rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: difference-in-difference estimates with face-to-face (F2F) index

||Monthly S|uspensions|Monthly|Closures|Weekly W|ork Hours|Monthl|y Income|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**[Panel A] Aggregate e**|**stimated ef**|**ects**|||||||
|Post Covid x F2F Index|2.831***|2.831***|0.304***|0.304***|-0.882***|-0.855***|-52.42|-83.02*|
||(0.616)|(0.616)|(0.0622)|(0.0622)|(0.160)|(0.157)|(47.81)|(45.17)|
|**[Panel B] Efects by m**|**onth**||||||||
|Feb x F2F Index|1.718**|1.718**|-0.0404**|-0.0404**|-0.684|-0.643|22.34|6.209|
||(0.683)|(0.683)|(0.0200)|(0.0200)|(0.417)|(0.414)|(108.7)|(102.8)|
|Mar x F2F Index|1.992***|1.992***|0.131***|0.131***|-0.567**|-0.533**|-69.19|-102.9|
||(0.592)|(0.592)|(0.0422)|(0.0422)|(0.251)|(0.253)|(89.79)|(87.18)|
|Apr x F2F Index|9.765***|9.765***|0.0951*|0.0951*|-1.765***|-1.739***|-0.876|-27.97|
||(1.359)|(1.359)|(0.0500)|(0.0500)|(0.414)|(0.414)|(81.81)|(80.48)|
|May x F2F Index|2.847***|2.847***|1.655***|1.655***|-1.240***|-1.274***|-165.6*|-198.5**|
||(0.743)|(0.743)|(0.399)|(0.399)|(0.331)|(0.328)|(98.91)|(93.61)|
|Jun x F2F Index|1.075*|1.075*|0.122***|0.122***|-0.641**|-0.638**|-21.81|-84.83|
||(0.589)|(0.589)|(0.0457)|(0.0457)|(0.256)|(0.252)|(75.14)|(72.32)|
|Jul x F2F Index|0.775|0.775|0.173**|0.173**|-0.366|-0.372|-48.58|-57.49|
||(0.478)|(0.478)|(0.0820)|(0.0820)|(0.246)|(0.242)|(78.87)|(74.75)|
|Aug x F2F Index|1.653**|1.653**|-0.0203|-0.0203|-0.438|-0.487|35.89|-0.937|
||(0.643)|(0.643)|(0.0657)|(0.0657)|(0.315)|(0.309)|(101.8)|(97.52)|
|Sep x F2F Index|-|-|-|-|-0.650**|-0.667**|-112.1|-161.1**|
||-|-|-|-|(0.271)|(0.272)|(70.48)|(68.83)|
|Observations|330,330|330,330|341,165|341,165|372,204|372,203|371,814|371,813|
|Observation level|Commune|Commune|Commune|Commune|Individual|Individual|Individual|Individual|
|Individual controls|-|-|-|-|No|Yes|No|Yes|
|Month fxed efects|No|Yes|No|Yes|No|Yes|No|Yes|
|Commune fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Note: Panel A presents the difference-in-difference estimates of the aggregate effect of COVID-19 on household business performance, following Equation 3. Panel B presents the estimated effect for each month after January 2020, following Equation 4. Data on household business suspension and closure filings are obtained from the Household Business Tax Census (HBTC). Data on individual work hours and income are obtained from the Vietnamese Labor Force Surveys. Data on the face-to-face (F2F) index is derived from Avdiu and Nayyar (2020), constructed using the 2019 HBTC industrial composition in each commune. Monthly income is detrended from pre-COVID linear trend and residualized from individual observables including age, gender, educational level, and occupation level. Standard errors are clustered at the commune level and presented in parentheses: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

20
# **3.2 The economic Impact of civil strife**

The prediction that civil strife would, by imposing a taoc _t_ on all assets in the commnunity, reduce incentives for investmnent and enterprise startups, is testable with available data. To do so, let **_Aj,_** be the asset stock held by householdj (in community i) in period _t._ Then households' investment, **Aj4,** - **_Aji,s,,_ cam** be regressed against a set of exogenous variables and initial conditions similar to equation (3) above.

