rafmacalaba/datause-encoder-data huggingface dataset:
Import Dynamics and Demands for Protection<sup>_∗†_</sup> Russell Hillberry<sup>_‡_</sup> Phillip McCalman<sup>_§_</sup>

**Key words:** Import dynamics, endogenous trade policy, anti-dumping **JEL codes:** F13, F14, F53

**Sector Board:** Economic Policy (EPOL)

> _∗_ An earlier version of this paper circulated under the title ‘What Triggers an Anti-dumping Petition? Finding the Devil in the Detail.’

> _†_ We would like to thank Bruce Blonigen, Chad Bown, Robert Feinberg, James Hartigan, John Ries, Georg Schaur, and Maurizio Zanardi for useful comments on earlier drafts, as well as audiences at the Universities of Adelaide, California-Santa Cruz, Melbourne, and Tennessee, Deakin and Purdue Universities, the U.S. International Trade Commission, the World Bank, the Australasian Trade Workshop, the Ljubljana and Rocky Mountain Empirical Trade conferences, and the Midwest International Economics Group conference.

> _‡_ Development Research Group, World Bank, 1818 H Street NW, Washington DC, 20433, United States. e-mail: rhillberry@worldbank.org

> _§_ Department of Economics, Level 4, FBE Building 105, 111 Barry Street, University of Melbourne, Melbourne, VIC 3010, Australia, e-mail: mccalman@unimelb.edu.au
can be divided into two groups, Mexico City and the Border States on one hand and the rest of the country on the other hand.

**Table 1: Per capita GDP by state and annual per capita growth, divided per region**

||**Per cap**|**ita GDP**|**(natio**|**nal aver**|**age=100)**|**Annua**|**l per cap**|**ita growt**|**h (perce**|**ntage)**|
|---|---|---|---|---|---|---|---|---|---|---|
||1970|1985|1993|2001|2006<br>|1970-85|1985-94|1994-95|1995-01|2001-06|
|**National**|**100**|**100**|**100**|**100**|**100**|**1.77**|**0.22**|**-7.95**|**2.79**|**1.61**|
|**Border**|**129**|**125**|**130**|**136**|**141**|**1.59**|**0.71**|**-7.05**|**3.32**|**2.24**|
|Baja California|145|129|128|125|123|0.96|0.21|-9.29|2.64|1.14|
|Coahuila|120|120|121|135|142|1.80|0.23|-2.15|3.70|2.59|
|Chihuahua|101|102|128|138|146|1.86|2.88|-8.50|4.02|2.71|
|Nuevo León|167|165|166|174|181|1.70|0.40|-8.43|3.49|2.49|
|Sonora|139|120|116|124|126|0.78|0.15|-3.91|2.73|2.01|
|Tamaulipas|105|103|100|104|107|1.67|0.16|-7.50|2.93|2.19|
|**Northern central**|**73**|**79**|**80**|**81**|**85**|**2.38**|**0.26**|**-6.09**|**2.70**|**2.61**|
|Aguascalientes<br>Baja California|79|85|108|124|126|2.31|3.12|-5.72|4.39|1.97|
|<br>Sur|139|116|132|125|121|0.57|1.39|-3.43|1.50|0.87|
|Durango|72|91|81|85|91|3.38|-0.93|-4.50|2.81|3.04|
|Nayarit|76|79|66|61|61|2.11|-1.94|-10.89|2.31|1.45|
|San Luis Potosí|58|70|73|73|80|3.04|0.91|-12.34|3.19|3.64|
|Sinaloa|94|85|87|81|81|1.12|0.05|-4.15|1.58|1.67|
|Zacatecas|51|60|55|56|61|2.81|-0.85|0.44|1.94|3.30|
|**Southern central**|**75**|**79**|**73**|**74**|**75**|**2.12**|**-0.59**|**-7.22**|**2.88**|**1.73**|
|Colima|86|107|103|97|99|3.31|-0.29|-6.09|1.42|2.17|
|Guanajuato|71|70|69|75|79|1.71|0.04|-5.22|3.67|2.56|
|Hidalgo|54|69|66|61|59|3.51|-0.59|-13.07|2.76|0.96|
|Jalisco|104|107|101|99|95|1.96|-0.53|-9.48|2.98|0.78|
|Michoacán|52|56|55|57|59|2.19|0.25|-3.11|2.33|2.08|
|Morelos|84|86|99|89|92|1.92|1.43|-12.06|2.35|2.19|
|Puebla|62|68|64|68|68|2.37|-0.54|-10.06|4.44|1.61|
|Querétaro|79|108|105|117|115|3.97|0.14|-5.80|3.91|1.22|
|Tlaxcala|46|75|53|56|51|5.25|-3.62|-5.79|3.21|-0.14|
|Veracruz|81|76|61|58|60|1.31|-2.01|-3.26|0.84|2.31|
|**Mexico City**|**162**|**144**|**154**|**146**|**140**|**0.98**|**0.85**|**-10.18**|**2.48**|**0.78**|
|Distrito Federal|192|190|244|250|242|1.70|3.10|-8.76|3.24|0.92|
|Est. de México|108|99|83|79|78|1.19|-1.87|-11.45|2.80|1.39|
|**South**|**49**|**62**|**60**|**57**|**56**|**3.44**|**-0.24**|**-5.73**|**1.71**|**1.12**|
|Chiapas|49|68|45|43|40|3.95|-4.33|-2.24|1.04|0.46|
|<br>Guerrero|52|57|58|53|51|2.44|0.45|-6.22|0.77|1.16|
|Oaxaca|35|51|45|43|42|4.32|-1.11|-5.62|1.45|1.27|
|Quintana Roo|100|110|188|149|130|2.42|5.95|-10.21|-0.05|-1.21|
|Yucatán|72|71|77|80|79|1.72|1.29|-7.96|3.21|1.40|

