rafmacalaba/data-use-mentions-extended huggingface dataset:
Overall, this reduced-form cross-country evidence is at most suggestive of the existence of poverty traps, but it is hardly conclusive. Moreover, this type of evidence tells us little about the specific mechanisms generating the poverty trap or the policy interventions required to escape from that poverty trap. More useful in this respect is a line of microeconometric evidence that focuses on finding direct evidence of the mechanisms underlying specific models of poverty traps. For example, McKenzie and Woodruff (2004) use data from microenterprises in Mexico to search for evidence of poverty traps based on non-convexities in the production function. They take seriously the argument that poverty traps might exist if there are large fixed costs to starting a business. If capital markets are imperfect and potential entrepreneurs are credit-constrained by their lack of collateral, then a poverty trap can exist since individuals who start out with low wealth are unable to finance potentially profitable investments in new businesses. They find that the fixed costs involved in starting up a small enterprise in Mexico are typically very low, in some sectors less than half the monthly wage of a low-wage Mexican worker. They also find that the returns to capital are very high even at very low levels of the capital stock, and cannot reject the hypothesis that returns are decreasing over the entire range of observed capital stocks. They conclude that their evidence is not consistent with this particular mechanism of poverty traps.

In contrast, a number of papers have found microeconometric evidence of spillovers or other externalities that could form the basis of poverty traps. For example, Jalan and Ravallion (2004) argue for the existence of spatial or geographic poverty traps. In a panel of households from China, they find that consumption growth at the household level increases with the local availability of “geographical capital” understood as the availability of roads, the local level of literacy, etc. Their evidence suggests that these aggregate factors (at the local level) increase the returns to capital faced by households. However, the main focus of the paper is to determine the impact of
# 6.  Discussion and looking ahead

Using a unique nationally-representative survey data set from Cambodia, this paper discusses how individual wealth is linked with time use. The findings show that ownership of different types of assets are closely associated with men’s and women’s time use, with significant heterogeneity in associations across sub-populations of men and women belonging to different occupational groups. The association of asset ownership with reduced time in unpaid work and greater time in paid work is particularly strong for women in off-farm work, and among those who own mobile phones, financial accounts, and motorized vehicles — all of which have sizable gender gaps in ownership for the overall sample. Among respondents in agriculture, on the other hand, the links between asset ownership and time use are more scattered, and particularly among women.

While the analysis is not conducted to estimate causal impacts, the regressions that account for both observed and unobserved factors associated with time allocation and asset ownership reveal that covariates related to asset ownership play stronger roles in time use analysis, in particular among women, vis-à-vis the competing proxies for socioeconomic status. The findings also underscore the need to differentiate exclusive versus joint ownership of and rights to a range of physical and financial assets to better understand the channels through which asset ownership may impact time use measures. Overall, the analysis demonstrates the utility of integrated, intra-household, individual-disaggregated data collection on asset ownership, time use and employment in lower-income contexts.

Looking forward, the importance of examining the heterogeneity in the associations between asset ownership and time use is relevant for the design, targeting and evaluations of future interventions that are aimed at improving individuals’ asset ownership and control, and thereby the ease with which they can pursue economic opportunities. This is particularly the case when the interventions, including randomized control trials, are localized and focused on specific sub-populations, say, female entrepreneurs. Individuals in off-farm work, for example, may be better able to use mobile phones, financial accounts and vehicles for time-saving services and resources. Choices over time
Engineering, Design and Technology_ , 2022.

- Ahlfeldt, Gabriel, Nathaniel Baum-Snow, and Remi Jedwab, _The Skyscraper Revolution: Global Economic Development and Land Savings_ , Mimeo, 2023.

- Akbar, Prottoy, Victor Couture, Gilles Duranton, and Adam Storeygard, “Mobility and Congestion in Urban India,” _American Economic Review_ , April 2023, _113_ (4), 1083–1111.

- Angel, Shlomo, Alejandro M Blei, Daniel L Civco, and Jason Parent, _Atlas of urban expansion_ , Lincoln Institute of Land Policy Cambridge, MA, 2012.

