rafmacalaba/datause-encoder-data huggingface dataset:
1989). Mothers were encouraged to converse with their children, to label things and actions in their environments and to play educational games with their children (Grantham-McGregor et al., 1987). Particular emphasis was placed on language development, the use of praise, and on improving the self-esteem of both the child and of the mother. At age 24 months, the curriculum was enriched to include concepts such as size, shape, position, quantity, color, etc based on the curriculum in Palmer (1971).

The focus of the weekly play sessions was on improving the quality of the interaction between mother and child. Mothers were encouraged to continue practicing the activities and games learned during the visits on a continuing basis beyond the home visitation time. At every visit, homemade toys were brought to the home and left for the mother and child to use until the next visit when they were replaced with new ones. The intervention was innovative both for its focus on activities to promote cognitive and language development and for its emphasis on direct mother-child interactions.

The nutritional intervention (comprising groups 2 and 3) was aimed at compensating for the nutritional deficiencies that may have caused stunting. The nutritional supplements were provided weekly for a two-year period. The supplements consisted of one kilogram of formula containing 66% of daily-recommended energy (calories), and 100% of daily-recommended protein (Walker et al., 1992). In addition, in an attempt to minimize sharing of the formula with other family members, the family also received 0.9 kilograms of cornmeal and skimmed milk powder. Despite this, sharing was common and uptake of the supplement decreased significantly during the intervention (Walker et al., 1991).

Of the 129 study participants, two of the participants dropped out before completion of the two-year program. The remaining 127 participants were surveyed at baseline, resurveyed immediately following the the end of the two-year intervention, and again at ages 7, 11, and 18. Our analysis is based on a re-interview of the sample in 2007-08 when the participants were approximately 22 years old, some 20 years after the original intervention.

4
# Table 1. Rearession<sup>EAuation for Agricultural Supplv in Sub-Saharan AfricaL</sup>

||All coun|tries<br>|Tree Crop Expo<br>|rters<br> <br>|Annual Crop Ey<br>|porters<br>6|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|_(5)_|()|
|Model|||||||
|Producer Price<br>Moving average<sup>(t and t-1)</sup>|0.202<br>(12.97)|0.240<br>(204.2)|0.017<br>(1.13)|_0.115_<br>(5.04)|0.940<br>(6.26)|0.920<br>(6.08)|
|Real effective exchange<sup>rate</sup><br>Moving average<sup>(t and t-1)</sup>|-0.104<br>(4.15)||-0.253<br>(-13.86)<br>||0330<br>(2.09)<br>|00009|
|Disasters variable|-0.0006<br>(-2.15)|-0.0010<br>(49.7)|-0.0003<br>(-0.43)<br>|-0.0016<br>(-1.69)<br>|0.0013<br>(0.92)<br>|.<br>(0.67)<br>0435|
|Weather variable|0.373<br>(21.64)|0382<br>(299)|0.153<br>(5.18)<br>|0.219<br>(5.54)<br>|0.440<br>(6.29)<br>|.<br>(5.7)<br>033|
|Intercept|4.12<br>(26.87)|3.44<br>(625)|5.62<br>(41.5)<br>|4.01<br>(36.8)<br>|-1.29<br>(-1.46)<br>|(0.47)<br>92|
|Degrees of freedom|331|332|219|220|91||

Note. Exports,<sup>producer prices, exchange rate, and the weather variable have been expressed in logarithms. Their coefricianct may be</sup> interpreted as<sup>elasticities.</sup> Figure in parentheses<sup>are t-values.</sup>
# **Table 5.1: Determinants of Environmental Lending**

||Environme|ntal Lend|ing<sup>a</sup><br>|Envir|onmental|AAA<sup>b</sup>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Log Overall Env.|0.700|0.425|0.457|0.305|0.433|0.407|
|Indicator|(10.35)**|(5.08)**|(4.91)**|(2.94)*|* (3.03)**|(2.45)*|
|Log Bank Non-Env.||0.437|0.402||-0.120|-0.018|
|Lending||(5.34)**|(4.70)**||(0.68)|(0.09)|
|Log Bank Env.|||||0.015|0.006|
|Lending|||||(0.22)|(0.08)|
|Log OED Success||1.149|1.071||0.864|0.969|
|Probability||(3.35)**|(2.97)**||(1.41)|(1.41)|
|AFR (Sub-Saharan|Africa)||0.245<br>(0.97)|||0.474<br>(0.72)|
|EAP (East Asia, P|acific)||0.367<br>(1.14)|||0.878<br>(1.28)|
|ECA (Europe, Cent|ral Asia)||0.813|||0.388|
||||(3.25)**|||(0.69)|
|MNA (Middle East,|N. Africa)||0.714<br>(2.02)*|||0.058<br>(0.08)|
|SAR (South Asia)|||0.459<br>(1.21)|||-0.375<br>(0.47)|
|Constant<sup>c</sup>|3.363<br>(31.89)**|-4.160<br>(2.85)**|-4.015<br>(2.64)**|4.292<br>(26.51)|1.227<br>** (0.44)|-0.169<br>(0.05)|
|Observations|92|91|91|63|59|59|
|R-squared|0.54|0.70|0.75|0.12|0.22|0.27|
|Adj. R-Sq.|0.53|0.69|0.72|0.11|0.16|0.14|

