rafmacalaba/datause-encoder-data huggingface dataset:
We also find some evidence that couples in scenario 1 are, compared to the overall sample, less likely to have certain assets (woman not owning a mobile phone) and infrastructure (no electricity). Couples in scenario 2, on the other hand, appear to be somewhat better off compared to the overall sample, with access to piped water, and less likely to have faced a shock affecting income or assets. Apart from the variables related to women’s status, parcel size and number of plots in the parcel also appear to be positively correlated with scenario 1, indicating that women tend to own smaller parcels. This could be because perceptions around ownership of specific parcels are more likely to be blurred as land size and diversity of cropping activities increase.

|**Table 6: Correlates of Reporting Discrepancies**|**in Parcel Report**<br>|**ed and Econom**<br>|**ic Ownership in T**<br>|**5/IHPS**<br>|
|---|---|---|---|---|
||**Discrepancy**|**scenario 1:**|**Discrepancy**|**scenario 2:**|
||**Husband: joint**<br>|**ownership,**<br>|**Wife: joint**<br>|**ownership,**<br>|
||**wife: she d**|**oes not own**|**husband: he d**|**oes not own**|
||**(1)**|**(2)**|**(3)**|**(4)**|
||**Reported**|**Economic**|**Reported**|**Economic**|
||-0.052***|-0.104**|0.065|0.002|
|HH head is female (Y=1 N=0)|[-2.63]|[-2.61]|[0.97]|[0.03]|
|Ptilil i Y=1 N=0|0.047|0.201***|-0.124|-0.004|
|arnea marrage ( )|[0.62]|[2.83]|[-1.15]|[-0.05]|
||-0.027|0.035|-0.063|0.098|
|Matrilineal marriage (Y=1 N=0)|[-0.37]|[0.53]|[-0.58]|[1.32]|
|Lenth of marriae (rs)|0|-0.001|0.002|0.002|
|g g y.|[0.28]|[-0.62]|[1.12]|[0.91]|
|Woman: age|-0.001|-0.004|-0.002|0.002|
||[-0.44]|[-1.12]|[-0.95]|[0.72]|
|Man: age|0|0.005*|0.001|-0.004|
||[-0.00]|[1.80]|[0.41]|[-1.46]|
|Woman: suffers from chronic illness|-0.037|-0.059|0.086*|0.05|
||[-0.98]|[-1.17]|[1.90]|[0.94]|
|M ff f hi ill|0.021|0|-0.031|-0.064|
|an: suers rom cronc ness|[0.45]|[0.01]|[-0.92]|[-1.27]|
|W hiht dtil ll|-0.015|-0.009|-0.004|-0.014|
|oman: ges eucaona eve|[-1.33]|[-0.47]|[-0.31]|[-0.60]|
|M hiht dtil ll|0.005|0|-0.006|-0.012|
|an: ges eucaona eve|[0.62]|[-0.02]|[-0.56]|[-0.68]|
|Woman: casual labor|0.001|-0.038|-0.008|-0.003|
||[0.06]|[-1.03]|[-0.28]|[-0.08]|
||0.024|0.073**|-0.03|-0.008|
|Man: casual labor|[1.07]|[2.18]|[-1.31]|[-0.23]|
|Household owns an enterrise or sho|-0.008|-0.056|0.026|-0.022|
|p p|[-0.26]|[-1.41]|[0.80]|[-0.47]|
|HH deendenc ratio|-0.036**|-0.004|-0.007|0|
|py|[-2.02]|[-0.17]|[-0.30]|[0.01]|
||0.011|0|0.008|-0.001|
|HH size|160|003|114|011|
||[.]|[-.]|[.]|[-.]|
|**Characteristics of parcel, market activity**<br>|||||
|**and decision-making**|||||
||0.031|0.094***|-0.021|-0.024|
|Garden: log land size (acres)|[1.22]|[2.75]|[-1.19]|[-0.83]|
|Gd b f lt|<br>0.003|<br>0.007*|<br>0|<br>0|
|aren: numer o pos|[0.93]|[1.89]|[-0.10]|[-0.02]|
|HH ll Y=1 N=0|-0.007|-0.023|0.011|-0.036|
|ses some crop ( )|[-0.36]|[-0.77]|[0.42]|[-1.18]|

26
all traded goods are storable to some extent, the certainty and knowledge of future tariff reductions provided firms with strong incentives to delay their purchases in advance of their implementation and then to stock up in their aftermath.

