rafmacalaba/datause-encoder-data huggingface dataset:
in some countries, and is also growing in MENA, although from a low base. Because health insurance is largely idiosyncratic, it has not been included in the econometric or business line analysis (Box 1).

## **<mark>Box 1 - Health Insurance</mark>**

**<mark>Health insurance shows little correlation with income levels or structural variables</mark>** <mark>.   As shown in Figure 8, health insurance appears to be driven by idiosyncratic factors and path dependencies.  The decisions of the KSA and Abu Dhabi to require expatriates to take out private health insurance (and the consideration other GCC countries are giving to this) reflects the high levels of their expatriate populations and a desire to deal with attendant fiscal pressures. The relatively high penetration levels in the Francophone countries and Jordan reflect gaps in the social insurance system.</mark>

<!-- Start of picture text --> Figure 8: Health insurance penetration versus income levels<br>7 1<br>6<br>5<br>Lebanon<br>4<br>3 1<br>Jordan<br>2 Morocco<br>Saudi Arabia<br>1 Tunisia<br>MoroccoJordanTunisiaIranLebanonSaudi ArabiaBahrain UAE Iran<br>0 EgyptSyria Libya<br>Kuwait Egypt<br>0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 0 Syria Libya<br>PCI 0 5,000 10,000 15,000 20,000<br>Source: AXCO reports and World Bank analysis<br>PA and Health Premiums/GDP %<br><!-- End of picture text -->

# **4. Industry Structure and Performance**

## _4.1. Overview of Market Structure_

**There are on average 25 licensed insurers per country, the bulk of which licensed as non-**

**life insurers or composites (Table 5).** The small number of life insurers reflects the low development of this sector in the region.  Some countries do not license composites any longer but the existing composites have been so far grandfathered.   The average market share of the three top companies is about 52%, but there are wide variations across countries, and some markets seem very concentrated.  State insurers still hold a significant share of the non-life market in 4 countries.  As shown in Figure 8, there is a positive correlation between the share of state insurers and the share of the three largest companies.   This is not surprising, reflecting the historical legacy of state monopoly in these countries, exercised through large companies.

13
fully specified and to be stationary.

The column “Sim III” presents the results for the longer time series with 100 years. It hints that the method has plausible asymptotic properties, i.e. standard errors decrease as we increase the length of time series, and the estimates converge to the true parameter values.

Finally, column “Sim IV” shows that as the number of choices decrease, the standard errors increase in both stages.

In the following tables, 5 and 6, we compare results of PPML and CCP based estimation strategies. We use “Simulation I” data with 20 _,_ 000 agents, then we repeat the exercise with 2 _,_ 000 and 4 _,_ 000 agents to demonstrate small sample properties of the estimators.

Table 5 shows estimated values and standard errors in parentheses, Λ<sup>_i_</sup> , using PPML and CCP methods with 2 _,_ 000 agents, with 4 _,_ 000 agents, with 20 _,_ 000 agents and with infinitely many agents<sup>14</sup> . We use equal weights in the non-parametric stage of CCP estimation. The last two columns show that both PPML and CCP methods converge to the true values, therefore they are asymptotically equivalent. With finite number of agents, PPML estimates are closer to the true values, especially when the sample size is small. However, we do not argue that PPML is more efficient than CCP, because the non-parametric stage of CCP estimation could be conducted with different weights and we cannot try all possible weighting vectors. When the number of choices are large, PPML is more convenient than non-parametric CCP estimation since it does not rely on taking logarithms of probabilities that can be very close to zero.

Table 6 presents the estimation results (and standard errors in parentheses) for _C/ν_ and 1 _/ν_ using different estimation methods. When we use a maximum likelihood based method, we assume that the econometrician knows the distribution of aggregate shocks to wages in order to use maximum likelihood estimation. Also we assume that the econometrican knows the true values of _η_ ’s, because otherwise repeating ML estimation procedure 300 times takes

> 14The econometrician can observe true switching probabilities _m_<sup>_ij_</sup> _t_<sup>whenthereareinfinitelymanyagents.</sup> But there may still be uncertainty due to the aggregate shocks to _wt_<sup>_i_.</sup>

18
earning, spending, saving, borrowing, and informal sharing. Rather than test hypotheses emerging from the economics literature, the researchers’ goal was to watch and listen, and only then try to make sense (inductively) of households’ observed choices.

