rafmacalaba/datause-encoder-data huggingface dataset:
variable definitions and sources, and Table 12 for countries included in the sample). Our sample period is 1995-2003. As previously noted, individual EU standards in this area often tend to cut across numerous HS product lines, which makes it desirable to aggregate the trade data to a higher level of generality. We therefore retain the distinction between clothes (HS 61-63), fabrics (HS 56-60), and fibers (HS 50-55) that was used above, and we aggregate all data to those categories.

We take the micro-founded gravity model formulation of Anderson and Van Wincoop (2003, 2004) as our starting point: log ( _X ijtk_ ) = log ( _E kjt_ )+ log ( _Yitk_ )− log ( _Ytk_ )+ ( 1 −σ _k_ ) log ( _tijtk_ )− ( 1 −σ _k_ ) log ( _Pjtk_ )− ( 1 −σ _k_ ) log (Π _itk_ )+ ε _ijtk_ (1) Where: _X ijtk_ = Exports from country i to country j in sector k for year t; _Yitk_ = Output of country i in sector k for year t; _E kjt_ = Expenditure of country j in sector k for year t;<sup>_Y_</sup> _t k_<sup>= Aggregate</sup> _k_ (world) output in sector k for year t; σ _k_ = Elasticity of substitution in sector k;<sup>_t_</sup> _ijt_<sup>= Trade costs</sup> _k_ facing exports from country i to country j in sector k for year t; ; ω _it_ = Country i’s output share _k k_ in sector k for year t; ω _jt_ = Country j’s expenditure share in sector k for year t; and ε _ijt_ = Random error term, satisfying the usual assumptions. Inward resistance dependence of exports from i to j on trade costs across all importers.

We modify the bilateral trade costs component of the standard model so as to explicitly include our standards counts, differentiating between the number of EU standards that are harmonized

- 15 -
on the Northern corridor between Mombasa and Nairobi); (3) Infrastructure condition; (4) Trans-shipment at multimodal facilities or at the border when trucks cannot go through and the merchandise needs to be unloaded to a vehicle of another nationality (common in Asia) and (5) Customs convoy requirements which besides slowing traffic can entail wasting days in transit, if convoys are not available daily.

The fact that virtually on every corridor in LDCs, initiation of transit takes as much time, as final clearance is potentially the single biggest anomaly in the current implementation of transit regimes. Transit initiation requires a simplified documentation (manifest) as compared with final clearance. For most shipments, the process should not include inspection or intervention of non-customs agencies. Finally, the Principal of transit is very often a large international forwarder, which can provide customs with appropriate guarantees. Unfortunately:

- Many gateway countries consider transit operations as a minor element of well conceived customs reform programs<sup>31</sup> ;

- Inadequacy of documents (In a recent mission to the Douala Bangui corridor we found seven transit documents, distributed by three different groups of agencies, none of them being an adequate transit manifest); and

- Risk management is absent: there is no incentive for compliant and reputable logistics operators.

# **5.3 The Observed Unpredictability of Lead Time**

Direct empirical evidence on the distribution curve of lead time over a full transit process is far from systematic. It is more easily available for partial transit processes (such as border-crossing or dwell time at container terminals in ports). As part of the preparation of the East Africa trade and Transport facilitation project, the authors were able to get extensive transit lead time data from one logistics operator on the Northern Corridor

> 31 In many coastal countries, transit import is a very small fraction of domestic traffic (often less than 10% in value).

