rafmacalaba/datause-encoder-data huggingface dataset:
procedures completed by the providers. Direct observation data collected in the facilities also allow us to consider other proxies for the quality of care, such as the length of the consultation and whether the procedures were explained to the patients.

We first estimate the overall differences in access to antenatal care by comparing coverage and effective coverage among women of different wealth levels. We use concentration indexes to characterize inequality in antenatal care, following previous work by Wagstaff, Paci, and Van Doorslaer (1991). we show that inequality in effective coverage is more than three times as large as inequality in coverage alone. We then estimate a wealth‐quality gradient and show that each standard deviation increase in household wealth is associated with a four‐percentage‐point increase in quality of antenatal care, as measured by protocol completeness, corresponding to eight percent of average quality in the lowest quintile. Similarly, the proportion of procedures received by women with secondary education is nine percentage points higher than that received by those without schooling.

Next, we decompose the observed wealth gradient into three main components. First, we expect health facilities in wealthier areas on average to be of higher quality, explaining at least part of the observed wealth‐quality gradient. Indeed, controlling for village of residence reduces the wealth‐quality gradient from 4 to 1.8 percentage points of completeness, suggesting that spatial differences explain about half of the overall gradient. The remaining wealth gap represents within‐village variation in the quality of care. The within‐village wealth gradient appears to operate mostly through facility choice: we show that wealthier women are more likely to visit a facility outside their catchment area or go to a hospital or other higher‐level facility. Nevertheless, we also find a within‐facility quality‐wealth relationship, suggesting that even conditional on the specific facility chosen, wealthier women receive higher quality of care.

To understand within‐facility differences in the quality of care, we use detailed exit interview data to analyze the specific services provided and prices charged to patients from various socioeconomic backgrounds. As expected, we find that the overall consultation price is positively correlated with the number of services provided, but also, conditional on quality, with the patient’s socioeconomic status. The positive association between wealth and price could be interpreted as evidence of cross‐ subsidization or price discrimination for profit maximization. If facilities charge wealthier women higher prices to subsidize service provision to the poor, we would expect the difference in prices paid by women of different wealth levels to decrease in the ratio of wealthy‐to‐poor, because each wealthy woman would need to subsidize fewer poor women. But we observe the opposite: the price differences 3
# **1. Introduction**

While economic theory has long advocated openness to trade as an important element of sound economic policy, the empirical evidence on the actual effects of trade liberalization on income distribution and welfare have been difficult to measure. Recently, the debate on the effects of globalization on poverty has rekindled the debate. At an analytical level, it is recognized that one must trace through the effects of trade policy changes on household welfare via the price transmission mechanism (say how much of the tariff reduction gets translated into changes in households' disposable income), via effects on household earnings (significant trade policy changes have general equilibrium repercussions via adjustments in factor markets), and via adjustments in the public sector (tariff revenue changes). It is also recognized that effects will not be evenly spread across the economic landscape, i.e., regional effects are likely to be important, as some regions will be isolated from the effects of changes in trade policy (often regions where markets are not developed and where the poor are concentrated). McCulloch et al. (2002) give a thorough description of these transmission channels.

In the past, two approaches have been used to try to identify the effects of trade policy changes on income distribution and poverty at the household level: ex-ante simulations and ex-post econometric analysis based on household survey data. This paper belongs to this latter category which goes back to Deaton (1989).<sup>1</sup> Recent work in that tradition includes Levinshon (1999), Edmonds and Pavcnik (2003), Goldberg and Pavcnik (2003) and Porto (2003). The distinctive feature of this paper, spelled out below, is the availability of a half-dozen household surveys spanning virtually the entire period of trade liberalization in Mexico. Arguably the analysis of such a dataset allows for a much better identification of the likely effects of trade liberalization on household welfare. I provide estimates of the effects of Mexican trade liberalization throughout the 1990s, relying on six household surveys spanning the period 1989 to 2000, with transmission effects of changes in trade policy being calculated on the basis of 12 important commodities in the consumption basket of Mexican households.