Absolute value of t statistics in parentheses significant at 5%; ** significant at 1% a Limited to countries with environmental lending

> n Limited to countries with environmental AAA

c LCR (Latin America, Caribbean) is the excluded regional dummy variable.

**___________________________________________________________________________**

This result suggests that a 1% increase in overall environmental problems is associated with a .70% increase in environmental lending. At the regional level, Figure 5.2 also suggests a good correspondence between overall environmental problems and environmental lending in countries where such lending has occurred. The relationship is very strong in East and South Asia (EAP, SAR), but it is also apparent in the other 13
Haelermans, C., Jacobs, M., Van Der Velden, R., Van Vugt, L., Van Wetten, S. 2022, Inequality in the Effects of Primary School Closures due to the COVID-19 Pandemic. _American Economic Association Papers and Proceedings_ 112: 303-307.

Hammerstein, S., König, C., Dreisörner T., Frey, A. (2021). Effects of COVID-19-Related School Closures on Student Achievement-A Systematic Review. _Frontiers in Psychology_ .

Jack, R., Halloran, C, Okun, J, Oster, E. 2022. Pandemic Schooling Mode and Student Test Scores. _American Economic Review: Insights_ .

Jakubowski, M., T. Gajderowicz and H.A. Patrinos. (2023). Global Learning Loss in Student Achievement: First Estimates using Comparable Reading Scores. University of Warsaw Working Paper.

Maldonado, J.E., De Witte, K. (2022). The effect of school closures on standardised student test outcomes. _British Educational Research Journal_ 48(1): 49-94.

McKinsey (2022). How COVID-19 caused a global learning crisis. <u>https://www.mckinsey.com/industries/education/our-insights/how-covid-19-caused-a-globallearning-crisis#</u> Patrinos, H.A. (2023). The Longer Students Were Out of School, the Less They Learned. _Journal of School Choice_ 17(2): 161-175.

Patrinos, H., Vegas, E., Carter-Rau, R. (2023). An Analysis of COVID-19 Student Learning Loss. In Jonathan Hamilton (Ed.), _The Oxford Research Encyclopedia of Economics and Finance_ . Oxford: Oxford University Press.

Pier, L., Hough, H. J., Christian, M., Bookman, N., Wilkenfeld, B., Miller, R. (2021). COVID19 and the educational equity crisis: Evidence on learning loss from the CORE data collaborative. Policy Analysis for California Education.

Psacharopoulos, G., Collis, V., Patrinos, H.A., Vegas, E. (2021). The COVID-19 cost of school closures in earnings and income across the world. _Comparative Education Review_ 65(2): 271287.

UNESCO (2020). UN Secretary-General warns of education catastrophe, pointing to UNESCO - estimate of 24 million learners at risk of dropping out. <u>https://en.unesco.org/news/secretary general-warns-education-catastrophe-pointing-unesco-estimate-24-million-learners-0</u> World Bank (2017). _World Development Report 2018: Learning to Realize Education’s Promise_ . Washington DC: World Bank.

Zierer, K. (2021). Effects of Pandemic-Related School Closures on Pupils’ Performance and Learning in Selected Countries: A Rapid Review. _Education Sciences_ 11(6): 252.

16
Caria, S., Gordon, G., Kasy, M., Quinn, S., Shamik, S., Teytelboym, A., 2020. An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan.

Cuevas, P. F., Inan O.K., Twose, A., Çelik, Ç., 2019. Vulnerability and Protection of Refugees in Turkey: Findings from the Rollout of the Largest Humanitarian Cash Assistance Program in the World. World Bank and World Food Programme.

