rafmacalaba/datause-encoder-data huggingface dataset:
geographical location) on growth.<sup>3</sup> The likely underestimation of inequality of opportunity could also lead to their measure of inequality of effort containing some amount of inequality of opportunity. This would imply that their finding of a positive impact of inequality of returns to effort on growth merits more scrutiny through research, as the authors themselves point out.

Also of relevance is a paper by Grimm (2011), which assesses the effect of inequality in health on economic growth, using a cross-national panel data set of 62 low and middle-income countries between 1985 and 2007. The gradient in child mortality over mothers’ education groups, used as a proxy for disparity in health conditions to which different socioeconomic groups of a country are exposed, is closely related to inequality in health opportunities. Even with such a narrow measure, the paper finds a significant negative effect of health inequality (between different socioeconomic groups) on economic growth, which is robust to different specifications, estimation methods, and time spells.

# **3. Methodological approach**

## **Data and measures of equality of opportunity**

This paper incorporates new measures of opportunities. The first is the Human Opportunity Index (henceforth, HOI), which is a measure developed by Barros et al. (2009). The HOI is a composite indicator that combines two elements:

- (i) The level of coverage of _basic opportunities_ necessary for human development, such as primary education, water and sanitation, and electricity.

- (ii) The degree to which the distribution of those opportunities is conditional on circumstances exogenous to children, such as gender, parental income, or household characteristics.

The formula to compute this index is where p̅ is the average coverage of an opportunity and D is the Dissimilarity Index (henceforth, D-Index) that measures inequality in opportunity due to circumstances that are exogenous. D is defined by:<sup>4</sup> where pk is the coverage rate for group _k_ (where each group is defined by a set of circumstances unique to that group) _,_ and ak is the share of group _k_ in total population. The D-Index is then a measure of the weighted average of the distance between group access and population access. It ranges from 0 to 100, in percentage terms, and in a situation of perfect equality of opportunity, takes the value zero. The D-Index has an interesting interpretation as the fraction of all available opportunities that need to be reallocated

> 3 Also, the likely underestimation of inequality of opportunity could lead to their measure of inequality of effort containing some amount of inequality of opportunity. This would imply that their finding of a positive impact of inequality of returns to effort on growth needs to be subjected to more scrutiny through research.

> 4 We also make use of the geometric version of the D-Index, which is more sensitive to inequalities within each subgroup and is decomposable by subgroups.

6
43

**Table 4.29: Poverty Simulation with Varying Poverty Line**

|**Poverty Line**|**Head Count**<br>**Index (P0 %)**|**Depth Index**<br>**(Pi %)**|**Severity Index**<br>**(P2 %)**|
|---|---|---|---|
|(Z=N237.246)|18.67|6.67|3.19|
|Elasticities: with respect to Mean Consumption|-1.37|-1.78|-2.19|
|(Z=N316.328) (1)|26.88|10.71|5.64|
|Elasticities: with respect to Mean Consumption|-1.20|-1.51|-1.80|
|(Z=N474.492) (1)|41.89|18.66|10.84|
|Elasticities: with respect to Mean Consumption|-1.00|-1.24|-1 .45|
|(Z=N553.574) (1)|48.95|22.46|13.43|
|Elasticities: with respect to Mean Consumption|-0.90|-1.16|-1.35|
|(Z=N632.656)<br>(1)|54.34|26.08|15.98|
|Elasticities: with respect to Mean Consumption|-0.82|-1.08|-1.26|
|(Z=N711.738)<br>(1)|59.58|29.52|18.47|
|Elasticities: with respect to Mean Consumption|-0.74|-1.02|-1.20|
|(Z=N790.820)<br>(1)|62.2|32.76|20.89|
|Elasticities: with respect to Mean Consumption|-0.67|-0.96|-1.14|

Note: Z= Poverty Line; (1) Indicates Poverty Line in 1985 prices used throughout the paper. Source: NCS, 1985/86 and 1992/93.

An important aspect of this analysis is shown above where poverty is simulated using different poverty lines (Z). The poverty measures used in this paper, are sensitive with respect to the poverty line chosen. The 1985/86 relative poverty line of Z=N395.41 is super-imposed on the 1992/93 expenditure, by using the mean estimated consumer price index. It is obvious that the choice of an index has a bearing on the number of poor and the depth and severity of poverty.

