rafmacalaba/datause-encoder-data huggingface dataset:
also comment on whether these relationships hold when considering the whole sample instead of the restricted small and medium firm sample, and by and large this seems to be the case. We use the OLS estimation results in table 1, column 4 as the base results for the small and medium firm sample as with OLS estimations there is a more straightforward interpretation with regards to the magnitude of the effects of interaction variables. However, it is worth noting that both the Logit and OLS estimations provide qualitatively similar results for economic growth

# _3.2 Small and Medium Firms and per Capita Economic Growth Interaction_

Column 1 of table 2 presents the estimation results of the interaction between small and medium firms and economic growth using the full sample of small, medium, and large firms. The interaction is negative, implying that economic growth reduces crime losses for small and medium firms to a greater extent than large firms. A one percent increase in per capital economic growth is associated with a 0.33 percent decline in crime losses for small and medium firms, statistically significant at the 1% level. The corresponding figure for large firms is 0.21 percent, statistically significant at the 5% level. The implication is that an increase in per capita economic growth is far more beneficial for small and medium firms than large firm with regards to crime. Thus pro-growth policies may be one way of insulating small and medium firms from crime.

# _3.3 Number of Police and per Capita Economic Growth Interaction_

The interaction term between the number of police and economic growth is positive and significant at the 5% level of significance for the sample of small and medium sized firms. The overall association of economic growth with crime losses for small and medium sized firms is still negative at the sample mean number of police after interacting economic growth with police.

15
where  _ic_ can be modelled either as a fixed effect or as a random effect and  _itc_ is supposed to follow a martingale that is with  _itc_ denoting an innovation term that is supposed to be normally, independently and 2 identically distributed, with mean zero and variance  _ct_ . Grouping households together by cohorts gives the possibility to estimate the model with repeated cross-section surveys. Estimating this model by focusing on second-order moments—as in Deaton and Paxson 2 (1994)—yields estimates of  _ct_  1 that can directly be used to predict the degree of household vulnerability in cohort _c_ . Indeed, by first drawing a value  ~ _itc_  1 in the normal distribution with mean zero and variance  ˆ  2 _ct_  1 , we obtain the probability to become poor in _t+1_ for household _i_ in cohort _c_ :

> where  (.) denotes the cumulative density of the standard normal distribution. Assuming,

> _c_ ˆ _c c_ ˆ _c_ for simplicity sake, that _xit_  1  _t_  1  _xit_  _t_ gives _v_ ˆ _itc_  Pr( _aitc_  1  _z_ | _xitc_ , _xitc_  1, _aitc_  1)  <sup></sup> ln _z_  lnˆ _aitc_   ~ _<u>itc</u>_ <u> 1</u> <sup></sup> , where  ˆ  2 _ct_  1 is the estimator of the   _ct_  1 

2 slope of the age profile for the asset disturbance term variance  _ct_ . Indeed, we propose to decompose the residual variance into age and cohort effects as follows: where  is a constant,  _ct_ is a cohort effect,  _at_ is an age effect, and _uct_ is an error term which is supposed to be independent and identically distributed and of mean zero. Then, assuming that the cohort effect is time invariant as it should asymptotically be the case (Verbeek, 2008), we estimate the first difference (from _t_ to _t+1_ ) of age effects―that is  ˆ _at_  1<sup></sup>  ˆ _at_ ―for each cohort in order to get  ˆ  2 _ct_  1.

Following the previous methodology, the estimation steps to obtain the vulnerability index can be summarized as follows:

- _Step 1._ Create a pseudo panel from repeated cross-section surveys. The rationale for this is to choose time-invariant characteristics to group households in each survey into cohorts.<sup>24</sup> The number of cells constituted equals the number of cohorts multiplied by the number of periods/surveys available for the analysis. Cell size

> 24 A cohort is typically defined by the year of birth, education level and localization.

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# **6.  Protective Impact of the Green Card during the 2008-2009 Economic Downturn**

In this Section, we make use of the Turkey Welfare Monitoring Survey (TWMS) to assess whether the Green Card had a protective impact for its beneficiaries during the recent economic downturn. Our hypothesis is that the Green Card did provide such protective impact by lowering the costs of both outpatient and inpatient care. If true, we would find Green Card holders to have reduced health consumption less than non-beneficiaries (of any insurance program). The analysis in this Section, hence, compares the health care utilization behavior of Green Card holding households with households with no insurance coverage. The counterfactual in the analysis therefore is households that did not have access to any form of health insurance (whether through the Green Card, through SGK coverage or private insurance). We estimate the different behavior of households with access to the Green Card and with no health insurance throughout the crisis using three different estimation methods: (i) a non-parametric technique, (ii) a parametric linear probability model (LPM) and maximum likelihood probit regressions; and (iii) propensity score matching.

