rafmacalaba/datause-encoder-data huggingface dataset:
assumption holds, the expected change in income in the absence of the program should be the same for participants and their comparison group.

Under _a priori_ plausible assumptions about the foregone income of participants based on evidence for the _Trabajar_ program, we find that the distribution of income shocks is reasonably well balanced between the sample of Jefes participants and the applicants not yet receiving the program. Table 4 gives the distributions of the income shocks for household and individual incomes for various assumptions. We see that the expected change of income in the absence of the program is balanced across the two groups when one assumes a foregone income of about one-third to one half, which is consistent with the estimates by Jalan and Ravallion (2003) for the _Trabajar_ program that preceded _Jefes_ . However, the tighter work requirements on _Trabajar_ may well mean that _Jefes_ forgone incomes are lower. The last column of Table 4 is based on our preferred estimates of foregone income of the _Jefes_ participants; we will explain how these estimates were obtained in the following sections. Again we find a good balance of the income changes between participants and applicants. Table 5 shows where the participants are found in the pre-crisis national distribution of income. For this purpose, _Jefes_ participants were assigned into national deciles based on household income per person in October 2001. We find that 15% of the participants were initially in the lowest decile of the income distribution; 40% are in the poorest 20%. And 90% were amongst the poorest 60%, which was about the official poverty rate at the time.

To throw further light on the role played by the eligibility criteria, Table 6 shows that unemployed heads of the households with dependents would have been highly concentrated in the bottom 20% of the income distribution before the program. The Table also gives results for a “theoretical” eligibility criteria close to the program’s official aim of reaching unemployed 13
# **Annex 1: Estimation Results**

_Table 1.1: Wage Premium (%) for the DTS, General Average HS Grade - OLS Estimate on_

||_L_<br>|_atest Salary_<br>|<br>|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|VARIABLES|RP and<br>DTS|RP and<br>DTS|RP and<br>DTS|RP and<br>DTS|RP and<br>DTS|
|DTS dummy (=1, RP=0)||0.118<sup>**</sup>|0.081|0.047|0.039|
|||(1.996)|(1.260)|(0.715)|(0.711)|
|Standardized HS grade|0.032|0.027|0.027|−0.029|0.011|
||(1.081)|(0.856)|(0.846)|(-0.923)|(0.249)|
|DTS<sup>*</sup>std. HS grade|||||-0.082***<br>(-3.698)|
|Years of schooling|0.072<sup>***</sup>|0.082<sup>***</sup>|0.072<sup>***</sup>|0.051<sup>**</sup>|0.047*|
||(3.708)|(3.813)|(3.175)|(2.178)|(2.046)|
|Work experience|0.008|0.011|0.009|0.017|0.016|
||(0.827)|(1.047)|(0.842)|(1.613)|(1.155)|
|Work experience<br>(squared)|−0.000|−0.000|−0.000|−0.000<sup>**</sup>|-0.000|
||(−1.227)|(−1.482)|(−1.239)|(−2.118)|(-1.500)|
|Female|0.011|0.019|0.044|0.032|0.036|
||(0.206)|(0.320)|(0.737)|(0.504)|(0.661)|
|Mother's education level|0.001|0.001|0.001|0.001<sup>a</sup>|0.001*|
||(1.104)|(1.357)|(1.369)|(1.752)|(2.005)|
|Constant|8.215<sup>***</sup>|8.024<sup>***</sup>|8.120<sup>***</sup>|8.689<sup>***</sup>|8.669***|
||(31.719)|(27.156)|(24.868)|(19.895)|(24.815)|
|Observations|399|356|354|356|354|
|R-squared|0.051|0.068|0.087|0.145|0.157|
|Enrollment Year||||||
|controlled|NO|NO|YES|NO|YES|
|TVI controlled|NO|NO|NO|YES|YES|

Source: _DTS Tracking Survey, 2015, World Bank. World Bank calculations._ Note: _Robust t-statistics in parentheses. * p<0.1; ** p<0.05; *** p<0.01_ 32
Policy Research Working Paper 9899

# **Abstract**

Access to infrastructure supports economic development through structural transformation. This paper investigates the links between investments in electricity, Internet, and transport infrastructure, in isolation and bundled, and economic development in the Lake Chad Region. Using data on the expansion of the paved road, electricity, and Internet networks over the past two decades and two instruments, it provides reduced-form estimates of the impacts of infrastructure investments on the sectoral composition of employment. Bundled infrastructure investments cause different patterns of structural transformation than isolated infrastructure investments. Bundled paved road and electricity investments is found to have reduced the agricultural employment share by 22 percentage points and increased the share of employment mostly in services. The paper then uses a spatial general equilibrium model to quantify the impacts of future regional transport investments, bundled with a large rural electrification program and trade facilitation measures to reduce border delays, on economic development in Nigeria, Cameroon, and Chad.

