rafmacalaba/datause-encoder-data huggingface dataset:
computed from the location probability estimates of Table 6. A comparison of column 5 of Table 8 with those of Table 5 would seem to suggest that no selectivity bias involved in the estimates reported in Table 5. None of the selectivity terms in Table 5 are statistically different from zero, and individual coefficient estimates are not significantly different in otherwise the same specifications across the two tables. However, the reason for this outcome is the high degree of collinearity between the selectivity term and the region dummies rather than true lack of statistical significance of the former. Indeed the outcome sharply contradicts what we seen Table 9 as strong evidence for the presence of selectivity bias in Table 5. The advantage of the reduced form specification that we estimated in Table 9 is precisely that it avoids the multicollineary problem of Table 8. We also see more plausible coefficient estimates in Table 9 than those in Table 8. This and the high values of the Hausman-Wu endogeneity test statistic reported at the bottom of column (4) to (7) lead to the conclusion that there is selectivity bias in the estimates of Table 5.<sup>26</sup> In estimating equation (12) in the first panel of Table 9, we replace the region dummies by probabilities of selection calculated from the choice probabilities of column 3 of Table 6. In the second panel of Table 9 we report the same correction for selectivity bias, but based on a multinomial selection equation in which location characteristics are assumed not to matter. This latter specification is directly estimated in Table 7, but can also be thought of as what we would obtain by imposing the restriction on equation (10) that all locations are identical in terms of observable characteristics.<sup>27</sup> In spite of the contrast between the two specifications of the selection process, the results are quite similar across the two panels in terms of the direction of selectivity bias though not in the magnitude of bias. The bias is larger when we assume that businesses self select on location characteristics as well as own characteristics. Since this is more consistent with profit maximizing behavior, and the selectivity corrected parameter estimates seem to be more

> 26 Corresponding GLS estimators of Table 5 are used as the efficient counterpart in the computation of the Hausman-Wu test statistics reported in Table 9.

> 27 In directly estimating this specification in Table 7 we are in effect assuming that businesses choose plant locations by some (non-profit maximizing) criterion that does not take into account location characteristics.

28
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water resources

# Irrigation

Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Water supply and sanitation

African Ministers’ Council on Water (AMCOW). 2010. _Country Status Overviews on Water Supply and Sanitation 2010_ . Regional Synthesis Report, August 2010, Addis Ababa Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. 2008a. ―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, Africa Region, World Bank, Washington, DC.

Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008b. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

Cain, Allan, Mary Daly, and Paul Robson. 2002. ―Basic Service Provision for the Urban Poor: The Experience of Development Workshop in Angola.‖ IIED Working Paper 8 on Poverty Reduction in Urban Areas, Human Settlement Program, IIED, London, UK.

Development Workshop—Angola, 2007. _Luanda Informal Water Sector Study Service Provision for the Peri-Urban Poor in Post-Conflict Angola_ . Draft Report, Luanda.

Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.‖ AICD Working Paper 13, Africa Region, World Bank, Washington, DC.

- Luta Contra Pobreza Urbana (LUPP). 2007. ―Community Model for Household Sanitation: Good Practice in the Musseques of Luanda.‖ LUPP Good Practice Series, LuandaMorella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. ―Climbing the Ladder: The State of Sanitation in SubSaharan Africa.‖ AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

- USAID (United States Agency for International Development). 2009. _Angola: Water and Sanitation Profile_ , Washington DCWHO (World Health Organization) Joint Monitoring Program (JMP). 2010a. ―Angola Estimates for the Use of Improved Drinking-Water Sources.‖ www.wssinfo.org/resources/documents.html.

- ———. 2010b. ―Angola Estimates for the Use of Improved Sanitation Facilities.‖ www.wssinfo.org/resources/documents.html.

