rafmacalaba/datause-encoder-data huggingface dataset:
for all δ > 0 . We think this characterization of preferences is most appropriately applied to the decision to send children to work outside of the family farm or business.<sup>1</sup> Hence, we use work for pay outside of the family farm or business as our definition of child labor in the empirical portion of this study. Of course, we examine the transfer's effect on other forms of work as well in our empirical analysis.

The discontinuity in child labor supply implied by the luxury axiom is central to BV's proof of the possibility of multiple equilibria for child labor supply in an economy.<sup>2</sup> Hence, the luxury axiom has been the focus of a large body of research. Tests of the luxury axiom typically look at whether child labor is income elastic. A negative income elasticity of child labor follows out of any Becker (1965) style time allocation model where leisure or an alternative use of time outside of work is a normal good (see for example, Cigno and Rosati 2005). In fact, the luxury axiom as codified in equation (1) does not imply that child labor is income elastic for all levels of income. Child labor does not respond to increases in income when such increases leave the household unable to cover subsistence costs, for example. Edmonds (2005) argues that, if we accept that households will vary in their perceptions of subsistence costs, the luxury axiom implies that the relationship between child labor and family living standards should be flat, and then decline rapidly in the range of perceived subsistence levels. He documents that the changes in child labor in Vietnam in the 1990s are consistent with what would be implied by the luxury axiom.

The baseline data in the BDH evaluation are consistent with the basic pattern implied by equation (1) of rapid declines in child labor after a certain standard of living is attained. Figure 1 contains the plot of participation rates in work for pay outside of the household for children 6-16 against the log of annual per capita expenditures in the baseline, pre-BDH, data. The curve in

> 1 Across countries, work for pay outside of the child's family is less prevalent and associated with lower school attendance rates than other forms of work.  The lower school attendance rates among children who work for pay outside of the household across countries can be explained by differences in hours worked (Edmonds 2007).  Work for pay outside of the household may be associated with less flexibility in hours worked and greater intensity for all of the reasons typically offered for the lumpiness of formal wage employment in the adult labor market.  Families may view work for pay outside of the home differently than other forms of work because of its greater intensity directly, the implied incompatibility with schooling, or discomfort with having children working outside of the home.

> 2 Swinnerton and Rogers (1999) emphasize that the multiple equilibrium result implicitly depends on the assumption that the distribution of capital income is sufficiently unequal.

6
**Information Treatments.** The Doctor and Patient Voucher treatments were cross-cut with a third within-clinic treatment. This “Patient Information” ( _PI_ ) was conveyed through a short video, which emphasized that all suspected malaria cases should be confirmed with a malaria test, noted that RDTs should be available for free at the clinic, and described recommended treatment for simple and severe malaria. The video also demonstrated an RDT test and how to recognize a positive test result. Finally, half the clinics were randomly selected to receive a more intensive training designed to increase providers’ trust in the diagnostic accuracy of RDTs. Both information treatments are analyzed in detail in a separate paper. We find limited evidence that the information treatments impacted the size of _PV_ and _DV_ treatment effects (see Appendix Tables B3 and B4 ).

The within-clinic randomization and associated data collection were conducted after the health worker trainings in November and December 2016, covering the end of the rainy season and therefore the period of highest malaria risk. We divided the 60 clinics into three cohorts of 20 based on geography. Each of the three cohorts rotated through two weeks of data collection and experimental intervention. Within each cohort, we randomly assigned each clinic to one of 20 intervention schedules depicted in Figure 2. Each clinic received two Control days, two Doctor Voucher days, and two Patient Voucher days. Although all clinics were informed of the upcoming study activities and interventions in advance, clinic staff did not receive prior notice of the actual intervention schedule – rather, our field officers informed clinic staff of the day’s intervention on the morning of an observation day.

# **3.2 Data Collection**

In order to identify the source of over-demand for malaria treatment, we require detailed data on prescription and treatment outcomes from patients at the clinic, as well as patients’ true malaria status. Our main analysis uses data from three sources.

