rafmacalaba/datause-encoder-data huggingface dataset:
Surprisingly little attention has been paid to whether differences in survey methods may affect the estimated returns to education. This is especially relevant for low income countries, where analysis more often relies on data generated by different surveys. This study focuses on two dimensions of survey design (i) the specific questions used in the questionnaire, considering two common approaches ‐ and (ii) whether the questions are answered by the respondent himself or herself, or by a another person.

The detail and wording of questions can have important effects on survey findings. In particular, labor questions have been shown to affect the labor statistics they generate, including those reflecting labor force participation and occupational distribution. This may be especially relevant in settings where there is substantial variation in the proportion of individuals who are employed in wage work versus household‐owned enterprises or home production (who are not directly remunerated in the form of a salary or wage). Likewise, challenges may arise in settings where employment is highly seasonal or where an important proportion of workers are casual laborers. These differences in the categorization of subjects (in labor force participation and wage work) may also affect the estimated returns to education, as they result in a different subsample of wage workers for whom the returns are estimated.

Several studies have investigated different aspects of questionnaire design, including question style and wording (open vs. closed questions; positive vs. negative statements; etc.) or the specific place of questions within the survey questionnaire (see Kalton and Schuman (1982) for a review). While the general conclusion is that question‐wording can have important effects, the direction of these effects is frequently unpredictable. Sustained research efforts to revise employment questions in the US provide interesting insights. Concerned that irregular, unpaid, and marginal activities may be underreported in the Current Population Survey (CPS), among others because people may not think of their activity as work, respondents were asked in a debriefing study to categorize different hypothetical situations as “work”, “job”, “business”, and so on. While the majority were able to classify consistent with CPS definitions, large minorities gave incorrect answers for each vignette. Thirty eight percent of the respondents for instance the advice to policy makers. Nevertheless this is still the dominant approach in the analysis of education and related policies, in particular in low income countries, and it is therefore useful to analyze their sensitivity to survey methods.

6
Table 3 Vietnam Household Living Standards Survey: Panel Sample Average Annual Household Income (in thousand Vietnamese Dong & PPP U.S. dollars)

||Fishing|Households|Rural H|ouseholds|All Ho|useholds|
|---|---|---|---|---|---|---|
||2002|2004|2002|2004|2002|2004|
|Mekong 4 (M4)|||||||
|observations|561|561|334|334|1113|1113|
|total income|3537|4224|2769|3507|3278|3883|
|in PPP U$S|1247|1489|976|1237|1156|1352|
|Mekong 6 (M6)|||||||
|observations|788|788|614|614|1706|1706|
|per capita income|3544|4281|2856|3598|3262|3881|
|in PPP U$S|1250|1509|1007|1269|1150|1368|
|South Vietnam (non-Mekong)|||||||
|observations|929|929|3560|3560|6127|6127|
|per capita income|3711|4107|3038|3736|3253|3873|
|in PPP U$S|1309|1448|1071|1317|1147|1366|

Note: Own calculations based on the panel sample of the Vietnam Household Living Standard Surveys, 2002 and 2004. Mekong 4 (M4) and Mekong 6 (M6) refer to two sets of Mekong provinces that specialize in catfish production: M4 includes An Giang, Can Tho, Dong Thap and Vinh Long, and M6 adds Soc Trang and Tien Giang.

39
This could result in girls living in bigger families (with more siblings) than boys (Barcellos et al., 2010). As already mentioned, bigger families may have scale (dis)economies that can affect individuals’ nutrient intakes. To the extent the unobserved fertility preference is timeinvariant, it could be eliminated by HFE method. Household income (proxied by expenditure) is also potentially endogenous as both nutrient consumption and health endowment of individuals may affect household income. In the literature, household expenditure is often instrumented by household landholding. However, Behrman and Deolalikar (1990) distinguish the effect of current income vis-` _a_ -vis permanent income on nutrient intakes, arguing that if households protect their nutrient intakes from short-term fluctuations, the income elasticities of nutrient intakes would be biased downward relative to the true household response to permanent income changes. They find that the effect of nutrient intake responses to both current and permanent income are quite small. Following Behrman and Deolalikar (1990), I use both current and permanent income (proxied by per capita landholding) measures as explanatory variables. If a household’s nutrient allocation decision based on an individual’s endowment and labor market productivity is time invariant, then this unobserved household characteristic that might influence household income is eliminated in HFE estimates.