This paper is a product of the Infrastructure Chief Economist Office. 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 author may be contacted at mlebrand@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 3

The distribution of the world's labor force by income group <!-- Start of picture text --> I<br>Lowincomeeconomies 43 211 312<br>* Agculture<br>D Scrvices<br>Middle income economies 4614 260 1i E<br>I Unemployed<br>High income economies 32 104 228 17<br>0 200 400 600 800 1000 1200 1400 1600<br>Number of workers (in millions)<br><!-- End of picture text --> 14
Table 8 reports similar country-specific results, but focusing now on the period 2005-2015, which as shown above was characterized by stronger negative average impacts of China’s supply shocks on exports of B&R economies. The estimates in this table reveal that the countries more negatively impacted by China supply shocks in this period were Brunei, Bhutan, Qatar, Latvia, United Arab Emirates and Oman. At the same time, China’s demand shocks had a positive impact on the exports of the Indonesia, India, Bahrain, Kuwait, Syria, Armenia, Poland, Russia, Slovenia, Ukraine and Mongolia.

**Table 9: Impacts of China shocks on exports of B&R industries, 2SLS, 1995-2015**

|Industry|Δ Supplycjt|Δ Demandcjt|
|---|---|---|
|Live animals and animal roducts|-2149.01|1859.58|
|p|(8523.23)|(6520.36)|
|bl d|247.16|39.90<br>***|
|Vegetae proucts|(190.50)|(12.22)|
|Food roducts|-338.36|287.04|
|p|(386.56)|(191.86)|
|Minerals|112.26|31.64|
||(365.22)|(72.78)|
|Fuels|-3.76|51.40|
||(23.35)|(39.04)|
|Chmil|196.36|43.95<br>**|
|ecas|(174.84)|(22.17)|
|Plastic or rubber|-202.02<br>***|-18.80|
||(65.34)|(28.85)|
|Hides and skins|132.55|0.24|
||(96.70)|(179.85)|
|Wd|-5070.24|-10917.25|
|oo|(58710.45)|(129295.96)|
|Ttil d lthi|-16.24<br>**|37.09<br>***|
|exes an cong|(8.16)|(11.03)|
|Footwear|52.29|177.75|
||(37.04)|(231.22)|
||-550.85<br>**|73.88|
|Stone and glass|(278.68)|(171.33)|
|Mtl|28.54<br>**|18.53|
|eas|(14.20)|(15.03)|
|Mhi d ltil|-33.84<br>***|40.08<br>**|
|acnery an eecrca|(12.79)|(18.42)|
||-414.50<br>**|124.48<br>***|
|Transportation|(190.07)|(37.09)|
||<br>-96.91<br>***|<br>288.61<br>***|
|Miscellaneous|||
|<br>Robust standard errors clustered by c|(34.30)<br>ountry, *** p<0.01, **|(89.56)<br>p<0.05, * p<0.1|

Table 9 reports estimates on the impacts of China’s supply and demand shocks on exports of B&R economies by sector during the period 1995-2015. The evidence in this table suggests that adverse effects of China’s supply shocks were felt more strongly in the sectors “Plastic or rubber”, “Textiles and Clothing”, “Stone and Glass”, “Machinery and electrical”, “Transportation” and 23
# **References**

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- Abay, K. A. (2020). Measurement errors in agricultural data and their implications on marginal returns to modern agricultural inputs. _Agricultural Economics 51_ (3), 323–41.

- Abay, K. A., G. T. Abate, C. B. Barrett, and T. Bernard (2019). Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity relationship in agriculture. _Journal of Development Economics 139_ , 171–84.

- Abay, K. A., L. E. Bevis, and C. B. Barrett (2021). Measurement error mechanisms matter: Agricultural intensification with farmer misperceptions and misreporting. _American Journal of Agricultural Economics 103_ (2), 498–522.

- Alix-Garcia, J. and D. L. Millimet (2023). Remotely incorrect? accounting for nonclassical measurement error in satellite data on deforestation. _Journal of the Association of Environmental and Resrouce Economists 10_ (5), 1335–67.

- Arag ́on, F. M., F. Oteiza, and J. Pablo Rud (2021). Climate change and agriculture: Subsistence farmers’ response to extreme heat. _American Economic Journal: Economic Policy 13_ (1), 1–35.