The picture that emerges is very different from the early microfinance vision. Collins et al. (2009) find that even if microfinance does not raise income or launch businesses, it may help households cope with the ups and downs of incomes and needs that arise through the year. A central finding of Collins et al. (2009) can be boiled down in terms of global poverty statistics: the hidden burden of living on $1 a day per person (or wherever the global poverty line is set) is that rarely does anyone actually receive $1 per person each and every day. Instead, farmers have high and low seasons, laborers have better and worse months, and many people are vulnerable to the ups and downs created by boom and bust economic business cycles. The financial problem of being poor, then, is both an issue of low resources on average and an issue of the uncertainty and unpredictability of those resources. Microfinance can then be an important asset in smoothing consumption in the sense of Deaton (1992), not just for investment. Not surprisingly, this is how Stuart Rutherford observes Grameen Bank’s microfinance customers actually using their money (Collins et al. 2009, chapter 6).

Put another way, even if poverty rates (defined by a given level of average income) are not noticeably affected by microfinance access, some of the consequences of being poor – such as having difficulty finding funds to meet health crises – may be ameliorated by having access to extra money when needed (Gertler et al. 2009). Such access may be vital during emergencies (and are due more attention from policy makers and researchers), but it is a very different story from the standard narrative upon which the microfinance sector was built. Indeed, it is a very different story from that behind efforts to address global poverty.

33
the middle class also implies more market-oriented economic policy on trade and finance. The impact of a larger middle class appears to be more robust than the impact on the same outcomes of lower poverty, lower inequality, and also of higher GDP per capita. Overall, our findings suggest that the development of the literature studying the association between income and socioeconomic factors may have been hindered by its limitation in measuring household income directly.

The paper is organized as follows. Section 2 discusses the dataset; Section 3 presents the empirical approach; Section 4 discusses the results, and Section 5 concludes.

# **2. Data Description**

The analysis is based on a cross-country panel dataset that contains information about headcount indexes for various thresholds that we have purposely built for the analysis. The dataset spans 672 yearly observations across 128 countries, from 1967 to 2009 (around 90 percent of the observations are however from the 1990s and 2000s). To compute the headcount indexes we draw from various World Bank collections of harmonized nationally representative household surveys that contain information on income or expenditures, and from simulated distributions of income and expenditures from the World Bank’s _PovCal_ database, whose parameters fit the distribution of nationally representative household surveys (see Table 1). Because of the nature of the primary data, 17 percent of the countries and 38 percent of the annual observations are from Latin America. The dataset is fairly balanced across levels of economic development: 21 percent of the observations are from high income countries, 37 percent from upper middle income, 30 percent from lower middle income, and 11 percent from low income countries. Because surveys tend to report information either for income or expenditures, we report for each country only one of the two measures: 57 percent of the sample reports information on income, and 43 percent on expenditures.

All income and expenditures data are in 2005 PPP US dollars. For each survey, we first correct current units for inflation using the national CPIs, and then convert them into 2005 US dollars PPP using the International Comparison Program (ICP) PPP conversion. Since the ECAPOV, PovCal and SEDLAC surveys are used to compute World Bank poverty figures, we used for these surveys the same conversion, weights and methodology that has been used to compute internationally comparable poverty data.

For the analysis in this paper, we have collapsed yearly observations into five-year averages. We have also dropped from the analysis countries with population of less than two million, and have 4
<!-- Start of picture text --> Poverty and Conflict Debt<br>Poverty rate ($1.9 a day), by sub-national region during 1992-2018<br>a>? =<br>Poverty rate (%)<br>Ml (75,100) a. ay’<br>Ml (50,75) eA<br>(25.50) e ce.<br>OY (10,25) é _ :<br>(2,10) * | ~<br>ID) (0,2) No data f¥<br>Conflict debt, by sub-national region during 1992-2018<br>se: a!a<br>= ,<br>|| Conflict debt \ er ><br>WB > oe ~ - ~ i$<br>WS (2[1 . 5,2.25)25.3) . 3ae . x: 4 q vw<br>I) [0.75,1.5)<br>(0,0.75) s<br>DD) ° No data ay<br>Source: World Bank and UCDP<br><!-- End of picture text -->
**Table 2: Individual, household and community characteristics**

