rafmacalaba/datause-encoder-data huggingface dataset:
Table 7. Contributions to the Decline in Moderate Poverty

||US$|1.25 a da|y||||||US$4 a d|ay|||||US$5|a day|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||||Costa|||||||||
||Bangladesh|Ghana|Nepal|Argentina|Brazil|Chile|Colombia|Rica|Ecuador|Honduras|Panama|Paraguay|Peru|Thailand|Moldova|Romania|
|**Poverty rate**|||||||||||||||||
|Initial period|57.7|38.2|73.7|27.5|43.1|23.2|61.6|29.2|51.5|66.1|43.4|50.3|45.8|31.4|93.8|75.3|
|Final period|40.3|23.5|53.1|14.6|27.6|11.8|39.5|18.9|33.4|52.1|29.9|33.0|30.0|16.6|58.7|33.2|
|Total change|-17.4|-14.7|-20.6|-13.0|-15.5|-11.4|-22.1|-10.2|-18.1|-14.1|-13.5|-17.3|-15.8|-14.8|-35.1|-42.2|
|**Full Decomposition**|||||||||||||||||
|Consumption-to-income ratio|-25.6|17.7|-6.9||||||||||5.1|-11.8|-20.9|15.8|
|Adult population|37.2|21.4|14.2|22.0|16.4|31.0|12.1|34.4|27.1|32.0|13.5|59.5|9.0|24.2|3.5|24.1|
|Occupation share|20.5|-3.6|13.4|16.7|10.9|-0.1|15.8|14.2|-3.3|-4.1|29.1|11.6|10.0|4.0|6.5|20.4|
|Labor income|60.2|49.6|50.4|35.2|41.6|48.2|38.3|17.7|53.3|55.8|30.2|33.2|62.5|47.5|37.3|1.9|
|Capital|7.8|5.0|5.9|-5.4|-0.7|-4.2|4.3|3.9|-0.7|3.8|-0.2|0.0|2.1|2.8|0.6|21.0|
|Pension||||14.9|17.9|15.8|4.6|23.6|6.2|3.2|10.4|-3.0|0.6||25.1|-2.8|
|Transfers|15.7|10.0|23.0|7.3|9.1|41.8|15.6|22.4|13.6|3.4|16.4|-1.0|10.0|33.1|37.2|8.7|
|Other nonlabor income|-15.9|||9.3|4.8|-32.5|9.4|-16.1|3.8|6.0|0.5|-0.3|0.7|0.3|10.6|11.0|
|Total change|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|Source: Author’s calculations wi<br>Consumption-based measures of<br>Colombia, Costa Rica, Ecuador, H|th data from<br>poverty are<br>onduras, Pa|SEDLAC (<br>used in th<br>nama, and|CEDLAS a<br>e case of B<br>Paraguay.|nd the Worl<br>angladesh,|d Bank), R<br>Ghana, Ne|IGA for<br>pal, Peru|Ghana and N<br>, Thailand, M|epal, and<br>oldova, a|from househ<br>nd Romania|old surveys<br>. Income-bas|for Bangla<br>ed measur|desh, Moldo<br>es of poverty|va, Rom<br>are use|ania, Peru, an<br>d in the case|d Thailand.<br>of Argentina,|Brazil, Chile,|

31
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# **Table 8. Descriptive Statistics Instrumental Variables**

||**Atla**|**ntic**|**Cen**|**tral**|**Coffee-G**|**rowing**|**So**|**uth**|**Total S**|**ample**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Average **|**Median **|**Average **|**Median**|**Average **|**Median **|**Average **|**Median **|**Average **|**Median**|
|Land Gini|0,72|0,71|0,54|0,51|0,66|0,65|0,73|0,72|0,66|0,67|
||(0,03)||(0,06)||(0,02)||(0,04)||(0,08)||
|_Dummy_Native Populations in Colonial|0,26||1,00||0,48||0,11||0,47||
|Times (1535-1540)|(0,44)||(0,00)||(0,50)||(0,32)||(0,50)||
|Number of households in the community|1.696||1.609||1.999||1.197||6.501||

Standard errors in parenthesis. Source: Instituto Geográfico Agustín Codazzi (IGAC) and Fernández (2010).