Spouses’ assets and education at marriage could be correlated with their unobserved characteristics, such as their preference for children’s (of particular sex) nutrition and health, which in turn could be correlated with household formation through marriage market selection. For instance, a man (woman) who wants healthy children may also choose an educated and/or wealthy wife (husband). So a wife’s (man’s) education and/or assets may appear to influence a child’s nutrition, even if for the same man (woman) changes in wife’s (husband’s) assets or education would not affect the child’s outcome. The effect of a wife’s (husband’s) bargaining measures on child’s nutritional allocation will be overestimated if husband (wife) with a high taste for children’s nutrition tend to choose educated and/or wealthier spouse. This could in turn lead to erroneous rejection of unitary model in favor of intrahousehold bargaining. Hence, another motivation for HFE is to eliminate spouses’ time-invariant unobserved characteristics that could be correlated with their bargaining measures and could influence individuals’ nutrient allocations.

The survey, as described in the next section, was conducted in four rounds. Using within household variation of individuals’ nutrient intakes in different rounds, and variation of time varying household characteristics across rounds, I estimate the following HFE version of equation (7): 13
Controls include household size, sex of household head, asset-based quintiles, community, age of respondent, educational status of respondent, marital status of respondent, parental status of respondent (indicator variables for having a child and being pregnant), orphan status of respondent (one parent or both parents being deceased), and month of interview at midline; all controls are from baseline data except month of midline interview.

**Table 7. Impact of EPAG on Fertility and Sexual Behaviors**

||**Panel 1: F**<br>|**ertility**|||
|---|---|---|---|---|
||Baseline mean|OLS|Controls|Observations|
||0.696|<br>-0.045***|||
|Any living children|[0.460]|(0.012)|Yes|3196|
|Any living children x Not<br>||-0.023**<br>|||
|pregnant at baseline||(0.011)|Yes|3196|
|Number of children<br>|1.549<br>|<br>-0.024<br>|||
|(conditional)|[0.798]|(0.026)|Yes|2353|
|Number of children|1.079|-0.071**|||
|(unconditional)|[0.975]|(0.028)|Yes|3188|
|Are you pregnant right|0.076|<br>0.057***|||
|now?|[0.264]|(0.012)|Yes|3169|
|Pregnant x Not||0.019*|||
|pregnant at baseline||(0.010)|Yes|3169|
|Desired number of|3.481|<br>-0.038|||
|children|[1.203]|(0.050)|Yes|3182|
||<br>**Panel 2: Se**<br>|<br>**xual Behavior**|||
||Baseline mean|OLS|Controls|Observations|
|Number of regular|1.003|0.016|||
|partners|[0.378]|(0.023)|Yes|3106|
|Number of casual|0.541|<br>0.065|||
|partners|[1.080]|(0.050)|Yes|3094|
|Ever used condom in<br>|0.710|<br>-0.016<br>|||
|your life?|[0.454]|(0.019)|Yes|3150|
|If yes, did you use a<br>condom last time you|||||
|had sex with your|0.553|<br>0.024|||
|regular partner?|[0.497]|(0.026)|Yes|2150|

Notes: Controls used are household size, sex of household head, asset based quintiles, community, age of adolescent, educational status of adolescent, marital status of respondent, orphan status of respondent (one parent or both parents being deceased), and month of interview at midline; all controls are from baseline data except month of midline interview.

42
**Figure 7: Kernel densities of non‐cognitive measures by age**

<!-- Start of picture text --> A. Conscientiousness B. Emotional stability<br>Z-Score of Goldberg Big 5 factor - z_gldconsc Z-Score of Goldberg Big 5 factor - z_gldemotstab<br>-4 -2 0 2 -4 -2 0 2<br>Score Score<br>18-24 yrs old 25-30 yrs old 18-24 yrs old 25-30 yrs old<br>31-40 yrs old 41-50 yrs old 31-40 yrs old 41-50 yrs old<br>. .<br>C. Kindness D. Cooperation<br>Z-Score of Goldberg Big 5 factor - z_gldkindness Z-Score of Goldberg Big 5 factor - z_gldcooperat<br>-6 -4 -2 0 2 -6 -4 -2 0 2 4<br>Score Score<br>18-24 yrs old 25-30 yrs old 18-24 yrs old 25-30 yrs old<br>31-40 yrs old 41-50 yrs old 31-40 yrs old 41-50 yrs old<br>. .<br>E. Extraversion F. Openness to experience<br>Z-Score of Goldberg Big 5 factor - z_gldextrav Z-Score of Goldberg Big 5 factor - z_gldopeness<br>-3 -2 -1 0 1 2 -4 -2 0 2<br>Score Score<br>18-24 yrs old 25-30 yrs old 18-24 yrs old 25-30 yrs old<br>31-40 yrs old 41-50 yrs old 31-40 yrs old 41-50 yrs old<br>. .<br>G. Perseverance of effort (grit) H. Consistency of interest (grit)<br>Z-Score of Duckworth Grit factor - z_grt2persist Z-Score of Duckworth Grit factor - z_grt2consist<br>-4 -2 0 2 -2 -1 0 1 2 3<br>Score Score<br>18-24 yrs old 25-30 yrs old 18-24 yrs old 25-30 yrs old<br>31-40 yrs old 41-50 yrs old 31-40 yrs old 41-50 yrs old<br>. .<br>.5 .5<br>.4 .4<br>Density .3.2 Density .3.2<br>.1 .1<br>0 0<br>.6 .6<br>.4 .4<br>Density Density<br>.2 .2<br>0 0<br>.4 .5<br>.4<br>.3<br>.3<br>.2<br>Density Density .2<br>.1<br>.1<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> Note: The only densities that are significantly different from others are those of consciousness for individuals aged 18 to 25 years old compared to any older group and for individuals aged 25‐30 years old compared to individuals older than 40 years old; those of kindness for all age groups when compared to individuals older than 40 years old; those of extraversion, emotional stability, and perseverance of effort for individuals aged 18‐24 compared to any older groups; those of openness between individuals aged 25‐30 years old and older than 40; and those of consistency of interest for individuals in the age range of 25‐30 compared to older groups.