When modeling the impact of a demand-side subsidy on health care utilization, we need to control for self-selection given that cardholders could have un-observable attributes which could be correlated with both program enrolment and the actual use of health services. Simply comparing medical care use between program participants (the treatment group) and non-participants (the control group) could otherwise result in a biased estimate of the program‘s effect. With experimental data, this bias would not be present as the random assignment of individuals to each group would balance the observable and unobservable individual characteristics affecting health utilization.

In the absence of experimental data, we make use of retrospective questions included in the TWMS. The TWMS recorded households‘ health care utilization at the beginning (October 2008) of the crisis and at the time of data collection (June 2009). With such a quasi-panel, we can isolate household fixed effects by estimating the probability of change in health care utilization for each household during the crisis. Comparing the estimated reduction in the probabilities of health care utilization of Green Card holding households versus households with no insurance coverage, then allows us to assess the protective impact of the program.

Table 5A provides summary statistics for household head characteristics with respect to insurance access, distinguishing Green card holders from those with access to the _SGK_ 11
related to the regular quinquennial population censuses.<sup>2</sup> The SPO estimates for 1965, 1975, and 1980, however, greatly exceed both level and increase of the explicit courts of unemployed, seeking employment, made by the censuses for these years and shown in Table 1.2 (compare with Graph 1.1). The censuses have an increase from 0.4 percent in 1965 to 3.6 percent in 1980 while the corresponding SPO estimates (chained) are 3.6 and 10.7 percent, respectively. The SPO estimates may perhaps also have considered the counts of economically active population with occupation "not adequately described and unknown" which increased from 5.1 percent in 1960 to 6.4 percent in 1965 and 10.9 percent in 1970 (age 15 and above) and those for "not adequately defined" which increased from 2.0 percent in 1970 to 4.1 percent in 1975 (age 12 and above). Be that as it may, it is generally acknowledged that population censuses are a poor source of information about unemployment and we should test the SPO estimates of unemployment against all other sources of information available. Other such sources are the special labor force surveys for urban areas, conducted for the years 1967-69 and 1982-85 and for rural areas 1973-74, with data for applications and placements, 1965-86, and for registered unemployed and new vacancies, 1962-86, all from the public labor placement offices. These series are presented in Graph 1.1 and/or Tables 1-.1, 3 and 4. The labor force surveys (1985, pp.vi-viii) are patterned upon the United States model. Assuming sampling and

-3-
# **References**

Acs, Zoltan, William Parsons and Spencer Tracy (2008) “High-Impact Firms: Gazelles Revisited”, _Small Business Research Summary_ No. 328, SBA Office of Advocacy.

Ayyagari, Meghana, Asli Demirguc-Kunt and Vojislav Maksimovic (2014) “Who creates jobs in developing countries?”, _Small Business Economics,_ forthcoming Bandiera, Oriana, Robin Burgess, Narayan Das, Selim Gulesci, Imran Rasul, Munshi Sulaiman (2013) “Can Basic Entrepreneurship Transform the Lives of the Poor?”, Mimeo. LSE.

Banerjee, Abhijit, Esther Duflo, Raghabendra Chattopadhyay and Jeremy Shapiro (2011) “Targeting the hard-core poor: an impact assessment”, Mimeo. MIT.

Behaghel, Luc, Bruno Crépon, Marc Gurgand, Thomas Le Barbanchon (2012) “Please Call Again: Correcting Non-Response Bias in Treatment Effect Models”, _IZA Working Paper_ no. 6751.

Birch, David L and James Medoff (1994). “Gazelles”, in Lewis C. Solmon and Alec R. Levenson, eds. _Labor Markets, Employment Policy and Job Creation_ , Boulder: Westview Press, pp159-168.

Blattman, Christopher, Nathan Fiala and Sebastian Martinez (2014) “Generating skilled selfemployment in developing countries: Experimental evidence from Uganda”, _Quarterly Journal of Economics_ 129(2): 697-752.

Bruhn, Miriam, Dean Karlan and Antoinette Schoar (2013) “The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico”, Mimeo. Yale.

Bruhn, Miriam and David McKenzie (2014) “Entry regulation and formalization of microenterprises in developing countries”, _World Bank Research Observer_ , 29(2): 186-201.