# **Table** **<u>9. Descriptive Statistics Instruments at Rural District Level</u>**

||**No Violent**<br>**Shock**|**Violent**<br>**Shock**|**Difference**<br>**between means**<br>**(t-test)**|**All**|
|---|---|---|---|---|
|Average time to rural district's urban|0,68|0,95|***|0,80|
|center|(0,47)|(0,94)||(0,72)|
|Problems in rural district due to water|0,42|0,58|***|0,49|
|access|(0,49)|(0,49)||(0,50)|
|Number of households|1.994|1.508||3.502|

Standard errors in parenthesis. Source: CLS-WILL: first wave – community questionnaire.

40
variable. The orders of magnitude are much lower however. This finding is consistent with the idea that unemployment needs to be defined more broadly in developing countries. Removing the inactive from our analysis indeed provides a very partial picture of the consequences of internal migration on local labor markets in Uganda.

## **6.2 Avoiding the violation of the exclusion restriction**

It may be that weather shocks in the source regions have an impact on the economic conditions in the destination region (and therefore on the employment probability of the nonmigrants in this region) that does not only transmit through internal migration, but through other channels, as for instance, when the destination region and the source regions are trade partners (or trade competitors). To control for these other channels, we include a proxy for economic growth in the destination region between 1997 and 2002 (the period over which the instrument could have an impact on the dependent variable that does not transmit through internal migration). This proxy is the growth rate in the nightlights intensity between 1997 and 2002. The estimates of the second stage of the IV probit analysis are reported in Table S3-3 in the appendix and show that our results are roughly unchanged by this control.

# **7 Conclusion**

This paper investigates the impact of weather-induced internal net in-migration rates on the employment probability of non-migrants in destination regions in Uganda. Consistent with the prediction of a simple theoretical model, our results reveal a larger negative impact than the one documented for developed countries: we find that a 10 percentage points increase 33
Policy Research Working Paper 9958

# **Abstract**

This paper identifies the impact of childcare responsibilities on adult labor supply in the context of COVID-19-related school closures in Kenya. It compares changes in parents’ labor participation after schools partly reopened in October 2020 for households with children in a grade eligible to return against households with children in adjacent grades. Using nationally-representative panel data from World Bank phone surveys in 2020–21, the findings show that the partial reopening increases affected adults’ weekly labor hours by 22 percent, with increases concentrated in household agriculture. The results suggest that school closures account for over 30 percent of the fall in average work hours in the first few months after COVID-19 cases were detected. The effects are driven by changes in household childcare burdens and child agricultural labor when a student returns to school. The impacts are not significantly different by sex of the adult. Although both women and men increased hours spent on childcare during the pandemic, women benefited more than men from reductions in childcare needs, but took on more of the childcare burden when the returning student was a net childcare provider. The results highlight the importance of siblings in household childcare and suggest that policies that increase childcare availability and affordability could increase adult labor supply in Kenya.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at upape@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 5.1. OLS results**

_Dependent variable: Logarithm of time (calendar days) spent in order to complete a procedure of land transaction (procedures No. 1, 2A, 2B, 3A, 3B and 8)_

