rafmacalaba/datause-encoder-data huggingface dataset:
Consumption could be a better measure of life satisfaction than income, since income gives a biased estimate of subjective well-being especially if we include individuals from different age groups (Clark, et al. (2008). But it is difficult to control for the measurement error associated with reported consumption level. We use monthly household consumption level as a proxy for own income. It could generate biased estimates if household size varies greatly. To avoid this we use per capita monthly household consumption for the estimation of life satisfaction.

We had to deal with missing observations as well. In a number of events in the retrospective section we have missing observations. As documented by LiTS these could be either zero (no event took place) or the reluctance to answer. We replace these missing observations with zero. In section 5, we include certain tests to verify that these missing observations as zero events do not account for any bias in the estimation. We examine these missing observations by country and other observable characteristics and find these to be random.

Also, these countries vary a lot in terms of population and physical area. The LiTS draws a nationally represented sample of 1000 people from each country. Since the Life in Transition Survey sample is unbalanced across PSUs, it requires a final weighting adjustment procedure to provide estimates at the national level. As a standard procedure we use PSU level weights for precise estimates at the national level.

# **3. Data and descriptive evidence**

We use Life in Transition Survey (LITS) data of 2006, a joint initiative of the European Bank for Reconstruction and Development (EBRD) and the World Bank, covering 27 countries<sup>8</sup> in Eastern European and Central Asia region further subdivided into European,

> 8 List of countries from different sub region, EU (Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia), Balkan (Albania, Bosnia, Croatia, FYR Macedonia, Montenegro, Serbia), CIS low income (Armenia, Azerbaijan, Georgia, Kyrgyzstan, Moldova, Tajikistan, Uzbekistan) and CIS middle income (Belarus, Kazakhstan, Russia, Ukraine)

8
and/or loans are denominated in (or linked to) a foreign currency, we are therefore likely to somewhat mismeasure the average interest rate spread. Ideally, one would want to calculate interest rates on loans and deposits in local currency. Unfortunately, such data is not widely available on a cross-country basis. We gather information on the degree of dollarization in each country from De Nicolo’, Honohan, and Ize (2003). DOLL is the share of deposits in foreign currency. All variables are for the year 2000, except for the variables from the Barth et al. database that are for either the year 1999 or 2000. Table 1 shows the country-averages of all the variables.

Finally, we control for the average level of bank operating efficiency through the ratio of operating expenses over total assets, orthogonalized with respect to PROP or LAW in order to capture the bank level of inefficiency that is not associated with the prevailing legal and regulatory framework.

After matching the available information for explanatory and dependent variables we ended up with a sample size of 106 countries for the estimation of the SPREAD1 regression. While some variables, such as SPREAD1 and PROP, are available for all sampled countries, others, such as SPREAD2, have a much smaller coverage as banks in many countries are not required to report interest income on loans separately from total interest income. As a result of lower data availability the number of countries for which we were able to obtain data for the explanatory and dependent variables for the SPREAD2 regression was limited to a total of 32.

As a first assessment of whether spreads are indeed lower in countries with better judicial efficiency, we look at the correlations between our country variables. The correlation matrix is reported in Table 2. We find that SPREAD1 and SPREAD2 are 12
**Figure 9: Priorities for Government Spending on Climate Change Adaptation**

(a) Government should spend more money to: (percentage of respondents) <!-- Start of picture text --> Improve extension services and provide seeds more 64<br>resilient to climate extremes 40<br>Provide clean drinking water 59 63<br>Invest in increasing availability of electricity 23 57<br>Invest in irrigation infrastructure 22 43<br>Provide more help and relief after natural disasters occur 34 42<br>Tajikistan<br>Provide more reliable weather forecasts 40<br>28 Kazakhstan<br>Invest in health facilities and provision of health services 33 50<br>Reforestation to prevent soil erosion 28 33<br>Develop disaster early‐warning systems 23 34<br>Facilitate out‐migration of people from areas prone to 22<br>high climate change risk 23<br>0 20 40 60 80<br>Percent of respondents<br><!-- End of picture text -->