**In-Clinic Survey.** On clinic observation days, enumerators approached all acutely ill patients at clinic intake and requested consent to participate in the study. We classified a patient as “acutely ill” if they were feeling sick and exhibited any of the following symptoms: fever, chills, excessive sweating, nausea, vomiting, diarrhea, poor appetite, headache, cough, weak-

20
# _<u>Family formation outcomes</u>_

The impacts of adolescent childbearing vary along the asset index dimension for family formation outcomes. Adolescent mothers from wealthier families are less likely to be single, and more likely to have a partner. The likelihood of being married does not seem to be affected differentially depending on the wealth of the household. This heterogeneity in the effect of adolescent childbearing suggests that the welfare of women plays a non-trivial role in the matching decisions – however, the ENADID data do not provide sufficient information to pinpoint the mechanism behind the observed effect.

# **4.2.2. Heterogeneity in impacts depending on community level characteristics**

Individual and household characteristics are not the only elements that can lessen or magnify the consequences of bringing up a child in adolescence. Certain features of the community may add yet another degree of difficulty to parenting a child as a teenager. Alternatively, the community may shoulder part of the burden – for example by providing educational opportunities or childcare. Following, we explore heterogeneity in the effects of adolescent childbearing depending on the municipal characteristics which provide imperfect proxies for educational and labor market opportunities in the municipality. Specifically, we analyze how labor market, education and welfare dependence outcomes differ depending on the fraction of residents aged 15 to 24 who are currently attending an educational institution and the fraction of employed who work 32 hours or less. These two characteristics are likely to capture educational and labor market opportunities in the municipality.

# _<u>Labor market outcomes</u>_

Raising a child differentially affects the labor market income, the probability of employment and the number of hours worked of teenage mothers depending on municipal-level characteristics. Although we do not observe an average effect of giving birth on labor market income (Table 5), we detect significant effects in specific labor markets, namely those characterized by high fractions of: individuals attending school (among people aged 15 to 24) and individuals who work less than 32 hours a week (among all employed).

15
- 49 -

- Hewlett, James. 1977. Changing patterns of household’s consumption of energy commodities. _Preceedings of the American Statistical Association Business and Economic Section_ , Part I, pp. 99-108.

- Komives, Kristin, Jonathan Halpern, Vivien Foster, Quentin Wodon. 2005. _Water, Electricity, and the Poor: Who Benefits from Utility Subsidies?_ The World Bank.

- Komives, Kristin, Jonathan Halpern, Vivien Foster, Quentin Wodon. 2006. The distributional incidence of residential water and electricity subsidies. Policy Research Working Paper No. 3878. The World Bank.

- Lam, Joseph, Danny Li, S.O. Cheung. 2003. An analysis of electricity end-use in airconditioned office buildings in Hong Kong. _Building and Environment_ , Vol. 38, pp. 493-498.

- Lampietti, Julian, Sudeshna Ghosh Banerjee, Amelia Branczik. 2007. _People and Power: Electricity Sector Reforms and the Poor in Europe and Central Asia_ . The World Bank: Washington.

- Maddala, G.S. 1983. _Limited-dependent and Qualitative Variables in Econometrics_ . Cambridge University Press.

- Matsukawa, Isamu, Nariyasu Ito. 1998. Household ownership of electric room air conditioners. _Energy Economics_ , Vol. 20, pp. 375-387.

- Matsukawa, Isam. 2001. Household response to optional peak-load pricing of electricity. _Journal of Regulatory Economics_ , Vol. 20, pp. 249-267.

- Nauges, Celine, Caroline van den Berg. 2009. Demand for piped and non-piped water supply services: Evidence from Southwest Sri Lanka. _Environmental and Resource Economics_ , Vol. 42, pp. 535-549.

- Nieswiadomy, Michael, David Molina. 1989. Comparing residential water demand estimates under decreasing and increasing block rates using household data. _Land Economics_ , Vol. 65, pp. 280-289.

- Pedro Antmann. 2009. Reducing technical and non-technical losses in the power sector. Background paper for the World Bank Group Energy Strategy. Washington DC: The World Bank.