||Model 5.1.1|Model 5.1.2|Model 5.1.3|Model 5.2.1|Model 5.2.2|Model 5.2.3|
|---|---|---|---|---|---|---|
|Constant|4.187***<br>(0.108)|4.059***<br>(0.198)|4.037***<br>(0.205)|4.984***<br>(0.441)|3.953***<br>(0.137)|3.947***<br>(0.136)|
|Procedure 1||||0.454***<br>(0.143)|0.480***<br>(0.132)|0.477***<br>(0.131)|
|Procedure 2B||||0.225<br>(0.186)|0.285*<br>(0.171)|0.288*<br>(0.171)|
|Procedure 3A||||0.223<br>(0.151)|0.241*<br>(0.137)|0.242*<br>(0.137)|
|Procedure 3B||||0.081(0.153)|0.100(0.143)|0.101(0.143)|
|Procedure 8|0.289***<br>(0.101)|0.320***<br>(0.115)|0.322***<br>(0.115)|0.583***<br>(0.166)|0.570***<br>(0.148)|0.568***<br>(0.148)|
|Kaliningrad oblast|1.226***<br>(0.143)|1.033***<br>(0.167)|1.043***<br>(0.169)|-0.054<br>(0.395)|1.220***<br>(0.144)|1.203***<br>(0.142)|
|Saint-Petersburg|1.455***<br>(0.396)|1.308***<br>(0.408)|1.326***<br>(0.411)||1.244***<br>(0.392)|1.218***<br>(0.390)|
|Leningrad oblast|0.935***<br>(0.244)|0.844***<br>(0.302)|0.845***<br>(0.303)|-0.328<br>(0.465)|0.835***<br>(0.243)|0.830***<br>(0.242)|
|Moscow|0.938***<br>(0.186)|0.772***<br>(0.207)|0.777***<br>(0.207)|-0.315<br>(0.413)|0.927***<br>(0.191)|0.919***<br>(0.190)|
|Moscow oblast|0.991***<br>(0.201)|0.775***<br>(0.223)|0.773***<br>(0.224)|-0.352<br>(0.420)|0.929***<br>(0.198)|0.933***<br>(0.198)|
|Sverdlovsk oblast|1.443***<br>(0.189)|1.291***<br>(0.209)|1.300***<br>(0.211)|0.194 (0.413)|1.417***<br>(0.189)|1.402***<br>(0.188)|
|Novgorod oblast|0.587**<br>(0.235)|0.457*<br>(0.269)|0.477*<br>(0.273)|-0.630<br>(0.447)|0.631***<br>(0.239)|0.594**<br>(0.234)|
|Tomsk oblast|0.570***<br>(0.141)|0.399**<br>(0.166)|0.420**<br>(0.174)|-0.752*<br>(0.401)|0.559***<br>(0.152)|0.521***<br>(0.142)|
|Khabarovsk krai|1.132***<br>(0.152)|1.013***<br>(0.167)|1.020***<br>(0.168)|-0.107<br>(0.396)|1.105***<br>(0.155)|1.093***<br>(0.154)|
|Irkutsk oblast|1.055***<br>(0.150)|0.949***<br>(0.184)|0.951***<br>(0.184)|-0.193<br>(0.404)|0.996***<br>(0.149)|0.992***<br>(0.149)|
|Rostov oblast||||-1.087***<br>(0.403)|||
|Perm oblast|1.507***<br>(0.142)|1.354***<br>(0.162)|1.362***<br>(0.163)|0.250 (0.393)|1.483***<br>(0.143)|1.470***<br>(0.141)|
|Novosibirsk oblast|1.802***<br>(0.142)|1.663***<br>(0.159)|1.675***<br>(0.162)|0.537 (0.391)|1.765***<br>(0.147)|1.745***<br>(0.144)|
|Nizhny Novgorod oblast|1.194***<br>(0.158)|1.027***<br>(0.176)|1.039***<br>(0.179)|-0.081<br>(0.399)|1.182***<br>(0.159)|1.162***<br>(0.156)|
|Sakhalinsk oblast|1.332***<br>(0.256)|1.251***<br>(0.268)|1.249***<br>(0.268)|0.053<br>(0.447)|1.225***<br>(0.253)|1.225***<br>(0.253)|
|Size of the land plot||0.035<br> (0.023)|0.034<br>(0.023)|0.026<br>(0.023)|||
|Not Obtained by BIS|||-0.032<br>(0.077)||-0.052<br>(0.071)||
|adj. R2|0.329|0.305|0.303|0.336|0.360|0.361|
|N|427|380|380|380|427|427|
|Mean of the dependent<br>variable|5.31|5.34|5.34|5.34|5.31|5.31|