- , Patrick Lamson-Hall, Alejandro Blei, Sharad Shingade, and Suman Kumar, “Densify and expand: A global analysis of recent urban growth,” _Sustainability_ , 2021, _13_ (7), 3835.

Bibri, Simon Elias, John Krogstie, and Mattias K ̈arrholm, “Compact city planning and development: Emerging practices and strategies for achieving the goals of sustainability,” _Developments in the built environment_ , 2020, _4_ , 100021.

- Blei, Alejandro and Shlomo Angel, “Global monitoring with the atlas of urban expansion,” _Urban Remote Sensing: Monitoring, Synthesis, and Modeling in the Urban Environment_ , 2021, pp. 247–282.

- Brueckner, Jan K. and Kala Seetharam Sridhar, “Measuring welfare gains from relaxation of land-use restrictions: The case of India’s building-height limits,” _Regional Science and Urban Economics_ , 2012, _42_ (6), 1061–1067.

- and Ruchi Singh, “Stringency of land-use regulation: Building heights in US cities,” _Journal of Urban Economics_ , 2020, _116_ , 103239.

- , Shihe Fu, Yizhen Gu, and Junfu Zhang, “Measuring the Stringency of Land Use Regulation: The Case of China’s Building Height Limits,” _The Review of Economics and Statistics_ , July 2017, _99_ (4), 663–677.
for purpose in climate migration research. To achieve this objective, there are methodological shortcomings that need to be resolved. The next subsection identifies the priority areas of a potential methodological agenda for unleashing the potential of LSMS-type surveys in the climate migration research domain over the short- and medium-term.

# **3.4 Priority areas and actions going forward: A tentative methodological agenda**

This proposed agenda focuses on improvements and innovations that can help tackle key research questions of the climate-migration nexus, as suggested by the conceptual framework presented in subsection 3.1 and for which multi-topic household surveys in general – and LSMS in particular – if properly tweaked, hold a significant comparative advantage (as discussed in the previous subsection).

> 27 Specifically, the LSMS has been supporting High-Frequency Phone Surveys (HFPS) in a number of low-income countries to track responses to and socioeconomic impacts of COVID-19. For more information about the LSMS-supported HFPS see <u>https://www.worldbank.org/en/programs/lsms/brief/lsms-launches-high-frequency-phone-surveys.</u>

18
# 5. Calibration

With the basic structure of the model specified, model behavior will depend importantly on the values of certain key parameters. As with the standard MFMod model the majority of parameters are estimated econometrically either on Jamaican data or in the cases where not enough data exists are imposed using results from the literature or cross-country regressions (Burns & Jooste, 2019). A similar approach is followed for this model, but with the added complication that many of the parameters are unobservable. Below is a brief description of the rationale for the default parameter values chosen. Results from sensitivity analyses for alternative choices are presented in Figure 5 and Figure 12.

## 5.1. Protection and adaptation capital

The actual effectiveness of adaptation capital is not well understood. Measuring it historically, would require knowing how much of a country’s capital is dedicated to adaptation and by how much this reduced damages that would have occurred in the absence of adaptation. One estimate (Multihazard Mitigation Council, 2018) suggests that the benefit-cost rate of hurricane adaptation is 10:1 at existing levels of adaptation investment. Importantly, this would not translate into a 1 = 10 unless adaptation investments equaled the expected value of hurricane damages (which is unlikely to be the case). γγ The literature discussed in Appendix B is somewhat vague in describing its calibration methods. Nevertheless, this work follows De Bruin et al (2009) in setting the curvature parameter of the protection function, 2 , to 0.3 (close to the inverse of their parameter of 3.6). The effectiveness parameter is set to 1 = 1 , implying that if an amount equal to the expected value of hurricane damages γγ were invested each year in adaptation, the productive capital stock would be protected from hurricanes. γγ