27
**Table 1: Baseline difference between treatment and control districts**

||(1)<br>Control|(2)<br>Treatment|(3)<br>Difference|(4)<br>p-value|
|---|---|---|---|---|
|Number of Girls Class 5 (District total)|11,953|7,082|4,871|0.0051|
|Number of Girls Class 6 (District total)|9,566<br>|4,750<br>|4,816|0.0028|
|Number of Girls Class 7 (District total)|8,959|4,152|4,807|0.0020|
|Number of Girls Class 8 (District total)|8,051|3,507|4,543|0.0018|
|Number of Girls Class 9 (District total)|6,487|2,516|3,971|0.0019|
|Number of Girls Class 10 (District total)|5,211|2,021|3,189|0.0020|
|Number of Boys Class 5 (District total)|14,641|12,670|1,971|0.2920|
|Number of Boys Class 6 (District total)|13,256|9,031|4,225|0.0226|
|Number of Boys Class 7 (District total)|11,564|7,621|3,943|0.0138|
|Number of Boys Class 8 (District total)|10,759|6,540|4,219|0.0074|
|Number of Boys Class 9 (District total)|9,618|5,478|4,140|0.0043|
|Number of Boys Class 10 (District total)|6,844|3,901|2,943|0.0067|
|Drinking Water (1 = Yes)|0.806|0.810|-0.004|0.9124|
|Electricity (1 = Yes)|0.473|0.314|0.159|0.0012|
|Toilets (1 = Yes)|0.491|0.482|0.009|0.7502|
|Boundary Wall (1 = Yes)|0.611|0.514|0.097|0.0008|
|Main Gate (1 = Yes)|0.675|0.577|0.098|0.0021|
|Play Ground (1 = Yes)|0.354|0.379|-0.025|0.3404|
|Library (1 = Yes)|0.167|0.187|-0.020|0.3727|
|Physics Lab (1 = Yes)|0.266|0.252|0.014|0.6320|
|Biology Lab (1 = Yes)|0.191|0.166|0.025|0.3447|
|Chemistry Lab (1 = Yes)|0.210|0.184|0.026|0.3566|
|Home Economics Lab (1 = Yes)|0.038|0.034|0.003|0.7323|
|Computer Lab Morning (1 = Yes)|0.046|0.036|0.010|0.1997|
|Computer Lab Evening (1 = Yes)|0.019|0.009|0.010|0.0440|
|Observations|19|15|||

Notes: Treatment districts have literacy rates < 40 percent. Control districts have literacy rates > 40 percent. Column 3 is calculated by subtracting column 2 from column 1. Enrollment figures are district totals for each grade and gender. The indicator variables describing school infrastructure and facilities are district averages representing the fraction of all public schools with the facility/infrastructure available. For instance, 49 percent of all schools in all control districts have toilets and 48 percent of all schools in all treatment districts have toilets. In contrast 61 percent of control district schools have a boundary wall while only 51 percent of treatment district schools do.

35
# **References**

- Bane, M. and D.Ellwood. 1986. ―Slipping Into and Out of Poverty: the Dynamics of Spells‖, _Journal of Human Resources_ , 21(1): 1-23.

- Barros, Ricardo Paes de, Carvalho, Mirela de, Franco, Samuel, and Mendoça, Rosane. 2006. ―Uma Análise das Principais Causas da Queda Recente na Desigualdade de Renda Brasileira.‖ In: _Revista Econômica_ . Volume 8, número 1, p.117-147. Universidade Federal Fluminense. Rio de Janeiro. Available in http://www.uff.br/revistaeconomica/V8N1/RICARDO.PDF

- Bourguignon, François and Francisco H. G. Ferreira. 2005.  ―Decomposing Changes in the Distribution of Household Incomes: Methodological Aspects‖. In François Bourguignon, Francisco H.G. Ferreira and Nora Lustig (eds), The Microeconomics of Income Distribution Dynamics in East Asia and Latin <u>America, pp. 17‐46 Washington, D.C.: The World Bank.</u>

- Bourguignon, François, Ferreira, Francisco H. G. and Leite Phillippe G. 2008. Beyond Oaxaca‐Blinder : Accounting for Differences in Households Income Distributions _. Journal of Economic Inequality_ , 6 (2): 117‐148

- Chen and Ravallion. 2008. ―The Developing World is Poorer Than We Thought, But No Less Successful in the Fight Against Poverty‖ World Bank Policy Research Working Paper 4703.

- Datt Gaurav and Martin Ravallion. 1992. Growth and Redistribution Components of Changes in Poverty Measures: A Decomposition with Applications to Brazil and India in the 1980s. _Journal of Development Economics_ , 38: 275‐296.

