rafmacalaba/datause-encoder-data huggingface dataset:
distributional changes—that is, how distributional changes that accompany growth interact with this measurement issue in a dynamic sense.

# 5 Discussion and conclusions

In summary, we have laid out a puzzle in the relationship among growth, inequality, and poverty in SSA and suggested that an inequality measurement problem lies at its heart. Consumption-based inequality measures miss important information at the top end of the welfare distribution, leading to an underestimation of inequality. Besides identifying the problem, we propose an economically viable solution to reduce this underestimation; we suggest that using information from income distributions collected under the FAO-RuLIS project to recalibrate the top end of the consumption distribution may provide a practical solution to this measurement problem. Overall, we calculate that by using consumption as a welfare measure, inequality is biased downward by 16 percent in SSA when using Gini as the preferred inequality measure and by about 20 percent when using the 90th percentile over the 50th percentile ratio. When correcting the inequality measures with the proposed method, we almost eliminate this downward bias. The evidence is certainly not conclusive, since we can apply the correction to only a small set of countries, but it sheds new light on the distributional changes in SSA in the past two decades. The value added of our work compared to similar contributions (among others, Chancel et al. 2019) lies in the fact that we use the original distributions as much as possible (consumption but also income), only in-sample information, and limit the amount of theoretical assumptions by “letting the data speak.” We conclude, however, on a more speculative note, posing some open questions that we hope will contribute to framing a research agenda centered on the revaluation of distributional issues in light of our new findings. What appears on the surface as a somewhat technical measurement issue has, in fact, implications that go far deeper. In this paper we have argued that the standard empirical toolkit available to development economists working on SSA has limited our ability to appreciate the magnitude and persistence of inequalities in the continent. We hope that, by beginning a process of refining and expanding this toolkit, we will have helped put in motion a process that will overcome what we believe is a technical bottleneck to understanding the effects of inequality in SSA.

Refocusing attention on inequality in SSA can have an effect in both academic and policy spaces. In the world of academic research, we hope to see more attention being placed on the collection and analysis 21
empirical approach and provides a discussion of the results. Section 4 provides concluding remarks.

# **2. SERVICE AVAILABILITY AND WELFARE: INSIGHTS & OBSERVATIONS**

# **2.1 Data**

Our analysis is based on the panel data from Vietnam Living Standards Survey (VNLSS) for 1992/1993 and 1997/1998. In addition to detailed information on household activities, these surveys include a very detailed community questionnaire and have information on the same communities and households for a subset of households in both years. This allows us to use the data in a panel form, which eases some of the statistical problems<sup>8</sup> . The first round of VNLSS was conducted from September 1992 to October 1993 and included a self weighted sample of 4800 households with two strata (urban and rural), while the community questionnaires were administered only in rural areas; a total of 120 communes. In the second round 6000 households were interviewed, 4305 of which were previously interviewed households, while the community questionnaire was administered to both rural and minor urban areas (156 communes). The data used in this study is collected from the same 111 rural communes and about 3300 rural households in both 1993 and 1998<sup>9</sup> . Unfortunately, the detailed information on the quality of some of the key services is only available in 1998, making both the detailed analysis of the impact of quality of services and the impact of the changes in that quality difficult. This reduces the precision of some of the service variables, and might cause some of the service quality variables seem not to have an impact.

# **2.2 Basic service availability**

As stated previously, Vietnam is almost unique among developing countries when it comes to basic service coverage. Figure 1 shows the distribution of some of the basic service availability for the years 1993 and 1998. Specifically, even in 1993, almost 88 percent of rural communes had electricity, 85 percent had roads (as opposed to 45 percent

> 8 The panel nature of our data ensures that we control for unobserved household characteristics, as opposed to repeated cross-section data.

> 9 Only rural communes in both years were included as those were the only ones interviewed for the community survey in both years (those that switched from urban to rural or vice versa were not included, in addition to those that underwent administrative changes).

6
The results highlight a need for better understanding the incentive effects and effectiveness of binding NTPs that are accompanied by legal enforcement mechanisms as compared to cooperation motivated by soft law types of NTPs. In the next sub-section we undertake an exploratory analysis of one such instrument that may play a role in influencing whether NTPs are implemented: official development assistance. The underlying hypothesis is that the impact of NTPs may be influenced by the use of complementary instruments that seek to improve non-trade outcomes.