Source: Calculations based on information from INEGI, excluding Campeche and Tabasco.

8
Risk Assessment of Burden of Disease and Injury Attributable to 67 Risk Factors and Risk Factor Clusters in 21 Regions, 1990–2010: A Systematic Analysis for the Global Burden of Disease Study 2010.” _The Lancet_ 380 (9859): 2224–60. doi:10.1016/S0140‐6736(12)61766‐8.

- MacCarty, N, D. Ogle, D. Still, T. Bond, C. Roden. 2008. “A laboratory Comparison of the global Warming Impact of Five Major Types of Biomass Cooking Stoves,” _Energy for Sustainable Development,_ Vol. 12: 5‐14.

- Masera, O., Edwards, R., Arnez, C.A., Berrueta, V., Johnson, M., Bracho, L.R., Riojas‐ RodrÌguez, H., Smith, K.R. 2007. "Impact of Patsari improved cookstoves on indoor air quality in Michoac·n, Mexico." _Energy for Sustainable Development_ Vol. 11:  45 ‐ 56.

- Nepal, Mani, Apsara Nepal, and Kristine Grimsrud. 2011. “Unbelievable but Improved Cookstoves Are Not Helpful in Reducing Firewood Demand in Nepal.” _Environment and Development Economics_ 16 (01): 1–23. doi:10.1017/S1355770X10000409.

- Oster, Emily. 2013. _Unobservable Selection and Coefficient Stability: Theory and Validation_ . National Bureau of Economic Research. http://www.nber.org/papers/w19054.

- Pandey, Apoorva, Pankaj Sadavarte, Anand B. Rao, and Chandra Venkataraman. 2014. “Trends in Multi‐Pollutant Emissions from a Technology‐Linked Inventory for India: II. Residential, Agricultural and Informal Industry Sectors.” _Atmospheric Environment_ 99: 341–52.