- Davis, B. et al. 2010. ―A Cross-Country Comparison of Rural Income Generating Activities‖ _World Development_ , 38(1): 48-63

- Essama-Nssah, B. 2012. ―Identification of Sources of Variation in Poverty Outcomes‖, World Bank Policy Research Working Papers, No. 5954.

- Juhn Chinhui, Murphy Kevin M. and Brooks Pierce. 1993. Wage Inequality and the Rise of Returns to Skill. _Journal of Political Economy_ , 101(3): 410‐442.

- Jaramillo, Miguel and Jaime Saavedra. 2011. ―Inequality in Post-Structural Reform Peru: The Role of Market Forces and Public Policy‖ in Luis Felipe Lopez-Calva and Nora Lustig (eds), <u>Declining Inequality in Latin America: A Decade of Progress? pp. 218-244 Baltimore: Brookings Institution</u> Press.

- Jenkins, S. 2011. _Changing Fortunes: Income Mobility and Poverty Dynamics in Britain._ New York: Oxford University Press Inc.

- Kolenikov Stanislav and Shorrocks Anthony. 2005. A Decomposition Analysis of Regional Poverty in Russia. _Review of Development Economics_ , 9(1): 25‐46.

- Lanjouw, Luoto, and David McKenzie. 2011. ―Using Repeated Cross-Sections to Explore Movements into and out of Poverty‖ World Bank Policy Research Working Paper No. 5550.

- McFadden, Daniel. 1974a. ―Conditional logit analysis of qualitative choice behavior,‖ in P. Zarembka (ed.) Frontiers in Econometrics, New York: Academic Press.

20
different from most of the available estimates in the literature, as we are interested in how household economic status vary with the education level of the father. Given that IFLS is a panel data set, we have household level income and consumption expenditure information for multiple rounds. To reduce the measurement error, we focus on the household consumption expenditure as a measure of economic status; it is widely-noted that expenditure suffers much less measurement error from transitory shocks because of consumption smoothing and thus is likely to be a more reliable indicator of a household’s economic status (see, for example, the discussion in Deaton (1997)).

The availability of 5 rounds of panel data allows us to provide estimates of returns to education for both the parental and children’s generations: we rely on the first three rounds (1993, 1997, 2000) for the father’s generation, and the last two rounds (2007 and 2014) for the children’s generation. For parents, we use the average of the 1993, 1997, and 2000 rounds of household expenditure (deflated by average rice price) data as a measure of household permanent income and estimate the income equation (equation (1) in the theoretical model in section (2)). The estimating equation for the fathers is given as follows: For the children, we take the average of the household expenditure in 2007 and 2014. The estimates are reported in Table 5. For parent’s generation, the intercepts are not different across rural and urban areas which can be interpreted as evidence that the rural-urban divide does not matter for the subset of households where the father has no schooling (about 5 percent of our sample). In contrast, the interaction of the rural dummy with the father’s education is negative and statistically significant at the 1 percent level. The evidence thus strongly suggests that returns to education at the household level was substantially lower in the rural areas. In terms of the parameters of the income equation (1) we have the following binary relations implied by the estimates in Table 5: _Y_<sup>ˆ</sup> 0<sup>_pr_</sup> = _Y_<sup>ˆ</sup> 0<sup>_pu_</sup> and ˆ ˆ _R_<sup>_pr_</sup> _< R_<sup>_pu_</sup> . The evidence on children’s generation also shows that the returns to education at the household level is substantially lower in the rural areas.

The lower returns to education in the rural areas affect intergenerational persistence in 22
Death scares: How potential work-migrants infer mortality rates from migrant deaths Maheshwor Shrestha<sup>_∗_</sup>

**JEL Codes** : F22, D83, O15, O12

**Keywords** : Migration, The law of small numbers, Nepal

> _∗_ The World Bank. Email: `mshrestha1@worldbank.org` . I am extremely grateful to my PhD advisers Esther Duflo, Abhijit Banerjee, and Benjamin Olken for their advice and guidance throughout this project. I also thank David Autor, Jie Bai, Seema Jayachandran, Tavneet Suri, Swarnim Wagle, and participants of the development and applied-micro lunches at MIT for helpful discussions and feedback. I am grateful to the Department of Foreign Employment and Foreign Employment Promotion Board in Nepal for providing me access to their database. This paper was written as part of the dissertation submitted to the Department of Economics at MIT.
Policy Research Working Paper 10309