# _3.5 Development assistance and NTPs_

Development assistance-funded projects and programs targeting issue areas addressed in NTPs may influence outcome performance. While there is an extensive literature investigating the effects of development assistance on trade, including so-called aid for trade projects and programs that seek to enhance trade capacity and facilitate trade,<sup>18</sup> little attention has been given to the role of ODA targeting the non-trade issue areas that are the focus of this paper. Aid recipients may or may not have a PTA with the donor, and these may or may not include NTPs, which may be binding or non-binding. Of interest is not only whether ODA varies with (type of) NTPs but whether overall ODA flows are affected by signing an NTP or a change in the type of NTP that a partner country signs.<sup>19</sup> Figure 5 plots the average amount of ODA (in USD million) received by countries that have not signed PTAs that include NTPs, those that have agreed to non-binding NTPs and those accepting binding provisions.<sup>20</sup> Countries that have accepted binding environmental provisions receive more ODA than signatories of PTAs that only include non-binding provisions. There is little difference between the latter set of countries and those that have not agreed to any NTPs. The opposite is true for aid for civil and human rights-related activities, where countries that have joined PTAs that include non-binding

> 18See e.g., Younas (2008); Pettersson and Johansson (2013); Hoekman and Shingal (2020).

> 19Hoekman _et al._ (2023) find that (lagged) bilateral aid is positively and significantly associated with the propensity of developing countries to accept PTAs that include NTPs, including legally-enforceable provisions.

> 20Table A-6 reports the t-tests for significance in mean differences.

21
Table 5. Impacts of PEP Migratory Status on Rights and Services

||Rights and<br>Services<br>(Index)|SISBEN<br>Access|Subsidized<br>Healthcare|Financial<br>Products|Transfers from<br>Government|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|1[PEP_i_ = 1]|4.424***|0.567***|0.267**|0.444***|0.221**|
||(0.917)|(0.127)|(0.118)|(0.110)|(0.097)|
|q-values|[0.001]|[0.001]|[0.010]|[0.001]|[0.010]|
|Obs. left|363|467|429|466|539|
|Obs. right|655|990|764|992|1,169|
|Observations|3,424|3,781|3,375|3,795|3,799|
|Mean values (Non-RAMV refugees)|0.000|0.023|0.015|0.030|0.065|
|Outcome Level|Individual|Household|Individual|Household|Household|

_Notes:_ Dependent variables: (i) Rights and Services (Index) is constructed using the outcome variables of columns (ii) to (vi) using the methodology of Kling et al. (2007): (ii) SISBEN Access is a dummy [=1] if the respondent is enrolled in the vulnerability assessment system; (iii) Subsidized Healthcare is a dummy [=1] if the respondent benefits from public healthcare; (iv) Financial Products is a dummy [=1] if the respondent has a savings account or other financial or banking products; (v) Transfers from Government is a dummy [=1] if the respondent received transfers from any official social assistance program. All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

49
Table 4 shows results from the IPW estimations of psychological wellbeing and government measures satisfaction outcomes. The results indicate positive relationships for ECT-R1 beneficiaries in the shortterm, who experienced significantly lower levels of concerns about food shortages, being sick with COVID-19, quarantine, and the lack of health care (besides COVID-19). These short-term relationships do not however last in the medium-term for R1 beneficiaries. ECT-R2 beneficiaries report experiencing lower levels of concerns concerning children being out of school and food shortages, however they are significantly more concerned about price increases, which may indicate a sense of relative deprivation. Receiving the ECT is also significantly correlated with the satisfaction with the government response in all estimations except for the medium term ECT-R1 beneficiaries.

**_Table 4_** _: Correlations between the ECT and subjective well-being measures, satisfaction with_ _<u>government response</u>_

||Not<br>Concerned<br>about<br>children|Not<br>Concerned<br>about food<br>shortages|Not<br>Concerned<br>about price<br>increases|Not<br>Concerned<br>about<br>being sick|Not<br>Concerned<br>about<br>quarantine|Not<br>Concerned<br>about lack<br>of other|Satisfaction<br>with<br>government<br>response|
|---|---|---|---|---|---|---|---|
||out of<br>school||**1. ECT-R1: Ro**|with<br>COVID-19<br>**und 1 (short-t**<br>|**erm) outcome**<br>|healthcare<br>**s**<br>||
|ECT-1|0.146|0.466***|0.062|0.414**|0.387***|0.222*|0.041***|
||0.096|0.127|0.045|0.114|0.118|0.120|0.009|
|Observations|568|569|569|569|567|567|555|
||**2.**<br>|**ECT-R1: Round**|**2(medium-te**|**rm) outcomes**|**(ECT-R2 bene**|**ficiaries exclu**|**ded)**|
|ECT-1|-0.305**|-0.064|-0.015|-0.028|0.217|0.122|0.017|
||0.055|0.108|0.040|0.195|0.184|0.199|0.015|
|Observations|648|649|649|649|649|648|647|
||**3.**|**ECT-R2: Round**|**2(medium-te**|**rm) outcomes**|**(ECT-R1 bene**|**ficiaries exclu**|**ded)**|
|ECT-2|0.594**|0.567**|-0.040**|-0.120|-0.045|0.177|0.035***|
||0.244|0.231|0.020|0.140|0.140|0.163|0.009|
|Observations|438|438|438|438|438|437|435|
|||**4**<br>|**. Pooled o**<br>|**utcomes of EC**<br>|**T-R1 and ECT-**<br>|**R2**<br>||
|ECT-1 and ECT-2|0.319***|0.473***|0.012|0.389***|0.389***|0.221**|0.046***|
||0.114|0.123|0.031|0.098|0.100|0.098|0.008|
|Observations<br>_Source_: Authors’ cal|911<br>culations from|913<br>CIMS (2020)|913|913|911|910|892|