## 5.2. Insurance

Premia for natural disaster insurance can vary widely, with empirical estimates rising as high 700% percent (Froot, 2001). Disaster insurance markups tend to be higher than those in other insurance policies,
|4.102|4.302|0.227|
|Total value of crops (inUSD)|||||1619.970|1739.327|0.491|1483.621|1543.083|0.760|
|Landowned|||||7.408|8.133|0.423|7.034|6.028|0.166|
|Trust, confidenceandneighboringvillage relat|ions||||||||||
|Interactswithneighbouring,villagemonthly||0.514|0.563|0.253|0.557|0.568|0.810|0.527|0.556|0.556|
|<br>Confident storing inneighboringvillage||0.596|0.652|0.176|0.634|0.669|0.392|0.559|0.698|0.003|
|Neighboringvillagememberwouldreturnlost|wallet|0.268|0.292|0.538|0.324|0.259|0.101|0.324|0.270|0.217|
|<br>Confidence in institutions index||0.086|-0.009|0.715|-0.029|0.011|0.881|-0.195|0.308|0.089|
|Kaxgutazespecific|||||||||||
|<br>Familiarwithwarrantage.|0.807|0.760|0.869|0.000|0.782|0.786|0.927|0.684|0.804|0.004|
|<br>Wouldstore:Sorghum|0.282|0.230|0.326|0.014|0.317|0.335|0.663|0.266|0.302|0.405|
|
household is underweight or not. We see that the incidence of undernutrition among women and children is lower when the male head is adequately nourished. However, substantial inequality in nutritional status is also evident, and the gender inequality goes in both directions. The majority of women in households where the male head is underweight are not undernourished, and there is a high incidence of undernutrition among women and children in households where the male head is not underweight. Table 6 also gives (in parentheses) the proportions of undernourished women and children found in the two groups of households, identified by whether the male head is underweight or not. (Note that the proportions sum to unity horizontally.) We see that the bulk of underweight women (74%) are found in households where the male head is not underweight and similarly for stunted (80%) and wasted (53%) children.

**_Wealth and other covariates:_** When we say that a household is “wealth-poor” we are referring to the DHS wealth index within a given country. The wealth index is constructed by taking variables

> 26 Using our estimates from Tables 2 and 4 to regress the log of the stunting rate on the log of GDP plus the log of the poverty rate, it is readily verified that the regression coefficients are -0.09 (s.e.= 0.06) and 0.42 (s.e.= 0.12) respectively.

13
sup>5</sup> Under HSP, the implementing partner directly compensated Jordanian landlords for rent on behalf of their refugee tenants, with the refugee household receiving rent-free housing for 9 to

> 4Determined using the Vulnerability Assessment Framework, a bi-annual survey of registered refugees in Jordan.

> 5The pre-analysis plan proposed the evaluation of the far less generous energy subsidy program as well. That will be the subject of future research.

8
are only classi...ed by citizenship (Germany, Greece, Italy, Japan and Korea).

The ...rst six columns in table 1 describe the availability of immigration data in OECD countries as well as the statistical sources. Some of these data are not available yet. Hence, provisory assumptions are used in this release of the database.

> 8In the International Migration Report (UN, 2002), the concept of country of birth is used for 159 countries while citizenship is used for 51 countries.

9In some receiving countries such as Germany, immigrants’ children keep their foreign citizenship.

9
- 17 -

_Q1_ -0.715 (0.132) *** -0.693 (0.215) *** _Q2_ -0.526 (0.124) *** -0.430 (0.191) ** _Q3_ -0.530 (0.110) *** -0.062 (0.179) _Q4_ -0.386 (0.101) *** 0.022 (0.170) ln _HHS_ -0.061 (0.070)<sup>-0.123 (0.123)</sup> ln _MINCSB_ -0.011 (0.027) 0.019 (0.049) ln _ROOM_ 0.180 (0.068) *** 0.189 (0.118) _HOME_ -0.074 (0.063) 0.155 (0.132) _POWR_ 0.206 (0.112) * -0.116 (0.196) _TAPW_ 0.190 (0.105) * -0.148 (0.192) _TOIL_ 0.050 (0.075)<sup>-0.058 (0.134)</sup> <u>Constant 10.986 (0.380)</u> *** <u>0.515 (1.301)</u> Obs. 858 No. of district 8 8 dummies Wald chi2 580.96 Inter-equation correlation parameters: _λ_ -0.426 (0.100) *** _ρ_ 0.766 (0.039) *** Endogenous selection test: Wald chi2 13.77 ***

