rafmacalaba/data-use-mentions-extended huggingface dataset:
36 To more formally assess the model’s ability to explain the observed post-ban changes in night lights, we run a dynamic event study analysis at the district-month level, assigning districts to the treatment or control group depending on whether they were above or below the median of fertilizer importance at baseline (see Figure VIIIc). The outcome variable is (the log of) average night light intensity and the treatment period is defined as starting in May 2021 (i.e. the month when the ban was implemented). The results, displayed in Appendix Figure A15, show that night lights decreased by 26.7 log points in treated (i.e. relatively fertilizer-dependent) districts relative to control (i.e. less fertilizer-dependent) districts eight months into the ban, corresponding to an income loss of 7.7%. Using the same assignment of districts into treated and control groups, model-predicted real income losses (Figure VIIIb) are 6.3 percentage points worse in treated districts than in control districts, on average. Therefore, by comparing the model’s predictions to the night lights evidence, we conclude that the model can explain 82% of the differential real income loss of fertilizer-dependent districts observed in the data during the relevant time period.

Finally, we take advantage of our night lights data to further confirm that our main results are not driven by the potentially confounding influence of the Covid-19 pandemic (see Section IV.A). Figure VIIId shows that the spatial pattern of income losses (proxied by night lights) in the period following the pandemic’s arrival in March 2020 was very different from the pattern of losses in the post-ban period (Figure VIIIa), indicating that potentially lingering effects of the pandemic cannot explain the negative economic effects that we attribute to the fertilizer ban.

# **VIII Conclusion**

Trade policy and geopolitical events increasingly affect the ability of many countries to access critical intermediate inputs. This paper quantifies the costs of a nationwide fertilizer shortage and explores its interaction with
proxied individuals, are consistent with Kilic et al. as they compare the two survey methods with both annual and weekly recall.

Finally, the findings in our paper have implications for a range of labor linked papers, especially those in rural settings that rely on similarly constructed data.<sup>5</sup> Large, primarily descriptive literatures currently exist on rural labor, gender and age differences in labor contributions, and rural income diversification. Our results suggest that all these estimates could be meaningfully impacted by reliance on long recall-based data and further distorted by the use of proxy reporting.

We discuss our survey experiment and data in Section 2 and empirical strategy in Section 3. Results are presented in Section 4, followed by a brief discussion in Section 5, and conclusion in Section 6.

# **2. Data**

Our survey experiment is built on a sample of households included in a separate study, the MwAPATA Instituteʼs Malawi Rural Agricultural Livelihood Survey (MRALS), conducted inperson in the fourth quarter of 2019. Their original sample is representative of farm households in rural areas of eight selected districts and used a multitopic household survey to collect information on demographics, health, socio-economic status, time use, and agricultural production from a sample of 3,259 households (Muyanga et al. 2020).<sup>6</sup>

# _2.1 Sample and survey structure_

Our analysis uses a sample of 701 households for whom a complete set of four quarterly phoneinterviews were successfully completed. Starting with the MRALS sample of 3,259 households only 2,435 households had contact phone numbers and were therefore eligible for inclusion in our phone-based survey. Stratifying by region, 1,505 eligible households were randomly selected to be included in our study. 1,020 of these households were successfully contacted and included in Round 1 and ultimately, 701 households were successfully reached for the full set of interviews.

> 5 See Dzanku (2020), Abay et al. (2019), Asfaw et al
Policy Research Working Paper 10691

# **Abstract**

This paper uses the context of the Islamic Republic of IranIraq war in 1980–88 to study the long-term impacts of exposure to the war during school years on educational attainment and labor market outcomes in Iraq. The analysis uses an event study and the Iraq Household Socio-Economic Surveys 2006–2007. The findings show that the Islamic Republic of Iran-Iraq conflict had a negative impact on the social welfare of men who were exposed to the war, including on social security, pension plan, health care, paid leave, and job permanence, while little impact on women is found. Additionally, the conflict reduced wages for both men and women. Furthermore, men who were exposed to the conflict were more likely to work in dangerous jobs or without air conditioning, while no evidence on this is found for women. The paper also shows the impact of the intensity of the Islamic Republic of Iran-Iraq war on educational attainment and labor market outcomes. It documents the education channel through which the war affects labor market outcomes, showing that the war decreased the educational levels of men and women born between 1971 and 1981. The findings are robust to a variety of robustness checks and falsification tests.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 hoangxuantrung3012@gmail.com.