_<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates. Psychological wellbeing outcomes are originally in a 5-point Likert scale format and are considered as continuous in the IPW estimation. The coefficients should</mark> 23
negative buyer’s remorse effect is considerably smaller in magnitude than the positive effect of IBLI coverage, however, suggesting that the comfort people derive from insurance coverage more than compensates for any regret they suffer once they realize they did not need coverage. Therefore, in our survey sample, insurance purchase is _ex ante_ optimal, on average.

This could reflect the nature of the sample we study. Pastoralists in southern Ethiopia’s Borana Zone face decline in informal social insurance institutions at a time when pastoral livelihoods are becoming more risky. As a result, Borana pastoralists may experience greater well-being as a result of having access to index insurance, even if it did not pay out in the short-term. These results suggest that for people with precarious livelihoods, even an imperfect, commercially priced insurance policy that does not pay out can leave them feeling better Our findings also show that estimations of the welfare effects of insurance ought not ignore potential _ex post_ impacts. Prior purchases of insurance may induce buyer’s remorse once a buyer realizes that, in retrospect, costly insurance proved unnecessary. Survey-based SWB measures can capture all of these prospective effects without resorting to strong assumptions about the arguments and functional form of utility functions.

SWB measures seem especially appropriate to establishing the impacts of commercial insurance. Commercial insurance policies, including IBLI, intrinsically involve a tradeoff between material and non-material well-being if policies are priced above actuarially fair premium rates so as to cover the costs of and ensure a profit margin for the underwriter. Theory suggests that actuarially fair insurance is welfare enhancing, regardless of whether it pays out, because most people are risk averse and insurance mitigates risk. But when insurance is not actuarially fair, and perhaps especially if it offers incomplete coverage, as is inevitably the case with index insurance products subject to basis risk, the _ex ante_ expected monetary loss (because premiums exceed expected indemnity payments over time) and the _ex post_ buyer’s remorse that might result if no insurable loss occurs, might negate the oft-assumed of insurance.

24
credible instruments for initial consumption is not self-evident, but we have access to rainfall data preceding the baseline survey. In the Kagera farming system, droughts and generally poor rainfall can present serious problems to crop income, so we use z-scores of rainfall deviations in the year preceding the baseline, with positive rainfall deviations truncated to zero. To allow for household level variation, we use as identifying instruments all the RHS variables interacted with this truncated z-score. The full first stage regression is reported in Appendix 2. The diagnostics for the instruments are encouraging: for example, the Cragg-Donald weak instrument F-stat is 8.06, while the Sargan over-identification statistic is 14.66 with a p-value of 0.55.

Table 5 offers the results. First, column (1) offers the IHHFE findings for the particular (somewhat smaller) sub-sample for whom we have full information on all the controls used. As before, a clear negative effect of a prime age death can be found. Column (2) uses a specification as in equation (3), using all the controls, including initial consumption, but the latter treated as exogenous. The impact of an adult mortality shock is again negative and significant at 5 percent: perfect risk-sharing is not taking place. The impact of any prime age death of a respondent in the initial household, our means of assessing the presence of at least some risk-sharing, is not significantly different from zero, implying that there is no evidence suggestive risk-sharing between the split-offs of the same initial household. However, this may a consequence of the presence of lagged consumption, a likely endogenous variable, as an explanatory variable. Column (3) therefore offers the same regression, this time using as discussed above the interaction of household characteristics with the truncated z-score of rainfall in the year preceding the baseline. The coefficient and standard error on deaths in 2000-2004 are virtually exactly the same as without instrumenting, suggesting that the likely presence of endogeneity 19
# **Annex 1. Detailed Information on Transport Expenditures in LSMS Surveys (12 Countries in LSMS Collection)**