In order to understand the impact in changes in mean consumption and Lorenz curves in general the elasticities of each of the poverty measures need to be estimated. This is also presented in Table 4.29. Since we are estimating point elasticities the estimate differs depending on which point we choose. The poverty lines were chosen depending on different possible rates of inflation. Other than elasticities we also indicate the general changes in poverty indicators. It is useful to note the responsiveness of poverty measures decreasing with higher levels of poverty lines.

# **4.7 Dominance Test of Poverty**

Apart from this broad changes in poverty due to changes in poverty line it is useful to do more detailed analysis of changes in poverty using dominance tests. This involves plotting the entire distribution of expenditures by cumulative proportion of population (or decile) in terms of regions, geographical locations and socio-economic groups.

The usual first test in this, "first order dominance", involves plotting the cumulative percent of people at successive levels of per capita expenditures. If we plot this for a country in two different periods and if period two is always below the initial period then it implies that poverty has declined over time and any change in poverty line will not change this result. However, if ever these lines were to intersect then the welfare implications of this will depend upon where one sets the poverty line and also vary according to different classifications. It is also
also faced much criticism, as fewer than 10 recruitment firms in Bangladesh handled all the recruitment interactions with Malaysia. The concerns of high cost and malpractice reemerged, and the program has been suspended since mid-2018. Bilateral talks are currently underway to resolve the issues and resume the flow of workers.

# 3. Data and empirical strategy

Our data come from 3,512 in-depth household interviews with G2G applicants between August and December of 2018. The first group is the Phase 1 lottery winners, referred to as T1, who won the lottery to migrate and were put in the first phase of intermediation. The second group comprises both Phase 2 and Phase 3 winners, referred to as T2, who won the lottery to migrate but were put into a deferred phase of intermediation. Eventually, this group only received partial (low) intermediation. The third group is the lottery losers, referred to as C, which will serve as the control group. **Figure 2** shows the various steps of the lottery program and the final study sample.

The survey included detailed modules on the migration, labor and earnings of all household members including the applicant and any other migrant members of the household. Interviews were conducted with the applicants themselves if they were present, or with a knowledgeable household member if the applicant was not present. The survey also had modules on household consumption, enterprises, housing and assets, debt position, and female decision-making of the household in Bangladesh. We also collected data on applicant and household characteristics that are unlikely to vary over time, as well as retrospective data on some pre-lottery outcomes.

## 3.1 Data collection

Constraints on data collection efforts limited the geographic coverage of the survey. Phase 1 winners (group T1) were spread thinly across Bangladesh. BMET limited the number of Phase 1 winners in each village to at most one applicant. Unions, which typically have about 6,000 households from a few villages, had an average of two Phase 1 (group T1) winners. To decrease the geographic scope of data collection efforts, we limited the survey to Dhaka (Mymensingh) and Chittagong Divisions.<sup>8</sup> We then randomly selected 49 of 223 upazilas from these divisions.<sup>9</sup> The survey was conducted in all 522 unions within the selected 49 upazilas.

> 8 The Mymensingh Division was formed in 2015, after the G2G lottery program, by combining the northern districts of the Dhaka Division. The survey was conducted in current Dhaka, Mymensingh, and Chittagong divisions.

> 9 The data extract we got from BMET had data from applicants in 223 of 258 upazilas. The discrepancy could be a result of the lottery not collecting data from upazilas with very high urban penetration.

10
# **6. Security**

## **Safety**

Feeling safe is important for a person to be able to go to school, work, the market, neighboring communities, towns, and cities, and to have social relations. In Haiti nearly 25 percent of youth do not feel safe in their homes (Table 6.1). A third of youth are afraid of visiting larger cities and more than one out of four fear going to the local market. One out of five considers going to a nightclub or bar to be dangerous while one out of eight of the age cohort 35–44 fears this. The metropolitan area, where over 45 percent of youth reside, is considered to be far more dangerous than rural areas. Variance between rural and urban areas is not found across age cohorts for the entire country; this indicates that not only youth but the entire population feel insecure and specifically so in Port-auPrince. Living in a stressful environment is an obvious risk factor for youth. More than 85 percent of youth concur that crime and insecurity are a very serious problem in Haiti. As a result of the perceived security situation only slightly more than one out of ten youth generally trust other people. Such a fundamental lack of trust in other persons affects youth in various social and economic ways; social networks deteriorate and effective and efficient markets are nonexistent.<sup>35</sup> Some simply cannot go to school because of a violent environment.