**28 |** P a g e
-32 -

"possible increases in the gains from trade associated with intercountry differences in factor endowments, represented by the per capita income variable, may have contributed to the observed results.." 1/ Furthermore, Noland's exercise on the industrial country subsample found that per capita GDP had a positive impact on the level of imports. Carliner also found a positive impact of per capita GNP on the index of intra-industry trade. These conflicting results suggest that there are both types of trade occurring -- i.e., the Heckscher-Ohlin type an.d the intra-industry type.

64. Fourth, the size of the market was hypothesized to have a positive impact on the level of imports by Bergsten/Cline, Balassa, Barbone, Carliner and Noland, all of whom indeed confirmed such a relationship. But Sato found a negative relationship on this score. Furthermore, while Bergsten/Cline, Balassa, Barbone and Sato used population as the proxy variaule for the size of the market, Carliner and Noland used CNP as the variable.

65. Fifth, the varying use of the income variable is an issue. Balassa used GDP in real terms and Carliner used CNP in real terms, but Bergsten/Cline and Noland used CNP or GDP adjusLed Lor **purtchIaing** power. This purchasing power adjustment may have made a difference as the latter two studies generally found Japan's import behavior not abnormal while Balassa and Carliner found Japan's import behavior abnormal.

66. The purchasing power parities (PPP) of national currencies have differed substantially from the prevailing exchange rates, and the 1/ Balassa (1986b), pp. 748-49.
- [14] Devarajan, S. and R. Singh. 2012. Government failure and poverty reduction in the CEMAC. In Bernardin Akitoby and Sharmini Coorey, eds. Oil wealth in Central Africa: Policies for inclusive growth. International Monetary Fund.

- [15] Eubank, N. 2012. Taxation, political accountability and foreign aid: lessons from Somaliland. Journal of Development Studies 48(4): 465480

- [16] Frankel, Jeffrey A. 2012. The Natural Resource Curse: A Survey of Diagnoses and Some Prescriptions. HKS Faculty Research Working Paper Series RWP12-014, John F. Kennedy School of Government, Harvard University.

- [17] Frynas, J. 2017. Sovereign Wealth Funds and the Resource Curse: Resource Funds and Governance in Resource-Rich Countries. In Douglas Cumming, Geoffrey Wood, Igor Filatotchev, and Juliane Reinecke, eds. The Oxford Handbook of Sovereign Wealth Funds. Oxford: Oxford University Press.

- [18] Gelb, Alan. 1988. Oil Windfalls: Blessing or Curse? Oxford University Press for the World Bank.

- [19] Giugale, M. and Nguyen, N. 2018. Money to the People: A Calculation of Direct Dividend Payments in Africa. International Economics and Economic Policy, 15: 1-19.

- [20] International Monetary Fund. 2018. World Economic Outlook. Washington, D.C.

- [21] Krause, B. Balancing Purse and Peace: Tax Collection, Public Goods and Protests. 2020. _Manuscript_

- [22] Lobel, F., T. Scot, and P. Zuniga. 2021. Corporate Taxation and Evasion Responses: Evidence from a Minimum Tax in Honduras. _Manuscript_

- [23] Lodono-Velez, J. and J. Avila-Mahecha. 2021. Enforcing Wealth Taxes in the Developing World: Quasi-experimental Evidence from Colombia. Amer. Econ. Rev.: Insights, Vol 3(2): 131-148.

