rafmacalaba/datause-encoder-data huggingface dataset:
The results show that in all cases these subsidies are regressive. Moreover, when a comparison is made between 2002 and 2006, it is clear that subsidies are becoming more regressive (Figures 10 and 11). Besides, as shown in Figure 12, errors of inclusion increased significantly, another indication of the failures associated with supply side subsidies. Overall, the analysis shows that share of subsidies accruing to the middle and high income households is increasing. The estimated Quasi-Gini coefficients for each subsidy in 2006 was 0.06 for AMBA, 0.2 for Colectivos, and 0.48 for SUBTE).

**Figures 10 and 11: Distributive incidence of transport subsidies in Greater Buenos Aires**

<!-- Start of picture text --> Year 2002 Year 2006<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Households deciles from poorest to richest Household deciles from poorest to richest<br>Bus Metro Train Neutral distribution<br>% of accumulated benefits % of accumulated benefits<br><!-- End of picture text --> Source: Bondorevsky (2007).

**Figures 12 and 13: Errors of exclusion and inclusion of transport subsidies in Greater Buenos Aires**

<!-- Start of picture text --> Year 2002 Year 2006<br>100% 100%<br>90% 91% 97% 90% 92% 89%<br>80% 73% 80% 75%<br>70% 70% 70% 68% 71%<br>60% 59% 57% 60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Bus Train Metro Bus Train Metro<br>errors of exclusion errors of inclusion Errors of exclusion Errors of inclusion<br><!-- End of picture text --> _Source_ : Bondorevsky (2007).

# _Mexico City_

A recent case study by Flynn (2007) shows that, contrary to popular beliefs, transport subsidies in Mexico city are not particularly pro-poor. Table 5 shows some of the distributive parameters of the different subsidies considered, as well as an estimate of the Ω coefficient based on the information presented in that study.

36
; few strategic areas. Improving access to maternal and child health services and changing health-seeking behaviour are two particularly important measures.

Access and quality of maternal and child health services im remote areas - Vietnam has an extensive health care delivery network with a large supply of health workers and well-organized national public health programs. However, the quality and use of service in poor and remote areas is low. In remote areas, where the density of population is low and it is not feasible to provide institution-based quality services, the government should consider providing round-the-year services through strengthened outreach, health posts, mobile teams and intensive campaigns. The poor provinces in the central and northern part of the country require special attention.

Tackling demand side issues - Children of poor women with low levels of education are more likely to die prematurely. These are the women who have lower demand for reproductive and child health services. Culturally sensitive communication activities will be required to generate better demand for reproductive and child health services. In addition, one of the major demand side constraints for utilization of child health services for poor households is the affordability of services. There is an urgent need to rationalize the health financing structure and make it more equitable. The Government needs to allocate more resources to improve the affordability of health services for the poor. There is a clear commitment to addressing this pressing need in the CPRGS.

# Malnutrition: achievements to date

Child malnutrition is still high - Unlike many other health outcome indicators where Vietnam outperforms expectations for its per capita income levels, child mainutrition levels remain one of the highest in the region: in 2000, one third of children under the age of five were underweight. Despite the good progress in recent years, Vietnam still has one of the highest rates of child malnutrition in the region. As shown in Table 8, the country lags behind many other developing countries and East Asia and Pacific countries. Even the latest figures at the end of the last decade do not compare favorably with other countries. Only countries in South Asia have performed consistently worse than Vietnam.

Table 8: Malnutrition - International Comparison

||PercentofUnderweightChildren (0-5 years)|
|---|---|
|Country|(period 1993 -2000)|
||a|

# Source: World Bank (2002c)

But, the current rate of malnutrition has to be viewed in the context of even higher levels in the recent past. In fact, there has been a remarkable decline in the prevalence of malnutrition in the last decade. Prevalence of malnutrition has come down from 52 percent in 1985 to 45 percent in 1995, 37 percent in 1999 and 33 percent in 2000.

26
should be noted that employment estimates from the two sources are not strictly comparable, and any differences may be the result of several factors, including the aforementioned recall bias, differences in the number, sequence and wording of questions, differences in the timing of surveys, and important distinctions between the populations represented.<sup>13</sup> More details on this comparison exercise can be found in Annex 4. The analysis suggests that despite some differences in magnitude, the HFPS and LFS capture similar employment dynamics in most countries with overlapping data (Figure A4.1). The share of respondents currently working in the HFPS is strongly correlated with the employment population rate reported in the ILO database (Pearson correlation coefficient=0.604, _p<_ 0.001; R<sup>2</sup> =0.869 controlling for country fixed effects; see Figure A4.2). Furthermore, discrepancies in terms of how closely employment trends mirror each other at the country level tend to be less consequential in country or population group aggregates, which is the level of analysis in this paper (Figure A4.3). The number of LICs with LFS data remains very small, and as such HFPS data continue to be one of the few available sources of information for many developing countries throughout the pandemic.