- Saatchi, S, M. Harris, S. Brown, M. Lefsky, E. Mitchard, W. Salas, B. Zutta, W. Buermann, S. Lewis, S. Hagen, S. Petrova, L. White, M. Silman, A. Morel. 2011. “Benchmark Map of Carbon Forest Carbon Stocks in Tropical Regions across Three Continents,” _Proceedings of the National Academy of Sciences_ vol. 108 (24): 9899‐9904.

- Smith, K. R., Uma, R., Kishore, V.V.N., Zhang, J., Joshi, V. and Khalil, M.A.K.2000 "Greenhouse Implications of Household Stoves: An Analysis for India." _Annual Review of Energy and the Environment_ , 25: 741–63

- Thakuri, M. B. M. 2009. “Revising the Need of Improved Stoves: Estimating Health, Time and Carbon Benefits,” SANDEE Working Paper No. 44‐09.  Kathmandu.

- UNDP. 2005. _Energizing the Millennium Development Goals: A Guide to Energy’s Role in Reducing Poverty_ . New York: UNDP.

- UNDP and WHO. 2009. _The Energy Access Situation in Developing Countries: A Review Focusing on the Least Developed Countries and Sub‐Saharan Africa_ . New York: UNDP.

- UNEP and WMO. 2011. _Integrated Assessment of Black Carbon and Tropospheric Ozone: Summary for Decision Makers_ . Nairobi: UNEP and WMO.

18
- Kalla, Joshua L. and David E. Broockman. 2023. “Which Narrative Strategies Durably Reduce Prejudice? Evidence from Field and Survey Experiments Supporting the Efficacy of Perspective-getting.” American <u>Journal</u> of Political Science 67(1):185–204.

- Kling, Jeffrey R., Jeffrey B. Liebman and Lawrence F. Katz. 2007. “Experimental Analysis of Neighborhood Effects.” Econometrica 75(1):83–119.

- Kosse, Fabian, Thomas Deckers, Pia Pinger, Hannah Schildberg-H ̈orisch and Armin Falk. 2020. “The Formation of Prosociality: Causal Evidence on the Role of Social Environment.” <u>Journal</u> of Political Economy 128(2):434–467.

- Lowe, Matt. 2021. “Types of Contact: A Field Experiment on Collaborative and Adversarial Caste Integration.” American Economic Review 111(6):1807–44.

- Mousa, Salma. 2020. “Building social cohesion between Christians and Muslims through soccer in post-ISIS Iraq.” Science 369(6505):866–870.

- Nabi, Robin L. and Melanie C. Green. 2015. “The Role of a Narrative’s Emotional Flow in Promoting Persuasive Outcomes.” Media Psychology 18(2):137–162.

- Paluck, Elizabeth Levy and Donald P. Green. 2009. “Prejudice Reduction: What Works? A Review and Assessment of Research and Practice.” Annual Review of Ps cholo <u>y gy</u>

- 60:339–367.

- Paluck, Elizabeth, Roni Porat, Chelsey Clark and Donald Green. 2021. “Prejudice Reduction: Progress and Challenges.” Annual Review of Psychology 72.

- P ́erez-Alb ́eniz, Alicia, Joaqu ́ın De Pa ́ul, Juan Etxeberr ́ıa, Mar ́ıa Paz Montes and Esther Torres. 2003. “Adaptaci ́on de Interpersonal Reactivity Index (IRI) al espa ̃nol.” Psicothema pp. 267–272.

- Simonovits, G ́abor, Gabor Kezdi and Peter Kardos. 2018. “Seeing the World Through the

30
suggesting a lag of a few days between exposure to pollution and health problems.<sup>21</sup> Finally, we take a weighted average of pollution levels of stations in the vicinity of the households. We use only data from stations within 8 kilometers of a household. Similar to previous work, we use an inverse distance interpolation method.<sup>22</sup> We trim some extreme and abnormal observations.<sup>23</sup> In particular, we drop observations with top 1% values of pollution and hours worked. We also drop observations from one station (located in the city center) for years 2007-2009 due to data collection problems and unusually volatile observations.<sup>24</sup> We use observations for this station for years 2010 and 2011 only.