- (b) Tajikistan: Most important priority for additional spending (totals sum to 100 percent)

<!-- Start of picture text --> 4 3 Invest in increasing availability of electricity<br>5<br>22<br>Improve extension services and provide seeds more<br>resilient to climate extremes<br>7 Provide clean drinking water<br>Provide more help and relief after natural disasters occur<br>Reforestation to prevent soil erosion<br>7<br>Provide more reliable weather forecasts<br>Invest in health facilities and provision of health services<br>8 18 Invest in irrigation infrastructure<br>Develop disaster early‐warning systems<br>Facilitate out‐migration of people from areas prone to<br>10<br>14 high climate change risk<br><!-- End of picture text -->

**Source:** Authors calculations based on P-LITS 2 data.
30

# <u>Figure 4: Catastrophic health expenditures and their distribution by consumption and income</u>

<!-- Start of picture text --> Percentage of Households Spending More than 10% of Budget on Health<br><!-- End of picture text --> <!-- Start of picture text --> Concentration Index of Out−of−Pocket Health Expenditures > 10% of Budget<br><!-- End of picture text --> <!-- Start of picture text --> 15–27 0.20–0.52<br>10–15 0.10–0.20<br>7–10 0.00–0.10<br>4–7 -0.10–0.00<br>3–4 -0.20–-0.10<br>0–3 -0.39–-0.20<br>no data no data<br><!-- End of picture text --> CI of Out−of−Pocket Health Expenditures > 10% of Budget, Consumption−Based <!-- Start of picture text --> CI of Out−of−Pocket Health Expenditures > 10% of Budget, Income−Based<br><!-- End of picture text --> <!-- Start of picture text --> 0.20–0.52 0.20–0.28<br>0.10–0.20 0.10–0.20<br>0.00–0.10 0.00–0.10<br>-0.10–0.00 -0.10–0.00<br>-0.20–-0.10 -0.20–-0.10<br>-0.20–-0.20 -0.38–-0.20<br>no data no data<br>IBRD 44309 | APRIL 2019<br><!-- End of picture text -->

Note: Surveys are the same in the left and right maps in each row, but not necessarily the same in the top and bottom maps in each column.
10. **Notwithstanding their lower income, education levels, and location, home ownership among the unbanked is high.** 61% of the unbanked in Bogotá own their home, showing a similar level of home ownership than the banked (66%, see figure 3.6). These levels are similar to home ownership rates in the United States (65%). The minimal difference in home-ownership rates for banked and unbanked suggests that access to bank loans is not a limiting factor in accessing homes. The high rate of ownership overall suggests that both unbanked and banked Colombians view their homes as key to their asset-building strategies. The high rate of home ownership among the unbanked indicates their capacity to accumulate assets, and, as a result, to potentially benefit from access to financial services.

**Figure 3. 6  Home-Ownership among the banked and unbanked**

<!-- Start of picture text --> Banked Unbanked<br>6%<br>4%<br>9%<br>13%<br>53% 52%<br>30% 33%<br>Owned by Household, totally paid Owned by Household, totally paid<br>Rented by Household Rented by Household<br>Owned by Household, being paid Owned by Household, being paid<br>Used by household without paying Used by household without paying<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 11. **The unbanked are further from access to banks than the banked.** The DANE survey does reveal that over 60% of the unbanked either don’t know where a bank branch is in relation to their homes, or report the closest banking institution to be over ten blocks away. In the case of the banked population in Bogotá, 50% of respondents report a distance to the next bank of over 10 blocks or an unknown distance. In particular, 22% of the unbanked live more than 20 blocks from the next bank compared only 13.5% of the banked in Bogotá.