# **Abstract**

This paper assesses the reliability and validity of cognitive and socioemotional skills measures and investigates the correlation between schooling, skills acquisition, and labor earnings. The primary data from Pakistan incorporates two innovations related to measurement and sampling. On measurement, the paper develops and implements a battery of instruments intended to capture cognitive and socioemotional skills among young adults. On sampling, the paper uses a panel that follows respondents from their original rural locations in 2003 to their residences in 2018, a period over which 38 percent of the respondents left their native villages. In terms of their validity and reliability, our skills measures compare favorably to previous measurement attempts in low- and middle-income countries. The following are documented in the data: (a) more years of schooling are correlated with higher cognitive and socioemotional skills; (b) labor earnings are correlated with cognitive and socioemotional skills as well as years of schooling; and (c) the earnings-skills correlations depend on respondents’ migration status. The magnitudes of the correlations between schooling and skills on the one hand and earnings and skills on the other are consistent with a widespread concern that such skills are underproduced in the schooling system.

This paper is a product of the Development Research Group, Development Economics. 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 dfilmer@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
Figure A11: Perception of economic conditions <!-- Start of picture text --> 2017<br>2016<br>2015<br>2017<br>2016<br>2015<br>0 20 40 60 80 100<br>Percent<br>Future onths)<br>economic<br>conditions(3m<br>Present<br>economic conditions<br><!-- End of picture text --> Very good/Much better Fairly good/Better Neither good nor bad/Same Fairly bad/Worse Very bad/Much worse Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure A12: Perception of living conditions <!-- Start of picture text --> 2017<br>2016<br>2015<br>2017<br>2016<br>2015<br>0 20 40 60 80 100<br>Percent<br>months)<br>Future living conditions (3<br>conditions<br>Present living<br><!-- End of picture text --> <!-- Start of picture text --> Very good/Much better<br>Fairly good/Better<br>Neither good nor bad/The same<br>Fairly bad/Worse<br>Very bad/Much worse<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure A13: Feeling in control over own life <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2015 2016 2017<br>No Control Some Control A Great Deal of Control Complete Control<br>Percent<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure A14: Satisfaction with life 23
Kakwani, N. C. (2004). Methods in measuring poverty matter: an Indian story. _One Pager Number 2_ , International Poverty Center, Brasil.

Kakwani, N.C., S. Khandker and H.H. Son (2004). Pro-poor growth: concepts and measurement with country case studies. _Working Paper Number 2004-1_ , International Poverty Center, Brasil.

Kakwani, N.C. and E.M. Pernia (2000). What is pro-poor growth? _Asian Development Review_ , vol. 18, pp. 1-16.

Kraay, Art. (2004). When is growth pro-poor? Cross-country evidence. _Policy Research Paper No. 3225._ Washington D.C.: The World Bank.

Lambert, P.J. (1984). Non-equiproportionate income growth, inequality and the income tax. _Public Finance/Finances Publiques_ , vol. 39, pp. 104-118 Osmani, S. (2005). Defining pro-poor growth. _One Pager Number 9,_ International Poverty Center, Brasil.

Ravallion, M. (2004). Defining pro-poor growth: a response to Kakwani. _One Pager Number 4,_ International Poverty Center, Brasil.

Ravallion, M. and S. Chen (2003). Measuring pro-poor growth. _Economics Letters_ , vol. 78, pp. 93-99.

Shorrocks, A. F. (1998). Deprivation profiles and deprivation indices. Pages 250-267 in Jenkins, S., Kaptein, S. A. and van Praag, B. (eds) _The Distribution of Welfare and Household Production: International Perspectives._ London: Cambridge University Press.

Shorrocks, A.F. (1999). Decomposition procedures for distributional analysis: a unified framework based on the Shapley value. _Mimeo,_ University of Essex.