Table 6.1: Feeling of Safety in Household. 2001 (percent)

|Agegroup|Metropolitan|Urban|Rural|Haiti|
|---|---|---|---|---|
|15–19 years|44.0|91.7|88.3|79.4|
|20–24years|38.6|88.1|83.8|69.6|
|15–24years|41.1|90.2|86.6|75.2|
|25–34 years|39.4|88.7|78.9|67.7|
|35–44years|40.7|81.9|81.1|70.2|

_Source:_ Own calculations based on ECVH 2001.

## **Crime and Domestic Violence**

Crime is a serious problem in Haiti: it has long-lasting financial effects on society and erodes human capital development. A general problem for addressing crime and especially domestic violence is the lack of data for the Caribbean. Moreover, problems of underreporting can be serious because of lack of trust in the interviewer or the simple fact that such issues are rarely addressed in public in Haiti; hiding from the truth is one of the main obstacles facing external agencies, as also noted by Moser and Lister (1999).

The LAC regional homicide rate is the highest in the world: 20 homicides per 100,000, which is twice that of the USA (Ayres 1998). In Haiti, most killings and kidnappings take place in Port-au-Prince. Violence is an integral part of the environment and works as a coping mechanism for unfortunate youth and adults. For poor youth living

> 35 In the US a negative relation between crime and schooling is generally observed (Eckstein and Wolpin 1999).

28
Table 8: Impact of therapy on human capital accumulation at the rapid resurvey.

||(1)|(2)<br>**Primary **|(3)<br>**outcomes**|(4)|(5)|(6)<br>**Secondary**|(7)<br> **outcomes**|(8)|
|---|---|---|---|---|---|---|---|---|
||Desired<br>fertility|Time<br>preferences|Prob. of<br>paid work|Compet-<br>encies|Educ.<br>aspirations|Pregnant<br>within 5yrs|Married<br>within 5yrs|Life<br>expectancy|
|_Rapid Resurvey (Immedi_|_ate post-inter_|_vention)_|||||||
|IPT-G|-0.043|0.055|0.024|0.103|-0.017|-0.027|-0.024|0.020|
||(0.058)|(0.029)|(0.011)|(0.058)|(0.018)|(0.022)|(0.023)|(0.013)|
|Control mean|3.531|0.489|0.812|1.307|0.855|0.421|0.410|0.505|
|Control SD|1.371|0.500|0.237|1.200|0.352|0.494|0.492|0.222|
|Observations|1639|1657|1648|1627|1561|1596|1598|1555|
|H0: IPT-G=0 p-values|0.461|0.065|0.039|0.078|0.356|0.224|0.314|0.139|
|FDR adj. q-values|0.461|0.104|0.104|0.104|0.356|0.356|0.356|0.356|