# **4 Employment dynamics during the pandemic**

This section uses the most comprehensive and up-to-date cross-country data available to (i) update the phone survey estimates of the initial impacts of the pandemic for a considerably larger set of countries vis-`a-vis analysis in previous papers; (ii) trace the evolution of employment, work stoppages and acquisition of new jobs; and (iii) examine transitions across sectors and types of employment throughout 2020 and 2021, focusing on the question of the quality of the employment gains following initial losses.

# **4.1 What are the main patterns of initial employment losses and subsequent employment gains?**

There are several ways through which labor market outcomes could be affected during the crisis. These include job losses, changes in jobs, and earnings losses which can be observed in

> 13For example, the HPFS question asking whether a respondent worked before the COVID-19 pandemic could be capturing broader recall of past employment which may or may not be coded as employment in a considerably more detailed LFS instrument. Respondents reporting not working in the HFPS after the onset of COVID-19 could still be classified as employed in the LFS as they are temporarily absent from work but believe they still have a job to go back to in the future. Differences in the magnitude of employment impacts are in line with expectations considering these factors. Also, the LFS available for comparison are conducted quarterly which does not line up with the timing of HFPS and therefore may not capture the same short-term disruptions to the labor market.

12
# **V. Results**

This section presents the OLS and instrumental variable estimates on whether robots lead to increases in export quality. First, we present our baseline estimation of quality for all countries and products and distinguish these effects across developed and developing country exporters. Second, we examine differences in quality gains across product types. In particular, we explore if robot diffusion results in a catch-up or a divergence in product quality over time, by contrasting the quality growth of products based on their initial distance from the “quality frontier”. We also consider heterogeneous quality gains across more vs less complex products and across new vs existing export varieties. Thirdly, we exploit novel data from the IFR to examine whether there are heterogeneous quality gains across different robot applications – contrasting more and less advanced robots which undertake handling, cutting and welding.

# **_i. Baseline_**

In this section, we examine whether the diffusion of robotics leads to quality gains overall and decompose these effects across country income groups – developed and developing economies. Earlier descriptive statistics show that developing countries have simultaneously experienced faster growth in robot adoption over the period 2000-2015, albeit from a far lower initial level, as well as convergence in the quality of their exports towards those of developed economies (see Figure 1 and 6). These trends may be suggestive of a potential link between robot diffusion and quality, however it is a priori unclear for which group of countries quality gains are expected to be stronger. On the one hand, emerging economies typically export lower-quality products, further from the frontier, and so have larger scope for quality increases (Henn et al., 2020). On the other hand, extensive research has shown that obtaining the benefits of technology requires a host of complementary assets, such as skills, organizational capital, management and so on (see for example Brynjolfsson and Hitt, 2000; Bresnahan et al., 2002; Brynjolfsson et al. 2002; Leung, 2004; Van Ark and Inklaar, 2006; Bloom et al. 2012; Akerman et al., 2015). These complementary assets are likely to be more readily available in higher-income economies.

Table 2 presents the results both for unit prices and export quality. We find robot diffusion is strongly related to increases in export quality overall for all countries in our sample (model 5) and this relationship appears to be causal (model 7). A 10% increase in robot stock is associated with 23
or the resource mobilization approach to uprisings. Nevertheless, this analysis suggests that _suffering_ is a relatively universal basis for non-violent uprisings.

# **Alternative measures of peaceful uprising**

Our measure of nonviolent uprisings based on the CNTS data set is collected through the New York Times and the coverage of such events is relatively limited in small, low-income countries with few ties to the U.S. Systematic time-invariant differences in coverage are filtered out by the country fixed effects, but the quality of New York Times news coverage might change over our sample period and such changes could be correlated with widespread human suffering in these countries, leading to selection bias. We therefore explore the robustness of our results using the data on nonviolent uprisings from the Major Episodes of Contention (MEC) data set (Chenoweth, 2015; Chenoweth & Ulfelder, 2017). Because nonviolent uprisings are identified through a review of global media (i.e. Associated Press and Agence France Press news stories within Factiva and LexisNexis), selection bias should be minimized.