Figure 3 displays the map of Lima city with the location of monitoring stations and census blocks both in the whole sample (yellow dots) and in the final sample used for this study (blue dots). Note that the final sample does not include observations located far from monitoring stations, especially in the north and east, but it is, otherwise, dispersed across the city.<sup>25</sup>

# **3.2 Empirical strategy**

The objective of the empirical analysis is to identify the causal effect of exposure to air pollution on labor supply. The first challenge that we face in achieving this is the likely presence of unobservable factors that may affect both pollution and number of hours worked. For instance, wealthier, better educated households may locate in less polluted areas. There could also be time-varying characteristics, such as seasonal variations in weather and health or local trends in labor markets and pollution levels that may create a spurious correlation between air pollution and labor supply.

Our empirical strategy addresses this potential source of endogeneity in two ways. First, we include a rich set of time-varying controls, such as temperature, humidity, week fixed effects and municipality-by-year fixed effects. Thus, our estimates are not subject to bias from any unobservable determinant of labor supply that is time varying but common across all households in Lima. As we control for municipality-by-year fixed effects, our estimates are also robust to the

> 21In the case of health outcomes, the reference period is two weeks before the interview date (i.e. weeks _t_ and _t −_ 1). In that case, we use measures of pollution in weeks _t −_ 1 and _t −_ 2.

> 22 We use the inverse of the Euclidean distance between the household and a monitoring station as weights.

> 23Using all available data produces similar, but less precise, results.

> 24According to communications with DIGESA’s personnel, from 2007 to 2009 this monitoring station was located in a place that did not fulfill the conditions stipulated in the data collection protocol. For this reason, the station was re-located in 2010.

> 25The final sample is not necessarily representative of Lima city given the changes made to the original sample, i.e., exclusion of areas further away from monitoring stations and non-panel observations.

9
August (which corresponds to the agricultural season for most crops grown in Nigeria) in 2019 and 2020. We then merge these data with the anonymized GPS coordinates with a random offset of the Enumeration Areas (EAs) where individuals reside, as provided in the public use GHS-Panel 2018/19 datasets.<sup>15</sup> Thus, following Vicente-Serrano et al. (2010), individuals are affected by drought when they reside in an EAs whose SPEI is less than ́1. Finally, we used the reports by IFRC to define flood disaster. Specifically, our flood measure takes the value 1 when the individual resides in the states affected by

# **2.2.5 Summary Statistics**

Table 1 displays summary statistics of the sample we use for the main analysis. This ́ sample includes children aged 5 18 years, and we exclude the respondents for whom the schools are still closed at the time of the survey in October 2020, those who are still on leave or refuse to go to school because they are afraid of contracting the coronavirus and those who are waiting for admission. This table indicates that 90% of children attended school before the schools’ closure compared to 82%, who are attending school during COVID-19, i.e. after the schools reopen in 2020.

In 2019, the average age of respondents in the sample is 9.92, and they are mostly the children of the household head (83%). Among the household heads, 41% have never been to school. The average household size is approximately 9. Slightly less than half of the individuals in the sample are girls (48%), and it is observed that 74% of the respondents live in rural areas. More than half (52%) of the individuals have been in contact with their teachers during the school closure period. Individuals were more affected by drought in 2019 than in 2020. Specifically, 56% of respondents were affected by the drought in 2019, compared to 41% in 2020. About half of the respondents (47%) for whom

> 15Given that the COVID-19 NLPS phone survey does not provide the updated GPS coordinates of EAs where interviewed households’ reside, we hypothesize that households interviewed in October 2020 reside in the same locality as in 2018/19. To test this assumption, we assess the households’ mobility for the previous rounds of the GHS survey. Of households surveyed, 93.7% in both the GHS - Panel 2015/16 and the GHS Panel 2018/19 have not changed location a 3-year interval, while 6.97% of households have moved within the same locality, close to the original location. This evidence, along with the context of COVID-19, which led the government to impose certain restrictions, reinforces the assumption that it is unlikely that households have moved from their original location in 2020.