- Barrios, S., L. Bertinelli, and E. Strobl (2010). Trends in rainfall and economic growth in Africa: A neglected cause of the African growth tragedy. _Review of Economics and Statistics 92_ (2), 350–66.

- Blankespoor, B., T. Croft, T. Dontamsetti, B. Mayala, and S. Murray (2021). Spatial anonymization: Guidance note prepared for the Inter-Secretariat working group on household surveys. UN Inter-secretariat Working Group on Household Surveys Task Force on Spatial Anonymization in Public-Use Household Survey Datasets. https://unstats.un.org/iswghs/taskforces/documents/Spatial ~~A~~ nonymization ~~R~~ eport ~~s~~ ubmit01272021 ~~I~~ SWGHS.pdf.

- Bosilovich, M., R. Lucchesi, and M. Suarez (2016). MERRA-2: File specification. GMAO Office Note No. 9 (Version 1.1). `http://gmao.gsfc.nasa.gov/pubs/office_notes` .

- Br ̈uckner, M. and A. Ciccone (2011). Rain and the democratic window of opportunity. _Econometrica 79_ (3), 923–47.

- Carletto, C., S. Gourlay, S. Murray, and A. Zezza (2017). Cheaper, faster, and more than good enough: Is GPS the new gold standard in land area measurement. _Survey Research Methods 11_ (3), 235–65.

- Central Statistics Agency of Ethiopia (CSA) (2014). Ethiopia Rural Socioeconomic Survey 20112012. Public Use Dataset. Ref: ETH ~~2~~ 011 ~~E~~ RSS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2053` on 6 September 2019.

- Central Statistics Agency of Ethiopia (CSA) (2015). Ethiopia Socioeconomic Survey 20132014. Public Use Dataset. Ref: ETH ~~2~~ 013 ~~E~~ SS ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2053` on 6 September 2019.

24
The magnitudes of the effects in the top panel are not easily interpretable. For this reason, the bottom panel of the figure studies the effect on the willingness to pay to avoid disadvantageous inequality. The willingness to pay is estimated using Fehr and Schmidt’s (1999) model of inequality aversion. In their model, the utility of an individual is given by: where x is the individual’s payoff and y is the payoff of the other individual. The second term in this function measures the disutility from disadvantageous inequality, and βDI the degree of aversion to disadvantageous inequality. The third term measures the disutility from advantageous inequality, and βAI the degree of aversion to advantageous inequality.<sup>34</sup> All ten trials of the sharing task are used to estimate βDI and βAI. The willingness to pay to avoid disadvantageous inequality is given by:<sup>35,36</sup>

> 34 Fehr and Schmidt (1999) assume that βDI ≥βAI, reflecting that individuals dislike more inequality that is to their disadvantage. They also assume that βAI < 1. If βAI were equal to one, the individual would be willing to throw away one dollar in order to reduce her relative advantage in one dollar. For the specific purposes of their paper, Fehr and Schmidt (1999) further assume that βAI ≥0. They recognize however that some individuals may like to be better off than others. There is evidence for example that children often choose to be at a relative advantage (Rochat et al. 2009; LoBue et al. 2009; Blake & McAuliffe 2011; Smith et al. 2013; Sheskin et al. 2014; Qiu et al. 2017).

> 35 The willingness to pay to avoid _advantageous_ inequality is given by:

WTPAI is the number of tokens the participant is willing to give up to _increase_ the other child’s payoff by 1 token. 36 The utility gain of choosing option _j_ over option _k_ is: where ∆xjk = xj −xk; ∆DIjk = max{yj −xj, 0} −max{yk −xk, 0}; ∆AIjk = max{xj −yj, 0} −max{xk −yk, 0}. We obtain estimates of βAI and βDI by estimating the following Probit model: Pr(Choose j over k) = Pr(Uj ≥Uk) = Φ(∆xjk −βDI∆DIjk −βAI∆AIjk) where Φ(∙) is the cumulative distributive function of the standard normal. Participants could indicate indifference between two options. In these cases, we randomized whether the choice was coded as having chosen option j or as having chosen option k. The fraction indicating indifference ranged from 0.43% to 4.16% depending on the trial.