De Mel, Suresh, David McKenzie and Christopher Woodruff (2008) “Returns to Capital in Microenterprises: Evidence from a Field Experiment”, _Quarterly Journal of Economics_ 123(4): 1329-72.

De Mel, Suresh, David McKenzie and Christopher Woodruff (2014) “Business Training and Female Enterprise Start-up, Growth, and Dynamics: Experimental evidence from Sri Lanka”, _Journal of Development Economics_ , 106: 199-210.

Duckworth, A.L., Peterson, C., Matthews, M.D., & Kelly, D.R. (2007) “Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology”, 9: 1087-1101.

Elkan, Walter (1988) “Entrepreneurs and Entrepreneurship in Africa”, _World Bank Research Observer_ 3(2): 171-188.

32
vulnerable populations are reached, especially given the higher poverty risk for families with young children (World Bank, 2012).

Second, there is a need to better understand constraints and effective mechanisms to increase access to and quality of ECE programs. Research on age of entry, program type and duration, and intensity of attendance is needed to understand which ECE programs are working and for whom. Program quality data are almost entirely lacking. Reliable administrative data on elements of quality, including teacher-child interactions, student-teacher ratios, mixed-aged classrooms, physical environments, availability of materials, curriculum, teacher training, incentive structures, and parent engagement, among other inputs, would be invaluable. Notably, it would be worth exploring the frequency and effectiveness of psychosocial stimulation in the classroom. Data on both supply- and demand-side barriers are needed, with analysis of the impacts of sociocultural norms and income inequalities, to allow for more targeted access to ECE and entry points for coordinated service provision.

Third, further research is needed to explore the role of gender-related norms, knowledge, and skills relevant to ECD. While this study provides an initial analysis of parental stimulation and ECD, further research is needed on differential patterns of early stimulation between women and men caregivers, how those might impact child development, and what approaches to parenting education programs are effective for women and men respectively.

# Limitations

Several constraints in data collection limit the conclusions we could draw. First, the respondents were predominantly male, which meant that most respondents were not the child’s primary caregiver. Second, an issue with cell-phone towers in Sindh resulted in under-sampling the province, however we were able to account for any selection bias with survey weights. Third, approximately 6 percent of Pakistani households that do not own a phone were excluded from our study. As such, our study results are only representative of the phone-owning households in Pakistan. Fourth, given the nature of the phone survey, all responses were caregiver-reported. Fifth, since the survey was cross-sectional, we could not draw any causal inferences from our regression analyses, and rather could only comment on associations between our exposures and outcomes of interest.

# Conclusion

This work sheds light on a major gap in the ECD literature as, prior to this study, no nationally representative data existed that quantified ECD among 0–3-year-olds in Pakistan. Moreover, it highlights the need to expand the reach of parenting education programs that explicitly teach 33
# **1. Introduction**

During 2020 and 2021, the COVID-19 pandemic affected the lives of everyone. To contain the spread of the disease, all governments imposed national lockdowns, travel restrictions, and social-distancing measures, including school closures. Latin America was not an exception. On average, in 2020 and 2021 schools were closed for 269 days. Given that a typical school year in the region takes 189 days, school closures represented a disruption of 1.42 years. National education systems provided remote learning options and other tools to hamper the learning interruptions, but undoubtedly the process of human capital formation of children and youths was affected. Due to this disruption, the pandemic will likely imply permanent lower levels of human capital for many individuals, and therefore lower earnings (see the surveys in Blanden _et al_ ., 2022 and Moscoviz and Evans, 2022).<sup>1</sup> In this paper we provide new evidence on the impact of the COVID-19 pandemic and the associated school closures on school dropouts and educational losses in Latin America. We take advantage of recently released microdata from national household surveys for 2020 that allows us to explore changes in school enrollment. To estimate the change in school enrollment associated with the pandemic, we run regressions of the outcome of interest—an indicator of whether the individual is enrolled in education or in private education—on a linear timetrend from 2009 to 2019, a binary indicator for the year 2020, and a set of controls. We show that in all countries enrollment rates in 2020 negatively deviate from the previous trend. On average (across the 12 Latin American countries in our sample, which add up to 87% of the regional school-age population), enrollment of children and young people aged 6 to 24 fell by around two percentage points. The decrease in enrollment in private education is also in this order of magnitude in absolute terms, but larger in relative terms because of the lower average baseline level. These results are in line with simulations

> 1 Psacharopoulos _et al_ . (2020) estimate that at the global level, the present value of a four-month school interruption implies an earnings loss of more than US$10,000 over the course of a lifetime. Using a structural model matched to US data, Fuchs-Schündeln _et al_ . (2020) find that earning losses amount to about 1% over the lifetimes.

