rafmacalaba/datause-encoder-data huggingface dataset:
There are marked differences in the incidence of Ninis by age group for women, but not for men. Approximately 10 percent of young men are Ninis, across both 15 to 18 and 19 to 24 year olds (see Table 1). In contrast, young women aged 19 to 24, are almost twice as likely to be Ninis than those aged 15 to 18; there are 38.4 female Ninis for every 100 females in the 19 to 24 age range, compared with only 22.3 in the 15 to 18 age range. For both age groups, a larger proportion of women are Ninis than are men: as a group females are three times more likely to be Ninis than men (9.7 percent vs. 30.5 percent).

<u>Table 1. Ninis in Mexico, by gender and age group</u>

|||_Male_<br>|||_Female_<br>||
|---|---|---|---|---|---|---|
||_(1)_<br>_15 to 18_|_(2)_<br>_19 to 24_|_(3)_<br>_15 to 24_|_(4)_<br>_15 to 18_|_(5)_<br>_19 to 24_|_(6)_<br>_15 to 24_|
|_Variable_|_y.o._|_y.o._|_y.o._|_y.o._|_y.o._|_y.o._|
|All|9.5|9.8|9.7|22.3|38.4|30.5|
|_Quintiles of_|_HH income per_|_capita_|||||
|Quintile 1|11.2|16.4|13.6|23.3|44.6|33.9|
|Quintile 2|11.6|13.6|12.4|31.1|52.8|40.8|
|Quintile 3|10.9|11.0|10.9|23.4|46.8|35.0|
|Quintile 4|8.3|7.2|7.7|19.3|31.4|25.7|
|Quintile 5|4.5|4.7|4.6|9.6|16.9|13.8|
|Urban|9.6|9.9|9.7|17.4|32.0|25.0|
|Rural|9.5|9.6|9.6|37.0|61.3|48.5|

_Notes_ : A person is considered a _Nini_ when s/he is not working nor studying. Calculations are based on a pooled sample of nearly 375,000 observations (persons) that had complete information on the variables of interest for the first and fifth interviews of the rotating panel (see text for description of data). Weights are used in the estimation. _Source_ : Authors' calculations based on the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

Young men and women from poorer families (quintiles 1 and 2) are more likely to be Ninis than their richer counterparts (quintile 5), irrespective of their age. Table 1 presents Nini rates by quintiles of household income per capita. For men in the 15 to 18 age group, for example, the likelihood of an individual from a rich family (quintile 5) being a Nini is almost 2.5 times higher than that of individuals from the poorest families (4.5 percent versus 11.2 percent). This trend is more marked for men in the older age group, where probabilities are 16.4 percent and 4.7 percent respectively, and even more marked for women in the 19 to 24 age group where 44.6 percent of the poor are Ninis, compared to only 16.9 percent of those in wealthier households. Note, however, that the probability of being a Nini only decreases monotonically 8
common forms of western democracy and governance. They may for instance, be indigenous councils, conclaves of elders, religious institutions or cultural performances.

Thirdly, entrepreneurial classes may become corrupt when they operate as monopolists, but indigenous entrepreneurial traditions, whether on the model of the Indian _bania_ community or the Chinese family firm, have proved critical in rapid economic development. These businessmen have a sense of the local market and can assess local patterns of consumption. Finally, the stability and proper working of local deliberative and civil society institutions are as important as ‘democracy’ at a national level. Centralization is often misguided. But any such democratic institutions must be legitimated by indigenous ideologies and practices if they are to be fully accepted.

# **References**

Acemoglu, Daron, Simon A Johnson and James A Robinson, ‘The colonial origins of comparative development: an empirical investigation’, _American Economic Review_ , December, 91 (2001a), 1369-14012.

- Acemoglu, Daron, Simon A Johnson and James A Robinson. ‘Reversal of fortune. Geography and institutions in the making of world income distribution’, NBER Working Paper no 8460. 2001b.

- Acemoglu, Daron and James A Robinson, _Economic origins of dictatorship and democracy_ (Cambridge, Cambridge University Press, 2006).