- [24] Luttmer, E. and M. Singhal. 2014. Tax Morale. J. Econ. Perspectives. Vol 28(4): 149168.

19
# **Figure 13: E-Registration and the Investment Climate**

<!-- Start of picture text --> Countries with e-registration Countries with out e-registration<br>47.8<br>50<br>41<br>40<br>32<br>28.9<br>30<br>20<br>10<br>7.7<br>10<br>0<br>Average Duration in days Average Number of Average cost of entry<br>Procedures<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

# **_7.3 Impact of e-registration on entry rates:_**

The data also reveal a significant role of modernized business registries in facilitating business creation. We find higher entry rates – defined as the number of new registrations divided by the stock of existing registrations – in those countries with e-registries compared to the ones without them. However, we cannot dismiss reverse causality, i.e. that registry modernization is demand-driven by a more robust private sector.

# **_7.4   The Impact of Electronic Registration: Guatemala, Sri Lanka and Jordan._**

The modernization of business registries is an important step in a successful private sector development strategy. If appropriate political and economical reforms take place, the country will require an efficient registry that can satisfy new businesses demands. Otherwise, the registry will become a bottleneck for entrepreneurs, not only encumbering the business creation process, but also discouraging the transition between the informal and formal sectors.

Our data suggest that the modernization process of business registries is usually a long process framed inside a larger national private sector development strategy. On average, countries draft five-year plans and the goal is to implement electronic registration and distribution. Figure 14 shows the timeline of new business registrations for three countries – Guatemala, Sri Lanka, and Jordan – that have successfully implemented a business registry modernization strategy.

29
**Table** 2: **Distribution of welfare across Economic Regions**

||_Head_<br>_in_|_count_<br>_dex_|_Poverty_|_Gap index_|_Rel_<br>_Cont_|_ative_<br>_ribution_|
|---|---|---|---|---|---|---|
|_Regions_|_1994_|_1998_|_1994_|_1998_|_1994_|_1998_|
|Ouest|40.1|40.8|11.9|12.0|16.4|16.1|
|Sud|45.1|37.3|14.0|12.0|9.0|6.8|
|Centre-Sud|51.4|55.5|14.6|19.7|27.8|28.3|
|Centre-Nord|61.2|61.2|20.9|18.2|31.6|30.6|
|Nord|50.1|42.3|18.7|11.3|6.1|5.9|
|Sud-Est|54.4|47.8|18.7|12.2|5.3|6.8|
|Ouaga-Bobo|7.8|11.2|1.5|2.7|1.8|2.7|
|Autres Villes|18.1|24.7|4.9|6.3|2.0|2.8|
|All Urban|10.4|15.9|2.5|4.0|3.8|5.6|
|All Rural|51.1|50.7|16.1|15.8|96.2|94.5|
|National|44.5|45.3|13.9|13.9|-|-|

Sources: Authors' calculations.

Vulnerability is another characteristic of rural poor which did not improve during the growth period. The poverty gap remains extremely high, even by Sub-Saharan African standards (I 6%).'8 A poverty gap this large reflects an initially large level of spread between average income of the poor and the poverty line, which persisted. Expressed as a fraction of the poverty line, the absolute deviation between the mean per capita expenditure and the poverty line in the first decile, though already high (60%), increased even further (65%), a sign of widening gap between the average income of the poor and the poverty line. This deterioration was systematic in all lowest deciles (Annex II).

However, while the incidence and contribution of rural areas to national poverty remain relatively stable, the urban contribution increased to about 6%, reflecting rapid deterioration of welfare which is apparent across all urban regions, including Ouaga-Bobo, where the poverty incidence increased to 11% in 1998. The incidence of poverty in "Autres Villes" exceeded the critical threshold of 20%. This rapid increase of poverty in urban areas contributed to emergence of numerous pockets of poverty in most urban suburbs and peripheral areas and is largely the result of massive internal migration which caused a significant increase in the population share of "Autres Villes" between 1994 and 1998.

This increase in urban poverty is also accompanied by persistence of high income inequality: the 10% of the population in the uppermost income decile continues to account for over 70% of aggregate national income, suggesting that the transfer of wealth from higher to lower income brackets did not occur; the Gini coefficient increased to .55 in 1998.19 To the extent that rising

> 18 This gap is much higher than Sub-Saharan African countries average which is less than 15%, World Bank (1996).