2
attendance. Most students in public schools come from socially disadvantaged backgrounds. Schools don’t have access to any significant amount of discretionary funds for maintenance and equipment.

# **_Knowledge of local agency and attitude to public action_**

Household respondents were asked whether there was any committee in the village to deal with issues related to education services. A startling 92 percent responded that they did not know of any such committee. Of those that claimed to know that such a committee exists, only 2 percent could name actual members of the VEC. (See Figure 15.)

**Figure 15. Has anyone heard of the VEC?**

<!-- Start of picture text --> Villagers Who Don't Know of a Village<br>Education Committee<br>Villagers Who Think there is a VEC<br>5.0% Villagers Who Believe there is a VEC, But Can't<br>92.4% 7.6% Name Any VEC Members<br>1.1% Villagers Who Can Name Only One or Two VEC<br>1.5% Members<br>(the Pradhan and/or Headmaster)<br>Villagers Who Can Name More VEC Members<br>than Just the Pradhan and Headmaster<br><!-- End of picture text -->

* Based on 2,803 household surveys in 4 random blocks in the District of Jaunpur, UP. Each household is weighted by total number of households in village divided by number households surveyed in village.

This high percent ignorance of the public school institutions remains at 92 percent after narrowing the sample down to only parents of public school children. It is clear that even if interested, most parents are uninformed of the very institutions designed to encourage their participation.

Very few households participate in any local governance at all. Only 14.2 percent of respondents know of a household member having ever attending a Gram Sabha (village meetings), that were institutionalized as part of a country-wide decentralization initiative in 1993. The overwhelming excuse given by parents (over 90 percent) is that they do not 19
**Table 2: Shares of Major Exporters and Importers in Tropical Timber Trade**

|Major Exporters<br>Country/Regions<br>Country/Etegions|Share of<br>World<br>Exports (%)<br>1990|_h/_|Major Importers<br>Countries/Regions|Share of<br>Imports (%)<br>1987|
|---|---|---|---|---|
|Countries|||||
|Malaysia|41.5|(19.6)|Japan|28.1|
|Indonesia|23.8|(13.8)|China|9.2|
|Singapore_a/_|4.6|-|USA|7.5|
|Brazil|3.3|(19.6)|South Korea|6.2|
|C&te d'Ivoire|2.1|(1.4)|Singapore|6.2|
|Papua New Guinea|1.7|(1.8)|United Kingdom|4.7|
|Cameroon|_1.5_|(1.8)|Hong Kong a/|4.2|
|Congo|1.4|(0.8)|||
|Gabon|1.4|(0.6)|||
|**Regions**|||||
|Asia|82.2|(52.6)|Asia|54.3|
|Latin America|5.4|(26.8)|EU|20.1|
|Africa|8.8|(17.9)|||

# Sources: World Bank, International Trade Division.

Notes: Timber trade in sawnwood, wood-based panels, (converted in roundwood equivalent) and logs. _a/_ Transit country.

_/ Inside the parenthesis are the world production shares of each of the exporters in 1990.

The principal **trading partners of the major developing country exporters** for nonconiferous timber products (logs, sawnwood and panels) are broken down and presented in Table 3 (wood manufactures and wood furniture are discussed later). Asia is the largest importer (54.3%) of tropical timber products; China, Japan and South Korea account for a little less than half of the total of these tropical timber products imports from Malaysia, Indonesia, Papua New Guinea and Myanmar. These statistics show that the dominant pattern in tropical timber trade is **from** Southeast Asian producing regions **to** East Asian import markets.