21
**Table 7: Investment in mutual fund Y (Increasing risk to socially responsible fund)**

||Dependent Variable|: Investment|in alternate (|Social) fund||||
|---|---|---|---|---|---|---|---|
||I|II|III|IV|V|VI|VII|
||Baseline|Controls|Decision<br>interaction|Risk<br>preference<br>interaction|Social<br>preference<br>interaction|All<br>interactions|Triple<br>interaction|
|Treatment (Social fund = 1)|188.600|215.7*|24.9|-389.0|159.5|-630.6|-715.8|
||(120.8)|(129.8)|(155.2)|(441.0)|(188.3)|(468.9)|(477.9)|
|Decision number (1 - 12)|-178.9***|-178.9***|-193.6***|-178.9***|-178.9***|-193.6***|-199.7***|
||(6.6)|(6.6)|(9.3)|(6.6)|(6.6)|(9.3)|(14.0)|
|Risk preferences||-28.5|-28.5|-138.9|-31.8|-141.4|-141.4|
|7 = Risk-seeking||(65.98)|(65.98)|(101.10)|(66.68)|(101.70)|(101.70)|
|Social preferences||0.256|0.256|0.173|0.041|-0.030|-0.329|
|Donation to charity||(0.58)|(0.58)|(0.58)|(0.78)|(0.78)|(0.93)|
|Treatment X|||29.35**|||29.35**|42.47**|
|Decision number|||(13.08)|||(13.08)|(19.31)|
|Treatment X||||198.0||197.2|197.2|
|Risk preference||||(138.1)||(138.6)|(138.6)|
|<br>Treatment X|||||0.502|0.476|1.209|
|Socialpreference|||||(1.21)|(1.21)|(1.44)|
|Social preference X|||||||0.046|
|Decision number|||||||(0.08)|
|Treatment X|||||||-0.113|
|Socialpreference X Decision|number||||||(0.12)|
|Constant|2959.0***|2268.8*|2364.2*|2223.8*|2202.5*|2256.5*|2296.4*|
||(95.43)|(1269.20)|(1269.90)|(1263.60)|(1284.00)|(1279.10)|(1281.00)|
|Additional Controls|NO|YES|YES|YES|YES|YES|YES|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. * 10%, ** 5%, *** 1% significance level. Additional controls include gender, age, financial literacy, investing experience, confidence that the charity was paid in line with instructions, and clarity of instructions (see table A.4 in the appendix B). Social preferences are measured in tokens donated to local charity, with a maximum of 400 tokens. Risk preferences are measured based on the number of times a subject selected the gamble in the risk preference measure. Financial literacy is measured by the number of correct answers the subject provided in the financial literacy questionnaire. Appendix A defines the main variables.
# **1. Introduction**

Employment opportunities for individuals in developing countries that require them to reside away from home often result in split households, where one or more members move, while the rest of the household remains behind. There are two primary pathways along which this sort of migration occurs: A family member moves abroad or, alternatively, to another part of the home country. Typically, there are welfare gains that accrue from this type of labor migration: Previous research has shown that origin households receiving international remittances are more likely to escape poverty; send children to school; make more investments in housing, health, and small businesses; and better withstand adverse shocks.<sup>2</sup> Recent work on domestic migration has also shown that this pathway, too, produces positive gains, with levels of individual consumption growing for movers relative to stayers.<sup>3</sup> In a sense, any dichotomy between domestic and international migration as an either-or choice is artificial: Notwithstanding formal barriers and resource constraints, households have both options open to them, and may elect to send members to another part of the home country or abroad for employment. How different are the welfare gains experienced by sending households when a family member relocates along one versus the other of these two migration pathways? An answer to this question has both economic and policy implications. It could indicate which migration pathway is more advantageous to pursue, helping inform decision-making within families. Similarly, it could inform labor policies designed to facilitate opportunities for individuals seeking work away from home.<sup>4</sup> Yet, the economics literature has given limited attention to comparing the relative welfare gains from these two migration channels.<sup>5</sup>

> 2 See Yang (2011) for a recent survey on the impact of international remittances.

> 3 See Beegle et al. (2011) and De Brauw et al. (2015).

> 4 For example, the Philippines recorded an almost equal number of domestic and international migrants in its 2010 census. But its labor policies are mainly focused on facilitating the employment of overseas Filipino workers (OFWs). In contrast, China is the second largest recipient of international remittances after India, but its government is more intent on regulating urban-rural migration, paying less attention to promoting overseas jobs for Chinese citizens.

> 5 Previous work includes Adams (1998), who looked at the impact of external and internal remittances on the accumulation of assets in rural Pakistan, finding that only external remittances increased agriculture land holdings; and Lokshin et al. (2010), who found that domestic migration did more to reduce poverty in Nepal than overseas migration.