# **VI. ROBUSTNESS**

37. Related to the selection endogeneity, one may wonder whether the instruments are statistically valid. To formally test this question, the instrumental variable (IV) regression is performed. Although this is a linear model, it is
Relative Efficiency<br>of Public||||
||Schools in Developing<br>Countries|Emmanuel Jimenez<br>Marlaine Lockheed<br>Vicente Paqueo|August 1988|T. Hawkins<br>33678|
|WPS73|Taxation and Output Growth in Africa|Jonathan Skinner|August 1988|A. Bhalla<br>60359|
|WPS74|Fiscal Stabilization<br>and Exchange Rate<br>Instability: A Theoretical<br>Approach<br>and Some Policy Conclusions<br>Using<br>Mexican Data|Andrew Feltenstein<br>Stephen Morris|August 1988|A. Bhalla<br>60359|
|WPS75|Welfare Dominance and the Design of||||
||Excise Taxation in the Cote d'lvoire|Shlomo Yitzhaki<br>Wayne Thirsk|August 1988|A. Bhalla<br>60359|
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However, these papers have considered the average impact and have not examined the timing of these effects and how the price impacts vary throughout the year. As the analyses conducted in this study show, the impact of these shocks is highly seasonal.

We match monthly data on retail prices of six staple cereals (teff, wheat, maize, sorghum, barley, and African millet) and wages of unskilled laborers in 82 markets over 17 years to data on district level annual growing season rainfall.

We find that the impact of the weather shock on grain prices is largest immediately following harvest in January. On average a 1 in 5-year moderate drought caused grain prices from January to May to increase by 9 percent. From May to October the impact of the rainfall shock on grain prices gradually decreases. During the lean season prior to the new harvest becoming available the impact of a moderate drought on grain prices is much lower at 3 percent. The sudden increase in prices at harvest time and the lack of inflationary impacts during the lean season is somewhat counter to commonly held views where the concern is around price increases during a prolonged lean season, not price increases at harvest time itself.

In contrast, unskilled nominal wages are not significantly impacted by weather shocks, implying that the real value of wages falls in the immediate aftermath of harvest when food prices rise. This is contrary to what has been found in India (Jayachandran 2006).

In addition to providing a robust estimate of the magnitude and timing of the impact of drought on local grain prices, we also examine whether this has changed over time. Ethiopia has experienced a prolonged period of particularly fast development in the timeframe of this study. As part of this development, it invested significantly in roads, basic services, agricultural extension and a rural safety net to respond to drought. The federal and regional road network increased from 26,500 km in 1997 to 100,000 km in 2015 (World Bank, 2016) and this occurred at the same time as mobile phone
items_ , which refer to the number of computers and cellphones; and (6) _IT services_ , which sums the home phone, internet and pay-TV; (7) _ln(imp cons)_ , is the imputed measure of nondurable expenditure on food personal products, and clothing. Data employed comes from 1997 ENCASEH and 2017-18 ENCEL. Panel A shows the result from a cross-section estimate which only uses data from 2017. Treatment is a dummy variable that identifies if the observation belongs to one of the 320 original Progresa RCT treatment localities. Panel B does a DiD estimation with 1997 and 2017 as the two periods considered. Panel C estimates an ANCOVA, which controls for the pre-existing level of the dependent variable (denoted as Y(97)). Controls include (from ENCASEH 1997): HH head age, education, speaking a dialect, HH size, % members under 15, dummies for home, productive land and draft animals ownership, and locality marginalization index. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

21
measures in the DHS surveys for India and Bangladesh. We posit that child wasting probability is a function of mother’s education,<sup>3</sup> household wealth, mother’s age, child birth order and child birth month.<sup>4</sup> We also incorporate controls for survey months, and fixed effects for Indian states [Bihar, Jharkhand, West

> 3 We have also tested husband’s education, which is statistically insignificant in all cases.