Crépon B, Duflo E, Gurgand M, Rathelot R, Zamora P., 2013. Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment. Q J Econ. May 1;128(2):531–80.

Deaton, A., and Zaidi, S., 2002. Guidelines for Constructing Consumption Aggregates for Welfare Analysis. LSMS Working Paper No. 135, World Bank.

Del Carpio, X.V., Demir Seker, S., Yener, A.L., 2018. Integrating refugees into the Turkish labour market. <mark>Forced Migration Review. 58. 11-13.</mark> Directorate General of Migration Management (DGMM), July 2020. Temporary Protection Statistics. Ministry of Interior, Directorate General of Migration Management.

Dutta, B., 2020, A citywide approach in urban Bangladesh. <mark>Forced Migration Review. 63. 30-32.</mark> <mark>Edmonds, Eric V., Kristin Mammen, and Douglas L. Miller. Rearranging the family? Income support and elderly living arrangements in a low-income country. Journal of Human resources 40, no. 1 (2005): 186-207.</mark> Erdoğan, M.M., 2019. Syrian Refugees in Turkey. Konrad Adenauer Foundation Publication.

Fan, J. 1992. Design-adaptive nonparametric regression. Journal of the American Statistical Association 87: 998–1004.

Fan, J., Gijbels, I., 1996. Local Polynomial Modelling and Its Applications. London: Chapman & Hall. Filmer, D., Friedman, J., Kandpal, E., Onishi, J., 2018. General equilibrium effects of targeted cash transfers: Nutrition impacts on non-beneficiary children. The World Bank.

Gilligan, M.J., Mvukiyehe E.N., Samii C., 2012. Reintegrating Rebels into Civilian Life: Quasi-Experimental Evidence from Burundi. J Conflict Resolut . Jun 28;57(4):598–626.

Hoddinott, J., Sandström, S., Upton, J., 2018. The Impact of Cash and Food Transfers: Evidence from a Randomized Intervention in Niger. Am J Agric Econ. 1;100(4):1032–49.

<mark>International Finance Corporation (IFC). 2018. Kakuma as a marketplace: a consumer and market study of a refugee camp and town in northwest Kenya (English). Washington, D.C.: World Bank Group.</mark> 35
around women in public transit. Social norm survey questions followed the same wording as that of the rider exit survey.

# **Sampling**

To select a representative sample of rush hour commuters, we use a simple sampling protocol based on ordering and counting individuals on the platform of each line at the main station, Central do Brasil, during the evening commute (5-8pm). Table A1, Panel B, summarizes patterns of response. A total of 1,078 commuters were approached, 555 women and 523 men. Ninety percent (90.1 percent) of women and 85.7 percent of men responded to the platform survey (Table A4 column 1), with an overall response rate of 87.9 percent.<sup>16</sup> We use administrative data on the number of SuperVia riders by line to apply sample weights to obtain estimates that are representative of the average rider.

After agreeing to participate in the platform survey, respondents are invited to participate in a series of IATs.<sup>17</sup> The IAT method and instruments are discussed in Section 4.3. Participants in the IATs are offered a compensation of R$30.00, or the equivalent of 7 hours of work at the minimum wage (about $7.50).<sup>18</sup> A booth was set up close to the platform and equipped with laptops on which participants took the test. The platform survey was conducted until 300 finished IATs were completed.<sup>19</sup> The response rate for the IATs is reported in Table A1. Conditional on being invited to take the IATs, the response rate was 40.6 percent. Women are slightly less likely to accept than men (38 percent versus 43.5 percent), but this difference is imprecisely estimated (Table A4 column 2: _p_ = 0 _._ 11). Women’s stated use of the reserved space is not significantly correlated with response to the IATs (Table A4,

> 16Among those who accepted to participate, 8 percent left mid-interview to board their train. See Online Appendix for the full protocol.

> 17For respondents who agree to participate in the IATs we randomize whether the platform survey is taken before or after completion of the IAT to control for priming effects differential fatigue in the instruments. We find that the order in which the respondents take either task does not affect the results (Table A5) 18 Eighty-six platform respondents were not invited to the IATs because they were illiterate, making completion of the task, requiring matching words and pictures, impractical, and 14 were excluded due to significant disruption caused by a samba party on the train platform.

> 19Nine IATs were discarded because the system was not able to compute the results, either due to the respondent appearing to provide random answers or application failure.