|**Indicator**<br>_(in ratio, if not indicated otherwise)_|Average|North|South|Female|Male|
|---|---|---|---|---|---|
|<br>Personal characteristics||||||
|Age, years|40.4|40.7|40.1|40.7|40.0|
|Kyrgyz|0.72|0.67|0.75 ***|0.71|0.73|
|Uzbek|0.12|0.01|0.20 ***|0.12|0.12|
|Of other ethnicity|0.15|0.32|0.05 ***|0.16|0.15|
|Years of schooling|10.3|10.4|10.3 **|10.3|10.4|
|Risk attitude, 0->10 scale|4.7|5.3|4.3 ***|4.2|5.3 ***|
|Labor market status||||||
|Employed|0.61|0.60|0.61|0.46|0.77 ***|
|Inactive|0.37|0.37|0.36|0.51|0.21 ***|
|Unemployed|0.03|0.03|0.02|0.03|0.02|
|Social capital||||||
|<br>Lived in the community, years|38|36|40 ***|38|39 *|
|<br>Member of a social group|0.12|0.13|0.10 ***|0.11|0.12|
|Can get financial help from others|0.81|0.76|0.85 ***|0.79|0.84 ***|
|<br>Trust, in a scale from 1->4||||||
|Trust in people in community|3.15|3.00|3.25 ***|3.14|3.15|
|<br>Trust in local government|2.70|2.51|2.84 ***|2.74|2.66 ***|
|Trust in central government|2.61|2.56|2.65 ***|2.66|2.56 ***|
|Trust in police|2.33|2.30|2.35 *|2.38|2.28 ***|
|Political attitudes||||||
|Supports current political system|0.15|0.21|0.11 ***|0.14|0.15|
|Supports Russian political system|0.20|0.23|0.18 ***|0.17|0.23 ***|
|Has no political system preference|0.23|0.18|0.27 ***|0.27|0.20 ***|
|Voted in last Presidential election|0.87|0.86|0.87|0.86|0.88 **|
|Is going to vote in local elections|0.78|0.76|0.79 **|0.76|0.80 ***|
|Household (HH) demographics||||||
|Female head|0.19|0.22|0.17 ***|0.24|0.14 ***|
|HH size, people|5.9|5.5|6.3 ***|5.9|5.9|
|Number of children|2.1|1.9|2.2 ***|2.1|2.1|
|Number of migrants|0.3|0.1|0.4 ***|0.3|0.2 *|
|Household economics||||||
|HH income, '000 Soms/month|18.4|18.2|18.5|18.1|18.7|
|Income per person, '000 Soms/month|3.5|3.7|3.3 ***|3.4|3.5|
|Remittances, '000 Soms/month|2.1|0.2|3.4 ***|2.2|2.1|
|Land size, ha|1.2|1.9|0.8 ***|1.2|1.3|
|Livestock units, sheep equivalent|22.7|25.2|21.0 ***|22.0|23.4|
|Number of cars|0.45|0.46|0.44|0.43|0.47 **|
|Asset index, HH level mean=100|108.1|133.0|91.3 ***|106.2|110.2|
|<br>Housing value, mln.Soms|0.90|0.86|0.92 ***|0.90|0.89|
|Community characteristics<br>1||||||
|<br>Population, '000 people|6.5|4.3|8.0 ***|6.5|6.5|
|Kindergarden is available|0.48|0.46|0.49 *|0.48|0.48|
|<br>Medical point is available|0.51|0.49|0.53 **|0.51|0.52|
|Frequent energy interruptions|0.12|0.09|0.14 ***|0.12|0.11|
|Clean water availability, %|80|87|75 ***|81|80|
|Irrigation water available, %|76|80|74 ***|76|76|
|Number of attending people|153|174|140 ***|155|151|

Sources: Life in Kyrgyzstan (LiK) Survey, 2011 and 2012 1/ Community data are from LiK 2011 wave The mean differences for North/South and female/male dimensions are tested using t-test. Significant differences are indicated by * p<0.1, ** p<0.05, *** p<0.01.