Indeed, we find strong evidence of _anticipatory slumps_ and _liberalization bumps_ in the response of trade flows to tariff cuts for US imports from Mexico of phased-out goods. We estimate these anticipatory dynamics by using a standard odds ratio or “tetrads” method (Head and Ries (2001), Head et al. (2010)) to construct trade flows, we then consider within-year growth rates of these. First, we find that in anticipation of an upcoming tariff reduction of 1%, imports dropped by a sizeable 6% in November and December relative to the middle of the year. Second, in the first few months after the tariff reduction, imports spiked by 12% with respect to the end of the previous year. The size of these high frequency reversals are large even in comparison to previously documented shortrun (annual) estimates of the trade elasticity. This paper is the first to our knowledge to document how (1) firms anticipate tariff reductions during gradual phaseouts and (2) that the trade elasticity can be quite large even at high frequencies.

Importantly, we find that the anticipatory dynamics are increasing in the degree of product storability. This suggests that trade flows and their consumption might significantly diverge during these episodes. Unfortunately, the consumption or usage of imports is not observed, especially at the level of aggregation used in the estimation of trade elasticities. To make progress, we propose a novel measure of the consumption of imports that builds on monthly trade data and the assumption that a constant fraction of inventory holdings are used for consumption. A product-source country specific inventory-usage ratio is obtained by exploiting the fact that more storable goods are traded less frequently.<sup>3</sup> We validate the measure of consumption by demonstrating that (1) it eliminates the anticipatory slumps documented for NAFTA’s phased-out goods, and (2) it is effective in replicating the actual consumption response in Monte Carlo simulations of a <u>(</u> _<u>s, s</u>_ <u>)</u> ordering model.

With our measure of consumed imports in hand, we examine how the anticipatory dynamics lead to deviations between the trade-flow and the trade-consumption elasticities using annual data. Consistent with the smooth behavior of consumed imports around the tariff changes, the annual or short-run trade-consumption elasticity is smaller

> 3This relationship emanates from the existence of per shipment ordering costs that lead firms to trade off the frequency of orders against the costs of holding inventories.

2
considered time period. Then, the import values from these five countries both in the chosen initial and final years are tabulated, together with their shares of total imports of the product in the destination market, and the corresponding compound annual growth rate. If the country for which the dashboard is made is not within these top five import sources, it should nevertheless be included in the tables, indicating its position in the ranking (e.g. Tunisia is not among France’s top five sources for the import of “other motor vehicle parts”, but it is nevertheless included at the bottom of the corresponding table for comparability purposes). The WITS GVC Module interface currently provides listings of competitors for products of interest at a global scale (based on export shares to the world) as part of the output of its product of interest query, but does not provide competitors’ lists for specific local markets.

Apart from the country‐level dashboards, the MC‐GVC database allows for the possibility to build intra‐ regional GVC trade dashboards, which are available for download directly from the WITS GVC Module interface through the intra‐region analysis query. Such dashboards show the value of gross exports and imports of every country in the selected world region of interest, to/from every other country within the same region, as well as the total gross exports and imports of that country in every GVC category to the remaining six regions of the world according to World Bank classification,<sup>29</sup> also showing these trade values as percentages of that country’s total trade and GDP.

Finally, inter‐regional GVC trade dashboards can also be built to show the trade value of gross exports and imports of GVC‐related products of each world region to itself and to other regions of the world. These dashboards are also directly downloadable from the WITS GVC Module through the inter‐region analysis query, and in addition to providing the total trade of all partner regions, this broadest‐level analysis also shows the trade values of GVC‐related products as a share of total trade of all partner regions.

> 29 The GVC Dashboard uses the following seven regions: East Asia – Pacific (EAS), Europe‐Central Asia (ECS), Latin America‐Caribbean (LCN), Middle East‐North Africa (MEA), North America (NAC), South Asia (SAS), and Sub‐Saharan Africa (SSA).