The MEC measure of nonviolent uprisings differs from the CNTS variable in several ways. First, it measures whether an episode (i.e. more than one event, occurring within a week of one another) is ongoing in a certain year. Hence, their variable is a dummy instead of a count variable. This is disadvantageous, because we lose considerable variation in the intensity of nonviolent uprisings. Second, in order for an event to be considered it needs to have at least 1,000 participants (instead of 100 participants for protests in the CNTS data set), and hence they only include _mass_ nonviolent uprisings. Third, for an episode to be included, an uprising should not only be anti-government, but its explicit goal should be to seek the removal of the incumbent. Hence, whereas the CNTS data also include acts of resistance in opposition to certain policies, the MEC data only include them if they have the maximalist aim of bringing about a regime change. Fourth, the data set covers episodes until 2013 and hence our period of 28
<!-- Start of picture text --> Bangladesh<br>190<br>170.<br>80. eee $$ or"weer<br>130 ~o--""<br>- oe<br>10 ————__-- =<br>0 |||1992 1996 2000 2005 sss2010<br>--=« Mean of Total Popuation Mean of Bottom 40%<br><!-- End of picture text -->

<!-- Start of picture text --> China<br>390<br>-<br>340 a<br>rae<br>290 aa<br>240 a<br>190 oe ea<br>--* -<br>1140 see<br> 90 1990 1993 1996 1999 2002 2005 2008 2009<br>---=~+Mean of Total Population ——=Mean of Bottom 40%<br><!-- End of picture text --> <!-- Start of picture text --> Brazil<br>129006.<br>170<br>150<br>130<br>——e<br>~ a* -<br>110 oe ae?<br>99<br>1995 1997 1999 2002 2004 2006 2008 2011<br>-=--Mean ofTotal Population ——— Mean of Bottom 40%<br><!-- End of picture text --> <!-- Start of picture text --> Uganda<br>190<br>“<br>170 7<br>PS<br>150 DZ<br>——s,<br>130 a<br>--*<br>110 ia<br>90<br>1992 1996 1999 2002 2006 2009<br>===-Mean of Total Population ——= Mean of Bottom 40%<br><!-- End of picture text -->
Liability Structure in Small-scale Finance: Evidence from a Natural Experiment<sup>_∗_</sup> Fenella Carpena, Shawn Cole, Jeremy Shapiro, and Bilal Zia<sup>_†_</sup>

# **1 Introduction**

Theory and evidence highlight financial market imperfections as a central cause of poverty and a key impedement to growth (Banerjee and Newman, 1993; Galor and Zeira, 1993; Rajan and Zingales, 1998). In theories of capital accumulation for example, financial market imperfections influence the poor’s ability to borrow for investments in education and physical capital. Additionally, in models explaining entrepreneurship, information asymmetries and transaction costs prevent profitable entreprenurial activities of the poor, who often have no collateral. Lack of access to financial services may thus play a crucial role in leaving many productive opportunities for the poor untapped, as well as in generating persistent income inequality and lower growth (Beck et al., 2008).<sup>1</sup> Microfinance, the provision of credit, savings and other financial services to low-income households and entrepreneurs, has exploded in popularity and coverage in recent years, particularly in meeting the large unmet demand for finance (Morduch, 1999; Armend ́ariz de Aghion and Morduch, 2010). Emerging markets and developed economies alike, including the United States, now provide microfinance services through a variety of public and private channels. The growth of microfinance has been unprecedented: between 2004 and 2008, the sector’s average annual asset growth rate was 39%, reaching US$60 billion in total assets by the end of 2008 (Chen et al., 2010). A careful evaluation of microfinance in Banerjee et al. (2009) has also revealed that microcredit does have important effects on business outcomes and the composition of household expenditure. The rapid

> _∗_ This project is a collaborative research effort with Saath Microfinance. We thank Saath, Xavier Gin ́e, David McKenzie, Petia Topalova, and workshop participants at the World Bank for helpful comments and suggestions. Stuti Tripathi provided excellent research assistance. Financial support from the World Bank, and the HBS Division of Faculty Research and Development, is gratefully acknowledged.

> _†_ World Bank ( `fcarpena@worldbank.org` ), Harvard Business School ( `scole@hbs.edu` ), Yale University

> ( `jeremy.shapiro@yale.edu` ), and World Bank ( `bzia@worldbank.org` ), respectively.