_(6) A1u -Ap,, =fiIjy,.,_ + _VZI_ + _6_ Sj, , §,, where _Al,_ - _Ai,-,_ is the net investment between 1 992 and 1999 and X is a set of initial household level conditions including the level of education and age of the householct head, **Z** is a set of time invariant community-level characteristics, especially urban and regional dummies, and Su., is a variable indicating the presence and intensity of civil strife at the community level. Use of the community-level variable on civil strife is appropriate because, even in if they are not directly affected, presence of civil conflict in a given community will affect households' behavior and lead them to adopt precautionary strategies. The fact that, for any given household, the amount of civil strife is exogenous allows us to overcome the endogeneity problem that affect attempts to recover the ingact of civil unrest from more aggregate data. While the range of right hand variables is constrained to those that could be obtained retrospectively in the 1999/2000 survey, the ability to include almost 10,0)0 households more than cornpensates for this shortcoming.

If civil war has led to significant changes in the compositioni of economic activity, one might be interested not only in the total amount of investment but also the sector in which such investment was undertaken. To do so, we use a probit regression with an indicator of non-agricultural enterprise startups as the dependent variable (7) _ESj,,_ **_=lXj,,,_** _+_ **_yZ'_** _+_ **_6St,+_** _qgj,_ where _ES,_ is a dummy variable that takes the value of one if, during the 1992-2000 period, household i started a new non-agricultural enterprise and zero otherwise. The remainder of the right hand side 18
would be consistent with predictions of increased markups for SNAP-ineligible products.<sup>55</sup> Therefore, there are at least two possible reasons for the large pass-through elasticity for SNAP-ineligible products. First, because SNAP benefits lead to a rise in disposable income for SNAP consumers, demand for SNAP-ineligible products could increase. Second, because of the multi-product nature of all grocery stores in our data, food and non-food products can become complementary due to shopping costs.<sup>56</sup> Multi-product retailers with asymmetric market power over food and non-food can leverage this complementarity to raise markups on non-food products when SNAP benefits increase.

# **6 Conclusion**

In this paper, we find evidence that large increases in government transfer payments in the form of electronic benefits for food increase retail prices in the grocery sector. Applying an instrumental-variable approach that uses state-specific program adjustments, we estimate that a 1% increase in SNAP benefits per population raises grocery store prices by about 0.08%. Using this reduced-form estimate as a sufficient statistic in a theoretical partial-equilibrium framework, we estimate that a marginal dollar of SNAP benefits increases producer surplus by about $ 0.5, increases SNAP consumer surplus by about $ 0.7, and decreases non-SNAP consumer surplus by about $ 0.4, or about $ 0.05 per non-SNAP consumer. The price response is larger in regions with higher proportions of SNAP participants and higher market concentration. Our estimates of MPCF out of SNAP are around 0.44 and consistent with those from recent literature.

These findings have several policy implications. First, if the objective of SNAP is to guarantee a floor of _real_ spending power on food, federal maximum benefits should be increased by about 7% to account for the price response.<sup>57</sup> Alternatively, this implies that programs that provide in-kind transfers, such as The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), would enable beneficiaries to receive the full benefit. However, this advantage must be weighed against the disadvantages of providing in-kind transfers, which could include shifting the price effect onto the government, limiting consumer choice, and leakages. Second, supply-side interventions in neighborhoods with

> 55Theoretically, Cowan (2004) shows that under imperfect competition such as monopoly and symmetric Cournot oligopoly, the quantity response can be negative for an increase in demand, depending on the curvature of demand.

> 56Thomassen et al. (2017) provide empirical evidence by applying a novel demand model to UK consumer data and find that product categories are complements due to shopping costs.

> 57Suppose an experiment raising benefits per population by 1%. This raises the price of food by 0.0724%, but since this cuts into the spending power of 100% of benefits, the real spending power of the marginal benefit gets reduced by 7.24%. The same logic applies to the case of extrapolating to inframarginal changes.