47
**Table 3:** Income and Trust Towards President and Municipal Government

|||**Trust**|**President**||**Trust Munici**|**pal Government**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Average Per Capita Family Income (in logs)|0.145**<br>[0.072]|0.157**<br>[0.070]|0.206***<br>[0.074]||0.083***<br>[0.026]||
|NTL Density at Municipality (in logs)||||0.080***||0.037*|
|||||[0.025]||[0.022]|
|Male||-0.005|-0.005|-0.004|-0.021*|-0.016*|
|||[0.013]|[0.013]|[0.011]|[0.012]|[0.009]|
|Age||0.003|0.003|0.002|-0.006***|-0.005***|
|||[0.002]|[0.002]|[0.002]|[0.002]|[0.001]|
|Age Squared||-0.000|-0.000|0.000|0.000***|0.000***|
|||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Years of Schooling||-0.006***|-0.006***|-0.005***|-0.009***|-0.007***|
|||[0.002]|[0.002]|[0.001]|[0.002]|[0.001]|
|Unemployed||0.011|0.010|0.007|-0.032|-0.019|
|||[0.020]|[0.020]|[0.017]|[0.022]|[0.019]|
|Living in Rural Area||0.047**|0.047**|0.039***|0.030*|0.022*|
|||[0.019]|[0.018]|[0.014]|[0.016]|[0.012]|
|Homicide Rates|||-0.036*|-0.022*|-0.025|-0.008|
||||[0.021]|[0.014]|[0.021]|[0.013]|
|FODES transfer per inhab. (previous year)|||-0.002|-0.001|0.004***|0.002***|
||||[0.001]|[0.001]|[0.001]|[0.001]|
|Observations|6,056|5,930|5,930|8,751|7,438|12,031|
|Number of Municipalities|42|42|42|88|45|114|
|Number of Waves|6|6|6|6|8|8|
|R-squared|0.113|0.125|0.126|0.122|0.045|0.045|

Notes: All regressions include municipality and year fixed effects. Robust standard errors clustered at the municipalityyear level in brackets. FODES refers to Development Municipal Fund Economic and Social transfers. Statistical significance: *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1.

16
Our paper contributes to the literature on curative health care for children in low-income countries, aided by novel daily panel data. Research on policies intended to improve acute care is still fairly rare, with some exceptions, such as Powell-Jackson et al. (2014).<sup>4</sup> The problem of underuse is known to be severe for preventive goods, where the benefits are nearly universal, but also often abstract and far off. Our results show that underuse is similarly pervasive at the extensive margin of acute care, that is, the decision to visit a provider. We show that subsidies can substantially improve utilization at this margin. These results contrast with Cohen et al. (2015) and Lopez et al. (2022), who examine the effect of subsidies for malaria drugs at the point of purchase. Both papers document that there are significant rates of overtreatment that increase with subsidization. Similarly, patient audit studies have shown low-quality diagnostic and treatment choices, which often result in non-indicated or mis-targeted treatment, in a range of low and low middle income contexts Das et al. (2016); Currie et al. (2014). The reasons for overtreatment range from financial incentives for providers to patients demanding powerful medicines. This points to a challenging twin problem, where underuse at the extensive margin, in the form of too few contacts with the health care system, occurs simultaneously with overuse at the intensive margin, in the form of overmedication conditional on such a contact. Subsidies that increase access are more likely to induce overuse at the intensive margin when there are fewer cost controls such as those implemented by Mali Health. Thus, efforts to reduce the cost of care likely require accompanying measures to limit non-indicated or overly expensive treatment.<sup>5</sup> An innovation in this paper is to collect illness spell data at home and apply hazard models to estimate the effects of the interventions on care seeking conditional on health status. In section 5.4, we discuss the advantages of this approach for making out-of-sample predictions and for avoiding selection effects that would occur in demand analysis based on health system contacts, as found for example in clinic records of health consultations or health insurance

> 4Das and S ́anchez-P ́aramo (2003) have weekly data on illness and document the variation in demand that comes from differential illness incidence, but do not study demand interventions.

> 5Also related is work by Fischer et al. (2014), who test for learning and reference dependence as they vary prices in door-to-door sales of over-the-counter drugs (but do not focus on over- or underuse).