- Reiss, Peter, Matthew White. 2005. Household electricity demand, revisited. _Review of Economic Studies_ , Vol. 72, pp. 853-883.

- Small, Kenneth, Kurt Van Dender. 2007. Fuel efficiency and motor vehicle travel: The declining rebound effect. _The Energy Journal_ , Vol. 28, pp. 25-51.

- Terza, Joseph, W.P. Welch. 1982. Estimating demand under block rates: Electricity and water. _Land Economics_ , Vol. 58, pp. 181-188.

- Terza, Joseph. Determinants of household electricity demand: A two-stage probit approach. _Southern Economic Journal_ , Vol. 52, pp. 1131-1139.
# **Table 7: Bundles of Rights Belonging to a Cluster by Country (Agricultural Land)**

|**Malawi**||||||**Tanzania**||||||**Ethiopia**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Type of right|s in order: be|quest, collatera|l, rent, sell|||Type of right|s in order: be|quest, collatera|l, rent, sell|||Type of right|s in order: beq|uest, collateral|, rent|||
|Not Hold**(N)**|, Jointly Hold|**(J)**, Exclusively H|old**(E)**|||Not Hold**(N)**|, Jointly Hold**(**|**J)**, Exclusively|Hold**(E)**|||Not Hold**(N)**|, Jointly Hold**(**|**J)**, Exclusively H|old**(E)**|||
|Cluster 1:<br>Ho|Mostly Not<br>ld|Cluster 2:|Mostly Joint|Cluster 3<br>Exclu|: Mostly<br>sive|Cluster 1:<br>Ho|Mostly Not<br>ld|Cluster 2:|Mostly Joint|Cluster<br>Excl|3: Mostly<br>usive|Cluster 1:<br>Ho|Mostly Not<br>ld|Cluster 2: M|ostly Joint|Cluster<br>Exc|3: Mostly<br>lusive|
|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|Rights<br>Bundle|%|
|NNNN|61.22|JJJJ|37.96|EEEE|75.67|NNNN|87.35|JJJJ|51.35|EEEE|82.21|NNN|65.42|JJJ|87.93|EEE|84.78|
|ENNN|6.72|JNJN|10.7|ENEE|11.12|NNJN|4.28|NJJJ|10.91|EEJE|4.99|ENN|14.39|JJE|3.16|JEE|4.72|
|JNNN|5.09|JNJJ|8.2|EEJE|2.15|NNEN|1.75|NNJJ|7.71|EEEN|3.64|JNN|10.75|JEJ|2.12|EEN|2.96|
|NNEN|4.99|EJJJ|5.35|EEEN|1.89|NNNE|1.56|NJJN|6.37|NEEE|3.13|NNE|6.12|EJJ|2.05|ENE|2.64|
|ENNE|4.02|JEEJ|4.43|EENE|1.63|ENNN|1.31|EJJE|6.37|ENEE|2.42|NNJ|1.97|JJN|1.90|NEE|1.27|
|ENEN|3.52|NNJJ|3.95|NEEE|1.58|NJNN|1.25|EEJJ|4.12|NNEE|1.61|NEN|0.68|NJJ|1.65|EEJ|1.27|
|NNNE|3.33|EEJJ|3.69|EEEJ|1.54|NENN|0.85|EJJJ|4.11|ENJE|0.44|NJN|0.67|JNJ|1.19|EJE|1.25|
|NNJN|2.54|JNNJ|3.52|JEEE|1.17|NNNJ|0.62|JEJJ|3.92|ENEN|0.35|||||JEN|0.52|
|NNEE|1.78|JJJE|2.65|ENJE|0.98|NNEJ|0.32|JEEJ|1.81|EJEE|0.35|||||ENJ|0.23|
|NNNJ|1.1|JEJJ|2.5|EJEE|0.97|ENNE|0.28|JJJN|1.03|NEEN|0.32|||||JNE|0.17|
|NJNN|1.03|JJEJ|2.3|JNEE|0.54|JNNN|0.20|JEJE|0.87|EEEJ|0.26|||||EJN|0.11|
|EENN|0.89|JEJE|2.08|ENEJ|0.33|NEJN|0.16|JJEJ|0.56|NENE|0.13|||||NJE|0.05|
|NEEN|0.8|JNJE|1.9|EEJN|0.22|NJEN|0.06|EJJN|0.48|EEJN|0.07|||||NEJ|0.02|
|JNEN|0.78|NJJN|1.79|NEJE|0.11|JNEN|0.02|JNJJ|0.20|NJEE|0.07|||||||
|ENJN|0.76|JJJN|1.74|JEEN|0.1|||NJNJ|0.14|JEEE|0.02|obs|746|obs|1,338|obs|1,844|
|NENN|0.33|EJJE|1.49|||||JJJE|0.06|||||||||
|JNNE|0.32|EJEJ|1.13|||||||||||||||
|ENNJ|0.24|ENJJ|0.79|||obs|612|obs|225|obs|472|||||||
|EJNN|0.21|JJNN|0.66|||||||||||||||
|NENE|0.11|NJJJ|0.59|||||||||||||||
|NNJE|0.1|JJEN|0.41|||||||||||||||
|NEJN|0.1|EJJN|0.36|||||||||||||||
|||JJEE|0.35|||||||||||||||
|||JNEJ<br>JJNJ<br>NJJE|0.3<br>0.29<br>0.29|||||||||||||||
|||JENJ|0.29|||||||||||||||
|||NEJJ|0.28|||||||||||||||
|obs|932|obs|303|obs|914|||||||||||||