Notes: This table shows ITT estimates of the impact of IPT-G on the primary and secondary human capital accumulation outcomes at the rapid resurvey, comparing the control group to the IPT-G only group; The full sample is used as the Cash treatment arm was implemented after the Rapid Resurvey; All regression models include randomization block fixed effects centered and fully interacted with treatment; Standard errors clustered at the club level in parentheses; Estimates at endline are weighted to account for the probability of selection for intensive tracking; Columns 1-4 show the primary outcomes: Desired fertility (Desired number of children at time of survey), time preferences (prefers 110,000 Uganda Shs in one month instead of 90,000 today), the self-reported probability of engaging in paid work at age 25, and the score obtained on the Competencies test; Columns 4-8 show the secondary outcomes: Educational aspirations (wishes to pursue some tertiary education), would like to become pregnant within 5 years (or if already pregnant), would like to get married within 5 years (or if already married), and perceived life expectancy (the likelihood of being alive at 40); The rows marked as _H0:..._ show the p-value for a t-test of the indicated null hypothesis on the regression coefficients and the corresponding q-values adjusting for the false discovery rate within primary and secondary outcomes and per round, as per Benjamini and Hochberg (1995); Sample size differences across outcomes are due to shorter tracking questionnaires being administered to difficult-to-reach subsets of CRs.
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE evaluate Kenya’s situation. Detailed comparisons will also be made with immediate regional neighbors in the East African Community.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the five years from 2003 to 2007, Kenya’s economy grew at an average annual rate of 5.3 percent, much better than the 2.3 percent recorded in the previous decade. Notwithstanding this improvement, current growth levels still fall short of the sustained 7 percent per annum needed to meet the Millenium Development Goals. Less than half of 1 percent of East Africa’s improved per capita growth performance during the 2000s can be credited to improved structural and stabilization policies (Calderon 2008); by contrast, almost 1 percent is related to improvements in the country’s infrastructure platform. Most of the boost was due to Kenya’s ICT revolution, while poor roads proved to be a drag on growth. Simulations suggest that if Kenya’s infrastructure could be improved to the level of the African leader—Mauritius—annual per capita growth rates would be 3.3 percent higher than they are at present. A substantial share of that impact would come from improvements in the power sector alone (figure 1).

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

a. Infrastructure’s contribution to annual per capita economic growth in selected countries, 2003–07, in percentage points <!-- Start of picture text --> 2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>telecom electricity roads<br>% of GDP<br><!-- End of picture text --> 2
The survey migration rates match the migration numbers provided by BMET. Assuming the same migration rate for the entire lottery program, our estimates suggest that about 10,700 would have migrated from the entire pool of 1.43 million applicants. This is only slightly higher than the official count of 9,800 provided by BMET officials in March 2018. This suggests that the rate of government intermediation in our sample is similar to that of the entire program even though we surveyed in divisions closer to the capital.

G2G intermediation led to migrants migrating much earlier than the control group. By the time of our survey, it had been 33 months since the migrants in the control group had migrated. That is, among those in the control group who migrated, they did so about 34 months after the lottery was conducted in February 2013. The T2 group migrated at about the same time as the control group – 33 months after the lottery. The T1 group migrated about 19 months earlier – 15 months after the lottery. Among the G2G migrants, the average T1 (Phase 1 winner) migrant migrated 14 months after the lottery, whereas the average T2 (Phase 2 and 3 winner) migrant migrated 26 months after the lottery. As expected, Malaysia was the most popular destination for the migrants in the T1 and T2 groups because of the nature of the government intermediation. Even without the government intermediation, Malaysia was still the destination for about a quarter of migrants in the control group. In that group, about two-thirds migrated to Gulf countries, including 22 percent going to Saudi Arabia, 21 percent to Oman, and 14 percent to Qatar.

A large share of applicants who migrated are still abroad. About 75 percent of the applicants from group T1 who migrated were still abroad at the time of the survey. The proportion is slightly higher for applicants who migrated from group T2 or the control group, at 78 percent. On average, the time of the survey was almost four years after the initial migration of applicants in group T1. This suggests that these applicants were able to either extend their stay abroad or were able to get another job abroad. Even though the government intermediation was only for one contract term, these migrants were able to use the first migration opportunity to extend the length of their migration episodes.

# **Impact on investments for migration**

We investigate whether the lottery winners take any initiative to better prepare themselves for work abroad. To elicit these investments, we asked applicants whether they made any investments in their language skills, other skills, or their health after the outcome of the lottery and before (any) migration. Such investments could potentially make their lives better in the destination and might also increase their income. **Table 2** shows the results, which are discussed below.

16
> TAsLE ~ ~ ~ ~ ~ IIIAElMYIT ~ ~ ~ ~ ~ ~ ~~~~~~~~~~Fl

OFTe RUSStIU Bale **of** Pnts cmtSd

- _1E_ - 8 categories of iprts: food, petroleun, other cnsumption goods, primary Intermediate goods, mafacturfng Intermediate goods, capital goods, non-factor services.