> 19 Short-run variations in income distribution of this magnitude are not unusual. A study on a sample of Latin American Countries shows similar rates of changes in the 1980's where the increase of Gini to _.56_ is accompanied by significant poverty increase, see Birdsall and Londono (1997)

9
restrictions) observed in developed economies. Within Africa, the Comoros provides a particularly insightful case study in evaluating national lockdown measures on socio-economic outcomes for several reasons. Firstly, by April 18, 2020, a month after the World Health Organization declared Covid-19 a global pandemic, the Comoros and Lesotho were the two countries in Africa that were still virus-free (Lone and Ahmad, 2020). The Comoros’s proactive measures led to the restriction of social activities following the President’s address on March 16, 2020. Furthermore, the national government enacted a complete national lockdown on March 23, 2020, over a month before the first confirmed case of Covid-19 on May 1, 2020. Thus, the Comoros is a typical example of a developing country in Africa that resorted to strict lockdown measures with low confirmed cases of Covid-19.

Secondly, households and individuals in developing countries are susceptible to shocks that have an adverse impact on their livelihood. Changes in commodity prices, climate-related shocks (drought and floods) as well as idiosyncratic shocks (illness and death) have a negative impact on their economic status, especially for the poor (Dercon 2002, 2004). In addition, the Comoros is one of the poorest countries in Africa (World Population Review, 2021)<sup>2</sup> and its geographical location increases its vulnerability to climate change shocks. The country was still recovering from Cyclone Kenneth, experienced in April 2019, when the Covid-19 pandemic hit and consequently led to lockdown measures (World Bank, 2020). The tourism sector is one of the country’s major contributors to economic activities and income generation, thus exposing the country to a decline in economic growth as a result of lockdown measures. Therefore, this analysis can guide future responses to similar economic shocks and crises that necessitate lockdown measures, especially for African countries.

> 2 The evaluation was based on gross domestic product as an economic measure and indicated the Comoros to be among Africa's 10 poorest countries.

3
# **Table 7: How does stratification vary with the number of Stratum?**

_Simulation results_

||Sa|mple Size 1|00|Sa|mple Size|300|
|---|---|---|---|---|---|---|
||Stratified<br>on 2<br>variables<br>(8 strata)|Stratified<br>on 3<br>variables<br> (24 strata)|Stratified<br>on 4<br>variables<br> (48 strata)|Stratified<br>on 2<br>variables<br>(8 strata)|Stratified<br>on 3<br>variables<br> (24 strata)|Stratified<br>on 4<br>variables<br> (48 strata)|
|**Panel A: Imbalance - 95th percentile of diff**|**erence in follow-up**|**means**|||||
|Microenterprise profits (Sri Lanka)|0.322|0.338|0.338|0.210|0.213|0.209|
|Child schooling (Indonesia)|0.399|0.346|0.369|0.219|0.211|0.212|
|Household expenditure (Indonesia)|0.337|0.335|0.343|0.194|0.193|0.191|
|Labor income (Mexico)|0.335|0.327|0.344|0.196|0.198|0.198|
|Height z-score (Pakistan)|0.297|0.299|0.310|0.186|0.191|0.189|
|Math test score (Pakistan)|0.285|0.298|0.316|0.180|0.181|0.184|
|**Panel B: Power: Proportion of p-values<0.1**|**0 when no strata du**|**mmies inc**|**luded**||||
|Microenterprise profits (Sri Lanka)|0.129|0.138|0.144|0.274|0.281|0.278|
|Child schooling (Indonesia)|0.303|0.267|0.273|0.585|0.574|0.562|
|Household expenditure (Indonesia)|0.852|0.850|0.845|0.999|1.000|1.000|
|Labor income (Mexico)|0.170|0.161|0.180|0.486|0.480|0.480|
|Height z-score (Pakistan)|0.286|0.295|0.297|0.757|0.757|0.756|
|Math test score (Pakistan)|0.236|0.245|0.254|0.654|0.649|0.650|
|**Panel C: Power: Proportion of p-values<0.1**<br>|**0 when strata dum**|**mies includ**|**ed**||||
|Microenterprise profits (Sri Lanka)|0.186|0.273|0.242|0.305|0.327|0.343|
|Child schooling (Indonesia)|0.278|0.301|0.255|0.596|0.596|0.589|
|Household expenditure (Indonesia)|0.904|0.914|0.876|1.000|1.000|1.000|
|Labor income (Mexico)|0.204|0.212|0.199|0.561|0.541|0.541|
|Height z-score (Pakistan)|0.487|0.463|0.457|0.849|0.843|0.854|
|Math test score(Pakistan)|0.464|0.464|0.399|0.792|0.790|0.781|

Notes: Statistics are based on 10,000 simulations of each method. Details on methods and variables are in Table A2.