Son, H.H. (2004). A note on pro-poor growth. _Economics Letters_ , vol. 82, pp. 307-314. Son, H.H. (2006). Assessing the pro-poorness of government fiscal policy in Thailand. _Working Paper No. 15_ , United Nations Development Program.

Watts, H. (1968). An economic definition of poverty. In D.P. Moynihan (ed.) _On Understanding Poverty: Perspectives from the Social Sciences._ New York: Basic Books 30
1983). This may be due to the social support provided by the group, or peer pressure, or some combination, and the question is whether such a mechanism can or should be applied to the sexual relationship. In the RESPECT study, we chose the clearest and most transparent method: paying each individual separately. Data analysis indicates that many of the individuals shared their rewards by buying household goods, but many others (both men and women) kept the funds to themselves and did not even share with their spouse.

# **_Skill-building to Aid Behavioral Change_**

In order to ensure that participants had the necessary tools to act on the incentives for sexual behavior change, we chose to offer monthly (sex segregated) group skillbuilding sessions (in addition to individual pre- and post-test counseling each four months). For this purpose we trained local facilitators to deliver a modified version of the Stepping Stones curriculum (Jewkes et al., 2008), with sessions on topics such as sexual health, condom use, relationship skills, decision-making skills, women‘s roles and gender-based violence. The last of these was particularly important due to the concern that women testing STI positive may be subject to violence; for this purpose we also carefully monitored for violence events. Fortunately, reported violence rates during the intervention were consistently lower than pre-intervention levels (Krishnan et al., 2011). We chose to make these group sessions voluntarily, and 20-30% of enrollees attended each month.

# **_Self-reported Degree of Behavioral Change Motivation from Incentives_**

To judge the overall perceived effectiveness of the incentive mechanisms used in RESPECT, table 4 presents enrollees‘ responses to direct questions regarding how much the incentives motivated sexual behavior change. At both 8-month and 12-month time points the low-value and high-value respondents were asked ―how much the cash rewards motivated sexual behavior change.‖ After 8-months, 36% of low-value arm (Tsh 10,000) respondents stated ―very much‖ and 31% stated ―none.‖ In the high-value arm (Tsh 20,000), the percent stating ―very much‖ was substantially higher at 59%, with only 21% reporting ―none.‖ The control arm respondents were also queried, but as part of this 21
# **I.  Introduction**

The link between the composition of public expenditure and growth in developing countries has been the subject of renewed attention in recent years. A number of studies have documented, in particular, that episodes of fiscal adjustment have often been associated with large cuts in public investment; such cuts have translated into adverse effects on growth and poverty reduction. The ongoing debate on “fiscal space” has also led to renewed thinking on this issue. Various observers have pointed out that, in this context, creating fiscal space for public investment in infrastructure, health, or education, for instance, involves dynamic trade-offs; to evaluate these trade-offs requires a dynamic framework that accounts explicitly for the various channels through which the composition of government expenditure and public capital affects the economy.<sup>1</sup> This paper contributes to the existing literature by proposing a dynamic, small-scale endogenous growth model to evaluate the effect of the composition of public expenditure on growth in low-income developing countries. A key feature of the model is that government spending is disaggregated into various components, including maintenance, security, and investment in education, health, and core infrastructure. In addition, it also accounts for the externalities associated with infrastructure, in terms of its impact on education and health (see Agénor and Moreno-Dodson (2007)). Deininger and Okidi (2003), for instance, in a study of Uganda, found that the benefit of education and health care for growth depends on complementary investments in electricity and other infrastructure.<sup>2</sup> In addition, the model accounts for improved political stability and reduction in violence. Political instability affects private sector confidence. As documented by Poirson (1998), for instance, using data on economic security

> 1Swaroop (1999) emphasized the need to strengthen the macro dimension in the analysis of World Bank public expenditure reviews (PERs). 2There is also some evidence suggesting that education may condition the benefits (or efficiency) of infrastructure. As noted by van de Walle (1995), for instance, investment in education may enhance the marginal impact of irrigation projects.

3