|**Survey Question**|**Albania**|**Burkina Faso**|**Cambodia**|**Ethiopia**|**Iraq**|**Malawi**|
|---|---|---|---|---|---|---|
|**_Non-food Expendit_**|**_ures during..._**||||||
|Past 7 days||Urban transport by taxi, bus,<br>tricycle or other||Transport||1. Public transport - Bicycle<br>Taxi<br>2. Public transport -<br>Bus/Minibus<br>3. Public transport - Other<br>(Truck, Oxcart, etc.)|
|Past 1 month|1. Fuels and lubricants<br>for personal vehicles<br>2. Passenger transport<br>by road (bus, minibus,<br>taxi, etc.) or railway<br>(EXCLUDE expenses to<br>travel to school and<br>health care facilities)||Gasoline, diesel, and<br>lubricant, and gas||(Include a lot of detailed<br>subcategories)<br>1. Fuel and oils for personal<br>transport equipment<br>2. Maintenance and repair<br>of personal transport<br>equipment<br>3. Wages of transport<br>services<br>4. Other transport services|1. Petrol or diesel<br>2. Motor vehicle service,<br>repair, or parts<br>3. Bicycle service, repair, or<br>parts|
|Past 3 months||1. Bus<br>2. Taxi<br>3. Other transport<br>4. Spare parts and accessories<br>5. Maintenance and repair of<br>vehicles, bicycles, motorcycles,<br>etc.<br>6. Other transportation costs<br>(excluding international travel)<br>7. New motor cars<br>8. New motorcycle<br>(motorcycles, scooters, etc.)<br>9. New bike<br>10. Second-hand motor car<br>11. Second-hand motorcycles<br>(motorcycles, scooters, etc.)<br>12. Second-hand bicycle<br>13. Cart<br>14. Super Essence<br>15. Gasoline - mixture<br>16. Diesel<br>17. Lubricants|Local travel||||

29
# **1.0 Introduction**

In Bangladesh, international migration has become an increasingly important avenue for employment and poverty reduction. In 2008 around 5.8 million workers were employed overseas, remittance flows amounted to around 10% of GDP and Bangladesh is now among the top ten remittance-receiving countries globally (World Bank 2008a). Almost two-thirds of Bangladesh’s remittances originate from the Middle East, followed by the United States. Returns to overseas employment are relatively high because of wage differentials even after adjusting for differences in cost of living. As a result, migrants are able to finance not only essential current consumption but also investments that contribute to permanent income gains for household members who remain at home inside Bangladesh.

But to what extent is this potential realized? And, by whom? Typically, the upfront cost of securing employment abroad is high. A significant portion of the international migration from Bangladesh is in the form of short-term, non-permanent migration, and potential migrants are recruited by private recruitment firms who supply labor to businesses in the Persian Gulf region or South East Asia under an explicit time-bound contract. The burden of financing agent fees, air fare, visa fees and other costs can be considerable for the poor. Further, it may well be that recruiters, who bear the responsibility for guaranteeing a smooth supply of adequately skilled and reliable workers, choose to minimize information asymmetries and moral hazard by recruiting within narrow social or community networks where information flows are better and labor contracts are easier to monitor and enforce. This is related to why the distribution of remittances is geographically skewed. The more prosperous regions in the Eastern part of the country have significantly more households receiving remittances than households residing in 2
**Figure 7: Market organisation in WCA (1990, 2008)**

<!-- Start of picture text --> West and Central Africa (1990) West and Central Africa (2008)<br>strong low regulation monopsony strong low regulation monopsony<br><!-- End of picture text --> Competition is ‘strong’ if many firms compete on prices and ‘limited’ if 2 or 3 firms exert price leadership. ‘Regulation’ reflects regional monopsonies or administratively allocated supply.

<!-- Start of picture text --> West and Central Africa (1990) West and Central Africa (2008)<br>No private capital Some private capital No private capital Some private capital<br>Only private capital Only private capital<br><!-- End of picture text --> ‘Some private capital’ implies that some of the ginning companies have opened up ownership to private investors. ‘No private capital’ implies that the State is sole owner of all the ginneries and ‘only private capital’ that the state is not part of the ownership of any of them.

<!-- Start of picture text --> West and Central Africa (1990) West and Central Africa (2008)<br>Fixed prices Market prices Fixed prices Market prices<br><!-- End of picture text --> ‘Market prices’ indicates that prices exclusively reflect market forces. ‘Fixed prices’ implies that the state or a public agency fixes prices throughout the year and the country.

WCA includes Benin, Burkina Faso, Cameroon, Central African Rep., Chad, Dem. Rep. of the Congo, Gambia, Ghana, Guinea, Guinea-Bissau, Ivory Coast, Mali, Niger, Nigeria, Senegal and Togo.

Source: Delpeuch and Leblois (2011) 25