rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 7 - Magnitude of Effect on Develo**<br>**Human Opportunity Index- Average M**<br>|**pment Outcomes**<br>**athematics, Reading and Scienc**<br>|**e Outcomesfor Level 2- PISA 200**<br>|**9**<br>|||
|---|---|---|---|---|---|
|Dependent Variable|Coefficient on HOI in IV<br>regression (for whole sample<br>without regional dummies)|Change in dependent variable<br>in response to 1 standard<br>deviation change in HOI|Ratio to 1 standard deviation<br>dependent variable|% change as a result of<br>a 1 point increase in HOI|% change as a result of<br>a 1 % increase in HOI|
|Log of GDP per capita, Average 2005-2010|0.035|0.773|0.718|3.50|2.31|
|Institutional Index, Average WBGI 2009|0.806|17.8|0.814|0.82|0.54|
|Infant Mortality|-0.436|-9.6|-0.956|-1.03|-0.68|
|**D-Index - Average Mathematics, **|**Reading and Science Outco**|**mesfor Level 2 - PISA 2009**||||
|Dependent Variable|Coefficient on HOI in IV<br>regression (for whole sample<br>without regional dummies)|Change in dependent variable<br>in response to 1 standard<br>deviation change in HOI|Ratio to 1 standard deviation<br>dependent variable|% change as a result of<br>a 1 point increase of D-Index|% change as a result of<br>a 1 % increase in HOI|
|Log of GDP per capita, Average 2005-2010|-0.097|-0.827|-0.767|-9.70|-1.02|
|Institutional Index, Average WBGI 2009|-2.221|-18.930|-0.865|-2.27|-0.24|
|Infant Mortality|1.203|10.253|1.017|2.85|0.30|
|<br>**Human Opportunity Index - Compl**|**eting Six Year of Education**|**on Time**||||
|Dependent Variable|Coefficient on HOI in IV<br>regression (for whole sample<br>without regional dummies)|Change in dependent variable<br>in response to 1 standard<br>deviation change in HOI|Ratio to 1 standard deviation<br>dependent variable|% change as a result of<br>a 1 point increase in HOI|% change as a result of<br>a 1 % increase in HOI|
|Log of GDP per capita, Average 2005-2010|0.038|0.910|0.803|3.76|0.99|
|Infant Mortality|-1.036|-25.079|-0.646|-0.63|-0.16|
|Malnutrition|-0.365|-8.836|-0.773|-0.84|-0.22|
|**~~D-Index - Completing Six Year of E~~**|**~~ducation on Time~~**|||||
|Dependent Variable|Coefficient on HOI in IV<br>regression (for whole sample<br>without regional dummies)|Change in dependent variable<br>in response to 1 standard<br>deviation change in Dindex|Ratio to 1 standard deviation<br>dependent variable|% change as a result of<br>a 1 point increase of D-Index|% change as a result of<br>a 1 % increase in HOI|
|Log of GDP per capita, Average 2005-2010|-0.067|-0.894|-0.789|-6.70|-1.65|
|Infant Mortality|1.846|24.641|0.635|1.12|0.28|
|Malnutrition|0.63|8.409|0.736|1.46|0.36|

27
Can Subjective Questions on Economic Welfare Be Trusted? Evidence for Three Developing Countries Martin Ravallion, Kristen Himelein and Kathleen Beegle<sup>1</sup>

**Key words:** Subjective welfare, subjective poverty, scales, heterogeneity, vignettes **JEL classifications:** C81, D60, I32

**Sector board** : Poverty Reduction (POV)

> 1 Ravallion is with Georgetown University, Beegle and Himelein are with the World Bank. This paper would not have been possible without the work of the _Tajikistan_ Living Standards Survey Team, including Diane Steele, Sasun Tsirunyan, Vladimir Kolchin, Farhod Khamidov, Oleksiy Ivaschenko, and staff of the National Statistics Committee of Tajikistan ( _Goskomstat_ ), Economic Development Initiatives in Bukoba, Tanzania, including Joachim De Weerdt and Albertus Kamanzi, Centro de Estudios y Documentación de la Frontera Occidental in Huehuetenango, Guatemala, and the Programa Regional de Seguridad Alimentaria y Nutricional para Centroamérica for funding the Guatemala portion of fieldwork. Helpful comments on this paper were received from Denis Cogneau, Sylvie Lambert and Claudia Senik. All views are those of the authors and do not reflect the views of their employers including the World Bank or its member countries.
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Benchmarking air transport indicators for Senegal and other select countries**

