rafmacalaba/datause-encoder-data huggingface dataset:
account. In practice, we assume that the instantaneous impacts apply only to a share of the households:<sup>27</sup> With a partial ability to smooth shocks, the total welfare impact is the sum of (1) the impacts on intertemporal welfare from a reduction in wealth ( ̃ , that can be approximated as a linear shock and is described in Section 3.1. and given by Eq. (22); and (2) a non-marginal instantaneous shock on income that affects basic needs and is measured in this section through Eq. (26). The result can be approximated as:

# **3.3 Social protection, transfers and welfare impacts**

To assess the ex post situation and the final impact on people’s welfare, we need to account for social protection and transfers. To assess the ex post welfare impacts, we need to include in the analysis the population that shares the risks, i.e. the country population for national social schemes and the world population for international support and foreign aid. Since the impact of almost all disasters is negligible at the global level, we will neglect the impact on the global population and only consider the country population.

The benefits from social protection and other government action are threefold:

- Social protection may put a limit to the welfare losses a household can suffer from, i.e. reduce . It is the case for instance if basic services (housing, health, or food) are provided at no

- cost to the poorest or those who have lost everything in the disaster.

- Social protection and other government action may mitigate instantaneous income losses and can help households smooth shocks over time (e.g., through facilitated access to credit and insurance, and other financial inclusion policies), so that fewer households suffer from an instantaneous income loss that is larger than their annual income, i.e. it can increase .

- Social protection may transfer some of the losses, even for those who are affected but have not lost all of their annual income, i.e. it can affect the impact on the net present value of the consumption flux in Eq. (18) (i.e., and ). It is the case if lost income is replaced by cash transfers (or cash-for-work programs) for the most affected, paid by the rest of the population, or in the presence of subsidized insurance. It is also the case if foreign aid is distributed to the population (in kind or in cash).<sup>28</sup>

> 27 It is well documented that the ability to smooth shocks is very different for poor and non-poor households. An option could be to assume that non-poor households are perfectly able to smooth shocks ( while poor households can unable to do so ( 0 . In that case, would be the fraction of non-poor in the country. Alternatively, can be calibrated using financial inclusion data and a criteria on the stock of assets needed to be able to smooth shocks (Carter et al., 2007; World Bank, 2013b).

> 28 See the impact of aid on aggregate consumption in Honduras after disasters in Mechler (2009, Fig. 5).

28
Table B.4: The Impact of Initial Conditions on Employment of Refugees by Years Since Migration

||(3)|(3’)|(6)|
|---|---|---|---|
|_Variable × Years since migration_||||
|Attitudes X 0-2 years|0.0117<sup>_∗∗_</sup>|0.0126|0.0122|
||(0.00533)|(0.00951)|(0.00948)|
|Attitudes X 3-5 years|0.0347<sup>_∗∗∗_</sup>|0.0363<sup>_∗∗∗_</sup>|0.0360<sup>_∗∗∗_</sup>|
||(0.00531)|(0.00928)|(0.00924)|
|Attitudes X 6-10 years|0.0306<sup>_∗∗∗_</sup>|0.0162<sup>_∗_</sup>|0.0159<sup>_∗_</sup>|
||(0.00516)|(0.00841)|(0.00840)|
|Attitudes X 11-15 years|0.0218<sup>_∗∗∗_</sup><br>(0.00548)|0.0148<sup>_∗_</sup><br>(0.00855)|0.0145<sup>_∗_</sup><br>(0.00850)|
|Attitudes X 15+ years|0.0175<sup>_∗∗∗_</sup>|0.00493|0.00438|
||(0.00603)|(0.0104)|(0.0104)|
|Unemployment X 0-2 years|-0.0134<sup>_∗∗_</sup>|-0.0119|-0.0113|
||(0.00642)|(0.0107)|(0.0105)|
|Unemployment X 3-5 years|-0.0268<sup>_∗∗∗_</sup><br>(0.00609)|-0.0212<sup>_∗_</sup><br>(0.0109)|-0.0205<sup>_∗_</sup><br>(0.0108)|
|Unemployment X 6-10 years|-0.0287<sup>_∗∗∗_</sup>|-0.0298<sup>_∗∗∗_</sup>|-0.0288<sup>_∗∗∗_</sup>|
||(0.00588)|(0.0104)|(0.0102)|
|Unemployment X 11-15 years|-0.0279<sup>_∗∗∗_</sup>|-0.0294<sup>_∗∗_</sup>|-0.0280<sup>_∗∗_</sup>|
||(0.00594)|(0.0115)|(0.0113)|
|Unemployment X 15+ years|-0.0362<sup>_∗∗∗_</sup>|-0.0324<sup>_∗∗_</sup>|-0.0309<sup>_∗∗_</sup>|
||(0.00664)|(0.0129)|(0.0127)|
|Network X 0-2 years|-0.00211|-0.00719|-0.00727|
||(0.00265)|(0.00573)|(0.00572)|
|Network X 3-5 years|-0.00781<sup>_∗∗_</sup>|-0.00706|-0.00704|
||(0.00312)|(0.00572)|(0.00571)|
|Network X 6-10 years|0.00546<sup>_∗∗_</sup>|0.00354|0.00350|
||(0.00271)|(0.00518)|(0.00515)|
|Network X 11-15 years|0.00457|0.00677|0.00709|
||(0.00279)|(0.00540)|(0.00532)|
|Network X 15+ years|0.00368|0.00267|0.00365|
||(0.00348)|(0.00620)|(0.00617)|
|Current year FE|Yes|Yes|Yes|
|Country of birth FE|Yes|Yes|Yes|
|Years since migration FE|Yes|Yes|Yes|
|Arrival year FE|Yes|Yes|Yes|
|Canton of arrival FE|Yes|Yes|Yes|
|Canton of arrival X Current year FE|Yes|Yes|Yes|
|Education FE|No|No|Yes|
|Sample|Main|Restricted|Restricted|
|Observations|696135|127737|127737|

