).

12
**Table 7 First stage instrumental variable regressions using all observations.**

||Imp.Transparency|Exp.Transparency|
|---|---|---|
|**Col. UK Importer**|**0.087*****|**0.407*****|
||[0.011]|[0.012]|
|**Col. UK Exporter**|**0.358*****|**0.105*****|
||[0.011]|[0.011]|
|Tariff (RG Weighted)|-0.127***|0.151***|
||[0.024]|[0.026]|
|NTB (RG Weighted)|-0.023|0.031|
||[0.021]|[0.022]|
|GDP Importer|0.040***|0.021***|
||[0.004]|[0.004]|
|GDP Exporter|0.019***|0.046***|
||[0.004]|[0.004]|
|Observations|29,376|29,376|
|R2|0.75|0.76|
|InstrumentF-Test|4691.78***|5156.51***|

Robust standard errors adjusted for clustering by country-pair in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%. Estimation method is OLS with fixed effects by country pair and HS 2-digit sector.

**Table 8 Simulated import and export gains by economy (% of baseline).**

||**Sc**|**enario I**|**Scen**|**ario II**|**Scena**|**rio III**|
|---|---|---|---|---|---|---|
||**Imports**|**Exports**|**Imports**|**Exports**|**Imports**|**Exports**|
|**AUS**|0.00|11.42|0.40|1.11|0.55|2.50|
|**CAN**|0.00|1.22|0.08|0.09|0.10|0.50|
|**CHL**|0.00|10.69|0.59|0.23|0.36|9.01|
|**CHN**|28.99|3.81|2.83|0.83|2.00|1.89|
|**HKG**|0.00|16.90|0.00|2.41|0.10|4.60|
|**IDN**|20.25|7.71|1.59|1.21|0.06|4.88|
|**JPN**|0.00|10.94|0.07|1.83|1.46|1.56|
|**KOR**|0.40|14.13|0.92|1.86|0.00|1.38|
|**MEX**|17.73|0.48|1.72|0.08|4.04|1.10|
|**MYS**|12.13|7.78|3.75|0.63|7.52|1.40|
|**NZL**|0.00|5.01|0.10|0.44|2.55|2.55|
|**PER**|31.00|2.04|3.88|0.17|0.71|2.53|
|**PHL**|47.59|8.21|0.20|0.44|11.15|1.38|
|**RUS**|100.66|13.93|5.44|1.50|5.90|1.95|
|**SGP**|0.00|12.90|0.00|0.63|7.59|1.32|
|**THA**|36.65|8.49|7.62|0.75|0.19|2.87|
|**USA**|0.00|8.46|0.03|0.45|1.22|2.12|
|**VNM**|73.55|5.41|8.16|1.19|0.00|7.24|

37
CONSUMPTION SUB-AGGREGATES 20 It remains possible that Engel curves in fact possess the desired linearity, or that changes in Engel curves due to changes in relative prices are negligible in real-world data. Accordingly, we use data from Rwanda, Uganda, and Tanzania and search across different goods to identify those that feature (nearly) linear Engel curves in a base period. We then use these goods to construct a sub-aggregate in a subsequent period. Consistent with theory, the performance of this method turns out to be extremely bad at measuring head-count poverty rates. One should _not_ construct sub-aggregates for this purpose by relying on the fact that Engel curves may be linear at some time and place, because they are unlikely to be linear in a different time or place since prices are likely to be different.

Perhaps there are better ways to construct a sub-aggregate? Our conditions are both necessary and sufficient, so theory tells us that the answer is “no”. Another idea often used in practice involves constructing a sub-aggregate by choosing goods with large expenditure shares. If one can do this in such a way that a very large percentage of _all_ households expenditures were in the sub-aggregate this would hold promise. But if Engel curves aren’t linear then there will be variation in expenditure shares across households, and identifying goods that have large shares on average will tend to select goods that have a low income elasticity. This is more or less the opposite of what one would wish, since expenditures on these goods will convey little information about underlying household resources.