*, **, *** Significance level of 10%, 5%, and 1%, respectively

22
_Central America and NAFTA_ 12 This demonstration is not entirely straightforward because we do not expect nor do we find a simple linear relationship between economic growth and exports of laborintensive manufactures. At low levels of capital abundance, we expect to see a positive association between growth in GDP and growth in net exports of labor-intensive manufactures. At high levels of capital abundance, we expect the relationship to reverse itself.

43. The relationship between growh and exports of labor-intensive goods was estimated using a data set for 62 countries and two years (1974 and 1988). To allow for the interaction of the level of GDP per worker and growth the following relationship was assumed: A(T1/Li) = a + i x %AGDP. x [0, + 02 GDP, /Li + 03(GDPi/L )<sup>2</sup> ] where A(T/L) is the absolute change in labor-intensive exports per worker (dollars), %AGDP is the annualized percentage increase in GDP in dollars, and GDP/L is GDP per worker in the initial period (1974). This functional form allows the association between growth %AGDPi and exports A(Ti/L1) to depend on the initial per capita GDP. The estimated regression is: Constant = -16.53; Std. Error of Y Est. = 408.95; R Square = 0.2995; No. of Observations = 62; Degrees of Freedom = 58

||%GDP|%GDPx(GDP/L)|%GDPx(GDP/L)<sup>2</sup>|
|---|---|---|---|
|Coefficient|0.69321|0.0712|-0.01579|
|Std. Err. of Coef.|0.54466|0.15496|0.00954|

44. From this estimated equation we can solve for the level of GDP per worker at which the association between growth and net exports turns from positive to negative. This is US$9,000 per worker. For countries with a GDP per worker at US$7,000 (low GDP per worker countries for now on), a half percentage point increase in the annualized rate of GDP growth approximately gave (during 1974-88) a US$5 increase in the exports of labor-intensive manufactures per worker.

45. In Figure E. 10 we show the very strong association between economic growth **and** increaises in labor-intensive trade that has existed in Central America, with Mexico also included. In 1970 Central America exported US$13 per worker of labor-intensive manufactured goods-Costa Rica had the highest exports per worker with US$23, well above the Mexican value of only US$5. In 1990 labor-intensive exports increased to _USS52_ per worker for Central America. During the two decades Costa Rica had the biggest absolute increase but Panama had the highest rate of growth of labor-intensive exports. The evolution of labor-intensive exports and the rate of growth of GDP is depicted in Figure E.9 where a clear positive relationship can be seen. Nicaragua, the
Table 8: Robustness of the interaction between treatment and trust to the inclusion of social norms on redistribution