Dependent variable is Employment dummy. Network is defined as log (nb of co-nationals+1). All regressions also include age, age squared, gender, marital status. Standard errors are clustered at the level Canton of arrival _×_ Year of arrival. * p < 0.10, ** p < 0.05, *** p < 0.01 60
**<mark>Table 16: Exploring the Presence of Omitted Variable Bias in Base OLS Regression Results Underlying the Mean Decomposition South, Pooled Category of Additional Covariates Integrated to the Base Regression</mark>**

|_VARIABLES_|_Base South_|_Manager characteristics_|_Plot characteristics_|_Geospatial characteristics_|_Crop fixed effects_|_Community characteristics_|
|---|---|---|---|---|---|---|
|Female manager=1|0.0484|-0.0921|0.0479|0.0102|0.106|0.0388|
||(0.132)|(0.201)|(0.132)|(0.133)|(0.128)|(0.138)|
|Age|0.00817|-0.00492|0.00756|0.0107|0.0138|0.0267|
||(0.0201)|(0.0234)|(0.0202)|(0.0202)|(0.0191)|(0.0220)|
|AgeSquared|-7.37e-05|0.000100|-7.37e-05|-9.80e-05|-0.000140|-0.000238|
||(0.000177)|(0.000210)|(0.000178)|(0.000179)|(0.000167)|(0.000193)|
|Years of schooling|0.00339|0.000171|0.00380|0.00355|0.00246|0.00375|
||(0.0117)|(0.0121)|(0.0117)|(0.0121)|(0.0104)|(0.0119)|
|Manager is nonmuslim†|-0.380|-0.201|-0.415|-0.258|-0.550*|-0.659*|
||(0.342)|(0.353)|(0.343)|(0.335)|(0.329)|(0.343)|
|Manager has nonfarm activity†|0.125|0.128|0.126|0.0859|0.141|0.146|
||(0.120)|(0.122)|(0.119)|(0.121)|(0.113)|(0.123)|
|Household female adult size(age 12-60)|-0.0962**|-0.133*|-0.0946**|-0.0907**|-0.0987***|-0.0930**|
||(0.0384)|(0.0716)|(0.0389)|(0.0383)|(0.0367)|(0.0385)|
|Household male adult size(age 12-60)|0.0739*|0.0195|0.0717*|0.0753*|0.0724*|0.0482|
||(0.0431)|(0.0677)|(0.0431)|(0.0433)|(0.0404)|(0.0416)|
|Child dependencyratio|-0.0449|-0.102|-0.0498|-0.0202|-0.0474|0.0261|
||(0.108)|(0.163)|(0.107)|(0.109)|(0.101)|(0.113)|
|Log [GPS-Land Area(HA)]|-0.746***|-0.765***|-0.749***|-0.748***|-0.788***|-0.742***|
||(0.139)|(0.141)|(0.138)|(0.140)|(0.144)|(0.145)|
|Log [GPS-Land Area(HA) Squared]|-0.0230|-0.0235|-0.0227|-0.0237|-0.0325|-0.0199|
||(0.0269)|(0.0273)|(0.0266)|(0.0276)|(0.0274)|(0.0279)|
|Plot distance to household|0.000950|0.000911|0.000900|0.000989|0.00209***|0.000801|
||(0.000661)|(0.000703)|(0.000625)|(0.000655)|(0.000653)|(0.000632)|
|Access to extension†|0.305|0.338|0.266|0.353|0.267|0.304|
||(0.230)|(0.227)|(0.230)|(0.223)|(0.209)|(0.237)|
|Grows cash crop†|-0.0235|-0.0405|0.280|-0.0350|-0.534|0.0935|
||(0.180)|(0.181)|(0.258)|(0.190)|(0.719)|(0.175)|
|Onlyone croponplot†|-0.640***|-0.641***|-0.650***|-0.536***|0.175|-0.600***|
|Robust standard errors inparentheses. ***p|(0.141)<br><0.01,**p<0.05|(0.145)<br>,*p<0.1. State fixed effects n|(0.142)<br>ot displayed.†indicates|(0.145)<br>dummyvariable|(0.170)|(0.147)|