Finally, as a practical matter, any measured consumption aggregate is likely to be an incomplete or a reduced aggregate approximation to the full aggregate. For instance, the number of consumption items (or categories) for which data are collected from households in LSMS surveys ranges from 37 to 305, with the mean being 137 and the median 130 (Beegle et al. 2010). Arguably the value of public goods and time and leisure should also be included in a comprehensive or “full” consumption expenditure aggregate for measurement of poverty, but in most cases this consumption is excluded. The consumption of publicly provided health and education, as well as the use value of durable goods are currently included in some countries, but excluded in others. These matters have been largely ignored in poverty tracking, but our results strongly suggest that they may be of first order importance.
characteristics of migrants (Singh 1998; Lusome and Bhagat 2006; Hnatkovska and Lahiri 2015). A more recent study (Pandey 2014) documents a slight upward trend in the overall level of migration from the early 1990s, primarily driven by increased intra-district and intra-state movements.<sup>4</sup> Studies by Bhattacharyya (1985), Munshi and Rosenzweig (2016) and Viswanathan and Kumar (2015) are exceptions that move beyond descriptive analyses. While the last paper examines how migration responds to environmental changes, the first two papers provide an explanation for the low levels of rural to urban migration in India. Bhattacharyya (1985) presents a theoretical framework for developing countries, in which migration decisions are more likely to be taken at the (extended) family level as opposed to the individual level, with the objective of increasing overall family income. Closely related, Munshi and Rosenzweig (2016) explore the linkages between the caste networks in rural areas and migration incentives. They argue that emigration of an income-earning individual reduces the family’s access to the caste network as a social safety net. This reduces the incentives for internal migration considerably. While this explanation addresses low rural to urban migration, where community networks exert a strong influence on the decisions of members, it does not explain why urban to urban migration is also low or why we observe differences in migration patterns across state borders.

Mobility across certain administrative boundaries can be costly, especially if these boundaries reflect differences in societal characteristics such as language, culture, laws and institutions or geographic barriers (Belot and Ederveen 2012). The first study to point out such a cost was by McCallum (1995) using trade as an example. McCallum showed that Canadian provinces adjacent to the United States trade more with their neighboring provinces than with the states in the U.S.<sup>5</sup> Subsequent studies have confirmed McCallum’s findings and unearthed evidence of a border cost in the case of 4Government of India (2017) also finds an upward trend in migration using estimates based not on actual migration but on railway passenger data and changes in the population within state and district level age cohorts.

> 5There is now an extensive literature on the role of national borders in trade, as reviewed in Anderson and van Wincoop (2003; 2004).

5
# **Table S1.5: Robustness: Short-term vs long-term enumerators**

|||_Short-term_|_enumerators_|||_Long-term en_|_umerators_||
|---|---|---|---|---|---|---|---|---|
||DP|FDC|NRM|UPC|DP|FDC|NRM|UPC|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Enumerator gender (male)|-0.923***|-0.654**|-0.127**|-0.664***|-0.669***|-0.557***|-0.080|-0.686***|
||(0.192)|(0.245)|(0.059)|(0.180)|(0.122)|(0.075)|(0.051)|(0.152)|
|Enumerator age|0.039**|0.049***|0.004|0.011|-0.074***|-0.085***|0.001|-0.104***|
||(0.016)|(0.012)|(0.006)|(0.022)|(0.016)|(0.015)|(0.010)|(0.030)|
|Enumerator education|-0.093|-0.014|-0.102|-0.102|0.178***|0.124**|-0.083*|0.040|
||(0.108)|(0.141)|(0.070)|(0.118)|(0.055)|(0.044)|(0.044)|(0.087)|
|Enumerator from urban locality|0.569***|0.740***|-0.022|0.551***|0.645***|0.668***|-0.010|0.436***|
||(0.180)|(0.225)|(0.054)|(0.166)|(0.122)|(0.109)|(0.044)|(0.058)|
|Observations|4,044|4,114|4,239|4,116|2,201|2,219|2,314|2,204|
|R-squared|0.452|0.423|0.241|0.438|0.675|0.618|0.353|0.642|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). _Long (short)-term enumerator_ is defined to be an enumerator who worked at least (less than) 25 days (which is the 75<sup>th</sup> percentile of the total number of work days during the survey for the population of enumerators). Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