40
26 Figure 2: Trade and Welfare Reduction Indexes, and Nominal Rate of Assistance for all covered products, 19 African countries, 1961 to 2004 <!-- Start of picture text --> (percent)<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>‐10<br>‐20<br>‐30<br>‐40<br>1961 1966 1971 1976 1981 1986 1991 1996 2001<br>TRI WRI NRA<br><!-- End of picture text --> Sources: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008)
**Different instrument specification** Next, we re-estimate our main specification by using a different shift-share instrument. Instead of using industries to calculate the instrument, we use 2,527 different occupations based on RAIS to calculate the national growth rates (shifters) and the local occupational shares. As in our main specification of the instrument, we exclude occupations related to the public sector as well as public firms. Results shown in Panel D of Table A.4 remain positive and significant and even have slightly higher point estimates. Nevertheless, in our main estimation strategy we prefer to follow Bartik (1991) to calculate the instrument and to use industries instead of occupations.

**Validity shift-share instrument** The recent literature has addressed the topic of the validity of shift-share instrument (see Goldsmith-Pinkham _et al._ (2020) and Borusyak _et al._ (2022)). As mentioned in Section 4 we follow the approach by Borusyak _et al._ (2022) to test our shift-share instrument for shock exogeneity.<sup>30</sup>

The authors suggest aggregating the data at the industry level to examine the conditional quasi-random shock assignment and the lack of correlation among shock residuals. Summary statistics for the shocks are presented in Table A.5. With a substantial number of shocks (8,579) and industries (660 at the 4-digit level and 278 at the 3-digit level), the statistics indicate that no single industry is significantly driving our observed effects. These findings demonstrate that we can mitigate concerns regarding the influence of any particular industry.

As a second step, we redo our analysis at the industry level as proposed by Borusyak _et al._ (2022). The authors show that the shift-share IV estimator equals the “second-stage coefficient from a _sn_ -weighted shock-level IV regression that uses the shocks _gn_ as the instrument”:<sup>31</sup> where _y_ ̄ _λ_<sup>_⊥_istheoriginal(residualized)outcome(exitrate),and</sup><sup>_x_ ̄</sup><sup>_⊥_</sup> _λ_<sup>istheoriginal(residual-</sup> ized) treatment (employment growth). As pointed out by the authors _y_ ̄ _λ_<sup>_⊥_“reflectstheaverage</sup> residualized outcome of the observations most exposed to the nth shock” and _x_ ̄<sup>_⊥_</sup> _λ_<sup>“isthesame</sup> weighted average of residualized treatment”. _x_ ̄<sup>_⊥_</sup> _λ_<sup>isinstrumentedby</sup><sup>_gλ_.</sup> Borusyak _et al._ (2022) show that their exposure-robust standard errors are asymptotically equivalent to the ones proposed by Adao _et al._ (2019), who note that locations with similar

> 30 While our main analysis is done at the family level, we do the robustness checks at the municipality level since our treatment is happening at the municipality level.

> 31In our case _sn_ equals _sλ_ .

20
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TABLE 12 Coverage for production function approach, filter-based, and expectations-based estimates: EMDEs