52
# **Table 7: Descriptive OLS Regression of Migration and Welfare Shocks Indices**

|||**OLS: Consi**<br>|**der Migrate=**<br>|**1**<br>||**OLS: Curre**<br>|**nt Migrant=**<br>|**1**<br>|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Food Shock Index|-0.004||||-0.009||||
||(0.005)||||(0.010)||||
|Services Shock Index||0.018***||||0.024***|||
|||(0.005)||||(0.008)|||
|Health Shock Index|||0.006**||||0.014**||
||||(0.003)||||(0.006)||
|Coping Shock Index||||0.019***||||0.000|
|||||(0.004)||||(0.008)|
|Constant|0.032***|0.027***|0.026***|0.034***|0.166***|0.115***|0.144***|0.173***|
||(0.005)|(0.006)|(0.005)|(0.005)|(0.011)|(0.011)|(0.011)|(0.013)|
|Number of observations|12,137|12,137|12,137|12,137|12,137|12,137|12,137|12,137|
|Calendar Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.005|0.009|0.006|0.008|0.004|0.006|0.005|0.004|
|Adjusted R2|0.004|0.008|0.005|0.007|0.003|0.005|0.004|0.003|

note: .01 - ***; .05 - **; .1 - *; 19
# **ROAD INERASTRUCTURE AND** ECONOMIC **DEVELOPMENT:** SOME **DIAGNOSTIC INDICATORS**

# by Cesar Queiroz and Surhid Gautam

_An investigation of the association between per capita income and the magnitude and quality of road infrastructure is carried out. The approach adopted is empirical in that selected variables on existing road networks are directly compared or correlated with a country's income. Cross-section analysis of data from 98 countries, and timeseries analysis of U.S. data since 1950 showed consistent and significant associations between economic development, in terms of per capita gross national product (GNP), and road infrastructure, in terms of per capita length of paved road network. The data show that the per capita stock of road infrastructure in high-income economies is dramatically greater than in middle and low-income economies. For instance, the average density of paved roads (km/million inhabitants) varies from 170 in lowincome economies to 1,660 in middle and 10,110 in high-income economies, the latter being 5,800 percent higher than the low-income group. Road condition also seems to be associated with economic development: the average density of paved roads in good condition (knmmillion inhabitants) varies from 40 in low-income economies to 470 in middle and 8,5.50 in high-income economies. The empirical information presented can be used as indicators of areas of weaknesses or strengths in a country's road infrastructure stock._

# Introduction

Road transport is an important sector of economic activity, especially in developing countries, where it plays an essential role in marketing agricultural products and providing access to health, education and agricultural inputs and extension services. The impact of road transportation in developed regions is also significant. As an example, in the United States it accounts for 15 per cent of the Gross National Product (GNP) and 84 percent of all spending on transportation (1). An efficient road system gives a country a competitive edge in moving goods economically. Conversely, lack of accessibility or poor road conditions are barriers to agriculture, industry and trade, and may hinder the entire development effort. Nevertheless, the contributions of transport to national development may be difficult to quantify in economic terms.