1 Reimer, 2002, offers a review of the literature.
seekers and 46,647 Venezuelans refugees, who had entered Brazil since July 2017. The quarterly flow of asylum seekers peaked in the third quarter of 2018, while the quarterly flow of migrants peaked in the last quarter of 2019. The Venezuelan refugees and migrants increased from about 14,000 in the first quarter of 2015 to about 263,000 in the second quarter of 2020.

<!-- Start of picture text --> 263392<br>252168<br>173298<br>127841<br>2019m1 2019m7 2020m1 2020m7<br>Time<br>Entries Exits<br>Stock<br>30000 300000<br>250000<br>20000<br>200000<br>10000<br>150000<br>0<br>100000<br><!-- End of picture text --> Figure 2: Refugee and Migration Trend of Venezuelans in Brazil Source: Author’s calculation from SISMIGRA and STIMAR.

In Brazil, estimates show that most Venezuelan refugees and migrants enter and settle in Roraima (50%) and Amazonas (19%), which is not surprising given that these states border the Republica Bolivariana de Venezuela at the north. The north region of Brazil is also the poorest traditionally, contributing only 4.7% of GDP in 2016 for instance. The population of Roraima in 2016 was about 0.5 million, one of the lowest among the states in Brazil and contributing only about 0.2% of the Brazilian GDP. Consequently, after the massive Venezuelan inflow, the Venezuelan refugees and migrants quickly comprised of about 30%<sup>10</sup> of the population of Roraima and the state received help from the federal government, UNHCR, faith based organizations and civil society partners to manage its response to the influx and provide humanitarian assistance. The initiative is called _“Opera ̧c ̃ao Acolhida”_ (Operation Welcome) and has three main programs: border management and documentation; provision of humanitarian assistance including shelter; and “Interiorization” which involves the voluntary relocation of Venezuelans from Roraima to other cities. The voluntary

> 10Author’s calculation from SISMIGRA and STIMAR. It may overestimate the Venezuelan population in Roraima since the database is not updated regularly.

6
3 based participation in the private sector can ease social tensions, especially<sup>in countries where</sup> there are strong divisions between the economicaUy dominant group, and other groups in society. Fourth, there is some evidence that a substantial role for SMEs contributes to<sup>a more</sup> equitable distribution of inconle.A/ Given these four lines of reasoning, the corollary follows that if government can intervene in a cost-effective manner to improve the prospects of SME success beyond what would be achieved in a wholly private marketplace, it should do so. Taken together, the two presumption **s** hift the terrain of cebate as to whether government **s** hould intervene to strengthen narketing and technological support systems<sup>from</sup> an ideological to an empirical one. Surprisingly, while there is ample documentation<sup>of failed</sup> atempts at intervention, there has been remarkably little empirical research as to whether collective (governmental, business association and NGO) interventions have been impoItant<sup>for</sup> the successful development of support systems.2f The research summaried here is the<sup>fruit of</sup> a susined effort to fill this gap.

Mefibgoggx- Our starting point for analysis of the role of collective interventions was with the SMEs themselves. We sought to learn from stuctred field inteviews how these firis took care of their marketing and technological tasks, to what extent they drew on<sup>external</sup> supports to undertake these taslcs, and what was the relative utilization and usefilness<sup>(as</sup> perceived by the finms themselves) of private and collective supports. Our goal in adopting this approach, rather than studying directly specific imtventions made by colective istuons, was to anchor any broader judgements as to the role of government in an ue ng of the dynamics of the private marketplace itself.

_fit_ See, for example the analysis in Fei, Ranis and Kuo (1979) of the relation between the evolution of income distrbution in Taiwan and the role of SMEs.