38
# Table 5. Incumbent Victories and Violent Incidents: Panel Regressions

|||Po<br>Dependent v<br>|isson Estima<br>ariable: Viol<br>|tes<br>ent incidents<br>||
|---|---|---|---|---|---|
||(1)<br>|(2)<br>|(3)|(4)<br>|(5)<br>|
|Incumbent victory _×_ post-election|0.052**|0.046**|0.031|0.045**|0.060*|
||(0.022)|(0.019)|(0.020)|(0.021)|(0.035)|
|Incumbent victory|0.0012<br>|–0.0034<br>|0.0034<br>|0.0038<br>|–0.013<br>|
||(0.012)|(0.010)|(0.011)|(0.011)|(0.021)|
|Margin _×_ post-election|–1.01*|–0.93**|–0.92**|–0.89*|–0.86**|
||(0.54)|(0.46)|(0.46)|(0.45)|(0.43)|
|Margin|–0.040|0.10|0.10|0.097|0.12|
||(0.40)|(0.32)|(0.31)|(0.30)|(0.29)|
|Margin _×_ incumbent victory _×_ post-election|0.24|0.35|0.35|0.33|0.31|
||(1.08)|(0.85)|(0.84)|(0.81)|(0.74)|
|Margin _×_ incumbent victory|–0.30|–0.46|–0.46|–0.44|–0.41|
||(0.56)|(0.45)|(0.45)|(0.43)|(0.41)|
|Post-election period (12 months)|–0.049***|–0.044***|–0.043***|–0.057***|–0.063***|
||(0.017)|(0.015)|(0.014)|(0.016)|(0.022)|
|Time trend (months)|||–0.000054<br>(0.00091)|0.00097<br>(0.00090)||
|Time trend squared (months)||||0.00013**<br>(0.000062)||
|Time trend _×_ incumbent victory|||0.0011<br>(0.0013)|0.000039<br>(0.0012)||
|Time trend squared _×_ incumbent victory||||–0.00013<br>(0.000078)||
|Gov. coalition _×_ inc. victory _×_ post-election|||||–0.021<br>(0.030)|
|Governing coalition _×_ incumbent victory|||||0.017|
||||||(0.018)|
|Governing coalition _×_ post-election|||||0.012|
||||||(0.019)|
|Governing coalition|||||0.0021<br>(0.0083)|
|Mean of dep. var.|0.023|0.023|0.023|0.023|0.023|
|Control variables|No|Yes|Yes|Yes|Yes|
|No. of municipalities<br>No. of observations|3672|3672|3672|3672|3672|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ All regressions are weighted by a triangular kernel with a bandwidth of 0.05. The unit of observation is the municipalitymonth. The sample is restricted to observations within 12 months of the 2007 election (the month of the election is dropped). Marginal effects calculated at the sample means are reported. Standard errors, clustered at the municipality level, are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels. Control variables are the same as in Table 3.

26
the EU-27, Japan, and the Russian Federation grew. Thus, the negative impact of the economic recession was not yet apparent in the annual import statistics for 2008 (see Table 1).

# **Table 1: Shifts in Top 15 World Apparel Importers: 1995, 2000, 2005, & 2007-2008**

[Top 15 by Year; Values in $US Billions, at Current Prices]

|**Country/ Region**|**1995**||**2000**||**2005**||**2007**|**2008**||
|---|---|---|---|---|---|---|---|---|---|
||**Value**|**%**|**Value**|**%**|**Value**|**%**|**Value**|**Value**|**%**|
|**World**|162.9||208.9||291.2||358.1|375.6||
|EU-27 (h)|74.2|45.5|83.2|39.8|131.5|45.2|165.0|177.7|47.3|
|United States|41.4|25.4|67.1|32.1|80.1|27.5|84.9|82.5|22.0|
|Japan|18.8|11.5|19.7|9.4|22.5|7.7|24.0|25.9|6.9|
|Russian Federation(a)|--||2.7|1.3|7.9|2.7|14.5|21.4|5.7|
|Canada (b)|2.7|1.7|3.7|1.8|6.0|2.1|7.8|8.5|2.3|
|Switzerland|3.8|2.3|3.2|1.5|4.5|1.5|5.2|5.8|1.5|
|United Arab Emirates (c)|1.3|0.8|--||1.8|0.6|5.0|5.5|1.5|
|Australia (b)|1.3|0.8|1.9|0.9|3.1|1.1|3.7|4.3|1.1|
|Korea,Republic of|1.1|0.7|1.3|0.6|2.9|1.0|4.3|4.2|1.1|
|Norway|1.4|0.9|1.3|0.6|1.8|0.6|2.3|2.7|0.7|
|Mexico (b, d)|1.9|1.2|3.6|1.7|2.5|0.9|2.5|2.5|0.7|
|China (e)|1.0|0.6|1.2|0.6|1.6|0.6|2.0|2.3|0.6|
|Singapore|1.6|1.0|1.9|0.9|2.1|0.7|2.4|2.2|0.6|
|Turkey|--||--||--||--|2.2|0.6|
|Saudi Arabia|--||--||1.5|0.5|1.9|--||
|Honduras (f)|--||1.3|0.6|--||--|--||
|TaipeiChinese|09|05|10|05|--||--|--||
|, <br>**Top 15 Share & % of Wo**|.<br>**rld Tota**|.<br>**l Impor**|.<br>**ts**|.||||||
||**151.3**|**92.9**|**193.0**|**92.4**|**269.9**|**92.7**|**325.5**|**347.8**|**92.6**|
|HongKong, China (g)|12.7||16.0||18.4||19.1|18.5||