This paper presents information which can be used as indicators of areas of weaknesses or strengths in a country's road infrastructure stock. The approach adopted is empirical in that selected variables on existing road networks are directly compared or correlated with a country's income. As pointed out by Owen (2), comparisons of income and road infrastructure are not meant to imply that a road by itself is capable of developing a country or region, but that it is a necessary element in the development process.
# _Journal: Applied Economics_ .

McKenzie, David, John Gibson, and Steven Stillman. 2010. “How Important is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration.” _Journal of the European Economic Association_ , 8(4): 913–945. McKenzie, David, John Gibson, and Steven Stillman. 2013. “A Land of Milk and Honey with Streets Paved with Gold: Do Emigrants Have Over-Optimistic Expectations about Incomes Abroad?” _Journal of Development Economics_ , 102: 116-127. National Statistics Office. 2011. “2011 Survey on Overseas Filipinos.” <u>www.census.gov.ph/sites/default/files/attachments/hsd/specialrelease/Tab1.pdf</u> OANDA. 2012. “Historical Exchange Rates.” Accessed June 28, 2012.

O’Neil, Kevin. 2004. “Labor Export as Government Policy: The Case of the Philippines.” Migration Information Source. Accessed 29 January, 2013.

Ortega, Javier. 2000. “Pareto-Improving Immigration in an Economy with Equilibrium Unemployment.” _Economic Journal_ , 110(460): 92–112.

Özden, Çağlar, Christopher R. Parsons, Maurice Schiff, and Terrie L. Walmsley . 2011. “Where on Earth is Everybody? The Evolution of Global Bilateral Migration 1960–2000.” _World Bank Economic Review,_ 25(1): 12-56.

Puri, Shivani, and Tineke Ritzema. 1999. “Migrant Worker Remittances, Micro-finance and the Informal Economy: Prospects and Issues.” ILO Working Paper No. 21. Ratha, Dilip, Sanket Mohapatra, and Ani Silwal. 2010. _Migration and Remittances_ _Factbook 2011._ Washington, D.C.: World Bank.

Ray, Sougata, Anup Kumar Sinha, and Shekhar Chaudhuri. 2007. “Making Bangladesh a Leading Manpower Exporter: Chasing a Dream of US $30 Billion Annual 24
# 2 Two debates on the e¤ects of school meals

Proponents of school meals have argued that school meals can foster educational achievement, thereby increasing people’s earnings potential and boosting economic growth. In answering the question of exactly how school meals can foster educational achievement, there have been two major lines of argument. The older argument is that school meals increase educational achievement by improving child nutrition. The second, more recent argument, is that school meals provide incentives for families to send their children to school.

The argument that school meals improve educational achievement through nutrition relies on two links: ...rst, that school meals improve nutrition; and second, that improved nutrition leads to better educational achievement. Most evidence on these two links is non-experimental, though there are some experimental studies on the link between school meals and nutrition.<sup>7</sup> Since child nutrition, child health and schooling re‡ect household preferences in human capital investments in the child, they might be correlated without any direct causal relationship between them. The evidence on the e¤ect of school meals on nutrition is mixed. The counter-argument to the e¤ectiveness of school meals at improving children’s nutritional status is that families adjust to school meals by reducing resources allocated to children who bene...t from the school meals, transferring them to other members of the households.<sup>8</sup> A few economic studies have tried to infer causal relationships between childhood nutrition and educational achievement by using longitudinal data or various exogenous shocks to nutrition as identi...ers.<sup>9</sup> While these studies document a positive relation between the two factors, they primarily document the e¤ects of early childhood nutrition, this is nutrition before a child reaches 18 months. Since school meals take place in schools, they do not reach children under the age of 18 months.

The more recent argument on school participation is a more promising motivation for funding for school meals. Casual observations from international organizations like the World Food Program suggest that subsidized school meals attract a high number of additional children to school.<sup>10</sup> However, it is hard to draw causal inferences or estimate the magnitude of the e¤ect from these observations. Schools that receive subsidies towards school meals tend to be chosen because they are disadvantaged, which makes it hard to compare them to schools that do not receive such subsidies. Similarly, schools that run feeding programs that are paid for by the pupils’parents will

> 7 See for example Jacoby et al. (1996), Grantham-McGregor et al. (1997), Powell et al. (1998) for experimental evidence and Grantham-McGregor et al. (1998), Chandler and Walker (1995), DelRosso (1999) for non-experimental evidence.