> 1See World Bank (2008) for a literature summary.

1
Table 6: O.L.S. and I.V. with alternative weighting scheme

|||||Dependen|t variable: P|r(birth teen=|1)|||
|---|---|---|---|---|---|---|---|---|---|
||Wh|ole sampl|e|A|t least KCPE||At least|secondaryc|omplete|
||O.L.S.|I.V.|I.V.|O.L.S.|I.V.|I.V.|O.L.S.|I.V.|I.V.|
|First-stage(al|l Second-stag|e RHS va|riables inc|luded,but not|reported)|||||
|Reform||-0.108|-0.057||0.363***|0.294**||0.703***|0.543***|
|||(0.11)|(0.10)||(0.11)|(0.11)||(0.17)|-0.17|
|Second-stage||||||||||
|Education|-0.043***|0.188|0.246|-0.059***|-0.180**|-0.206**|-0.038***|-0.163***|-0.194***|
||(0.00)|(0.38)|(0.76)|(0.00)|(0.07)|(0.10)|(0.00)|(0.05)|(0.07)|
|Year|1.591***|-5.945|-7.742|1.515***|6.781**|7.817*|0.400|7.509**|9.023**|
||(0.37)|(12.55)|(25.10)|(0.46)|(3.25)|(4.08)|(0.62)|(3.07)|(4.09)|
|Year<sup>2</sup>|-0.000***|0.001|0.002|-0.000***|-0.002**|-0.002*|-0.000|-0.002**|-0.002**|
||(0.00)|(0.00)|(0.01)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Muslim|0.021||0.564|0.042||-0.056|-0.024||-0.123|
||(0.02)||(1.45)|(0.03)||(0.08)|(0.03)||(0.08)|
|City|-0.011||0.155|0.001||0.009|-0.041||0.069|
||(0.03)||(0.43)|(0.04)||(0.05)|(0.04)||(0.08)|
|Town|0.023||0.307|0.025||-0.025|-0.012||0.009|
||(0.03)||(0.74)|(0.03)||(0.06)|(0.04)||(0.06)|
|Countryside|0.024||0.692|0.001||-0.208|-0.013||-0.129|
||(0.03)||(1.73)|(0.03)||(0.15)|(0.03)||(0.08)|
|Abroad|-0.032||0.147|0.010||0.011|-0.012||-0.041|
||(0.06)||(0.51)|(0.07)||(0.11)|(0.08)||(0.14)|
|Embu|0.179***||-0.254|0.140***||-0.012|0.048||-0.139|
||(0.03)||(1.11)|(0.03)||(0.11)|(0.04)||(0.11)|
|Kalenjin|0.195***||-0.491|0.161***||0.042|0.045||-0.065|
||(0.02)||(1.79)|(0.03)||(0.09)|(0.04)||(0.08)|
|Kamba|0.169***||-0.699|0.147***||0.061|0.094**||0.065|
||(0.02)||(2.24)|(0.03)||(0.08)|(0.04)||(0.07)|
|Kikuyu|0.153***||-0.739|0.100***||-0.035|0.065||-0.115|
||(0.02)||(2.32)|(0.03)||(0.10)|(0.04)||(0.10)|
|Kisii|0.219***||-0.463|0.204***||0.078|0.126***||-0.003|
||(0.02)||(1.78)|(0.03)||(0.10)|(0.04)||(0.09)|
|Luhya|0.261***||-0.218|0.232***||0.032|0.146***||-0.051|
||(0.02)||(1.26)|(0.03)||(0.14)|(0.04)||(0.11)|
|Luo|0.249***||-0.182|0.205***||-0.018|0.090**||-0.1|
||(0.03)||(1.13)|(0.04)||(0.15)|(0.05)||(0.11)|
|Masai|0.032||-0.040|0.030||-0.207|-0.031||-0.357**|
||(0.03)||(0.24)|(0.04)||(0.17)|(0.05)||(0.17)|
|Meru|0.045||-0.444|0.022||-0.131|0.039||-0.125|
||(0.03)||(1.29)|(0.04)||(0.12)|(0.05)||(0.11)|
|Mijikenda|<br>0.069**||<br>-0.032|<br>0.056||<br>-0.260|<br>0.041||<br>-0.276|
|/ Swahili|(0.03)||(0.30)|(0.04)||(0.23)|(0.05)||(0.19)|
|Recall|-0.001|-0.006|-0.005|0.005***|0.029*|0.036*|-0.013***|0.043*|0.057*|
||(0.00)|(0.01)|(0.01)|(0.00)|(0.02)|(0.02)|(0.00)|(0.02)|(0.03)|
|Survey FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