12. **In conclusion, the majority of the unbanked share the characteristics of the urban underclass** with lower income, lower education, closer links to the informal sector (and consequent economic and social insecurity), with a greater tendency to live outside higher income census districts where banks, commerce, religious and health centers are located. The unbanked show no particular difference of age or gender distribution from either the banked population or from the population at large nor do they differ from the overall population in terms of home-ownership. At the same time, the data reviewed above 49
While the introduction of these measures has to be understood in the context of replacing market discipline with supervisory discipline, given that the former is absent in light of an implicit guarantee, this heavy-handed regulation is partly ineffective and comes at a high cost. Banks can get around the branching requirement by opening new branches just outside the urban areas – given that the urban areas in Bangladesh are still growing, soon these branches will be inside the urban areas – against the spirit of the regulation. Nevertheless, this regulation causes sub-optimal allocation of bank branches. Regulations such as for branch opening or prescribing bonuses and benefits for board members and executives cause additional transaction costs and cause inefficiencies in banks’ operations. Vetting the appointments of new CEOs by Bangladesh Banks seems rather in the old tradition of operating a financial system than regulating it. Finally, similar governance measures were introduced later and are still not being enforced for the banks that need them most – the NCBs.

Another indication that bank supervision in Bangladesh is heavy-handed compared to other countries is the number of professional bank supervisor per institution, which stood at eight in 1997, substantially above the average of 2.6 for a sample of over 100 countries.<sup>28</sup> Figure 17 illustrates the interaction of government guarantee and supervisory approach. On the horizontal axis is the degree of government guarantee and on the vertical axis the degree of supervisory intervention. Given that the Bangladeshi banking system enjoys an implicit guarantee for depositors and owners, there are no incentives for monitoring and discipline by market participants. The absence of both market and supervisory discipline (south east corner) resulted in easy abuse by banks and high risk of fragility in the past. Bangladesh Bank has thus opted to introduce the above discussed approach of supervisory intervention (north east corner).

> 28 Source is Barth, Caprio and Levine (2006).  Unfortunately, there are no more recent data available. Of course, this high number of bank supervisors also reflects overstaffing in the Bangladesh Bank, and there might certainly be a bi-directional causality between the size of the supervision department and supervisory interference into banks.

33
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function decreases in its 'acceleration' since the incremental wind speed does little additional damage to the gross destruction already done.

To provide some perspective of the context of this risk market, it is estimated that the ESC countries generate currently about $150 million in insurance premiums. Most ESC countries face a high level of risk. The islands are in the front line of Atlantic storm activity. A number of the islands have significant earthquake risk as well. A few countries have a volcano risk, most notably Montserrat, where an eruption in 1996 caused extensive damage.

Projections by risk management firms show that the East Caribbean region can expect 2.5 storms every year. Severe hurricanes, defined as category 3, 4 and 5, have, up until the late 1 990s been less common. Category 3 hurricanes can be expected to occur every second year, and Category 5 storms every fifth year.

Reviewing recent hurricane experience, the following records are obtained:

||Hurricanes which Affected Caribbean<br>|
|---|---|
||Countries Between 1970-1999|
|**Year**|**Hurricane**<br>**Country**|
|1974|Carmen<br>Jamaica|
|1974|Fifi<br>Belize, Jamaica|
|1979|David<br>Dominica<br> <br>|
|1980|Allen<br>Barbados, St. Lucia,|
||St. Vincent, Jamaica|
|1984|Klaus<br>Dominica|
|1985|Gloria<br>Antigua, St.Kitts, Turks<br>& Caicos, Anguilla|
|1987|Emily<br>Grenada, St. Vincent|
|1987|Floyd<br>Bahamas|
|1988|Gilbert<br>Dominica, Jamaica,|
||G. Cayman, St. Lucia|
|1988|Joan<br>Tobago|
|1989|Hugo<br>Antigua, Dominica,<br>Guadeloupe,|
||Montserrat, St. Kitts|
|1992|Andrew<br>Bahamas|
|1994|Debbie<br>St. Lucia|
|1995|Iris<br>Antigua, Dominica,|
||St. Vincent, T & T|
|1995|Erin<br>Bahamas|
|1995|Marilyn<br>Dominica|
|1995|Luis<br>Anguilla, Antigua,|
||Dominica, Montserrat,|
||St. Kitts & Nevis|
|1998|Georges<br>St. Kitts & Nevis,<br>Antigua|
|1999|Floyd<br>Bahamas|
|1999|Jose<br>St. Kitts, Antigua|
|1999|Lenny<br>St. Lucia, Grenada,<br>|
||St. Kitts, Dominica,|
||St. Vincent|
Of course, the two shadow rate equations are not incompatible. Some or all of the other variables shown may be endogenous. If both UIP and domestic monetary equilibrium (with the money demand function adopted) prevail, then the two shadow interest rates will be equal. Thus, for example, in a fixed exchange rate regime, the money stock may adjust to ensure that the money market equilibrium condition (2) prevails, with i,° = _i,+._