-U - For some studies of the role of technical and mar support, see Hogan. Keesing and Singer (1991); Schmitz and Musyck (1993); Cortes, Berry and Lshaq (1987);<sup>Berry and</sup> Mazumdar (1991); and Nugent (1990). Rhee, Ross-Larson and Pursell (1984) used a... methodology similar to the present one to examine the acquisition of technical and maketing. capability by large firms.
Figure 6 Theil index for OECD imports excluding China, 1963-2006 Extra-OECD imports Extra-OECD imports excluding China <!-- Start of picture text --> 3.3<br>3.3<br>3.2<br>3.2<br>Theil<br>3.1 3.1<br>3 3<br>2.9 Theil 2.9<br>2.8 2.8<br>2.7<br>2.7<br>1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003<br><!-- End of picture text --> Table 7 Regression results, OECD import concentration on time trend, excluding China

|Regressors:|Herfindahl|Theil|
|---|---|---|
|time|-0.004***|-0.013***|
||(-17.08)|(-24.61)|
|timesq|0.000*|0.000|
||(1.925)|(0.0720)|
|Constant|0.419***|3.302***|
||(218.4)|(677.4)|
|Observations|53763|53763|
|R-squared|0.531|0.581|
|Product FE|yes|yes|

Notes: t statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 Figure 7 confirms that the increased concentration of OECD imports on China occurs at the intensive margin: In recent years, no new product line opened between China and its OECD trade partners.

26
community radio; this variation is exogenous to village-level characteristics. We therefore study this area, taking advantage of this natural experiment in radio access.<sup>6</sup> A map of our study area is provided in Appendix 1. To determine radio access, village-level key informants listed which radio stations they were able to receive. In addition, enumerators used their own transistors to verify and expand on this if they received additional signals. The codes of the radio listed as accessible to the village were then matched with the radio survey data to examine the nature of programming available to the village. Table 1 summarizes the number and types of radio stations covering this study area.

**Table 1:  Access to Radio Stations in North Benin**

||No. of<br>stations<br>accessible|No. of<br>sampled<br>villages|Average<br>Signal<br>Strength|Average num<br>broadcast o<br>mo|ber of programs<br>ver previous 3<br>nths|
|---|---|---|---|---|---|
|Type of Radio<br>station:|to the<br>region|covered<br>(Total=210)||Health|Education|
|Government-<br>owned public|3|200|2667<br>watts|40|33|
|Private Non-<br>commercial or<br>Community|21|195|231<br>watts|138|50.6|
|Private<br>Commercial|10|49|476<br>watts|27|21|
|Religious|2|71|.|36|.|

All of the community radio stations list the provision of information on health and education as “very important” to their objectives. The average number of health programs (138) is more than three times as many as public or private commercial radio. The average number of education programs reported by station managers is almost double for community radio compared to public radio, and almost three times that of private commercial radio. Hence, access to more community radio stations is associated with exposure to more programming on the value of health and education services.

The importance of public interest programming for community radios emerges in part because, of the 21 community radio stations to which our village sample has access, 14 are dependent on foreign donors and three on non-governmental organizations for funding; none receive meaningful revenues from advertising. European donors funded community radio across several African countries to promote local development initiatives in remote and rural areas (Gratz, 1999). Donors believed that these small radio stations would communicate public interest messages

> 6 Only the northern commune of Parakou is excluded from our study because it exhibits relatively little withincommune variation in radio access.  It is the second largest city of Benin, after the capital, Cotonou, in the south.  All other communes in the north are included.

8
Figure 3: PMT performance and sample size <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Undercoverage (%) Leakage (%) Eligibility (%) Poverty rate (%)<br>N=100 N=250 N=500 N=750 N=1000 N=10000 Full sample (N=21,757)<br>Percent<br><!-- End of picture text --> Figure 3 presents the results of these simulations. Each tick mark on the X‐axis represents results from a simulation of a certain sample size. The different lines represent undercoverage, leakage, eligibility, and poverty rates estimated for different samples. Figure A1 plots PMT scores estimated from the full sample on the Y‐axis against the one estimated from a smaller sample on the X‐axis. In Figure 3, we see that undercoverage is the lowest for the smallest samples. This is because the consumption distributions predicted by the PMT model based on larger samples have thinner tails than the distribution of actual consumption based on the full sample (Figure A2).<sup>14</sup> In other words, consumption estimated from larger samples assigns fewer people as poor than there actually exist at any given poverty line. We can also see this in the yellow line in Figure 3, which is sloped slightly downwards. The graph also suggests a trade‐off between undercoverage on the one hand and leakage and eligibility on the other hand for samples smaller than 1,000 households. We see the lines leveling off between sample size of 750 and 1,000 households, suggesting the optimal sample size to be around 1,000 households.