|**Economy**|**Production**<br>**function appro**|**ach**<br>**Univariate and**<br>**multivariate filters**|**WEO expectations**|
|---|---|---|---|
|**EMDEs**||||
|Albania|1998-2032|2008-2024|1993-2021|
|Argentina|1998-2032|2004-2024|1990-2021|
|Bolivia|1998-2032|1990-2024|1990-2021|
|Brazil|1998-2032|1990-2024|1990-2021|
|Bulgaria|2000-2032|2000-2024|2000-2021|
|Cameroon|1998-2032|1999-2024|1990-2021|
|Chile|1998-2032|1996-2024|1990-2021|
|China|1998-2032|1992-2024|1990-2021|
|Colombia|1998-2032|2000-2024|1990-2021|
|Costa Rica|1998-2032|1991-2024|1990-2021|
|Dominican Republic|1998-2032|2007-2024|1990-2021|
|Ecuador|1998-2032|2001-2024|1990-2021|
|Egypt, Arab Rep.|1998-2032|2007-2024|1990-2021|
|Guatemala|1998-2032|2001-2024|1990-2021|
|Honduras|1998-2032|2000-2024|1990-2021|
|Hungary|1998-2032|1998-2024|1990-2021|
|India|1998-2032|1997-2024|1990-2021|
|Indonesia|1998-2032|2001-2024|1990-2021|
|Iran, Islamic Rep.|1998-2032|2012-2024|1990-2021|
|Jordan|1998-2032|1992-2024|1990-2021|
|Kazakhstan|1998-2032|1996-2024|1993-2021|
|Kenya|1998-2032|2009-2024|1990-2021|
|Lesotho|1998-2032|2007-2024|1990-2021|
|Malaysia|1998-2032|2005-2024|1990-2021|
|Mexico|1998-2032|2000-2024|1990-2021|
|Mongolia|1998-2032|2010-2024|1993-2021|
|Morocco|1998-2032|1998-2024|1990-2021|
|Namibia|1998-2032|2000-2024|1994-2021|
|Nicaragua|1998-2032|2006-2024|1990-2021|
|Paraguay|1998-2032|1994-2024|1990-2021|
|Peru|1998-2032|1998-2024|1990-2021|
|Philippines|1998-2032|1998-2024|1990-2021|
|Poland|1998-2032|1996-2024|1990-2021|
|Romania|1998-2032|1995-2024|1993-2021|
|South Africa|1998-2032|1981-2024|1990-2021|
|Thailand|1998-2032|1993-2024|1990-2021|
|Tunisia|1998-2032|2000-2024|1990-2021|
|Turkey|1998-2032|2001-2024|1990-2021|
|Uruguay|1998-2032|1997-2024|1990-2021|
|Vietnam|2013-2032|2008-2024|1990-2021|

Source: World Bank.

Note: Includes only countries with available data from 2001. Forecasts for filterbased estimates for 2022Q2-2024Q4 are based on the lag structure of the estimation. Forecasts for production function-based estimates are derived as described in Kilic Celik, Kose, and Ohnsorge (2023). Univariate filters: HodrickPrescott, Baxter and King, Christiano and Fitzgerald, Butterworth, and unobserved component model.

59
the impacts of climate change on agricultural productivity in the absence of adaptation using panel data at the district level (Deschenes and Greenstone 2007). Combining these estimates of the impacts of climate change on the returns to land and labor with the household specific information on endowments of land and labor, one is able to derive household-specific impacts of the climate change on consumption which is a prerequisite for a proper distributional analysis.<sup>21</sup> The main results emerging from the study of Jacoby et al. (2011) are as follows: First, the substantial fall in agricultural productivity (17 percent overall inclusive of adaptation) that is predicted as a result of warming will translate into a much more modest consumption decline (of 6 percent on average) for the majority of households. This is because these households derive the bulk of their income from wage employment and (rural) wages are estimated to fall by only a third as much as agricultural productivity. The same general pattern is observed in the case of no adaptation.<sup>22</sup> Second, climate change will have heterogeneous impacts across geographical areas and across the income distribution (see Figure 1). Ignoring cereal price effects, climate change appears "progressive" insofar as wealthier households suffer proportionally greater consumption losses. A household in the top percentile of the per capita expenditure distribution would experience nearly a two percentage point greater decline in consumption than a household in the bottom percentile. This progressivity is driven by the skewed land distribution and the fact that larger landowners are concentrated in the higher percentiles. By contrast, temperature-induced wage declines are relatively more costly to the poor than to the rich, mainly because the poor tend to engage in climate sensitive agricultural employment. Third, once the welfare effects or rising cereal prices are taken into account; climate change impacts are regressive, falling more heavily on the poor than the rich. This is true in urban areas, where it is assumed that cereal price effects are the only welfare consequence of climate change, as well as in rural areas, where the beneficial impact of higher prices to agricultural producers offsets the decline in land productivity.

> 21  Thus, in contrast to the seven types of households considered in Hertel et al. (2010) in this model there is a continuum of households.

> 22 The estimates show that in the absence of adaptation a 1 degree Celsius increase in annual temperature reduces gross productivity per hectare in the range between 24 and 31 percent, which translate into a much smaller decline in consumption between 10.9 and 11.3 percent.