# _Previous literature: majcor contributions to measuring liberalization_

Two main approaches in the literature draw on this equilibrium framework to assess the degree of capital account openness. The first, due to Edwards (1985) and Edwards and Khan (1985), defines an open capital accounts as one which satistfies UIP. This approach nests the UIP in a broader maintained hypothesis encompassing both the domestic monetary equilibrium and UIP conditions as special cases. For example, we can estimate<sup>36</sup> the parameter (p in a regression defined by: (4 ) The value _(o_ = 1 is interpreted as complete capital mobility, q = 0 as a closed financial system.

The Edwards-Khan approach requires the use of data on interest rates. However, such data may not be available, at least for some of the relevant domestic financial markets. For example, published official interest rates may be applicable only to a fraction of borrowings and allocate(d by administrative directive. If there is a parallel market, then the unobserved interest rate on that market may give a better indication of the degree to which the financial system is integrated in the World economy. In order to deal with the unobserved interest rate issue, Haque and Montiel (1991) proposed a way of solving for the unobserved shadow interest rate that would prevail in the absence of capital mobility.

In contrast to Edwards and Khan, who use the shadow rate _i°_ defined as the rate that clears the money market for the observed money stock, Haque and Mcntiel use the concept of the hypothetical money stock m" that would prevail if there were no capital flows. They then define a new shadow rate i' as the rate that would clear the money market for the hypothetical money stock. Specifically, let _mt_ in equation (2) be replaced with _m'_ defined by: where kP is the net private capital flow, and vt is a measurement error. The new shadow interest rate is then defined by: The true (but unobserved) interest rate _it_ is now hypothesized to be a weighted average of the UIP and the hypothetical closed-market shadow interest rate i' giving: 3 Provided we have some empirical substitute for the unobserved exchange rate expectation.

43
# **Figure A3.3: Average Per Passenger Mile Fuel Consumption According to ICAO Distance Classes of All Aircraft in ICAO Methodology in Phase III Sample (60% first class and 60% business class load factors assumed).**

<!-- Start of picture text --> 0.7 0.10<br>0.09<br>0.6<br>0.08<br>0.5 0.07<br>0.06<br>0.4<br>0.05<br>0.3 Avg Footprint per Pax/Mile<br>0.04<br>Avg Fuel per Seat/Mile<br>0.2 0.03<br>0.02<br>0.1<br>0.01<br>0 0.00<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16<br>Distance Class<br>(Kg)<br>Average Fuel Burn Per Seat Mile (Kg)<br>Average CO2 Footprint Per Passsenger Mile<br><!-- End of picture text -->

# **Figure A3.4: Bank Carbon Footprint by Business and First Class Load Factors, FY12**

<!-- Start of picture text --> 250,000<br> 200,000<br> 150,000<br>Combined Carbon<br>Footprint<br> 100,000<br>Headquarters Carbon<br>Footprint<br> 50,000<br> -<br>0.5 0.6 0.7 0.8 0.9<br>Business and First Class Load Factor<br>CO2 Footprint (Tons)<br><!-- End of picture text --> 33
above and just below the cutoff for receiving a “small” transfer—US $45 versus no scholarship. We find clear evidence of diminishing marginal returns to transfer size. Indeed, we cannot rule out that the additional $15 has no effect on enrollment over and above the first $45.