|||Me|asure of**"**redist|ribution values**"**||
|---|---|---|---|---|---|
|||People are poor|Success is a|Society should|Composite|
|||<br>because of bad<br>luck or insjustice<br>(not laziness)<br>(1)|matter of hard<br>work<br>(2)|<br>make incomes<br>more equal<br>(3)|<br>index<br>(4)|
|(A) Aggregate/al|l proposals|||||
|Mean giving|Treatment|0.12|0.09|0.08|0.11|
|||[0.065]*|[0.055]|[0.056]|[0.066]*|
||Treatment*Trust|-0.10|-0.06|-0.03|-0.09|
|||[0.033]***|[0.022]**|[0.017]*|[0.036]**|
||Redistribution values|0.05|0.11|-0.02|-0.03|
|||[0.044]|[0.037]***|[0.039]|[0.050]|
|Frequent giver|Treatment|0.18|0.13|0.13|0.17|
|||[0.091]*|[0.077]*|[0.078]|[0.097]*|
||Treatment*Trust|-0.18|-0.12|-0.10|-0.17|
|||[0.060]***|[0.046]***|[0.038]***|[0.057]***|
||Values redistribution|0.08|0.12|-0.07|-0.04|
|||[0.064]|[0.049]**|[0.058]|[0.099]|
|(B) Individual pro|posals|||||
|Unconditional|Treatment|0.17|0.14|0.14|0.16|
|cash transfer||[0.089]*|[0.076]*|[0.080]*|[0.089]*|
||Treatment*Trust|-0.17|-0.12|-0.10|-0.16|
|||[0.054]***|[0.041]***|[0.034]***|[0.051]***|
||Redistribution values|0.07|0.10|-0.06|-0.03|
|||[0.045]|[0.041]**|[0.044]|[0.048]|
|Unconditional|Treatment|0.10|0.07|0.06|0.09|
|fd f||[0.057]*|[0.049]|[0.050]|[0.056]|
|oo transer|Treatment*Trust|-0.10|-0.06|-0.04|-0.09|
|||[0.037]***|[0.028]**|[0.025]|[0.034]***|
||Redistribution values|0.05|0.12|-0.06|-0.05|
|||[0.055]|[0.050]**|[0.053]|[0.056]|
|Unconditional|Treatment|0.15<br>|0.10<br>|0.09<br>|0.14<br>|
|cash transfer and||[0.084]*|[0.076]|[0.071]|[0.092]|
|<br>hl|Treatment*Trust|-0.11<br>|-0.04<br>|-0.01<br>|-0.10<br>|
|scoo||[0.040]***|[0.029]|[0.013]|[0.043]**|
||Redistribution values|0.08|0.10|0.04|-0.01|
|||[0.051]|[0.049]**|[0.053]|[0.196]|
|Cash transfer|Treatment|0.06|0.04|0.03|0.06|
|conditional on||[0.083]|[0.068]|[0.068]|[0.089]|
|<br>|Treatment*Trust|-0.03|0.00|0.02|-0.02|
|training||[0.030]|[0.004]|[0.201]|[0.027]|
||Redistribution values|0.01|0.10|-0.01|-0.03|
|||[0.038]|[0.044]**|[0.056]|[0.061]|
|Number of observ|ations|346|405|407|340|

Notes: The estimation method is a linear probability model. All regressions include controls for trust, gender, three education levels, location in the capital city, and number of cars in the household. The dependent variable is the giving rate. Results in column 1 control for believing that people are poor because of bad luck or injustice rather than laziness. Results in column 2 control for believing that success is a matter of hard work rather than luck or connections. Results in column 3 control for believing that society should redistribute income. Results in column 4 control for the composite index made of all three values constructed via polychoric principal-components model. Standard errors clustered at the PSU level using wild bootstrap-t are reported in brackets. ***, **, * significant at 1%, 5%, and 10% level.

30
Contractual Savings In Countries With A Small Financial Sector

- [351 **Vittas,** D. (2000) Pension Reform and Capital Market Development: "Feasibility" and "Impact" Preconditions. _The World Bank, Policy Research Working Paper Series,_ No. 2414.

- [36] **Vittas, D., and M. Skully** (1991) Overview of Contractual Savings Institutions. _The World Bank, Policy Research Working Paper Series,_ No. 605.

- [37] **Von Gersdorff, H.** (1997) Pension Reform in Bolivia: Innovative Solutions to Common Problems. _The World Bank, Policy Research Working Paper Series,_ No. 1832.

- 30-
# **4. Descriptive Statistics**

The aim of this section is to describe the characteristics of the different groups included in the survey, and to illustrate their living conditions, as well as their future migration plans. A preliminary analysis of these data has already been done by (Etang-Ndip et al., 2015). Most of the surveyed people were displaced in April 2012, when Gao, Kidal and Timbuktu were occupied by rebels. A second wave of displacement occurred in June 2012 and a third one in January 2013.