33
Overall, based on 596 observations or data points, the returns to higher education are greater than the returns to primary education in 83 percent of cases. The returns to higher education are greater than the returns to secondary education in 97 percent of cases. And, in terms of latest year data for 104 economies, the returns to higher education are greater than the returns to primary education in 73 percent of cases, and the returns to higher education are greater than the returns to secondary education in 95 percent of cases. However, the returns to secondary schooling are greater than the returns to primary schooling only 33 percent of the time. While in some countries the pattern of increase is not monotonic with the level of education, a generally _mixed_ pattern of returns is observed across most countries (Table 1).

Table 1: Education-Earnings Relationship

||% of countries|
|---|---|
|Higher > Primary|73.3|
|Higher > Secondary|95.2|
|Secondary > Primary|32.7|

|Computed from Montenegro and Patrinos (2014)|
|---|

The very sharp declines, and low overall rate, for secondary education are a bit misleading. There is an _option value_ to completing secondary education – and primary as well. The _option value_ is the value of obtaining further education and the rewards accompanying it (Weisbrod 1962). It is distinct from the conventionally accepted educational benefit of standard expected wage gain. In the case of secondary education, there is option value arising from the fact that completion of secondary education provides a benefit other than wage returns: the option to invest in post-secondary levels of education, which are not available to those who do not complete secondary education. Estimates from Bangladesh suggest significant returns to the option value (Shafiq 2007). Thus, we must keep that in mind. Focus should be on the returns to higher education.

# **Causes of the changed pattern**

The returns to schooling have not declined very much over time, while the supply of schooling has increased. This suggests that the supply of schooling is not keeping up with demand. There 13
# **2 Survey Procedure**

Two sets of surveys were used, the HBS and the LFS. The HBS is carried out about every seven years, while the LFS is conducted every year. The HBS is used to determine total family expenditure, which we convert into expenditure per capita expressed in USD. The Moroccan HBS includes two different surveys: the 2000–2001 National Survey on Consumption and Expenditure (NSCE) and the 2006–2007 National Living Standards Survey (NLSS), which both measure household expenditure and are representative of the national and regional context and of urban and rural areas.

The NSCE covers 15,000 households and was administered between November 2000 and October 2000. The 2006–2007 NLSS, administered between December 2006 and November 2007, was smaller and covered only 7,200 households. The two questionnaires share many common modules, of which the most relevant address sociodemographic characteristics, habitat, expenditures, durable goods, education, health, and employment. The NSCE also has a module on transfers, subjective indicators of well-being, nutrition, and a module administered to the community to measure access to services.

Both surveys have the same structure. For urban areas, strata include the region, province, city size (large, medium, and small) and five types of housing. For rural areas, the strata are regions and provinces. The common points between the two surveys, however, end here. All the surveys conducted up to 2005, including the NSCE, have a master sample frame that is derived from the 1994 population census. The surveys conducted after 2005 are, instead, based on the 2004 census.

The 2001 NCSE follows a two-stage sampling process, while the 2007 NLSS follows a three-stage sampling process. In the first stage of NSCE, 1.250 Primary Sampling Units (PSUs) of approximately 300 households each were extracted from the 1994 population census. In the second stage, a dozen households per PSU were extracted randomly to constitute the final sample. In the first stage of NLSS, 1,848 PSUs of approximately 600 households each were selected from the 2004 population census. The second stage subdivided each PSU into twelve Secondary Sample Units (SSUs), representing about 50 households each, and then randomly selected six of the twelve SSUs from each PSU. In the third stage, a constant number of households was selected randomly from each SSU.

The LFS, which was first launched in 1976, follows a sampling process that is similar to that of the 2007 NLSS and shares a number of questions with the two censuses on which the population frame is based. The LFS questionnaires share only a few questions with the other two censuses, relating to sociodemographic characteristics, habitat, education, health and employment, but, significantly, do not share any information about expenditure.