13
# TABLE II

SOCIOECONOMIC CHARACTERISTICS OF HOUSEHOLDS IN THE ESTIMATION SAMPLE

|Variable|Estimation<br>sample survey|Household<br>Survey|
|---|---|---|
|Female|75.4|52.0|
|Age|52.5|32.1|
|Married|60.8|38.6|
|Tertiary education|38.0|19.4|
|Home owner|88.9|71.2|
|Number of rooms|3.3|3.1|
|Number of families|1.1|1.0|

_Notes_ : Individual characteristics in the estimation sample survey are from the member of the household that is responsible for paying the gas bills. Statistics on households from Argentina's Household Survey ( _Encuesta Permanente de Hogares_ ) refer to households living in Buenos Aires. “Number of families” refers to the number of different families living in the household. “Number of rooms” refers to the total number of rooms in the household.

27
|**Table 7.(Log) Weekly hours**|**worked by ge**<br>(1)|**nder: Tobit mo**<br>(2)|**del(coeff. esti**<br>(3)|**mates)**<br>(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Tot Hours**<br>**worked**|**Tot Hours**<br>**worked**|**Tot Hours**<br>**worked**|**H.Wage**<br>**Empl.**|**H. Paid**<br>**Self-empl.**|**H. Unpaid**<br>**work**|**H. Wage**<br>**Empl.**|**H. Paid**<br>**Self-empl.**|**H. Unpaid**<br>**work**|**H. Wage**<br>**Empl.**|**H. Paid**<br>**Self-empl.**|**H. Unpaid**<br>**work**|
|||||||**F**|**EMALE**||||||
|Any hh migrant|-1.306|||-0.330*|0.426|0.19|||||||
||(1.50)|||(1.86)|(1.19)|(1.52)|||||||
|**Current hh migrants**||-2.089**|0.018||||-0.261|0.036|-0.041|0.202|0.452|-0.292|
|||(2.14)|(0.01)||||(1.25)|(0.09)|(0.29)|(0.61)|(0.71)|(1.21)|
|**Past hh migrants**||-0.349|-0.442||||-0.232|0.751**|0.127|-0.241|0.716*|0.113|
|||(0.38)|(0.49)||||(1.19)|(2.05)|(1.00)|(1.24)|(1.96)|(0.89)|
|**Past indiv. migration**||-2.994|-3.016||||0.868**|-1.682|-1.321***|0.788*|-1.704|-1.219***|
|||(1.14)|(1.15)||||(1.99)|(1.31)|(3.05)|(1.80)|(1.32)|(2.82)|
|Rem. from current int.nal mig.|||-0.339*|||||||-0.074**|-0.068|0.031|
||||(1.92)|||||||(2.02)|(1.00)|(1.21)|
|Rem/gifts from abroad –last 12 m|||-0.352**|||||||-0.035|-0.210***|0.044*|
||||(2.14)|||||||(1.04)|(2.63)|(1.86)|
|Rem/gifts from Albania-last 12 m.|||-0.392**|||||||-0.122***|0.132|0.014|
||||(1.96)|||||||(2.87)|(1.60)|(0.49)|
|Other non-lab income|||-0.686***|||||||-0.023|-0.119|-0.094***|
||||(3.89)|||||||(0.69)|(1.60)|(3.34)|
|Observations|2852|2852|2852|2852|2852|2852|2852|2852|2852|2852|2852|2852|
||||||||**MALE**||||||
|Any hh migrant|-2.901***|||0.007|-0.822***|0.37|||||||
||(3.37)|||(0.04)|(3.13)|(1.44)|||||||
|**Current hh migrants**||-3.222***|-2.384||||0.146|-0.941***|0.113|0.27|-0.796*|0.059|
|||(3.52)|(1.63)||||(0.90)|(3.36)|(0.41)|(1.05)|(1.80)|(0.13)|
|**Past hh migrants**||-1.642|-1.745||||-0.341|-0.404|0.754**|-0.346|-0.425|0.747**|
|||(1.30)|(1.39)||||(1.43)|(1.02)|(2.21)|(1.45)|(1.08)|(2.20)|
|**Past indiv. migration**||-0.479|-0.465||||0.22|-0.148|-0.673**|0.226|-0.152|-0.664**|
|||(0.56)|(0.55)||||(1.49)|(0.59)|(2.54)|(1.52)|(0.60)|(2.51)|
|Rem. from current int.nal mig.|||-0.154|||||||-0.021|-0.02|0.002|
||||(0.93)|||||||(0.71)|(0.40)|(0.04)|
|Rem/gifts from abroad –last 12 m|||-0.535***|||||||-0.049*|-0.014|-0.061|
||||(3.45)|||||||(1.77)|(0.29)|(1.17)|
|Rem/gifts from Albania-last 12 m.|||0.154|||||||0.008|0.111**|-0.093|
||||(0.84)|||||||(0.24)|(2.03)|(1.53)|
|Other non-lab income|||-0.708***|||||||-0.025|-0.059|-0.137***|
||||(4.76)|||||||(0.94)|(1.29)|(2.64)|
|Observations|3740|3740|3740|3740|3740|3740|3740|3740|3740|3740|3740|3740|