7
Figure 1: Quality Growth vs. Dispersion <!-- Start of picture text --> .08 .08<br>United Kingdom United Kingdom<br>JapanSwitzld JapanSwitzld<br>.06 Portugal ArgentItalySwedenAustraliaFranceIrelandAustriaGermanyNethldsDenma .06 Portugal ItalySwedenAustraliaFranceAustriaGermanyNethldsDenma<br>Hongkong Bel_lux Norway Hongkong<br>.04 Bngldsh Sri_lka El SalvadorPeruEgyptVenezGuatmalaIndonesPolandPhilippinesThailandArab_emColombiaBrazilChinaIndiaTaiwanKoreaSpainIsraelCanadaMexicoSingaporeFinland .04 Sri_lka GuatmalaPhilippinesThailandColombiaBrazilChinaIndiaTaiwanKoreaSpainIsraelCanadaMexicoBel_luxSingaporeFinland<br>Bngldsh Indones<br>Dom_repPakistan South AfricaTurkeyMalaysia Pakistan TurkeyMalaysia<br>Macau Dom_rep<br>.02 Cos_rica Macau<br>Hondura .02<br>.04 .06 .08 .1 .04 .06 .08 .1<br>Standard deviation Standard deviation<br>(a) Countries with > 50 Products (b) Countries with > 100 Products<br>.08<br>United Kingdom<br>.07 Japan Switzld<br>.06 France<br>Italy Germany<br>Hongkong<br>.05<br>China Taiwan Canada Mexico<br>.04 Korea<br>.07 .075 .08 .085 .09 .095<br>Standard deviation<br>Quality growth Quality growth<br>Quality growth<br><!-- End of picture text -->

# **(c) Countries with** _>_ **500 Products**

**Notes:** This figure plots the median quality growth against the standard deviation of quality growth for countries that export at least 50 (panel a), 100 (panel b), or 500 (panel c) HS10 products to the US.

10
The multidimensional nature of job quality makes it difficult to measure and standardize across countries. We use the ordinal framework developed by Sen (1976) and Alkire and Foster (2011) for measuring poverty to estimate job quality within Latin America. We find that job quality began to significantly increase around 2004. We all find that the best predictors of job quality are age, gender, education, formal employment, and union membership; while job benefits are the job quality dimensions that are best correlated with living “out” of poverty.

The paper is organized as follows. The next two sections discuss the data used in the study and the measurement of job quality across countries, employment sectors, and time. Section 4 investigates which job and personal characteristics are associated with higher job quality. Section 5 looks at the relationship between job quality and family poverty. Finally, sections 6 and 7 offer robustness checks and concludes the discussion.

# **2 Data**

The data in this study were taken from the _Socio-Economic Database for Latin America and the Caribbean_ (SEDLAC). The SEDLAC is a harmonized dataset compiled from over 300 household surveys across 24 countries. More details about the database can be found on their website, http://sedlac.econo.unlp.edu.ar, and from CEDLAS and the World Bank (2012). The data in this report were taken from a subset of 17 countries between 1995 and 2012, with available years varying by coun7
aversion (1- _σ_ ) is estimated to be 2.7. The structural labor literature typically estimates values between 1 and 5 (van der Klaauw and Wolpin (2008)) and the discount factor is estimated at 0.999 (French and Jones (2011) report estimates between 0.85 and 1.12). The estimated utility of leisure (Ul) parameters indicate that females value leisure (i.e., nonmarket work) more than males. Also, married women, women with children and women who work part-time derive greater utility of leisure.

The parameter estimates provide some insights into what leads individuals to work formally or informally, which is helpful in understanding heterogeneous behavioral responses to pension policies. First, we find significant costs to switching between the formal and informal sectors for both men and women. The costs are similar in magnitude to the cost of re-entering the labor market after a period of not working. Both entry costs and switching costs are higher for women. These labor market frictions will tend to mitigate labor supply responses to pension policy changes, particularly for women.

Second, we find heterogeneity in the way individuals value informal sector work, as captured by the estimated unobserved type coefficients. Some types incur non-pecuniary costs when working informally (types 1, 2 and 3 for women and type 2 for men), while other types perceive benefits. This heterogeneity may reflect differences in individuals’ attributes that affect suitability for informal sector work as well as preference heterogeneity. Turning to the estimated parameters of the formal and informal log earnings offer equations, we find that the estimated intercept varies significantly across types. For example, for a given schooling and experience level, type 4 males have higher earnings offers in the formal sector, but their informal earnings offers are similar to other types. Thus, type 4 males have a comparative advantage in the formal sector.

Additionally, the estimated returns to schooling and experience, both allowed to be quadratic, differ in the formal and informal sectors. For example, for men, the informal sector offers lower returns to experience. The returns to schooling are initially higher in the informal sector and then lower as schooling levels increase in comparison to the formal sector. Heterogeneity in schooling levels is an additional source of comparative advantage to working in the formal sector.<sup>39</sup> Moreover, once a worker accumulates sufficient levels of

> 39Younger birth cohorts have higher schooling levels on average and therefore tend to have stronger attachment to the formal sector.