4
where _αi_ 1 + _αi_ 2 = 1. We assume that donors have conflicting preferences in how they weigh public goods across countries: Note that we can capture a situation of no conflict by setting _α_ 1 _j_ = 0 _._ 5. Otherwise, donor preferences are in conflict. Without any loss of generality we assume that _α_ 11 _≥_ 0 _._ 5. Asymmetry in their preferences is largest if _α_ 11 = 1. In that case, each donor cares only for one recipient country, with one donor for each recipient. Assumption 1 can easily be justified. For example, the colonial history of many donors can explain such conflicting biases, as donors may favor their former colonies. Empirical studies on aid allocation have indeed shown that colonial ties matter (e.g. Alesina and Dollar, 2000; Rajan and Subramanian, 2008).

Each of the two recipient governments _j_ = 1 _,_ 2 chooses its policy _pj ∈_ [0 _,_ 1]. Policy _pj_ affects the allocation of recipient _j_ ’s resources _rj_ and total receipts of foreign aid _Aj_ to the production of a public good _Gj_ . Total resources for recipient _j_ equal _Rj_ = _rj_ + _Aj_ . The public good equals We think of _pj_ as ‘control of corruption.’ The larger _pj_ , the more of a country’s resources are used for the production of a public good. We assume that _f_ is a strictly positive and concave function, where _f_<sup>_′_</sup> (0) _>_ 1 and _f_<sup>_′_</sup> ( _rj_ ) _<_ 1. These assumptions assure an interior solution to the recipient’s optimization problem. Note that _Gj_ is a pure public good as aid by either player increases _Gj_ .

Recipient _j_ uses the fraction _pj_ of all resources _Rj_ to produce the public good, _Gj_ , and the other part of the resources, (1 _− pj_ ) _Rj_ , are public funds used for _j_ ’s private gain, i.e. lucrative salaries, corruption, embezzlement, etc. Assume that the government cares for both the public and the private good, where recipient _j_ ’s utility is given by A recipient government maximizes _uj_ by choosing _pj_ . Note that _uj_ is the objective function of the government in an aid recipient country, which is different from the objective function of the general public. We assume that the general public in country _j_ cares about the public good _Gj_ . It follows that donors care about the well-being of the general public.

With _Aj_ = 0, the recipient _j_ ’s first-order condition equals 11
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reasons—and may be a very acceptable method for adults if they self-report information, based on our research findings—their ability to provide reliable child labor statistics needs to be further considered. These few additional screening questions in the detailed questionnaire—to clarify the meaning of the concept of work—come at very little cost for survey field work.

Our results are also an implicit plea for additional, similar survey experiments, as they leave important questions unanswered. Whereas the experiments used in this paper (especially the short vs. detailed questionnaire one) focus on existing survey instruments, future work may want to explore the effects that newly designed instruments would have. In particular, combining survey instruments with direct observation or diary keeping could be especially useful to find out what approach works best, and to help define a “gold standard,” on which there is currently no agreement. Another fruitful way forward would be to implement survey experiments to investigate issues related to the System of National Accounts categorization. The experiment used in this paper, while not well suited to address these issues, indicates that how respondents classify children’s work may not always be clear. Finally, a more precise way to identify a “pure” proxy effect would involve comparing data on the same person from proxy and self-response. Although this type of experiment could not be implemented in the setting available to us, this is certainly something worth considering for future work under different conditions.

23
Table 7: Patient’s Knowledge of Alternative Care Availability (percentages)

|||TY|PE||L|OCATION<br>Other|||
|---|---|---|---|---|---|---|---|---|
||Public|Private|Religious|NGO|Capital|Urban|Rural|Total|
|Total competition|33.7|63.8|35.1|77.6|64.8|38.6|30.4|38.9|
|Formal sector|23.3|53.3|21.6|69.4|59.9|32.5|16.2|28.1|
|Public clinics|12.2|29.8|14.4|52.1|38.3|18.2|7.7|15.9|
|Private clinics|4.9|21.5|3.9|20.8|22.6|5.9|2.7|7.3|
|Religious and NGO|1.2|6.1|1.0|8.3|7.4|0.9|0.6|2.0|
|Informal sector|15.9|30.1|20.0|26.5|27.8|9.1|18.6|18.7|
|Docteur Choukous|9.7|26.3|9.2|10.4|21.7|4.7|10.6|11.7|
|Traditional healers|9.2|11.4|14.1|16.7|13.0|5.3|11.3|10.5|

Source: Chad 2004 QSDS and authors’ calculations.