> 4 We include birth month to incorporate the potential effects of seasonal food supply fluctuations on mothers’ post-partum nutrient intake.  This follows Dasgupta et al. (2018), who find that child health measures in Bangladesh are higher for birth dates in September - December, when monsoon floods sharply increase the availability of fish protein.

5
in 2003, but recovered to the previous level in 2007.

In Panel B we add our measure of TFP to the set of regressions discussed above. We find positive and significant effects – i.e. firms that have higher TFP are more likely to make

> 17 Our measure of operating income to sales is only indirectly related to firm profitability. More commonly used measures of profitability are ROA (return on assets) or ROE (return on equity), which are not available in ICA data.

24
Table 3. Intergenerational mobility among the rural population of PROSPERA: change in relative <u>position</u>

||(1)|(2)|
|---|---|---|
|VARIABLES|Height|Years of schooling|
|Provider’s relative position (Rank)|0.354***<br>(0.018)|0.239***<br>(0.018)|
|Constant|0.323***|0.286***|
||(0.012)|(0.010)|
|Observations|4,502|4,425|
|R-squared|0.125|0.066|

Notes: Standard clustered errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed based on data from the Evaluation of Rural Households Survey (ENCEL) 2017.

Table 4. Intergenerational mobility height and schooling estimated simultaneously

||(1)|(2)|
|---|---|---|
|VARIABLES|Height|Education|
|Provider’s height|0.317***<br>(0.016)||
|Provider ́s education||0.221***<br>(0.015)|
|Individual’s education|0.298***<br>(0.061)||
|Individual’s height||0.161***<br>(0.044)|
|Constant|0.230***<br>(0.023)|0.211***<br>(0.021)|
|Observations|4,093|4,093|
|Adjusted R-squared|0.
As noted, agricultural production, land and labor use are covered in the 2016 HIES, in a one-year recall format. To ensure that farmers are referring to the 2015 agricultural season, we restrict the sample to agricultural households (rural households engaged in farming) interviewed between January-April 2016; i.e., before the 2016 planting season was underway. Our empirical analysis of agricultural production is by necessity crosssectional, comparing post-Ebola production outcomes between districts differentially affected by the crisis.

There are two threats to validity of a causal interpretation of our estimates: (1) Omitted variables correlated with both agricultural production outcomes and the incidence of EVD; and (2) Simultaneity between _kuu_ labor use and Ebola. In other words, where farmers rely more heavily on _kuu_ labor, Ebola could have spread more quickly. Simultaneity bias would lead to a _positive_ association between _kuu_ labor and Ebola, as suggested by the higher _kuu_ share in severely affected counties noted above.

To address these issues, we control for whether the household is in a “severely affected” county as defined by FAO, thus focusing on variation _within_ highly affected areas as well as _within_ less affected areas. We also control for distance to major roads from the center point of each Enumeration Area (so all the household records within an EA should have the same value), as Ebola may have spread along road networks that are more likely to be placed where agriculture productivity is particularly high. Finally, the production regressions also control for rainfall patterns<sup>11</sup> (total rainfall at Enumeration Area level in 2015) that may have influenced rice planting choices and yields.

> 11 The rainfall source data were Climate Prediction Center (CPC) Africa Rainfall Climatology Version 2.0 (ARC2).

12
provides more information about the context, the instrument and the implementation of the survey experiment. Section 3 provides the description of, and rational for, the calculation of the improved constructs, and discusses reliability and internal consistency. It also shows predictive validity results, using agricultural yield and practices as outcome variables, and comparing results with the naïve and the improved constructs. Section 4 presents additional analysis related to measurement error and derives lessons and practical recommendations for skills measurement. Section 5 concludes and the appendix provides more details on methodologies and data, as well as additional empirical results.

# **2. THE SETTING, THE SAMPLE, AND THE QUESTIONNAIRE DESIGN**

# **2.1. Setting**

6