# _PMT performance and survey period_

Another important question in the design of PMT pilots is the survey period. Ideally, the survey would be fielded uniformly across the quarters so that the consumption estimates (which often rely on a recall or diary of consumption in the previous week or two weeks) are not biased by seasonality. However, doing this in practice may not be feasible due to logistical or budgetary reasons. This raises the following question: how does the performance of the PMT model change across different calendar quarters? We address this question by implementing Model 1 on quarterly subsamples of HIES 2016. In other words, we do not change the variables in the PMT model, but only estimate its coefficients for the quarterly subsamples and predict consumption that can be rescaled to obtain the PMT scores. These PMT scores and the actual consumption values are then used to measure the performance metrics of undercoverage, leakage, and eligibility.

14 The Law of Large Numbers suggests that the estimate of the consumption based on larger samples will have a smaller variance than estimates based on smaller samples.

15
across sectors to the CGE model based on the GTAP database.. The Institute for Applied System Analysis (IIASA) reports PM10 and other secondary emissions for India, which corresponded to almost to 8.7 million tons in 2010. The emission coefficients based on Garbaccio et al. (2000) were updated to reproduce the aggregate PM10 and other small particles level for India in 2005 and then extrapolated following the growth assumptions in BAU.

25. Table 3 in Annex 1 represents the relative shares of PM10 emissions by sector and energy (αi,j).

26. Emissions from productive activities and the respective coefficients (β _i_ ) were calculated as follows:

- First, the shares of production activities and process (XP) and energy use (C) related emissions in total emissions (E) were calculated as per the Garbaccio et al. (2000) study.

- Second, sector-specific emission coefficients in Garbaccio et al. (2000) were readjusted according to the GTAP classification in proportion to the sector‘s contribution to overall PM10 emissions and overall emission estimates from the IIASA model.

27. On the basis of equation (1) and the CGE simulations, the increase in PM10 emissions and other particulate emissions over time was calculated as a function of the demand for each type of energy by sectors (C _i,j_ ), and the economic expansion of production activities (XP _i_ ).

28. Second, the emissions coefficients αij and βi are modified over time to account for the improvements in the emission-capturing technologies, ; through (a) a shift to cleaner coal (imported coal has lower emissions per unit of energy than domestic coal and its share in the total amount of coal used in India is rising); and (b) other measures such as coal washing. These reductions in emissions are partly driven by administrative measures, and partly by trade factors and such improvements are included in the BAU. The rates of decline in unit emission are for these reasons taken from micro studies (see Cropper et al, 2012). Further reductions in the coefficients may be achieved through a tax on PM10 and similar emissions. Such reductions in the coefficients reflect the impact of further pollution control measures that will be introduced as a result of the tax.<sup>21</sup> 29. The energy demand in value (US$) for four fuel types — coal, crude oil, oil and coal products, and natural gas — were obtained for 2010–2030 using the CGE model. This was converted into volume in terms of Thousand Tons of Oil Equivalent (TTOE) using appropriate factors.

# **Main Results**

## **_<u>PM10 and other particle emissions</u>_**

30. Fossil fuel use, the primary cause of pollution, is expected to decrease under BAU due to a declining share of coal in the overall energy demand (although coal would still dominate in 2030); to greater emissions capture; and to the shift to cleaner coal. Demand for refined oil products and electricity, however, will still increase considerably. As a result, the share of emissions from productive activities in total

> 21 The PM10/CO2 elasticity varies across scenarios, the average is found to be 1.62 which means that 1 unit of CO2 abatement will bring 1.62 unit of PM10 abatement.