Source: (WTO, 2010); Apparel represented by SITC Code 84 --Indicates country not in Top 15 that year (a) Estimated value: coverage: includes intra-trade; (b) Method of valuation: imports are valued f.o.b.; (c) Estimated value; (d) Coverage: Includes processing zones; (e) Trade system: prior to 1992: CT data reported in HS; (f) First year processing zone trade included; break in data continuity with data from earlier years; (g) Value of Hong Kong, China not included in world totals due to large portion re-exported and not retained; (h) EU values include intra-EU trade; values only represent EU-15 in 1995.

A closer look at the shifting apparel imports of the United States, the EU-15, and Japan provides more detailed evidence of the impact of the economic recession on global apparel supply and demand.

**United States.** In 2008, U.S. consumers spent $200 billion on apparel, down 3.6% from 2007, and apparel spending in the first quarter of 2009 was also down 10% from the same period in the previous year (Driscoll & Wang, 2009). Apparel sold and consumed in the United States has a very high import ratio, which has been increasing for decades. In 2006, the estimated overall apparel import penetration was 94% (Clothesource, 2008).  In 2008, the percentage of imports to apparent U.S. consumption of men’s, women’s, and children’s apparel ranged from a

4
However, it is also important to note that the gap between the consumption aggregates from these two sources does not imply that the NSS overestimates poverty. Some of the gap is due to errors in NAS consumption, which is determined residually in India, after subtracting other components of domestic absorption from output at the commodity level. There are also differences in the definition of consumption, and there are things included in NAS consumption that should not be in a measure of household living standards.<sup>26</sup> Some degree of under-reporting of consumption by respondents, or selective compliance with the NSS’s randomized assignments, is inevitable. However, it is expected that this is more of a problem for estimating consumption by the rich than the poor.<sup>27</sup> While we cannot rule out the possibility that such problems lead us to overestimate poverty in India—and an external review of the procedures used by the NSS, in the light of international best practice, is called for in our view—it is hard to justify the practice used by some analysts of replacing the mean from the NSS by consumption per capita from the NAS, while assuming that inequality is correctly measured by the NSS.

For the for the same reason that the consumption aggregates from the NSS are diverging from the private consumption component of domestic absorption as estimated by the NAS, one cannot rule out the possibility that the increase in inequality in India is being underestimated by the NSS. That depends on why we are seeing the divergence between NSS and NAS aggregates; if it stems from a failure of the surveys to fully capture the rising consumptions of the rich then it is not clear that there will be much bias in the poverty measures based on the surveys.<sup>28</sup>

# **4. Results**

We begin with an overview of the trends in the variables of interest, both for the 50 year period and the periods before and after 1991. We then present the estimated growth elasticities of poverty reduction, also looking separately at urban and rural areas and their interaction. _Trends over time_

> 26 For further discussion of the differences between the two data sources see Sundaram and Tendulkar (2001), Ravallion (2000, 2003), Sen (2005) and Deaton (2005).

> 27 There is evidence from other sources consistent with that expectation; see Banerjee and Piketty (2006) on income under-reporting by India’s rich.

> 28 For a more complete discussion of this issue see Korinek et al. (2006).

15