32
Despite the significant progress in poverty reduction, shared prosperity and upward economic mobility in recent years, a sizable segment of the population is still at risk of falling into poverty or dropping from the middle class. Estimates of income mobility between 2004 and 2011 suggest that the majority of those who exited poverty were near the poverty line in 2004 and find themselves just above the poverty line in 2011 with a median income around US$5 PPP per day (World Bank, 2013). Very few of these households crossed the middle class threshold. Similarly, most of those who did enter the middle class in this period were already near the middle class line in 2004. Having entered the middle class by 2011, they have a median income close to US$13 PPP per day, barely over the middle class line.

In sum, most of households that exited poverty in Uruguay in recent years have experienced only a modest improvement in welfare that did not allow them to move into the middle class and thus have high risk of falling back into poverty. Thus an additional challenge for the government is to identify this segment of the population and adequately protect it against potential negative shocks.

In the simulation, welfare changes are influenced mostly through the labor market and remittances. Even though labor income is the main component of total income, accounting for 52 percent, other income and other transfers account for 42 percent of the total household income. These two components, which include, among others, pensions and health insurance payments, are of particular importance in the simulations since they are government transfers that are a stable source of income that is kept constant during the simulation of an economic crisis. Therefore, the ability of the model to predict household welfare changes is limited to the labor market and remittances channel. Along these lines, the results of the model can be seen as a bound which the government can augment by taking action using the automatic stabilizers such as unemployment insurance and increasing social safety nets during the time of crisis.

**Figure 8: Average income composition 2011**

<!-- Start of picture text --> 60%<br>52.6%<br>50%<br>40%<br>30% 27.3%<br>20%<br>14.5%<br>10%<br>4.3%<br>1.1% 0.2%<br>0%<br>Labor Other Other Domestic Capital International<br>income income transfers transfers income remittances<br>Source: WB staff calculations based on ECH 2011.<br>Note: Sample representative at the national level.<br>Including Montevideo, and urban and rural areas of the Interior region<br><!-- End of picture text --> 7
<!-- Start of picture text --> 0.7<br><!-- End of picture text --> <!-- Start of picture text --> 0.7<br>«-- Daily Maximum Drawdown USDBTC<br>0.6 | —— Daily Maximum Drawdown USDMXN<br>— Daily Maximum Drawdown USDEUR<br>0.5<br>0.1 an A | hi \ Ph aie, | We<br>° o"" * ow “® ar<br>time<br><!-- End of picture text -->

<!-- Start of picture text --> time<br><!-- End of picture text -->
# **Encouraging Service Delivery to the Poor:  Does Money Talk When Health Workers Are Pro-Poor?**

Sheheryar Banuri (University of East Anglia); Damien de Walque (World Bank); Philip Keefer (Inter-American Development Bank); Paul Jacob Robyn (World Bank)<sup>1</sup> JEL Codes: C91; H83; I14; I32; J45; M52 Keywords: public health, access, motivation, performance, inequality, socioeconomic status 1 We are extremely grateful for a fruitful collaboration with the Ministry of Health in Burkina Faso and in particular with Philippe Compaore, Baly Ouattara, Abdoul Karim Ouedraogo, Boukary Tapsoba and Paul Toe. We are also very grateful to all participants in the experiment and those who facilitated it, in particular the direction, staff and students from the Faculté de Médecine de l’Université de Ouagadougou, the Ecole Nationale de Santé Publique and the Ecole de Santé privée Sainte Edwige, as well as the direction and staff of the health facilities in the regions of Boucle du Mouhoun, Centre-Nord, Centre-Ouest and Nord. The data were collected by Centre Muraz with special thanks to Hervé Hien, Nicolas Meda and all staff implementing the lab in the field experiment. We are also grateful to Nouna Health Research Centre, through Maurice Ye who developed the clinical-based scenarios for the videos. Special thanks to Bambio Yiriyibin for coordinating and supervising the experiment and to Boubakar Diallo and the team from Les Films du Dromadaire for producing the videos. We are also grateful to Ousmane Diadie Haidara, Maurice Ye, Ellen Green, participants of the Measuring the Tricky Things Workshop (Oxford), and participants of the Centre for Behavioral and Experimental Social Science (CBESS) seminar series for helpful comments. This research is funded by the Health Results Innovation Trust Funds (HRITF) at the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.