_Notes_ Absolute value of t statistics in brackets; * significant at 10%; ** significant at 5%; *** significant at 1%. All individual, household and regional level controls are included as in Table 6.

27
relative incomes. In short, social welfare functions that place greater weight on poorer individuals will be more responsive to variations in the income share of the poorest.

At first glance, the finding of greater volatility in income shares of the poorest seems plausible, to the extent that the poor experience proportionately greater income shocks. However, in this section we show that greater volatility in the income shares of the poorest may also to some extent simply be a consequence of sampling variation, rather than reflecting any true differences in the population variance of income shares of the poorest. This in turn suggests that the share of the variance of growth in social welfare due to growth in average incomes might be understated in our results thus far in Table 2-Table 4, and particularly so for more bottom-sensitive social welfare functions.

We illustrate this point by considering the thought experiment of drawing random samples of data on income at two points in time from a given population, which we can think of as the two endpoints of one of the “spells” we study in this paper. We assume the samples are drawn independently of each other (corresponding to household surveys that are repeated cross-sections of households, as is the case in most countries in our sample, rather than true panels that track individual households over time). We also assume that the population distributions of income from which we are sampling is lognormal (which is a reasonable approximation in most countries, see Lopez and Serven (2006)). Finally, we assume that the standard deviation of log income is the same for the two distributions. This provides us with a benchmark of what to expect when there is no change in inequality in the population over the spell. However, measured inequality, which can be summarized by the sample standard deviation of log incomes, will be different in the two samples, due to purely random sampling variation.

In Appendix A we derive an analytical expression for the standard deviation of the growth rates of the income shares of different percentiles of the income distribution corresponding to this thought experiment, which we plot in the bottom panel of Figure 5. This pattern is remarkably similar to what we see in the actual data, with substantially higher volatility in the growth rate of the income shares of the poorest deciles, despite the fact that there are no changes in relative incomes between the two population distributions. This suggests that some of the apparent differences in the importance of growth in average incomes for growth in social welfare that we saw in Table 2-Table 4 may simply reflect the differential importance of sampling variation across decile shares.

17
account. In this paper, we rely on the simplest form of general equilibrium models, namely the Social Accounting Matrix framework (SAM hereafter). A SAM is primarily a data framework which functions as a double-entry square matrix recording in columns payments (or expenditures) and in rows receipts (or incomes) of transactions made by the different activities, commodities, and agents in the economy. When SAMs are used as models, for example to assess the impact of quantity or price shocks, they are typically static models with fixed technical coefficients (i.e., Leontief technology) and prices. A key advantage of SAMs over Input-Output tables is that data from household surveys on incomes and consumption patterns can be integrated into the analysis. On the other hand, as compared to computable general equilibrium models, SAMs cannot take into account behavioral reactions to various types of shocks.

There is a very large literature on the use of SAMs for economic work which cannot be reviewed here. In developing countries, examples of studies include Adelman and Taylor (1990), Dorosh (1994), Taylor and Adelman (1996), Thorbecke and Jung (1996), Khan (1999), Arndt et al. (2000), Bautista et al (2001), and Taylor et al. (2002). A good review of the SAM approach to modeling can be found in Thorbecke (2000) which builds on an earlier synthesis by Defourny and Thorbecke (1984). While SAMs have a number of limitations, one of those we wish to emphasize here is that the “traditional” SAM model assumes that average expenditure propensities hold for exogenous demand shocks, implying income elasticities equal to one. A more realistic alternative mentioned in Pyatt and Round (1979) and Lewis and Thorbecke (1992) is to use marginal expenditure propensities.

While most of the applications of the SAM technique have focused on the impact of exogenous quantity or demand shocks, in this paper, our objective is instead to use a recent SAM for Ghana to assess the potential impact of the increase in oil and food prices on the cost of 4