<u>Notes: The table reports rights bundle for a parcel-individual level observation that is assigned into a cluster.</u> 23
# **Appendices**

Table A1: <u>Clustering — Impacts of hosting refugees on household welfare and poverty</u>

|||All househol<br>|ds<br>||
|---|---|---|---|---|
||Consumption|Consumption poverty|Wealth|Wealth poverty|
||(1)|(2)|(3)|(4)|
|Refugee Intensity|-0.189**|0.179***|0.047|-0.007|
||(0.076)|(0.059)|(0.049)|(0.044)|
|Constant|9.725***|-0.896**|0.455|0.651**|
||(0.446)|(0.375)|(0.339)|(0.324)|
|Round FEs|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|
|R-squared|0.068|0.043|0.188|0.115|
|# of Woredas|317|317|317|317|
|# of EAs|433|433|433|433|
|N|12937|12937|13522|13522|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on logarithm of total household consumption expenditure per capita (column (1)), a binary measure of household consumption poverty status (column (2)), “started log” of household wealth score (column (3)), and a binary measure of household wealth poverty status (column (4)). Household sample weights from ESS2 applied. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 characteristics are not reported here, but can be available on request. Robust standard errors clustered at woreda level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

51
6 Figure 3: Characteristics of U.S. arm’s-length and intra-firm trade

- (A) Share of intra-firm exports in total U.S. exports

- (B) Share of intra-firm exports in total U.S. imports

<!-- Start of picture text --> Percent of U.S. exports Percent of U.S. imports<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Overall AEs EMDEs Overall AEs EMDEs<br><!-- End of picture text -->

- (C) Structure of U.S. exports

<!-- Start of picture text --> Intermediates<br>Percent of U.S. exports<br>Capital goods<br>Final goods<br>100<br>14 12<br>80<br>30<br>33<br>60<br>40<br>53 58<br>20<br>0<br>Arm's length Related party<br><!-- End of picture text -->

- (D) Structure of U.S. imports

<!-- Start of picture text --> Intermediates<br>Percent of U.S. imports<br>Capital goods<br>Final goods<br>100<br>16<br>29<br>80<br>36<br>60 22<br>40<br>49 48<br>20<br>0<br>Arm's length Related party<br><!-- End of picture text -->

- (E) Evolution of intra-firm exports in total U.S. exports

- (F) Evolution of intra-firm imports in total U.S. imports

<!-- Start of picture text --> Percent of U.S. exports 2002 Percent of U.S. imports 2002<br>60 2005 60 2005<br>2009<br>2009<br>2010<br>50 2010 50<br>2014<br>2014<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Overall AEs EMDEs Overall AEs EMDEs<br><!-- End of picture text --> Source: U.S. Census Bureau.