2
Table 7. Decomposition of the gap into intra and inter components, by skill.

||total|intra|intra<br>lsbc|intra<br>hsbc|intra<br>lswc|intra<br>hswc|inter|inter<br>lsbc|inter<br>hsbc|inter<br>lswc|inter<br>hswc|
|---|---|---|---|---|---|---|---|---|---|---|---|
|2004|0.6361|0.6610|0.0388|0.0251|0.0979|0.4992|-0.0250|0.7531|0.6853|-0.3332|-1.1302|
||(0.0267)|(0.0301)|(0.0055)|(0.0041)|(0.0120)|(0.0265)|(0.0117)|(0.0500)|(0.0480)|(0.0647)|(0.0825)|
|2005|0.6877|0.7470|0.0469|0.0378|0.1124|0.5500|-0.0593|0.8716|0.6539|-0.4091|-1.1757|
||(0.0266)|(0.0307)|(0.0069)|(0.0052)|(0.0123)|(0.0295)|(0.0120)|(0.0664)|(0.0548)|(0.0654)|(0.0820)|
|2006|0.6629|0.7394|0.0483|0.0366|0.1142|0.5402|-0.0764|0.9530|0.7110|-0.4455|-1.2949|
||(0.0270)|(0.0298)|(0.0062)|(0.0057)|(0.0127)|(0.0271)|(0.0119)|(0.0536)|(0.0576)|(0.0692)|(0.0885)|
|2007|0.6284|0.7397|0.0519|0.0313|0.0842|0.5723|-0.1113|0.8269|0.9082|-0.4008|-1.4456|
||(0.0283)|(0.0316)|(0.0075)|(0.0041)|(0.0118)|(0.0302)|(0.0142)|(0.0598)|(0.0533)|(0.0647)|(0.0976)|
|2008|0.5903|0.7468|0.0516|0.0210|0.0861|0.5880|-0.1564|0.9719|0.7866|-0.3798|-1.5351|
||(0.0219)|(0.0217)|(0.0051)|(0.0033)|(0.0086)|(0.0217)|(0.0119)|(0.0545)|(0.0430)|(0.0560)|(0.0821)|
|2009|0.5406|0.6858|0.0501|0.0150|0.0994|0.5213|-0.1452|1.1199|0.6246|-0.3937|-1.4959|
||(0.0176)|(0.0198)|(0.0051)|(0.0022)|(0.0085)|(0.0192)|(0.0103)|(0.0479)|(0.0340)|(0.0482)|(0.0721)|
|2010|0.4830|0.6177|0.0494|0.0188|0.1354|0.4141|-0.1347|1.1316|0.5664|-0.5188|-1.3139|
||(0.0182)|(0.0182)|(0.0044)|(0.0027)|(0.0096)|(0.0154)|(0.0089)|(0.0466)|(0.0287)|(0.0534)|(0.0700)|
|2011|0.4431|0.5881|0.0512|0.0207|0.1115|0.4048|-0.1451|1.2036|0.5253|-0.4559|-1.4180|
||(0.0240)|(0.0257)|(0.0066)|(0.0035)|(0.0118)|(0.0232)|(0.0141)|(0.0694)|(0.0516)|(0.0730)|(0.0946)|
|2012|0.4368|0.5418|0.0532|0.0259|0.0921|0.3707|-0.1051|1.1893|0.5310|-0.5012|-1.3242|
||(0.0252)|(0.0255)|(0.0074)|(0.0049)|(0.0115)|(0.0227)|(0.0124)|(0.0647)|(0.0527)|(0.0680)|(0.0962)|
|2013|0.4354|0.5537|0.0489|0.0142|0.1023|0.3883|-0.1184|1.0724|0.5630|-0.5575|-1.1962|
||(0.0251)|(0.0244)|(0.0058)|(0.0032)|(0.0106)|(0.0219)|(0.0123)|(0.0608)|(0.0429)|(0.0655)|(0.0902)|
|2014|0.3823|0.4724|0.0480|0.0148|0.0739|0.3356|-0.0901|1.0234|0.5885|-0.4511|-1.2509|
||(0.0233)|(0.0241)|(0.0051)|(0.0030)|(0.0099)|(0.0229)|(0.0099)|(0.0622)|(0.0421)|(0.0679)|(0.0932)|
|2015|0.4509|0.5632|0.0621|0.0200|0.1121|0.3690|-0.1123|1.2015|0.5037|-0.6316|-1.1859|
||(0.0225)|(0.0232)|(0.0059)|(0.0034)|(0.0121)|(0.0195)|(0.0112)|(0.0641)|(0.0419)|(0.0718)|(0.0900)|

Notes: bootstrapped standard errors in parentheses (200 replications); All estimates are significant at 1% significance level; lsbc is low-skill blue-collar occupations, hsbc is high-skill blue-collar occupations; lswc is low-skill white-collar occupations and hswc is high-skill white-collar occupations.

21