# **4.1 Demographic characteristics**

Looking at the composition of our sample between refugees, returnees and IDPs, it is possible to claim that if they had not already done so by August 2014 (the time of the baseline) few returned between 2014 and 2015. The transition probability from being internally displaced in our sample to having returned to Northern Mali over the 12 waves is 2.4%. The same probability is even lower for refugees: only 0.3%. However, it should be pointed out that some refugee respondents changed migration status several times over the period considered in the survey. Having returned (early) to Northern Mali did not always lead to a stable condition either: the transition probabilities from returnee to IDP or to refugee are 1.1% and 0.2% respectively.

As we can see from Figure 1, the majority of the sample is Songhai and Kel Tamasheq (almost everybody identified themselves as Figure 1: Ethnic composition Muslim). There are clear differences in <mark>Displaced Refugee</mark> migration decisions between ethnic groups. The reaction of most of Arab and Kel <mark>Returnee Total</mark> Tamasheq origin was to leave the country, while most Songhai people preferred to go south, to Bamako, or, by the time of our Tamasheq Arab survey, had already returned to Northern SonghaiBella PeulhOther Mali. In fact, as pointed out in (Etang-Ndip Analytic weights used Source: LDPS 2014-15 et al., 2015), IDPs and returnees have a similar ethnic composition because 94% of returnees in our sample were IDPs. Far fewer returnees in the sampled cities of Gao, Tombouctou and Kidal returned from refugee camps in the neighboring countries for the simple reason that most refugees used to live in towns and villages outside the regional capitals of Northern Mali.

6
Functions (RIF). The section highlights the impact of economic factors such as sector employment, access to human capital services, and income sources among others on household consumption. RIF regressions not only allow for the observation of changes in such impact across time, but also to determine the impact of the selected welfare determinants across the various income groups of the society. Section VI concludes.

# **II. Data Used and Methodological Issues**

The paper uses a panel of cross-sectional household surveys referred to as the Jordan Household Expenditure and Income Survey (HEIS) and conducted by the Jordanian Department of Statistics (DoS) in the years 2002, 2008 and 2010. The questionnaires of the three surveys are very similar and offer information on household demographics; households’ dwellings, properties, and assets; individuals’ education, employment status, and economic activities; household productive activities, income, and income sources; regional residency; distance and access of households to various services and utilities along with individuals’ health status<sup>7</sup> . Additionally, the surveys collect information on household expenditure and consumption from own production, gifts and offerings, of various food and non-food items, along with household spending on other different services such as education, health and entertainment. The information is collected using an expenditure diary methodology and captures the overall consumption of Jordanian households all year long<sup>8</sup> .

Utilizing the above data, the paper defines the household welfare<sup>9</sup> measure as being per capita household consumption in real terms. Additionally, welfare growth is depicted as the changes that occurred to the level of such consumption between 2002 and 2010 in a first instance, and 2008-2010 in a second one. The paper calculates consumption as the sum of household expenditure on all goods and services on the one hand, and the estimated value of 7 The information from bullet (vi) is not available in all questionnaires and can be found mainly in the 2010 survey. In 2002 a separate survey has been conducted on these issues. The paper uses data on access to services from this particular survey, aggregates it on a sub-regional level and uses it in the regression analysis and counterfactual decompositions performed in later sections of the study.

> 8 A note of caution should be taken into account related the surveys design. The household surveys in Jordan might underestimate the expenditure of the richest segment of the population as the largest share of the non-respondents and the households that dropped the questionnaire are considered as richer Jordanians. Hence the samples might consequently be skewed. Therefore results for the upper income distribution must be approached with caution. The representation of expenditure surveys for the richest parts of the population is a common problem in the literature. 9 Referred to often in this study as simply “welfare”.

4