Note: AE stands for advanced economies.

A.B.E.F. U.S. exports and imports of goods, average for 2002-14. The residual to 100 percent is the share of arm’s-length trade in total U.S. goods exports or imports with the world, advanced economies (AEs) or EMDEs. The shares are broadly stable over the period.

C.D. 2014 averages. The classification into intermediates, capital, and final goods is according to the Broad Economic Categories (BEC) rev.4 classification of goods according to their use. Category 51—passenger motor cars—has been excluded.
since 1980 (or .10 to .14 for the 1985-2000 period), consistent with the results presented above and previous results from gravity models.

# **VI. Industry Results**

Our results imply that there has been an increase in the importance of distance for a large share of industries. In this section, we examine which sectors and type of goods have been subject to these shifts. First, we look at the industries at the one-digit SITC level. Second, we split the data by market structure. We use the Rauch (1999) classifications, which split goods into three categories (i) those traded on an organized exchange, (ii) reference priced goods, and (iii) all other commodities. The first two categories are meant to capture goods which are roughly homogeneous, the third category is primarily differentiated goods—goods where brand and producer are relatively more important.

Table 4 reports results for the goods at the one-digit SITC level. For all categories, a greater share of industries display a bias toward an increasing importance of distance (negative coefficient) than towards a decreasing importance of distance (positive coefficient). The increase in the elasticity is especially important for animals, food and live animals, chemicals, and other goods. In terms of the total number of goods that show a negative and significant change in the elasticity, basic manufactures, chemicals and food and live animals are most important. Crude materials and mineral fuels show very little change in their sensitivity to distance in terms of the percentage of industries that are negative and significant. In part, this may be because production of these products is determined largely by natural resources, and as a result, production and trade are changing only marginally over time. In addition, in the case of mineral fuels, this 18
# **Table 6. Correlation coefficients between measurement residuals and selected variables.**

# All observations

||_Dummy_|_Dummy Asia_|_Dummy_|_Dummy_||||
|---|---|---|---|---|---|---|---|
||_EU Asia_|_Pacific_|_America_|_Africa ME_|_Gdp_cap_|_Pop_|_R_g/gdp_|
|_mesresWB-CCI_|-0.0604|-0.1405|-0.2507***|0.3621***|-0.0090|-0.2122**|-0.1182|
||(0.5071)|(0.1211)|(0.0052)|(0.0000)|(0.9242)|(0.0185)|(0.1946)|
|_mesresTI-CPI_|-0.1297|-0.0810|-0.2854***|0.4109***|0.0510|-0.2234**|-0.1010|
||(0.1527)|(0.3732)|(0.0014)|(0.0000)|(0.5916)|(0.0130)|(0.2683)|
||_Free_press_|_Free_speech_|_Voice_acc_|_Emp_right_|_Democ_|_Checks_|_Stability_|
|_mesresWB-CCI_|0.0368|-0.0739|-0.0604|-0.0677|-0.2687***|-0.2180**|0.1518*|
||(0.6876)|(0.4163)|(0.5067)|(0.4572)|(0.0033)|(0.0177)<br>|(0.0938)|
|_mesresTI-CPI_|0.1579*|-0.1221|-0.1620|-0.1415|-0.3310***|-<br>0.2807***|0.1113|
||(0.0823)|(0.1786)|(0.0735) *|(0.1184)|(0.0003)|(0.0021)|(0.2205)|