_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
(2003) estimates of ad-valorem equivalents of speci fi c tariffs. We also need information on tariffs in the rest-of-the-world to estimate the impact that a potential 33% reduction in bound tariffs in Doha will have on world prices.<sup>34</sup> Import and export data comes from the UN’s Comtrade. We take the average between 2001 and 2003 to smooth any year speci fi c shock. If data is missing for a particular country, then we use data for 2000 to calculate the average. For the few countries for which there is no data available between 2000 and 2003, we mirror the data using partners’ trade data. The partial equilibrium model also requires estimates of import demand and export supply elasticities at the tariff line level for every country in the sample. These are taken from Kee, Nicita and Olarreaga (2004a, 2004b).<sup>35</sup> Table A3 in the Appendix provides some summary statistics. The export-weighted tariff faced by bene fi ciaries countries in the European Union is zero, whereas it can be non-zero in Japan and the United States. This is because the European Union has a very generous LDC speci fi c preference program (Everything But Arms), whereas Japan and the United States preferences to LDC generally have exemptions. Notably, Bangladesh bene fi ts from almost no preference on its $ 2.6 billion of exports to the United States, whereas it faces an export weighted tariff of almost 11 percent. Similarly Vanuatu faces an export-weighted average tariff of almost 18 percent on its 17 million dollar of exports to the United States. These LDCs represent a negligible share of total TRIAD imports, as assumed in the model. Bangladesh’s share, the largest, is only 0.17 percent of total TRIAD imports.

# 3.3 Empirical Results

Table 1 provides estimates of the subsidy that the TRIAD would pay in the absence of tariff reductions, which, as we previously noted, is equal to the tariff revenue currently forgone due
, et al., 2000) and to create new jobs (Audretsch, et al., 2006; McMillan and Woodruff. 2002). For example, studies using longitudinal data sets on

> 3 Our results are also consistent with de Mel, et al. (2012) who find that firms in Sri Lanka rationally refrain from formalizing, since they see few benefits from doing so – while a few firms seem to be suboptimally informal. Furthermore, their results suggest that a relatively modest increase in the net benefits to firms of formalizing could dramatically increase the rate of formalization.

4
1%<br>Back office Manufacturing<br>13.4% Back office<br>Post sales<br>Post manufacture Post manufacture<br>Post sales<br>4.4%<br>48.1% 33.2%<br>8.3%<br>Source: EIS data from Turkey; author’s calculations.<br><!-- End of picture text --> 12 A full categorization of all services into production stages by their 3‐digit NACE code is presented in Table A2 in Annex 1.

19
Philip R. and Gian Maria Milesi-Ferretti, (2007). "The External Wealth of Nations Mark II" , _Journal of International Economics_ 73, 223-250, November 2007

- Mankiw, N.G., D. Romer, and D.N. Weil (1992), “A Contribution to the Empirics of Economic Growth”, _The Quarterly Journal of Economics_ 107(2):407-437.

- Mauro, Paolo.(1995).  “Corruption and Growth.” _Quarterly Journal of Economics_ , 1995, 110(3), pp. 681-712.

- Mincer, J. (1974) _Schooling, Experience and Earnings_ . Columbia University Press: New York.

- Mundlak, Y. (1996) "Production Function Estimation: Reviving the Primal," _Econometrica_ 64: 431-38 .

- Paldam, Martin, and Gert Tinggaard Svendse (2006) “Social Capital Database for a CrossCountry Study.” In _Trust, Social Capital and Economic Growth: an International Comparison_ , ed. M. Paldam and G. T. Svendsen. Cheltenham, U.K.: Edward Elgar.

- Psacharopoulos, G., Patrinos, H., 2004. Returns to investment in education: A further update. _Education Economics_ 12(2), 111-134.