16
differential effect associated with proxy or short modules with respect to land holdings, household size, assets, and the child’s age. For boys, only land holdings are associated with a differential effect of the short questionnaire. Boys from households with larger land size have less of a gap between hours reported by the short vs. detailed modules. Proxy reporting yields greater hours (as in Table 5) but this increase is attenuated for older boys, and is reversed (i.e., proxy respondents generate fewer hours than self-respondents) for boys living in households with a higher level of assets. Although other results are not statistically significant, the small sample size permits only the statistically significant detection of large effects.

Results in Tables 7 and 8 indicate that different survey methods may generate employment statistics that are not only different at the mean, but also vary as key covariates vary. That is, the estimation of the economic relationships of interest to the researcher can be affected by the survey method. For both girls’ and boys’ LFP, we find that the size of the effect of the short versus detailed module varies depending on household assets and the child’s age. Given the central importance in the literature of the effect of household wealth and household size on girls’ participation in domestic duties—like childcare and food preparation—and economic activities—like the processing of food for market sale—our results raise important questions on the empirical estimation of these effects and point to the need for more research in this direction.

# _Cost implications_

Alternative survey designs will have cost implications that have to be weighed against the value of “better” data. The difference in length between the detailed and the short module we used in our experiment was small; using the detailed module added only a few minutes to the average duration of the interview, according to field work reports from enumerators and supervisors. The cost implication of using a detailed rather than a short module, therefore, is also small. The additional cost of printing slightly longer questionnaires and the extra data entry requirement are only marginally larger for the detailed questionnaire.

19
## Figure 7

# Hungary: Sources and Disposition **of** Commercial Bank Revenues

- - % of Average Total Assets (699,137 BHUF) (basP- _cn_ 1990 IAS Data for the four largest commerdal banks)

<!-- Start of picture text --> Net Interest Margin<br>Direct Cost of Intermediatlon<br>interest income,, 15.4<br>0.6 staff costs<br>..:,:4.8 : .<br>interest expense 10.6<br>0.0 depreciabon<br>5.9 3.0 other operating expenses<br>0.9 provisions<br>7.7 . 1.3 income taxes<br>Gross Operati,ng<br>Income<br>Net Non-Interest Income<br>netfeeincome ss;. 5. 13<br>Cost of Capital<br>other net non-interestincome 1.6 .9<br>0.8 dividends<br>foreign exchange valuation gans 0.1<br>1.0 retained earnings<br><!-- End of picture text --> <!-- Start of picture text --> Direct and Implict Taxes<br>-% of average -<br>assets deposits<br>1.) income taxes 1.3<br>2.) dividends paid to State 0.3<br>3.) cost of required reserves 1.4 4.3<br>4.) cost of required liquidity 0.3<br>5.) tax on household interest earnings 0.0 0.0<br>Sum 3.4<br>Sources: Banks Sft Estmale<br><!-- End of picture text -->
# **Does Land Fragmentation Increase the Cost of Cultivation? Evidence from India**

# **1. Introduction**

Concerns about land fragmentation as a constraint to agricultural productivity have long been raised in the policy debate. Many studies find that, irrespective of the potential risk diversification benefits it may bring, having a cultivator’s land distributed among many small fragments, often at large distances from each other, increases production cost by requiring more time to move between fragments and set up equipment or by making use of machinery entirely impossible. Efforts to quantify associated effects face two challenges. First, the number of fragments (or average fragment size) and the distance between them are two distinctly different dimensions of land fragmentation that are not necessarily highly correlated and thus cannot be used interchangeably or as substitutes for each other. Second, producers may try to mitigate negative fragmentation-induced impacts via land market participation or crop choice. Failure to take such behavior into account may bias empirical results. Finally, the impact of fragmentation may not be homogenous across producers and differentiating impacts, for example across farm size classes, could be important to understand the underlying mechanisms.