Excluding the ten biggest outliers

||_Dummy_|_Dummy Asia_|_Dummy_|_Dummy_||||
|---|---|---|---|---|---|---|---|
||_EU Asia_|_Pacific_|_America_|_Africa ME_|_Gdp_cap_|_Pop_|_R_g/gdp_|
|_mesresWB-CCI_|-0.0400|-0.1297|-0.1928**|0.2860***|0.0478|-0.2234**|-0.0933|
||(0.6737)|(0.1708)|(0.0408)|(0.0021)|(0.6315)|(0.0174)|(0.3278)|
|_mesresTI-CPI_|-0.1200|-0.0758|-0.2216**|0.3476***|0.1124|-0.2319**|-0.0772|
||(0.2056)|(0.4247)|(0.0184)|(0.0002)|(0.2585)|(0.0134)|(0.4186)|
||_Free_press_|_Free_speech_|_Voice_acc_|_Emp_right_|_Democ_|_Checks_|_Stability_|
|_mesresWB-CCI_|-0.0221|-0.0673|0.0019|-0.0391|-0.1882*|-0.2234**|0.2073**|
||(0.8171)|(0.4787)|(0.9839)|(0.6904)|(0.0511)|(0.0195)|(0.0276)|
|_mesresTI-CPI_|0.1159|-0.1104|-0.1161|-0.1153|-0.2682***|-0.2818***|0.1619*|
||(0.2238)|(0.2443)|(0.2208)|(0.2241)|(0.050)|(0.0030)|(0.0866)|

_Notes_ : Number of observations. Panel A: between 124 and 117; Panel B: between 104 and 113. _P_ -values between parentheses. * _p_ -value < .1; ** _p_ -value < .05; *** _p_ -value < .001. For a description of the data, see Appendix B. Countries for which _PACI_ z = 0 and Pr zero_ cases zPACI_ > 0.015 and countries for which _PACI_ z > 10000 have been excluded.

22
tween the two sets is revealing. Policy makers see events changing in the present, and expect those changes to continue into the future, while farmers, at least for the climate-related events, do not extrapolate current trends into the future nearly as much.

# **3.3 Household survey**

The third element of our empirical study was the analysis of a survey that was administered to gather baseline data for a Red Cross climate change program, and to evaluate the effectiveness of a first set of workshops that were designed to promote climate change adaptation. The workshops, which had taken place in the communities of Chiguidela and nearby Malhazene, had been motivated by the qualitative observation that many farmers attributed observed climate changes to supernatural causes, and that their efforts to adapt to the observed changes could in fact make them worse.

Figure 5—Map showing the study area, including the two survey villages Chiguidela and Malhazene, the district seat Ch—kwe, the provincial capital, Xai Xai, roadways, and the Limpopo River. Image Source: Global Land Cover Facility, http://glcf.umiacs.umd.edu.

15
COMTRADE database to obtain iceberg transport cost by taking the ratio of the importing country’s c.i.f unit values to exporting country’s f.o.b unit values for each 4-digit SITC product for many countries between 1984 and 2008. While this methodology allows for the measurement of transport costs to vary across countries, product, and time, Feenstra and Romalis (2014) reported that there is a large amount of measurement error in the unit values, and there are many instances when the c.i.f unit value is less than the f.o.b. unit value.

**Stylized Fact 13.** _Matched partner c.i.f./f.o.b. ratios from IMF and UN data are often inaccurate measures of the ad-valorem freight rates._

the for international trade recommend that countries re- Recently, official reporting guidelines port bilateral trade distinguished by five different modes of transport. Thus, the COMTRADE Plus database reports f.o.b. and c.i.f values by mode of transportation. In December 2020, UNCTAD, in collaboration with the World Bank, launched the Global Transport Cost database that has information on transport costs for 105 importing countries and 200 exporting countries. This dataset has the most extensive coverage of any public transport data so far, but it is available only for 2016. The system that has been put in place will allow for more updates and expansion in the future.

# **3.3 Direct Measures of Transportation Costs**

Several data sets contain direct measures of transport costs. We start our discussion of these data with the product-level customs data sets. The United States, New Zealand, and 11 Latin American countries report data on freight expenditures (including insurance) at 10-digit HS product level that are collected using importers’ customs declarations. These data provide a direct measurement of transport costs by mode of transportation together with trade values, quantities, and port of entry (for the US data). Several studies have employed this data to understand the trends in transport costs since the 1950s (Hummels, 2007; Hummels and Schaur, 2010), to model importers’ choice between air and ocean (Hummels and Schaur, 2013), to explore the nature and the determinants of international transport costs and the effect of freight rate on the patterns and quality of trade (Hummels and Skiba, 18