3 I _DFI0_ OFI, (Lfe*Lfp), a I (Lfgttfpl.

here: k - 1987. 1988. 1989

# Bulymie of ha -gnts cntd

|Lfe<br>Lfd|-<br>.|foreign borrowing by the<br>central<br>beatn 03<br>foreignborrorn<br>by the<br>ccmerclat<br>bssks (net)|
|---|---|---|
|Lfp<br>DFI|-<br>-|<br> <br>foreign<br>borrecng<br>bythe<br>private non-finwnclet<br>sector<br>(net)<br>not directforeign<br>investment|
|Lsfp|-|short-termcapital<br>inflows toprivet<br>sector|
|Kel||netcapital<br>notelsere<br>ircluded|
||-|**I**fg<br>nterest<br>on Lfg|
|NsllUpf<br>Isfp<br>Esgf<br>|-<br>-<br>-|interest<br>an foreian borrwing<br>by central<br>_bank_an<br>**cecrclat**<br>**bait**<br>Intereston**Lsfp**<br>Interestan short-tem<br>foreign<br>debt<br>|
|11Wi<br>Prof||interton Knol<br>reittances<br>of profits<br>nddividends|
|C<br>Utp<br>Sf<br>UFA|-<br>-<br>-|rsrce<br>balat<br>trfer<br>to the private sector<br>current<br>ecountbal<br>Interestan NFA|

NMIin

**K=S**

gi4ifc, a gifc, I gdf .(1 . kgr) kIl GDPx% a 0Pfc , Ti - Subg TOTIApS a l *Ip TOTUCFaI a so + Sp; Sf TOTIUCuarS * C + l + Cp. Ip - o TOTUACurU a GDPfc Ti -abg OODg, Lsfp, Lsfg, Prof and Tfp are projected in a similar way.
Apart from single-member households, relatively small households have the largest drops in terms of levels and shares. But these households are the same with the highest health expenditure levels and shares before the crisis. And these may be the households with the highest total consumptions, which we know have large health expenditure drops (table 4.16). Shortly, in our multivariable analysis, we control for this to disentangle any confounding effect.

Table 4.19. Health expenditures by household head's education

||||Shares|||Level||
|---|---|---|---|---|---|---|---|
|||Pre-cnsis|Post-cnsis|Change|Pre-crnsis|Post-cnsis|Change|
|_Illiterate-_||||||||
||Mean|0.019|0.015|-0.004|1.446|1.690|0.244|
||Std.err.|0.013|0008|0.015|0.909|1.049|1.249|
||Obs.|47||||||
|_Primary_||||||||
|_education:_||||||||
||Mean|0.091|0.053|-0.038|11.675|4.638|-7.037|
||Std.err.|0.014|0.010|0.017|2.179|1.182|2.427|
||Obs.|331||||||
|_Secondary_<br>_education_||||||||
||Mean|0.105|0.081|-0.024|14.256|8.803|-5.453|
||Std.err.|0.008|0.008|0.011|1.501|0.984|1.665|
||Obs.|1173||||||
|_Higher_||||||||
|_education_||||||||
||Mean|0139|0.094|-0.045|33.162|12.477|-20.686|
||Std.err.<br>Obs.|0.020<br>215|0.016|0.022|6.527|2.717|6.716|

From table 4.19, we can see that health expenditures increase monotonically with household head's education. This is true in terms of both per capita expenditure levels and budget shares. Households with a head with higher education have the highest expenditures in health in Moldova. At the same time, it is this type of households the ones that see their expenditure on health decreasing the most.

# _Regression analysis_

We run an OLS regression where the dependent variable is the change in health expenditure shares. Regressors include the household variables used in the analysis of consumption vulnerability plus variables to measure the time to get to both clinics and hospitals and the approximate distance to these institutions. As in our analysis for consumption, the inclusion of the year quarter dummies intends to control for any trend in expenditure shares (see a discussion on this in the consumption section). The results are in table 4.20.

Coefficients for other towns and rural are both statistically different than zero, implying that health care expenditure shares drop more dramatically outside large cities, relative to 32