|Country|Senegal|Côte d’Ivoire|Ghana|Nigeria|
|---|---|---|---|---|
|**Traffic (2007)**|||||
|Domestic seats (seats per year)|130,000|—|144,183|1,199,572|
|Seats for international travel within Africa (seats per<br>year)|1,260,000|851,003|909,819|1,373,745|
|Seats for intercontinental travel (seats per year)|1,230,000|297,891|832,895|2,437,702|
|Seats available per capita|0.23|0.06|0.08|0.09|
|Herfindahl-Hirschmann Index—air transport market<br>(%)|11.64|9.75|6.28|11.28|
|**Quality**|||||
|Percent of seat-km in newer aircraft|98|90.9|96.8|71.42|
|Percent of seat-km in medium or smaller aircraft|39.3|52.3|15.7|29.6|
|Percent of carriers passing IATA/IOSA audit|50.0|0|0|28.6|
|FAA/IASA audit status|No audit|Fail|Fail|No audit|

_Source_ : Bofinger 2009. Derived from the AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. All data as of 2007 are based on estimations and computations of scheduled advertised seats, as published by the Seabury Aviation Data Group. This captures 98 percent of worldwide traffic, but a percentage of African traffic is not captured by these data.

FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit. — = Not available.

On par with this growth trend, Senegal’s international connectivity has been progressively increasing, while domestic connectivity has remained constant. Intercontinental city pairs went up from 13 in 2004 to 18 in 2007, and international city pairs went from 20 to 24 during the same period (figure 10b). International services are competitive with a Herfindahl-Hirschmann Index of only 10 percent (table 8).

**Figure 10. Evolution of seats and city pairs in Senegal, 2001–07**

<!-- Start of picture text --> a. Seats b. City pairs<br>3,000,000 50<br>45<br>2,500,000<br>40<br>35<br>2,000,000<br>30<br>1,500,000 25<br>20<br>1,000,000<br>15<br>10<br>500,000<br>5<br>- 0<br>2001 2004 2007 2001 2004 2007<br>Total International Intercontinental Domestic Total International Intercontinental Domestic<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009. Derived from the AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems.

19
4 where CR is the variance of _R._ The estimator of 3, is equal to which, from eqn (1), shows that 61 is equal to _C._ Readers familiar with the RII will note that eqn (2) is essentially the same as the regression equation used to compute the RII, the difference being that the RII is typically computed using grouped rather than individual-level data. The division of the LHS through by p simply means that the coefficient 81 is the RII rather than the Slope Index of Inequality (SII) [18]. The only difference, then, between eqn (2) and the-equation used to compute the RuI is that the LHS contains the variance of the rank variable. This, however, approaches 1/12 as the sample size grows, and can therefore be treated approximately as a constant across samples. Thus the RII and C ought to rank distributions the same-there is little to choose between the two measurement approaches, though the concentration curve has the attraction of facilitating graphical comparisons of malnutrition inequalities.