17
|**Annex table 2: Impact of a ch**|**ange in the pric**|**e of cereals **|**on produc**|**ers- **|**Urban an**|**d Rural **|**Areas**||
|---|---|---|---|---|---|---|---|---|
||**-25%**|**-12.5%**|**No chang**|**e**|**12.5%**|**25%**|**50%**|**100%**|
|||||**Ric**|**e**||||
|**Poverty, urban**|||||||||
|Headcount index of poverty|25.65|25.65|**25.51**||25.47|25.40|25.39|25.33|
|Poverty gap|7.89|7.82|**7.76**||7.74|7.74|7.73|7.71|
|Squared poverty gap|3.48|3.38|**3.35**||3.34|3.34|3.34|3.33|
|**Poverty, rural**|||||||||
|Headcount index of poverty|58.74|58.40|**57.63**||57.05|56.93|56.18|55.62|
|Poverty gap|21.40|21.02|**20.78**||20.63|20.51|20.33|20.07|
|Squared poverty gap|10.58|10.29|**10.17**||10.09|10.04|9.94|9.80|
||||**Millet**|**and**|**sorghum**||||
|**Poverty, urban**|||||||||
|Headcount index of poverty|25.58|25.55|**25.51**||25.51|25.41|25.41|25.17|
|Poverty gap|7.79|7.77|**7.76**||7.74|7.73|7.70|7.65|
|Squared poverty gap|3.35|3.37|**3.34**||3.33|3.31|3.27|1.45|
|**Poverty, rural**|||||||||
|Headcount index of poverty|58.08|57.90|**57.63**||57.63|57.24|56.90|55.95|
|Poverty gap|21.04|20.91|**20.78**||20.66|20.54|20.32|19.93|
|Squared poverty gap|10.34|10.25|**10.17**||10.09|10.02|9.89|9.67|
|||||**Cor**|**n**||||
|**Poverty, urban**|||||||||
|Headcount index of poverty|25.51|25.51|**25.51**||25.27|25.27|25.27|25.05|
|Poverty gap|7.91|7.83|**7.76**||7.68|7.67|7.66|7.62|
|Squared poverty gap|3.57|3.43|**3.35**||3.32|3.31|3.30|3.29|
|**Poverty, rural**|||||||||
|Headcount index of poverty|57.66|57.64|**57.63**||57.56|57.56|57.43|57.06|
|Poverty gap|20.94|20.86|**20.78**||20.71|20.63|20.48|20.22|
|Squared poverty gap|10.33|10.25|**10.17**||10.10|10.03|9.92|9.75|
||||**All ide**|**ntifie**|**d cereals**||||
|**Poverty, urban**|||||||||
|Headcount index of poverty|25.68|25.76|**25.51**||25.23|25.11|25.11|24.46|
|Poverty gap|7.91|8.08|**7.76**||7.66|7.63|7.57|7.48|
|Squared poverty gap|3.48|3.73|**3.35**||3.31|3.29|3.25|3.20|
|**Poverty, rural**|||||||||
|Headcount index of poverty|58.72|59.04|**57.63**||56.98|56.34|55.11|53.58|
|Poverty gap|21.22|21.83|**20.78**||20.43|20.13|19.60|18.76|
|Squaredpoverty gap|10.45|10.92|**10.17**||9.95|9.76|9.44|8.97|

Source: Authors’ estimation using ELIM 2006.
Table 4: Summary of counterfactual scenarios for transport investments

|Scenario|Country|Road infrastructure||Policies|
|---|---|---|---|---|
|||Baseline<br>|||
|1|Djibouti, Ethiopia,<br>Kenya,Somalia|Speed and road conditions from latest<br>surveys|High <br>lays|Border De-|
||With t|ransport infrastructure investments|||
|2.1|Kenya,<br>Somalia,<br>Ethiopia|Corridor 1: Kismayo, Lamu and Mo-<br>gadishu Corridor with 3093km of re-<br>habilitation or new roads|High <br>lays|Border De-|
|2.2|Djibouti, Ethiopia|Corridor 2: Assab and Djibouti Cor-<br>ridor with 649km of rehabilitation or<br>new roads|High <br>lays|Border De-|
|2.3|Somalia, Djibouti|Corridor 3:<br>Berbera and Djibouti<br>Corridor with 1003km of rehabilita-<br>tion or new roads|High <br>lays|Border De-|
|2.4|Djibouti, Ethiopia,<br>Somalia|Corridor 4: Mogadishu, Berbera and<br>Bossasso Corridor with 2550km of<br>rehabilitation or new roads|High <br>lays|Border De-|
||With|border infrastructure investments|||
|3|Djibouti, Ethiopia,<br>Kenya, Somalia|Corridors 1–4|50% <br>borde<br>borde<br>corrid|reduction in<br>r<br>times<br>at<br>r posts along<br>ors 1–4|

We create counterfactuals using the transport networks from each country described in Section 3. Figure A10 in the appendix shows the new corridors and investments in border posts. We assume a speed of 70 kilometers an hour for the new corridors and reduce the time by half at the border in some of the scenarios. Finally we create counterfactuals combining both road and electricity investments to study the interaction effects from combining infrastructure investments (Table 5).

21