# 6  Measuring and Assessing Bypassing

Having examined in the previous sections the characteristics of health service provision as well as patients’ characteristics we now examine how these affect choices of health facilities. The focus is on individuals’ decision to bypass available health facilities.

## **6.1. Measuring Bypassing**

Previous work on bypassing, e.g. Akin and Hutchinson (1999) or Leonard et al. (2002), have mostly relied on a theoretical measure of bypassing. In contrast to this approach, this paper measures bypassing using data collected from patients about their knowledge of existing alternative providers in their neighborhood. Therefore, a patient is said to have engaged in bypassing if and only if (1) she knows about other available providers, and (2) the facility she attended is not the closest from her household within the pool of providers available.

Akin and Hutchinson (1999) study bypassing in one district in Sri Lanka. They match a facility dataset which contains the complete set of facilities in the district with a sample of households surveyed in the same district. Both household and facility geographic information is available, they can therefore compute for each household the distance between the household and all facilities in the district. In the household survey, individuals indicate the facility they visit during their last illness spell. Bypassing is then assumed if the facility visited is the not the nearest to the 12
# **8. Tables**

## Table 1

Levinsohn-Petrin productivity estimation

||Coefficients|Std. Err.|
|---|---|---|
|White collar|0,148***|0,026|
|Blue collar hours|0,234***|0,036|
|Materials|0,314***|0,053|
|Machinery capital|0,120*|0,063|
|Electricity|0,200***|0,088|
|Number of observations|5903||
|Number of establishments|685||
|Wald test of constant returns t|o scale:<br>Chi2 =1,51(p|= 0,22)|
|Note: Dependent variable is gr<br>*significant at10%;**signific|oss output. All variables a<br>ant at 5%;***significant a|re in logs<br>t1%|

|Table 2<br>Demand shock estimation<br>Three-stage least squares re|gression|
|---|---|
||Obs<br>Parameters|
|Demand equation|5903<br>9|
|Supply equation|5903<br>16|
||**Coef,**<br>**Std. Err.**|
|Demand equation||
|Price|-1,156*<br>0,619|
|Supply equation||
|Price|0,863*<br>0,520|
|Total factor productivity|0,006*<br>0,003|
|Wage Index|-0,376**<br>0,166|
|Note: Dependent variable is<br>Endogenous variables: gross<br>Exogenous variables not rep<br>digit ISIC industry dummies (<br>* significant at 10%;** signifi|gross output.<br>output and price.<br>orted: year dummies (supply) and 3<br>demand)<br>cant at 5%;*** significant at 1%|

20
Policy Research Working Paper 7404

# **Abstract**

This study uses a randomized experiment to evaluate the impacts of the training and internship program piloted in Nairobi, Mombasa and Kisumu counties by the Kenya Private Sector Alliance and the Government of Kenya with support from the World Bank’s Kenya Youth Empowerment Project. The program provided three months of classroom-based technical training coupled with three months of internships in private firms to vulnerable youths between ages 15 and 29 years, with vulnerable being defined as those out of school and/or with no permanent job. The analysis in this paper is based on survey data collected before the program started (July 2012) and 15 months after the program ended (July 2014). The results of the impact evaluation show that the program has been successful in placing youths in paid jobs and has contributed to an increase of 15 percent in current employment among male participants. The evaluation also found that the program has had positive effects on wage earnings, especially those of females and among older males, with wages increasing by about K Sh 5,000 for males and by K Sh 7,500 for females. With a total unit cost of K Sh 97,000 per beneficiary, an estimated K Sh 6,768 monthly wage for males and K Sh 9,623 monthly wage for females, the program’s benefits exceeded the costs for males and females. The program also encouraged youths to participate in either (certified) skills training or an internship program, - and helped to increase the probability of participants’ open ing a bank account and accumulating savings (for females).

This paper is a product of the Social Protection and Labor Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mhonorati@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