# **STATISTICAL** INFERENCE

When undertaking cross-country (or temporal) comparisons, one needs to bear in mind that the malnutrition data are derived from survey data and are hence subject to sampling variation. It is useful, therefore, to couple numerical comparisons of the index C with statistical tests to assess the statistical significance of any inter-country (or temporal) differences. An attraction of the convenient regression-eqn (2) above-is that it provides a standard error for the concentration index C. This standard error is not, however, wholly accurate, since the observations in each regression equation are not independent of one another due to the nature of the R, variable. The following standard error estimator, derived by Kakwani et al. [15], takes into account the serial correlation in the data: where being the ordinate of _L(s),_ with qo = 0. It is this estimator that is used, rather than that in eqn (2), which is used in section IV.
|**Table 4: Sectoral Emp**|**loyment Elasticitie**<br>Nati|**s**<br>onal|No<br>∆<br>log(<br>Em|rth<br>−<br>03 13<br>)<sup>s</sup><br>d<br>p|So|uth|
|---|---|---|---|---|---|---|
||Light<br>manufacturing<br>(1)|Heavy<br>manufacturing<br>(2)|Light<br>manufacturing<br>(3)|Heavy<br>manufacturing<br>(4)|Light<br>manufacturing<br>(5)|Heavy<br>manufacturing<br>(6)|
|−<br>∆<br>03 13<br>log(<br>)<br>d<br>MEmp|0.210**|0.312***|0.558***|0.259|0.164|0.355***|
||(0.087)|(0.105)|(0.156)|(0.323)|(0.106)|(0.111)|
|′≠<br>−<br>∆<br>,<br>03 13<br>log(<br>)<br>r d<br>d<br>MEmp|0.451***|0.254*|–0.219|–0.172|1.109***|0.554*|
||(0.147)|(0.132)|(0.272)|(0.360)|(0.326)|(0.282)|
|−<br>∆<br>93 03<br>log(<br>)<br>d<br>IFirm|–0.403|–0.032|–0.353|–0.086|–0.891|0.204|
||(0.625)|(0.966)|(0.966)|(1.411)|(1.180)|(1.045)|
|−<br>∆<br>83 93<br>log(<br>)<br>d<br>IFirm|–0.225|–0.970|–0.237|–1.006|0.282|–0.936|
||(0.706)|(0.843)|(0.780)|(1.206)|(1.269)|(1.245)|
|−<br>∆<br>93 03<br>log(<br>)<br>d<br>SFirm|–1.214|1.945|–3.546|–0.173|–0.932|2.822|
||(1.679)|(1.688)|(3.058)|(3.468)|(2.698)|(2.933)|
|−<br>∆<br>83 93<br>log(<br>)<br>d<br>SFirm|3.569*|3.240*|0.565|2.313|6.719*|3.760|
||(1.933)|(1.855)|(2.433)|(2.888)|(3.977)|(4.078)|
|Constant|–1.853|–2.792**|3.197|0.489|–6.849***|–5.569***|
||(1.261)|(1.135)|(2.209)|(2.320)|(1.617)|(1.676)|
|_N_|136|136|67|67|69|69|
|_R_<sup>_2_</sup>-statistic|0.167|0.175|0.188|0.052|0.400|0.331|

_<mark>Source:</mark>_ <mark>Authors’ estimates based on data from the 2003 National Industrial Census, the 2014 Integrated Business Establishment Survey, and GSS (2016).</mark> _Note:_ This table reports estimates for equation 1. Dependent variable = district-level employment in the given sector. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

25
Table A.22: Under-5 EFM with different recall periods

||Re|call period|from interv|iew|
|---|---|---|---|---|
||10 years|12 years|15 years|20 years|
|**Outcome: Death before age 5**|(1)|(2)|(3)|(4)|
|_First girl_*_Female_|1.737*|2.172**|2.715***|2.768***|
||(0.897)|(0.780)|(0.705)|(0.588)|
|_First girl_*_Female_*_Post1_|-0.364|-0.964|-1.516|-1.525*|
||(0.992)|(0.976)|(0.919)|(0.768)|
|_First girl_*_Female_*_Post2_|-0.954|-1.380|-1.944**|-1.996**|
||(1.098)|(0.998)|(0.878)|(0.744)|
|N|134,986|169,344|206,830|227,129|
|_Xijt_|x|x|x|x|
|_Firstborn Girl_ x Birth year FE|x|x|x|x|
|_Female_ x Birth year FE|x|x|x|x|
|_Female_ x State FE|x|x|x|x|
|_Female_ x Birth order FE|x|x|x|x|
|Birth order x Birth year FE|x|x|x|x|
|Birth order x State FE|x|x|x|x|
|State x Birth year FE|x|x|x|x|
|_Firstborn girl_ x State FE|x|x|x|x|
|_Firstborn girl_ x Birth order FE|x|x|x|x|

NOTES: Sample of second- and higher-order births. Each column is a separate regression. Column (1)(4) include samples with recall period within 10,12, 15, and 20 years. Specification same as column (3) of Table 3. The outcome is an indicator for death before age 5. We drop children that are less than 5 years old to allow each child in the sample full exposure to the risk of under-5 mortality. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE. The vector _Xijt_ comprises mother’s age at birth, household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