> 8 See for example Jacoby (2002), Long (1991) and Powell (1983).

> 9 See for example Glewwe and Jacoby (1995), Glewwe, Jacoby and King (2001), Alderman et al. (1997) and Behrman and Lavy (1997).

> 10 Global School Feeding Report (2002), WFP.

4
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strike were left entirely to union discretion, the required advance notification to the employer cut from five to two days, and strikes in certain strategic sectors were no longer banned. Finally, firing costs were raised. The penalty levied on employers for unjustified dismissal, a category encompassing most separations considered legitimate for economic reasons in the US, increased by four times from 10% to 40,% of the accumulated separation account (FGTS, Fundo de Garantia por Tempo de Serviço).

To date, the most comprehensive work relating these changes to the functioning of the labor market was undertaken by Paes de Barros and Corseuil (2001) who find that separation rates decreased after the Constitutional changes for short employment spells and increased for longer spells, but find inconclusive results on the impacts on flows into informality from the formal sector. However, again, matching models suggest that several of these reforms would lead to a reduction in hiring (job finding) rates as opposed to the separations that Paes de Barros and Corsueil study. By exploiting cross industry variation in proxies related to these reforms, we find suggestive evidence that the Constitutional reform had very strong impacts through this second channel.

We begin by exploring the cyclical behavior of gross labor flows to shed light on the nature of the role of the informal sector in the labor market. We then examine the determinants of the movements in flows that underlie the secular increase in informality across the period.

# **II. Data**

We draw on the Monthly Employment Survey (Pesquisa Mensal de Emprego, hereafter PME<sup>5</sup> ) that conducts extensive monthly household interviews in 6 of the major metropolitan regions<sup>6</sup> and covers roughly 25% of the national labor market. The questionnaire is extensive in its coverage of participation in the labor market, wages, hours worked, etc. that are traditionally found in such employment surveys. The PME is

> 5 For descriptions of the methodology underlying the Pesquisa Mensal de Emprego, see Sedlacek, Barros and Varandas (1990), IBGE (1991) and Oliveira (1999).

> 6 São Paulo, Rio de Janeiro, Belo Horizonte, Porto Alegre, Recife and Salvador.

6
outcomes of interest, including microenterprise profits, labor income, school attendance, household expenditure, test scores, and child anthropometrics.

The first panel data set covers microenterprises in Sri Lanka and comes from de Mel et al. (2008). This data was collected as part of an actual randomized experiment, but we keep only data for firms that were in the control group during the first treatment round. The data set contains information on firms’ profits, assets and many other firm characteristics. It also includes detailed measures of the firm owners’ entrepreneurial ability, risk aversion, and other characteristics that are thought to be correlated with profits. The simulations we perform for this data set are meant to mimic a randomized experiment that administers a treatment aimed at increasing firms’ profits, such as a business training program.

The second data set is a sub-sample of the Mexican employment survey (ENE). Our sub-sample includes heads of household between 20 and 65 years of age who were first interviewed in the second quarter of 2002 and who were re-interviewed in the following four quarters. We only keep individuals who were employed during the baseline survey and imagine a treatment that aims at increasing their income, such as a training program or a nutrition program.

The third data set comes from the Indonesian Family Live Survey (IFLS).<sup>10</sup> We use 1997 data as the baseline and 2000 data as the follow-up, and simulate two different interventions with the IFLS data. First, we keep only children aged 10-16 in 1997 that were in the 6<sup>th</sup> grade and in school. These children then receive a simulated treatment aimed at keeping them in school (in the actual data, about 26 percent have dropped out 3 years later). Second, we create a sample of households and simulate a treatment that increases household expenditure per capita.

The fourth data set comprises child and household data from the LEAPS project in Pakistan (Andrabi et al. 2008). We focus on children aged 8 to 12 at baseline and examine two child outcome variables: math test scores and height z-scores<sup>11</sup> . The simulated treatments increase test scores or z-scores of these children. There is a wide

> 10 See http://www.rand.org/labor/FLS/IFLS/.

> 11 We also have data on English test scores and weight z-scores and performed all simulations with these outcomes. The results are very close to the results using math test scores and height z-scores and are available from the authors upon request.

- 12 -
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