- Appadurai, Arjun, ‘The capacity to aspire: culture and the terms of recognition’, in Rao and Walton (eds.) 2004, pp. 59-84.

Austin, Gareth, ‘Resources and strategies south of the Saharah. Long term dynamics of African Economic development’, papers presented to African Studies Association, Washington DC, Nov. 2005.

- Bayly, C. A., _Rulers, townsmen and bazaars. North Indian society in the age of British expansion 1780-1870_ (Cambridge, Cambridge University Press, 1983)

- Bayly, C. A. , _Empire and information. Intelligence gathering and social communication in India 1780-1970_ (New Delhi, 1<sup>st</sup> Indian edn., Cambridge University Press,1999)

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**Table 7: Wages in the public sector (public observations only)**

||**(1)**<br>**Log of Mo**<br>(pooled|**(2)**<br>**nthly Salary**<br>sample)|**(3)**<br>**Desirabil**<br>(PHC/CH|**(4)**<br>**ity index**<br>C sample)|
|---|---|---|---|---|
||Binary<br>regressions|Multiple<br>regression|Binary<br>regressions|Multiple<br>regression|
|Percentage of checklist items|0.002|-0.001|0.005|0.004|
||(0.003)|(0.002)|(0.009)|(0.009)|
|Time spent with SP (minutes)|-0.049*|-0.010|-0.056|-0.076|
||(0.025)|(0.014)|(0.073)|(0.076)|
|Correct Treatment|0.011|-0.097**|-0.268|-0.146|
||(0.059)|(0.048)|(0.235)|(0.201)|
|Has MBBS|1.056***|1.289***|||
||(0.168)|(0.175)|||
|Has some qualification|-0.094|0.851***|||
||(0.367)|(0.301)|||
|Age of provider|0.011**|0.018***|0.052***|0.060**|
||(0.006)|(0.006)|(0.019)|(0.024)|
|Gender of provider (1=Male)|0.114|0.128|-0.580|-0.887|
||(0.188)|(0.106)|(0.509)|(0.739)|
|Born in same district|-0.384***|0.020|-0.178|0.113|
||(0.146)|(0.081)|(0.444)|(0.431)|
|Is a dual provider|0.578***|0.145*|0.021|-0.169|
||(0.135)|(0.085)|(0.402)|(0.523)|
|Constant||8.056***||-1.361|
|||(0.315)||(1.188)|
|R2||0.623||0.155|
|Number of observations||302||183|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. The desirability index is a constructed using principal component analysis of proximity to several amenities (paved road, bus stop, railway station, Internet, post-office and bank), availability of infrastructure (stethoscope, spyghamometer, torchlight, weighing scale, hand washing facility, drinking water, staff toilet, patient toilet, fridge, sterilizers, electric connection, electric supply, power generator, telephone, computer, IV drip, cots/beds, disposable syringes), and PHC size (number of staff and number of patients). In binary regressions columns, each coefficient represents a separate regression of prices on the row variable, a constant and district fixed effects. Multiple regressions include district fixed effects.

39
costs.<sup>30</sup> However, the fact that providers almost never violated the rules of the vouchers in _DV_ , but did in _PV_ , suggests that gatekeeping costs matter in our context.

A final question is whether differences between _PV_ and _DV_ reflect differential rates of patients “stockpiling” ACTs for future use. Differences between _PV_ and _DV_ due to stockpiling would be consistent with patientdriven demand, but may not occur in a long-run equilibrium where subsidies are permanent. We check for stockpiling using our home survey data, where we cycled through the list of all medications purchased at the clinic and asked if the patient was currently taking the medication.<sup>31</sup> Appendix Table B13, column 2, shows that 94 percent of individuals who purchased ACTs for simple malaria at the clinic reported taking ACTs during the home survey, with no significant differences by voucher condition. Barring the possibility of poor self-reporting, this suggests that stockpiling is not a significant driver of the higher purchase rates in _PV_ relative to _DV_ .