68
Table (G7) Impacts of the Program on Household Consumption and Expenditures

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Diversity of food purchases|0.75**|(0.36)|[0.66]|-0.02|(1.03)|277|
|Food exp, typical week (USD PPP)|-6.56|(44.64)|[1.00]|277.97|(157.96)|287|
|Food exp, last week (USD PPP)|-14.50|(18.36)|[1.00]|189.65|(112.09)|1,090|
|Value of gifted food, last month (USD PPP)|9.60|(8.15)|[1.00]|2.09|(14.55)|297|
|Food consumption, last year|651.14|(2,517.53)|[1.00]|14,409.39|(9,974.92)|257|
|Non-food exp, last month (USD PPP)|13.49|(156.37)|[1.00]|831.77|(874.02)|1,016|
|Diversity of non-food purchases|-0.03|(0.15)|[1.00]|0.01|(1.01)|1,054|
|Value of gifted non-food, last yr (USD PPP)|-18.87|(19.03)|[1.00]|11.68|(186.07)|1,100|
|Value of all durables acquired, last yr (USD PPP)|752.90|(862.95)|[1.00]|354.88|(1,977.68)|296|
|Value of all durables gifted, last yr (USD PPP)|146.24|(179.81)|[1.00]|16.05|(174.57)|296|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: Diversity of food purchases is a standardized measure of the number of months per year the household consumes food from 9 different food groups. Food exp refers to the PPP adjusted amount of money spent in a typical week or last week. Value of gifted food is the value of food given to the household as formal or informal assistance in the past month. Food consumption, last year is an annualized measure of food consumption calculated from weekly food expenditure * 4.3 * months that food was consumed + annualized weekly food produced + annualized monthly food assistance. Non-food expenditure includes expenditures in the last 30 days from 9 different non-food expenditure categories. Diversity of non-food purchases is calculated in the same way as described above for food purchases. Value of gifted non-food includes all items from 9 non-food categories received as gifts in the past 12 months. Value of all durables acquired includes durables from 9 categories purchased or gifted to the household. Value of durables gifted includes all durables the household received as gifts.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the pre-analysis plan.

45
_Business_ represents characteristics of the business that operates in tandem with FINCA agent activities. Like agent age, we expect that the age of the agent’s business could be positively linked to agent transactions to the extent that more firmly established businesses inspire trust and/or already have experience with a wider number of potential clients. We also hoped to explore how the nature of the business affects agent transactions. To proxy for interactions between an agent’s established and banking businesses, we tried a number of variables including the number of days per week (and hours per day) that the business is open, the number of employees of the business, and the economic sector of the agent’s business.<sup>22</sup> We were unable to derive robust results for those variables, and thus rely on the value of stock (meaning inventory) in the retail business as a simple summary indicator of how demanding are an agent’s duties to maintain that business. To the extent that demands from the retail business on an agent’s time are high, it might make it harder (and relatively less financially lucrative) for her/him to fully pursue banking, and thus we would expect a negative relationship between retail stock value and agent transactions.

_Market_ represents a set of variables that describe the demographic characteristics of the municipality including population, population density, commercial development, and average income level. Both population and population density are taken from official sources for the most recent year for which data are available.<sup>23</sup> We expect both to be positively linked to the number and volume of agent transactions. Average income levels and the level of commercial development were derived by Experts SARL Consulting, as described above. Lacking a census of businesses and a representative survey of households, we asked Experts SARL to sort municipalities into one of three categories for these variables – high, medium, and low. Our hope was to derive coarse,

> 22 Unfortunately, we were only able to categorize agents’ businesses as providing either services or manufactured goods. A finer delineation of the types of goods and services that agents sell might have produced better insights into the types of businesses that are best suited to accommodate and foster agent transactions.

> 23 Population figures are taken from the most recent census, which occurred in 2004.

14
regression for explaining the FFE stipend. This first stage regression included X and a dummy variable for village participation. To be a valid instrumental variable, village participation must not affect child labor or schooling controlling for the variables in the second-stage regressions. Under this identifying assumption and with normally distributed errors, we can consistently estimate the coefficient on the FFE stipend in the probits for child labor and schooling as long as we control for the residuals from the first-stage regression. '° The coefficient on the residuals also provides an exogeneity test.

Given the way the program is targeted, village-level participation is a suitable instrument for participation at the individual level provided one includes a set of geographic control variables for village placement (Ravallion and Wodon, 1998). The set of geographic controls include distances to school; the type of school (govermmental, private, NGO); a series of school quality variables reported in the community survey; land distribution; irrigation intensity; road quality; electrification; distance and time to thana and district headquarters and to Dhaka; distance to various facilities (health care, Banks, governnent agencies); incidence of natural disasters; attitudes to women's employment, education and family planning; average schooling levels of the head and spouse; majority religion of the village; and population size of the village. These were (jointly) very good predictors of program placement. A probit regression of whether the village had the program on the geographic control variables gave a pseudo-R<sup>2</sup> of 0.55 (Chisquare of 91.7 which is significant at the 0.5% level, with 166 observations).