9
14 Table 3. Panel Data Correlations: 1988.01 - 1992.09

|Lat|in Americ|a||Asia|||||
|---|---|---|---|---|---|---|---|---|
||Net<br>Equity|Net<br>Equity<br>(with<br>real<br>interest<br>rates)|Gross<br>Bond|Gross<br>Bond<br>(with<br>real<br>interest<br>rates)|Net<br>Equity|Net<br>Equity<br>(with<br>real<br>interest<br>rates)|Gross<br>Bond|Gross<br>Bond<br>(with<br>real<br>interest<br>rates)|
|Credit|0.1_4||0.253||0.052||0.334||
|Second.|0.029||0.142||N.A.||N.A.||
|US INP|-0.129||-0.282||-0.222||-0.12"'||
|Black|_-0.054_||-0.134||-0.083||0.141||
|USAT|-0.094|-0.094|-0.324|-0.273|-0.253|-0.221|-0.117|-0.143|
|USAC|-0.099|-0.103|-0.329|-0.295|-0.256|-0.235|-0.122|-0.147|
|USAG|-0.092|-0.024|-0.315|-0.006|-0.211|0.016|-0.119|-0.071|
|USAL|-0.100|-0.102|-0.332|-0.295|-0.253|-0.229|-0.128|-0.153|
|USA3i|-0.092|-0.075|-0.336|-0.219|-0.234|-0.141|-0.123|-0.135|
|PCi|-0.098|-0.093|-0.336|-0.260|-0.248|-0.195|-0.125|-0.148|

Sources: International Financial Statistics, IMF. US Treasury, Salomon Brothers Institutional Investors, and World Currency Year book. Note: USAT, USAC, USAG, USAL and USA3i interest rates are US Treasury bill rate (IFS line 66c), certificate of deposit rate (IFS line 601c), long term rate (ten-year: IFS line 61), LIBOR (three-month: IFS line 60ldd) and medium term rate (threeyear: IFS line 61a), respectively. PCi stands for the first principal component of the five interest rates (it explains 95 % of the variance of these (nominal) interest rates).<sup>12</sup> Brady bonds have been taken out of gross bond flows for Latin America (the results are very different with Brady bonds incorporated). N.A. denotes not available.

Table 4 reports the correlation coefficients among the explanatory variables by region. As expected, the various interest i-ates are highly correlated. The correlation between these interest rates and the industrial production variable is likewise high. There is also a significant (negative) correlation between secondary market prices and interest rates (and the industrial production variable). As interest rates dxclined over much of this period, secondary market '<sup>2</sup> When using real interest rates, the first principal component explains equally well the individual real interest rates, except for the long-term (ten-year) real interest rate, where only 51 % is explained by the first principal component.
measures in our summary index, in the interest of reporting all survey measures used from each family.

3. Hypothetical discount rate We also attempted to measure the discount rate in a second way (again, mainly for the methodological study mentioned above). As in Holt and Laury (2002), we asked respondents a series of hypothetical inter-temporal choices for larger amounts of money (on the order of US$10-30, about a week’s wages). This was organized as two lists of 11 binary decisions, with a fixed amount right now versus a varying amount in two weeks (or two weeks versus four weeks for the second list). The delayed amount started as strictly less than the sooner amount (e.g. 1000 LD now or 900 in the future), then equal to, and then larger and larger until it was four times as big (1000 LD now or 4000 LD in future).

We calculated discount rates based on each respondent’s first switch from a present preference to a future preference.<sup>13</sup> Those who preferred 900 LD in the future over 1000 LD in the present received a discount rate of .9, while those who always preferred money earlier received a discount rate of 4. We then took the average of the inverse of the present (now versus 2 weeks) and future (in 2 weeks versus 4 weeks) discount rate as our measure of patience, and the difference between future and present as our measure of time inconsistency.

4. Self-reported survey questions We asked respondents six qualitative questions to gauge their self-reported levels of patience and time inconsistency.<sup>14</sup> For example, respondents were asked to place themselves on a ladder from 0 (least patient) to 5 (most patient) as one measure of self-reported patience, and how much they agree with statements such as “When I get money, I spend it quickly” as a proxy of time inconsistency. Specific questions are displayed in Table D.3.