23
for the decision for a woman to work becomes: Importantly, the variance has a non-linear relationship with the probability of a terrorist attack. Specically, it is very low for low and high probabilities of a terrorist attack, as specied by the solid line in Figure 5. Of course, a full specication of risk would consider the probability of attack times the impact of the event. Thus, the risk would be higher if deaths were involved, as represented by the dashed line. Note, however, that risk rises and then falls due to "banality"; or the psychological adjustment workers make when terrorist events become common and therefore aects behavior similarly to very low-probability terrorist events. In other words, people get used to the violence. In the middle of the range, there is much more uncertainty, which itself is a cost.

If we allow the variables in (1) to be randomized and then aggregated across individuals, simple comparative static predictions match those suggested in the literature. Specically, the randomized and aggregated version of (1) predicts that, holding all else equal, higher wages ( _w_ ) will be associated with higher FLFP. In addition, having a working partner (generally a male) will increase _h_ and make FLFP less likely. When a working mate is lost, the reservation wage _h_ falls, making FLFP more likely. This is the added worker eect described in the literature, which could be a result of terrorism (if a mate were a victim of terrorism, for example). Increases in _c_ due to terrorism, such as rising transportation costs, will drive down FLFP linearly. And as discussed above, the nonlinear variance eect may introduce nonlinearity in the results. We evaluate all of these predictions using the data described above using the estimation methodology described in Section 4.

# 2.2 The intensive margin of labor supply

Instead of choosing whether to participate or to not participate in the labor market, women could choose the number of hours they work in a dierent way in the presence of violence. To formally allow for this possibility we follow Ashenfelter [1980] and Serneels [2002]. Consider a representative household that maximizes household utility subject to a shared budget constraint. After maximizing, an individual's labor supply (in number of hours) can be written as a function of her wage ( _wi_ ), a time constraint ( _H i_ ), labor income from the other household members ( _wjHj_ ), and a xed cost of entering the labor force ( _fi_ ).<sup>4</sup>

> 4This cost may include the opportunity cost (e.g. reservation wage or value of foregone household work) and safety (terrorism risk).

5
TABLE 3 Variance decompositions, over time: Headline CPI (percent)

||1970-2017|1970-85|1986-2000|2001-17|
|---|---|---|---|---|
|Factor||All c|ountries||
|Global|11.9<br>(18.8, 2.9 - 29.4)|15.5<br>(25.8, 4.1 - 40.1)|9.9<br>(16.2, 1.5 - 29.0)|22.1<br>(20.7, 8.6 - 30.9)|
|Group|3.9<br>(11.6, 0.7 - 16.6)|7.9<br>(12.2, 2.3 - 18.2)|6.7<br>(19.4, 2.8 - 22.0)|9.0<br>(13.7, 3.8 -19.5)|
|Global + Group|27.2<br>(30.4,14.0 - 44.2)|31.0<br>(38.0,18.3 - 59.5)|34.8<br>(35.6,16.1 - 48.7)|33.6<br>(34.4,19.0 - 49.3)|
|Factor||Advance|d economies||
|Global|24.3<br>(25.6, 10.4 - 35.2)|31.6<br>(30.8, 10.8 - 39.7)|22.0<br>(22.9, 9.9 - 35.5)|27.3<br>(26.1, 21.6 - 32.9)|
|Group|8.1<br>(12.7, 4.2 - 18.4)|18.0<br>(18.1, 7.3 - 24.0)|15.7<br>(21.0, 4.9 - 33.6)|20.5<br>(22.7, 9.4 - 34.8)|
|Global + Group|37.2<br>(38.3,30.6 - 49.6)|47.2<br>(48.8,36.3 - 64.6)|45.0<br>(43.9,26.9 - 55.4)|50.7<br>(48.8,39.1 - 62.8)|
|Factor||EM|DEs||
|Global|10.4<br>(16.4, 2.2 - 21.8)|13.6<br>(24.1, 2.9 - 38.9)|6.6<br>(14.0, 0.7 - 26.7)|18.1<br>(18.9, 5.8 - 30.3)|
|Group|<br>2.1<br>(11.3, 0.5 - 12.7)|<br>6.3<br>(10.3, 1.9 - 15.1)|<br>6.4<br>(18.9, 2.7 - 19.0)|<br>7.8<br>(10.7, 3.2 - 16.9)|
|Global + Group|<br>21.4<br>(27.8,11.5 - 41.7)|<br>25.8<br>(34.4,13.8 - 52.3)|<br>27.4<br>(32.9,13.0 - 43.8)|<br>28.9<br>(29.6,17.2 - 39.4)|