The rest of the paper proceeds as follows. In Section 2 we briefly discuss the CSP program, the data, and our identification strategy. Section 3 presents our main set of results and robustness checks. We conclude in Section 4.

# **2. Program, data, and identification strategy**<sup>**2**</sup>

<u>Program: The program we analyze, the CSP, offers “scholarships” to 6</u><sup>th</sup> grade students conditional on enrolling in school in 7<sup>th</sup> grade, the first year of lower secondary school. These scholarships are renewable for the three years of lower secondary school, conditional on enrollment, regular attendance, and on-time promotion from one grade to the next. Although they are referred to as “scholarships” by the government and donors, these are effectively child-specific CCTs.3 The money is transferred to the parents, conditional on school enrollment and regular attendance, and program officials do not monitor how the money is spent.

In the initial program period we evaluate, the program operated in 100 of the approximately 800 lower secondary schools in Cambodia. These schools were selected on the basis of administrative data that indicated that poverty rates in the areas served by these schools were high and, by implication, secondary school enrollment rates low. The selection of CSP recipients within eligible schools was done in three stages. First, using administrative data from the 100 CSP schools, program officials identified all of the primary “feeder” schools for every CSP school. (A primary school was designated a feeder school if it had sent graduating students to a given CSP school in recent years.) Second, within feeder schools _all_ 6<sup>th</sup> graders were asked to complete a CSP “application” form—regardless of whether these students or their parents had previously expressed an interest in attending secondary school. The application form

> 2 This description of program design and data sources draws from Filmer and Schady (2009a).

> 3 The is in contrast to many other CCT programs which identify households for program participation, and then make transfers conditional of the behaviors of all children (and adults) within those households.

4
market constraints and childcare duties. Blundell _et al._ (2018), for example, show that childcare duties play an important role in labor supply decisions – especially for mothers.<sup>12</sup> Our research findings add to the existing literature by examining program exits and their relationship with local employment growth. We find that families exposed to “rising tides” in the labor market are not only more likely to exit BF but also more likely to have members transition into formal employment. This suggests that BF’s design does not create strong disincentives against formal work participation even after being exposed to the program for some time.

The rest of the paper is structured as follows. Section 2 describes the institutional design of BF, its interaction with the rest of the tax benefit system and the data used. Section 3 presents some descriptive statistics followed by Section 4, where the methods used are explained. Section 5 shows results and robustness checks before we conclude in Section 6.

# **2 Institutional Design and Data**

## **2.1 Bolsa Familia Program**

Brazil was among the first countries in the world to embrace CCTs. Launched in 2003, BF aimed to alleviate both temporary and chronic poverty and fostered human capital development for children. When BF was first introduced, it also sought to consolidate several sub-national programs, such as _Bolsa Escola_ and a gas subsidy (Lindert _et al._ , 2007; Bastagli, 2011; Fietz _et al._ , 2021). This section describes the rules and structure of BF as they applied in 2019. Since then, the program has undergone significant changes, including its replacement by _Aux ́ılio Brasil_ in 2021 and further modifications in 2023.

To receive assistance under the 2019 framework, families had to be registered in the single registry Cadastro Unico<sup> ́</sup> (CadUnico).<sup>13</sup> BF consisted of two benefits: (i) the basic benefit and (ii) the variable benefit. As of 2019, the final year of our panel dataset, the monthly basic benefit was paid to families with a monthly per capita income of less than R$ 89 (USD 21.4). Families whose per capita income remained below R$ 89 (USD 21.4) also received a supplement so that the household would reach at least a monthly per capita income of R$ 89 (USD 21.4). The variable benefit was granted to individuals with a monthly per capita income below the

> additional hours of informal work).

> 12Other references are Aguilar-Gomez _et al._ (2019) for Mexico, Berniell _et al._ (2021) for Chile, and Heath (2017) for theoretical evidence from Ghana.

> 13Individuals with a per capita income below 0.5 of the minimum wage (MW) were eligible to register in CadUnico. For more information about the registry, see Section 2.2.

5