By reporting all measures collected in the endline survey, three-quarters of our time preference measures are hypothetical rather than based on incentivized games. For robustness purposes, in Table D.3 we also report a summary index of the incentivized games only.

# **D.4 Anti-criminal and anti-violent values**

Table D.4 displays control means and long-term treatment effects for survey questions in the anticriminal and anti-violent values index.

# **D.5 Other outcomes**

Table D.5 displays the control group means and long-term impacts of all the survey questions underlying the variables in Table 8, including prosocial behavior, post-traumatic stress, personality traits, quality of appearance, substance abuse, quality of social networks, well being, and executive function. We describe each of these measures in Section 6.6 of the main paper.

> 13Enumerators continued down the list, and (oddly) a nontrivial fraction switched multiple times. We use the first switch only. Furthermore, about 17% of respondents preferred less money in the future as a commitment device, especially if they were expecting a large purchase coming soon.

> 14Dohmen et al. (2011) and Jamison and Karlan (2011)show that basic self-reported attitudes on risk and time preferences can be externally valid.

xxvii
are provided in Panel B. In all the specifications, there is a large and positive relationship between the likelihood of having a quality certificate (log odds and marginal effects) and informal competition. This relationship is statistically significant at less than the 1 percent level or close to it. Without any other controls (except for country, year, and industry fixed effects), a unit increase in informal competition is associated with an increase in the probability of a firm having a quality certificate by 17.4 percentage points (the marginal effect in Panel B, column 1), which is significant at the 1 percent level. This impact increases to 21.2 percentage points when we add the remaining controls to the specification (see columns 2-4 in Panel B). Alternatively, a one standard deviation increase in informal competition leads to an increase in the probability of certification by 2.9 to 3.6 percentage points across the different specifications. For the final baseline specification (column 4, Panel B), the corresponding increase is 3.6 percentage points. This is an economically large impact given that only 10.4 percent of the firms in the sample have a quality certificate.

Regarding the controls, the likelihood of certification (marginal effect and log odds ratio) is significantly higher for firms that are larger, more productive in terms of sales per worker, older, have foreign owners, use technology licensed from a foreign company, and have their own website. Higher exports are also associated with an increase in the likelihood of certification, but this becomes weak and statistically insignificant when some of the other controls are included in the specification.

# **6. Robustness**

# 6.1 _Additional controls_

23
_5_ to all households and 50 percent with respect to urban families.' Starting with 1978, female-headed households are classified according to whether they have children. Over this period, female-headed households increased their share of total households by 18 percent. Not surprisingly, the fastest growth took place in the 10 largest cities in Brazil.<sup>5</sup> Femaleheaded households without children increased faster in every region, especially in the metropolitan areas. The large cities with the fastest growth in female-headed households were Rio de Janeiro in the southeast, and Fortaleza in the northeast.

# What is Meant by a Female-Headed **Household?**

Initially, household surveys sought to define the head of household in order to have a reference point in each household and to avoid double counting. Over time, headship has come to imply normative assumptions about authority and income-earning responsibility. 'fhese acsumptions do appear to have an empirical base, as estimates of earnings functions using household survey data find a positive effect of headship after controlling for human capital variables and gender.<sup>6</sup> Recently, however, the designation of headship in national household surveys (the basis for much of the analysis of female-headed households) has been questioned. It is alleged that household surveys reflect the norms of the household membet questioned, or of the interviewer, rather than the relative economic importance within the household in designating the head, and that this bias has resulted in an underreporting of 4 The census data used by both authors provide data on households and families. Goldani (1989) analyzed urban famnilies, while Merrick and Schmink (1983) analyzed households. Not shown in Table 1, which combines both sets of data, is the ratio of families to households. However, it is known that the ratio of families to households declined from 1960 to 1980 (Fox, 1982), and it also appears that the ratio of femaleheaded families to female-headed households also declined. This is probably a reflection of two trends in Brazil: (a) declining number of multi-fanily households; and (b) the declining share of women employed as domestics, a common employment for female heads of families in multi-family households.

I These cities are actually an agglomeration of cities known as metropolitan areas, and include the major city and surrounding suburbs. In this paper, we use the term city' to refer to these agglomerations.

6 See, for example, Birdsall and Fox (1985) for Brazil.