**Hawthorne and Experimenter Demand Effects.** It is also important to ask how our results might be influenced by Hawthorne or experimenter demand effects. This could be especially relevant for doctor behavior; although all clinics were formally told that the study was not a performance review and that all data would be kept strictly confidential, clinics were informed that the study was being conducted in collaboration with the National Malaria Control Program and the National Directorate of Health. Clinics also knew that the pre-intervention training was part of the study; this training emphasized the importance of adhering to official Malian policy when treating malaria, meaning that only patients with confirmed positive test results should receive an antimalarial prescription. We therefore expect Hawthorne effects to have increased the likelihood of providers adhering to official treatment guidelines.

Since treatments rotated within clinic, this would impact behavior in _all_ treatment arms, which would bias us away from finding _PV_ - _DV_ differences in outcomes. Consistent with this (and the hypothesis that Hawthorne effects decay across observation days, see e.g. Leonard and Masatu (2010)),

> 30A related possibility would be if providers perceived more patients in _PV_ to be “likely compliers” with prescribed simple malaria treatment, if for example more patients explicitly requested simple malaria treatment.

> 31ACT treatment courses last for three days and individuals were interviewed the day after the clinic visit, so the treatment course should be ongoing at the time of the home survey.

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around 6.6 in 2007 (Figure 12). Behind this aggregate trend, however, there are differences across income groups: while the z-score has been decreasing in high- and upper-middle income countries since 2005, there has been no clear trend in low and lower-middle income countries (Figure 14). Considering the 12 year period, however, z-scores are lower in 2007 than 1995 in the median highincome and lower-middle-income countries, but similar in low and upper-middle income countries. Considering correlation across countries in 2007, we find a positive but weak correlation between profitability, as measured by ROA and ROE, and stability, as measured by the z-score.

The banking trends documented over the recent years through 2007 match well the boom period leading up to the global financial crisis that started in 2007, especially in high-income countries, with low and even further declining net interest margins (forcing banks to look for alternative income sources), rising profitability, as proxied by higher returns on assets and equity and declining stability, as obvious by lower z-scores. Given increasing returns on assets, the lower z- scores in the years leading up to 2007 can be explained either by lower capital – likely in the context of the transition towards Basel II in many high- and middle-income countries – or due to higher volatility of returns. It is interesting to note that low and lower-middle income countries have not shown similar banking trends over the past years, but have also not seen the same degree of financial deepening as high-income countries and documented in section II. _In hindsight,_ these recent trends of low banking margins, search for higher profits, and declining stability in high-income countries illustrate the circumstances surrounding the financial sector boom that resulted in the 2007 financial crisis.

# **IV. Capital Markets and Insurance Sector**

We include several indicators of capital market development and the size of the insurance sector. The equity market indicators are based on raw data from the Emerging Market Database; the bond market indicators are based on raw data from the BIS banking statistics and the insurance data are based on raw data from Swiss Re.

Take first equity market indicators. As discussed above, **Stock market capitalization to GDP** equals the value of listed shares divided by GDP. It indicates the size of the stock market relative to the size of the economy. **Stock market total value traded to GDP** equals total shares traded on the stock market exchange divided by GDP. It measures the activity of the stock market 10
<!-- Start of picture text --> Chart 5A: Average income level of world exports of other private services, 1995-<br>2006<br>1995 2000 2005<br>year<br>.61<br>.6<br>.59<br>.58<br>.57<br>Average income level of exports of other private services (index)<br><!-- End of picture text --> _Source: IMF Balance of Payments Statistics_ .

Other private services exports are services other than transport and travel and cover most skill-intensive business services. We compute the weighted average of per capita GDP of the exporting countries, with the weights being the share of each country in the total exports of other private services.

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23 capital<sup>20</sup> . But this demand for association interpretation is not supported by the results in table 4 which show strong spillover impacts.