This paper contributes to the literature by using a national level data set from India to estimate the impact of fragmentation on cost of production in ways that account for these challenges. By focusing on inherited land, an unbiased estimate of fragmentation on input costs is derived. We estimate a translog cost function and associated share equations with measures of fragmentation on the right hand side to recover the elasticity of total production cost and key factor shares with respect to fragmentation as distinct from total land area. To distinguish size- and distance-related effects of fragmentation, we use the Simpson index (as a measure of fragment numbers) as well as mean distances between fragments or fragments and the homestead. To eliminate the scope for endogenous adjustments to fragmented holding structures via land market participation or crop choice, we restrict the sample to households who cultivate only inherited fragments and, within these, conduct analysis only for fragments planted with paddy or wheat. Finally, to allow inferences on the channels through which production cost may be affected and account for the possibility of heterogeneity across farm size groups, we compute the elasticity of different inputs’ cost share with respect to fragmentation and estimate regressions by farm size class.

We find that the effects of fragmentation vary with farm size and that fragmentation-induced increases in the unit cost of production are largest for marginal farmers. A one standard deviation increase in the level of fragmentation as measured by the Simpson index is estimated to raise cost of production by 9.8% on 2
sectors and restricts the equalization of labor productivity (nominal value-added per worker) across sectors. Introducing time-scripts, the labor allocation p ]′ can be written as a function of final consumption shares p , trade shares pLL, and productivity gaps = [llaappaa, llmmppaa, llssppaa p p p p p CC<sup>�</sup> � (7)NN<sup>�</sup> TT where and denote the time-invariant matrices of labor shares pp pp and sectoral linkages .<sup>3</sup> LL = FF�CC<sup>�</sup> , NN<sup>�</sup> , TT �BB , MM Therefore, function pp pp which combines equations (5) and (6) helps solve for the resulting labor jjpp jjkkpp BB MM ββ μμ allocation when one or more vectors among FF(. ) p and p are counterfactually altered. jjpp CC<sup>�</sup> , NN<sup>�</sup> TT

# **3   Data**

The quantitative application of equation (7) requires time-series data on four variables– employment shares p , final consumption shares p , trade shares p and labor market wedges p . Equation (6) allows the construction of LL p using CC<sup>�</sup> p and p . Hence, in essence, the analysis hinges on the NN<sup>�</sup> TT availability of p , p , p , and TT p . LL VV<sup>�</sup> In addition, I also need the estimates of time-invariant labor share vector LL CC<sup>�</sup> NN<sup>�</sup> VV<sup>�</sup> and intermediate share matrix . Equation (7) allows quantifying the impact of over-time changes in pp and on BB sectoral allocation. However, the unavailability of these data for Central American economies hinders pp pp pp MM BB MM this investigation.

I source the required data from multiple sources. However, the raw data appear spurious for some country-years. Perhaps, for this reason, the literature has not undertaken a long-term sectoral analysis of the Central American economies, especially when it comes to labor allocation. I make several adjustments to the raw data before using them. The data appendix contains the details of these adjustments. Still, I note the nature of adjustments briefly as I provide a summary of each series. **Employment shares (** p **):** The employment data are taken from the International Labor Organization’s (ILO) Key Indicators of the Labor Market database (ILO, 2018) that reports LL employment by sector. The database classifies total employment into six sectors – agriculture, manufacturing, construction, mining and utilities, trade and transportation (includes personal &

> 3 Sinha (2019) contains the detailed derivations of the accounting equations.

8