Notes: All numbers indicate percentage contributions of the global and group-specific factors to the variance of headline CPI inflation. The contributions of global and group-specific inflation factors are estimated using the dynamic factor model described in Annex 2.1. The data set includes 99 countries (25 advanced economies and 74 EMDEs). In each pair of rows, the numbers in the first row indicate medians across countries. The first number in the second row (in parentheses) is the unweighted mean across countries. The second and third numbers in the second row (in parentheses) indicate the interquartile range.

26
BBP outpatient utilization remains low. This happens because although the BBP relieves some financial burden in relation to doctor fees, outpatient health care requires large additional out-ofpocket expenditures (mainly on prescription medicines). The rest of the paper is structured as follows: section 2 describes the data and our methodological strategy; section 3 discusses the main results; the article ends with a brief conclusion.

# **2. Data and Methodology**

Data used for the purposes of this study comes from the 2004 Armenian Integrated Living Standard Survey (ILCS). The survey has been conducted since 1996 by the National Statistical Service of Armenia, with support from the World Bank, USAID, and other donors. The survey collects information on household characteristics, such as household assets, intrahousehold transfers, and housing; and on individual characteristics, such as migration, health, education, and employment. Data are stratified, nationally representative, and provide detailed information on household income and consumption. The sample of the 2004 ILCS is based on the population structure of the 2001 population census. It includes 6,816 households and 26,614 individuals. The data contains 43 urban sample units and 216 rural sample units, which makes the survey representative at the level of the eleven main regions in Armenia (or Marzs).

The health module of the survey collects information on morbidity, access, and expenditures on health by type of doctor, type of expenditure (i.e. official vs. unofficial payments), type of health provider, self-perceptions of health status, and postnatal services. The following variables were used in the analysis: individual and household characteristics (such as per capita consumption, region, strata, sex, age, and employment status), cost of doctor visits, overall health expenditures, health care utilization, and type of treatment. We define morbidity as a dummy variable taking the value of 1 if the individual claimed to have been sick/injured in the past four weeks. Among those, an additional dummy was constructed for those who claimed having been seriously ill. Another important variable used is a dummy for those who were 6
We have organized the rest of our paper as follows. Section 2 describes our data. Section 3 discusses econometric issues. We present estimation results in Section 4. Section 5 is summary and conclusion.

# **II. Data sources and measurement**

Our data come primarily from the World Bank’s Investment Climate Survey of India for the year 2003.<sup>12</sup> The survey covered 1856 manufacturing establishments sampled from 40 cities, which are the top 3 or 4 cities of the largest industrial concentration from each of 12 of India’s 15 largest states. These 12 states are Andhra Pradesh, Delhi, Gujarat, Karnataka, Kerala, Haryana, Maharashtra, Madhya Pradesh, Punjab, Tamil Nadu, Uttar Pradesh, and West Bengal. Between them the 12 states account for well over 90 percent of India’s industrial GDP. The 3 or 4 cities covered in each state also accounted for the bulk of manufacturing outputs of their respective states. In each city, samples were drawn exclusively from the main exporting or import competing manufacturing industries: food, textiles, garments, leather goods, drugs and pharmaceuticals, chemical, consumer electronics, electrical white goods, auto parts, fabricated metals, and machinery.

Table 1 draws up the business profile of establishments in the sample in terms of scale, age, start up conditions and productivity. The average plant has just over a hundred workers over the full sample, but with significant variation in scale across sectors, ranging from average employment size of under forty in consumer electronics to more than 250 in textiles. We are thus dealing with what is essentially a population of small to medium sized establishments, although the sample does include a sizeable number of large scale plants. The vast majority of the full sample are also owner managed, most

> 12 However, we use these in conjunction with similar World Bank Surveys for other developing economies for the purpose of estimating the reference technology in terms of which the productivity indices we relate to business environment and geographic variables in the paper have been calculated. We also draw on other (secondary) sources including the Annual Survey of Industries of the Central Statistical Office of India for data on indicators of economic geography.

8