> **10** Datt and Ravallion (1994, Appendix 1) prove consistency for a more general simultaneous tobit model (generalizing Smith and Blundell, 1986, to allow for a censored endogenous variable). The consistency proof for our case is a minor variation. If the FFE stipend was continuous (rather than censored) then our estimation method would be the same as that proposed by Rivers and Vuong (1988).

13
activities and where regulation makes it difficult to establish new banks. Branches are, instead, more common in host countries with high corporate taxes ― possibly because of the greater ease allowed by this structure in shifting profits across borders ― and in poor countries, perhaps because of fewer market opportunities.

Second, our results suggest that different organizational forms are associated with different degrees of penetration in the host market. Branches are more likely when foreign operations are smaller in size and do not have a retail orientation.

Third, host-country risks matter, and in particular, economic and political risks have opposite effects. Branches are less common in countries with highly risky macroeconomic environments, where parent banks seem to prefer the shield of “hard” limited liability provided by subsidiaries to the “soft” protection of ring-fencing. However, when it comes to risks stemming from possible government intervention and other major political events, parent banks are more likely to operate as branches. This is not necessarily surprising. In view of legal provisions that shield parent banks from the liabilities of their foreign branches in events such as wars, insurrections, or arbitrary actions by foreign governments. Under such circumstances, banks are actually more exposed as subsidiaries, which typically have higher capital and reserve requirements and larger investments in local fixed assets relative to branches.

The literature on banks’ organizational form has been scant. Our paper relates to early studies on the operations of international banks during the 1970s-1980s (see Goldberg and Saunders 1980; 1981a, b; Goldberg and Johnson 1990; Miller and Parkhe 1998, among others). However, these studies looked at the determinants of each type of organizational form in isolation and did not take into account banks’ decision to enter a given market.

4
intended for sale. To some extent, these shifts may be expected, since the end-of-season survey was (for the most part) conducted after the main harvest. However, the magnitude of labor reallocations is still surprising, considering that farmers also grow other crops which follow a somewhat different production calendar. In addition, the bottom panel of Table 4 shows that household members are more likely to report some of their products (i.e. maize, goats and chickens) as being intended for sale at the end rather than at the beginning of the season.<sup>30</sup> This change in the self-reported intended use of output for specific products contributes to the increase in the share of farmers producing for sale over the course of the agricultural season, though, clearly, it not as important a factor as farmers’ labor reallocation across products.

**Table 4: Changes in labor allocation across products and their intended use over the season, Malawi**

|**Start-of-season**|**End-of-season**|**N(combined)**|
|---|---|---|
|_Products worked on during last 7 days_|||
|Any crop<br>0.90|0.51***|386|
|(0.02)|(0.04)||
|Maize<br>0.79|0.07***|386|
|(0.03)|(0.02)||
|Irish potato<br>0.09|0.10|386|
|(0.02)|(0.02)||
|Pigeon pea<br>0.43|0.21***|386|
|(0.04)|(0.03)||
|Any livestock<br>0.46|0.73***|386|
|(0.04)|(0.03)||
|Goats<br>0.20|0.33***|386|
|(0.03)|(0.03)||
|Chicken<br>0.27|0.45***|386|
|(0.03)|(0.04)||
|Any fish<br>0.02|0.03|386|
|(0.01)|(0.01)||
|_Fraction of output intended for sale_|||
|Maize<br>0.11|0.21*|167|
|(0.02)|(0.09)||
|Irish potato<br>1.00<br>(0.00)|0.83***<br>(0.03)|36|
|Pigeon pea<br>0.40|0.44|124|
|(0.04)|(0.05)||
|Goats<br>0.75|0.88**|103|
|(0.05)|(0.03)||
|Chicken<br>0.20|0.31**|139|
|(0.03)|(0.04)||

_Notes:_ Only those products are reported for which at least 10 respondents report the intended use at the start- and endof-season. Standard errors in parentheses; * denotes the difference between the start- and end-of-season is significant at 10%, ** at 5%, *** at 1%. Based on the Malawi Agricultural Labor Survey.

> 30 The opposite trend occurs for Irish potatoes, but they are not farmed as widely as the other products in the study area.

18