15
world-wide attention on issues related to clean cooking fuels.<sup>2</sup> A separate global alliance, known as Global Alliance for Clean Cookstoves (GACC), has been also initiated under a global partnership of public and private sectors to foster the adoption of clean cookstoves and fuels in 100 million households by 2020 (GACC, 2011). The World Bank has recently launched a number of regional clean cooking initiatives, such as the Africa Clean Cooking Energy Solutions to promote enterprise-based, large-scale dissemination and adoption of clean cooking solutions and the East Asia and Pacific region’s Clean Stove Initiative (CSI) to scale up access to advanced cooking stoves for rural poor households through country-specific technical assistance and a regional knowledge-sharing and cooperation forum. Besides these global initiatives, there are several initiatives to promote clean cooking. For example, in India, the government has launched National Biomass Cookstoves Program in 2009 to provide 160 million ICS to households currently using solid fuels (Venkataraman et al., 2010).

A key knowledge gap that has emerged in developing these regional efforts to scale up adoption of ICS involves the economics of household cooking energy uses. There have been few recent assessments of the economic rationale for carrying out supply and demand-side interventions such as adopting cleaner cooking technologies and fuels, and implementing community-based fuelwood management practices. More needs to be done to assess costs and benefits of household energy interventions using the latest information. In particular, new developments such as commercial availability of advanced cookstoves and eligibility of ICS projects for carbon mitigation funds – should be considered in the economic analysis as potential beneficial interventions. The objective of this report is to review the literature on the household choice for cooking fuels and economic assessments of household cooking energy transitions. In particular, this report highlights the factors influencing household cooking fuel choice and the challenges faced by empirical studies in estimating opportunity costs of biomass fuel collection.<sup>3</sup>

> 2 IEA (2012) estimated that nearly US$1 trillion in cumulative investment is needed to achieve SE4ALL by 2030. More specifically, a study by Global Energy Assessment (GEA) estimated that US$30 to 41 billion in annual investment is needed to achieve universal access to modern energy services by 2030 (GEA, 2012). 3 We use ScienceDirect, Google Scholar, JSTOR and ISI Web of Science databases, and individual websites of several international organizations (World Bank, UN, UNDP, UNEP, UNICEF, WHO, GTZ, IEA/OECD, ADB, EPA, SEI, EPA, USAID) for searching relevant articles including both published and unpublished articles.

3
Policy Research Working Paper 8071

# **Abstract**

To assess the conventional view that assets uniformly improve childhood development through wealth effects, this paper tests whether different types of assets have different effects on child education. The analysis indicates that household durables and housing quality have the expected positive effects, but agricultural assets have adverse effects on highest grade completed and no effects on exam performance. Extending the standard agricultural-household model by explicitly including child labor, the study uses three waves of panel data from Tanzania to estimate the effects of household assets on child education. The analysis corrects for the endogeneity of assets and uses a Hausman-Taylor instrumental variable panel data estimator to identify the effects of time-invariant observables and more efficiently control for time-invariant unobservables. The negative effect of agricultural assets is more pronounced among rural children and children from farming households, presumably due to the higher opportunity cost of their schooling.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at k.kafle@ifad.org, djolliffe@worldbank.org or alexwn@illinois.edu.

_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
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<!-- Start of picture text --> Fig 8<br>Arrecrs: Firm Net Positions 91.1-92.3<br>Net Poyables (1991.1 prices)<br>2.8<br>2.6-<br>2.4-<br>2.2-<br>2<br>1.6<br>1.4<br>1.2<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>91.1 91.2 91.5 91.4 92.1 92.2 92.3<br>0 Asset Size Class I + Asset Size 11 o Asset Size III<br>7<br><!-- End of picture text --> Fig 9 Ratio of Interest to Sales 1991.1 - 1992.3 <!-- Start of picture text --> 0.11<br>0.1<br>0.09 /<br>0.08<br>0.07<br>0.06<br>2.05<br>0.04-<br>0.05<br>0.02-<br>0.01<br>91.1 91.2 91.5 91.4 92.1 92.2 92.3<br>0 Asset Size Class I + AS 2 0 AS 3<br><!-- End of picture text -->