Before moving to the next section detailing the mechanisms of effect, let us pause and admit the results are somewhat an embarrassment of riches. We would not have guessed at impacts as large as those estimated, especially given all the obvious empirical difficulties in measurement, equation specification, etc. The instrumental variable estimates from SCPS imply that a one standard deviation increase in village social capital increases the income of all households in the village by approximately 50 percent and those using the HRDS by 20 percent. In more concrete terms, if half the village are members of one group (with average characteristics) this village would have a social capital index that is higher by one standard deviation than a village where group membership was zero<sup>2</sup> l. While increasing average membership by one-half group per household (or changing group characteristics to a similar degree) is a substantial shift in social behavior, the estimates suggest this would increase expected incomes by 20 to 50 percent, which is an impressively large impact.

Either of these impacts on income is very large relative to other well-known determinants of income, such as schooling or physical assets. A one standard deviation increase in education, which is an additional 3 years of schooling per adult, would increase 20 This is true unless all of the income variation across households is due to village effects, while in fact nearly all the observed variation in household incomes is due to non-cluster related household effects.

**21** Since the index is multiplicative between group membership and the characteristics of groups matter and since the index is normalized twice it requires some working back to find out that, evaluated at the average group characteristics, increasing group membership by .5 would increase the social capital index by one standard deviation.
effect of growth on poverty reduction, while θ 2 reveals its _composition_ effect. Negative signs are expected for both coefficients if growth helps reduce poverty and if the labor intensity of growing sectors has an additional impact on poverty alleviation.

In order to estimate equation (14), it is crucial to obtain data on labor intensities by sector and country. As explained above, we derive these data from information on sectoral value added from World Bank (2005) and payments to unskilled workers from the Global Trade Analysis Project (GTAP). We focus on unskilled workers as they are likely to best represent the poor in each country.

Equation (14) provides a direct test of the model, and this is our basic and preferred specification. However, there are other possibilities. First, if we believe that labor intensities are technological driven and common across countries, then we can use a single _li_<sup>_s_</sup> _i_ ratio for each sector for all countries. This may be a good strategy if we are uncertain as to the quality of the data on labor intensities per country. We implement this specification by replacing the country-specific labor intensities in equation (16) by their corresponding sample median per sector. Second, a discrete or categorical version of the test can be derived by assuming that sectoral growth can have either a high or a low impact on poverty reduction depending on whether its labor intensity _li_<sup>_s_</sup> _i_ is, respectively, above or below a certain threshold, which we set equal to 1. This approach is useful if we are still uncertain as to the precise measure of labor intensities but don’t believe that they are common across countries. We implement this specification by allocating sectors into two groups according to their labor intensity, regressing poverty changes on the growth rates of high and low labor-intensity groups, and then testing for the difference between their respective coefficients. Notice that the composition of these groups can vary from country to country.

Table 3 presents the estimation results for the direct regression implied by the model (column 2), the two alternative specifications (columns 3 and 4), and a benchmark 15
In rural Tanzania, Ms M, brings her 9 month old to the local health clinic, carrying the child on her back. When she enters, Dr. K (an Assistant Medical Officer with O-level education and 4 years of medical training) asks her what the problem is. Still standing in front of his desk, she replies that her daughter has a fever. Dr. K fills a prescription for malaria based on this statement, even though he cannot see the child, much less observe her condition. The consultation and medicine are both free and Ms. M leaves the facility with the prescribed medicine. During the exit interview a nurse on our team notes that the child is suffering from severe pneumonia. The health facility has the medicine to treat both malaria and pneumonia. Dr. K is trained in the diagnosis and treatment for these diseases and saw only 25 patients that day. Yet, but for the intervention of the nurse on our research team, the child would have died. Indeed, a survey in rural Tanzania found that 79 percent of children who die of malaria sought care at modern health facilities (de Savigny et al. 2004).

Taking these two cases as a starting point, this paper documents the quality of medical advice in low-income countries. We being with an overview of the evidence that in many lowincome countries, the problem of access to health care has improved, at least if measured by facilities, doctor visits, and access to drugs. We then introduce a rapidly developing literature on measuring the quality of health care in low income countries. Two broad approaches have been used. In the “vignette” approach, a medical provider may be presented with hypothetical cases, and their responses may be compared to a checklist of essential procedures. The other approach is direct observation of doctors.