13
**Figure 23. Semiconductors lead exports**

<!-- Start of picture text --> Contributions to export growth<br>16<br>Semiconductors<br>14 Other manufactures<br>Garments & textiles<br>Mineral and petroleum products<br>12 Agro-based and forest products<br>Export growth<br>10<br>8<br>6<br>4<br>2<br>0<br>2003 2004 2005 2006<br>-2<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>2003<br>-2<br><!-- End of picture text --> <!-- Start of picture text --> 2004<br><!-- End of picture text --> <!-- Start of picture text --> 2005<br><!-- End of picture text --> <!-- Start of picture text --> 2006<br><!-- End of picture text --> _Source:_ National Statistics Office, 2007.

**Figure 24. TFP is leading the way**

<!-- Start of picture text --> Growth accounting<br>4.0<br>2.0<br>0.0<br>-2.0<br>-4.0<br>-6.0<br>-8.0<br> 1960-80 1981-85 1986-03 2004-06<br>COntribution to growth<br>Physical K Human K TFP<br><!-- End of picture text --> _Source:_ World Bank, 2007a.

**Total factor productivity (TFP) - rather than capital or labor - spurred recent growth.**

Although TFP growth has been much lower than in the rest of East Asia (World Bank, 2005a), it picked up significantly during 2004-06. While two years might be too short of a time span for reaching robust conclusions, the recent spike might be capturing cyclical effects in capacity utilization and the lagged effect of past structural reforms (Figure 24).

28
Facility (CPLF)—to enhance liquidity during the height of the crisis, European banks only had the standard discount window of the ECB (along with currency swap agreements, although these likewise had to be intermediated by the ECB). For the U.S., CDS interacted with the second phase of the crisis is also positive and significant, meaning that the positive association increased during this phase, which is consistent with the policy response explanation. For the EU, the analagous interaction term is negative and significant, meaning that EU bank lending became more sensitive to solvency problems during the acute second phase of the crisis as well.

The results on liquidity also add some nuance to the benchmark estimates. For U.S. banks, Libor-OIS is associated with decreased lending to developing countries (in specifications ( _S1_ ) and ( _S2_ )), and the sensitivity to Libor-OIS does not change during either phase of the crisis. For EU banks, on the other hand, the Euribor-Eonia spread is associated with increased lending during normal times, but decreased lending during the second phase of the crisis. This can be seen in the overall foreign claims patterns in Figure 2.

As for the uncertainty channel, U.S. lending appears to have been negatively associated with volatility during the first phase of the crisis only, and insensitive to it at other times. EU lending is negatively associated with volatility in good times and bad, but was especially so during the second phase of the crisis, despite the efforts of the Vienna Initiative to prop up credit provision to Eastern Europe.

# **5 What is the Role of the Interest Rate?**

Up till this stage, our examination of the three main transmission channels has taken the form of Equation (7), which deliberately suppresses any discussion of internal demand conditions in developing countries. To the extent that we incorporated demand considerations, this was through a measure—changes in demand conditions reported by the Federal Reserve—which proxies for external demand conditions, mainly from high-income countries. While we certainly recognize that (7) does not fully account for internal loan demand, our preference for this approach—at least at the outset—was conditioned by a desire to focus which transmission channels affect the supply side, along with the desire to introduce interaction effects between the crisis and the main coefficients of interest ( _β_ 1, _β_ 2, and _β_ 3).

In this section, we substitute the generic measure of demand conditions with country-specific interest rates. Accordingly, (7) can be re-expressed as where _rit_ is the domestic lending rate in country _i_ at time _t_ , which now more fully approximates the potential effect of local demand for bank loans. Of course, since interest rates are codetermined by bank lending as well as saver deposits, _r_ is endogenous. Consequently, we require an instrument to identify the loan demand function, and (8) is estimated via instrumental variables (IV). For this purpose, we rely on country demographics; in particular, we exploit the 18