These two tools that measure process quality—medical vignettes and direct observation of the doctor-patient interaction—have proved quite informative. For example, doctors in Tanzania complete less than a quarter of the essential checklist for patients with classic 4
The estimate of _γ_ represents how much larger the share of the population responding ”yes” to the IPV indicator increased in disaster-affected regions than in the rest of the country. Importantly, these results are a likely underestimate of the impacts in the worst-affected regions given the omission of the worst-affected areas from the fourth phase of the survey and the delay in interviewing households in the region on which this analysis focuses.

# **Section 5b: Robustness Checks of the Identification Strategy**

As discussed in the Introduction, there are three critical assumptions for _γ_ to identify the arguably causal impact of the earthquake. Specifically, there cannot be substantial differences between urban and more remote populations across provinces, the survey design and implementation needs to be roughly identical across provinces, and the IPV change in the rest of the country need to be a good counterfactual for the change in disaster-affected regions in absence of the earthquake. A variety of robustness checks illustrate that these conditions are likely met and that the results are being driven by the earthquake.

First, we illustrate that the empirical strategy uncovers no significant differences in individual characteristics that would be impossible to be affected by the disaster, where differences in the sampling strategy or in how urban and more remote regions varied by province would result in different samples where such differences would likely be evident. Tables 1 and 2 reports the changes in each individual control variable used in the baseline specification in disaster-affected regions in column (1) following the disaster, the change in the rest of the country in column (3), and reports the difference between the changes in column (5). Table 1 reports the changes for the Men’s Survey and Table 2 reports the changes for the Women’s Survey.

Consistent with the identification strategy, the results illustrate that there were few differences in the changes between disaster-affected regions. As expected by going from urban to rural and remote areas over time, the estimates illustrate that households are socioeconomically worse off following the earthquake in the fourth phase in both the disaster-affected regions and the rest of the country. However, in the difficult-to-adjust characteristics, there is no difference in the changes between disaster-affected regions and the rest of the country. Of the 38 variables captured in both the Men’s and Women’s Surveys in Tables 1 and 2, the vast majority are small in magnitude and imprecisely estimated. Only one of the differences in column (5) is statistically significant at the 5 Sant’Anna 2020; Goodman-Bacon 2021; Sun and Abraham 2021; Baker et al. 2022; Borusyak et al. forthcoming; etc.).

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# **4. Exploratory Analysis of the Clustering of Cross-Country Growth Rates**

Before assessing the strength of spillovers in a regression framework, we apply exploratory spatial data analysis (ESDA) techniques which assess the extent to which growth rates across neighboring countries are spatially autocorrelated. We use the average annual logarithmic growth rate of real GDP per capita as our measure of economic growth. We make use of the same sample-period of 1970-2000 on which the regression analysis of section 5 focuses, splitting this into 5-yearly cross-sections.

To provide a general indication of whether or not there is evidence of significant clustering of growth rates for each 5-year period, we use Moran's I statistic (Moran, 1948), which is defined as: where _gi_ and _gj_ denote the growth rates of countries _i_ and _j_ respectively, _wij_ the corresponding weight in the neighborhood weights matrix, and _M_ the mean rate of growth in the sample. Table 1 shows that, for **W3** (RTA definition of neighborhoods), Moran's I is positive for all sub-periods, with the exception of 1995-2000. This indicates the presence of positive global spatial autocorrelation.<sup>21</sup> Furthermore, using a permutation based approach to inference<sup>22</sup> , this spatial autocorrelation is significant at the

> 21 A possible explanation for the negative Moran's I statistic for 1995-2000 might rest with the impacts of the 1997 Asian financial crisis.

> 22 For a discussion of different methods of inference see Anselin (1992, p 133-135).  In implementing the permutation approach, we used 999 permutations.

15