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relationship-more stable economies may promote greater financial sector development."<sup>7</sup> Clearly, the legal origin of a country, which has been linked to financial development, is a predetermined and exogenous variable in the above estimations.'<sup>8</sup> Hence, we are able to use it to instrument for financial development in our cross-country data set. Our cross-country data set also has some important disadvantages compared to the panel we used for the main results. Not the least of which is the small size of our data set; data availability restricts our analysis to only 27 countries. In addition, while we believe that the additional variables we add are important components of the country-specific fixed effect, we do not believe they include all important country characteristics. As a result of these disadvantages, we are cautious in our interpretation of the cross-country results and present them only as supplementary to our main results. Table 6 presents the results of this estimation for the variability of per capita consumption growth.1<sup>9</sup>

# [See Table 6.]

Our results for the per capita consumption growth regressions are generally supportive of the conclusions we drew above. Higher levels of _BANK, PRIVATE_ and _PRIVY_ are associated with lower volatility of per capita consumption. Per capita GDP and investment regressions, not reported here, generate negative but insignificant coefficients in three of the four GDP regressions and all of the investment regressions.

The coefficients on the additional country characteristics we were able to include in the cross-country regressions also suggest some interesting conclusions. Stronger accounting standards are associated with lower variability of consumption in two regressions, while exchange rate volatility is robustly and positively related to consumption variability in three out of four specifications. Surprisingly, less corrupt governments are

> 1<sup>7</sup> While our use of initial financial development and subsequent volatility somewhat addresses this issue, it is interesting to reverse this estimation and examine the effect of initial volatility and subsequent financial development.
Table 7. Linear regression model estimates for the aggregate production function parameters (dependent variable: - <u>log output per worker log emissions per worker)</u>

||skills measure<br>not rescaled|skills and<br>capital measure<br>rescaled|
|---|---|---|
|log emissions per worker (negative)|0.24***|0.24***|
||(0.04)|(0.03)|
|log skills per worker x (log capital per worker - log emissions per worker)|0.07<br>(0.09)|0.07***<br>(0.003)|
|log capital per worker - log emissions per worker|0.30<br>(0.46)||
|log skills per worker|-0.37<br>(1.01)||
|Industry fixed effects included|yes|yes|
|Constant|4.07|5.63***|
||(4.83)|(0.19)|
|Observations|317|317|
|R-square|0.96|0.96|

Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted as ***, **, and *, respectively.

_Estimating the skill premium_ In steady state, the skill premium, h, is constant. This was estimated for each country using the PIAAC data set with a simple, univariate linear regression model with earnings as the dependent ww variable and literacy skills, rescaled as described above, as the independent variable. This estimate of h is used to determine the country-specific capital augmenting technology factor, , by setting such that equation (2) holds. KK,ii ww AA KK,ii AA _Estimating the skills cost function_ In equilibrium, individual
in which policies of import substitution predominated. (It goes without saying that such policies are anomalous from the perspective of the World Bank’s Doing Business database and Cato Institute’s Economic Freedom indicators [World Bank, various years; Gwartney, Lawson, and Hall 2012]).

But in Chapter 7, Firpo and Pieri argue that by the late 1970s, the country had run out of room for continuous and long-term structural change, at least along broad intersectoral lines, and had to rely on within-sector enhancements—like investing in human capital and new technologies (in agriculture especially) and improving institutions. In fact, they assert that efforts aimed at reversing this natural trend (by enlarging manufacturing and contracting agriculture) failed “and the early years of the 1980s of slow growth can serve as evidence of those efforts.” So what path remains open for Brazil today? The authors argue that the Brazilian experience suggests a return to the old policies is likely to fail. They believe horizontal, across-the-board policies are more likely to spur productivity within sectors than selective policies that give priority to some sectors over others. Given where Brazil stands in term of its stage of development, it is reasonable to expect that future growth will have to rely predominantly on investment in fundamentals (institutions and human capital), and that broad patterns of structural change will play a comparatively small role. In particular, it will be difficult for Brazil to reindustrialize. But there are still strategic opportunities that could be exploited by a nimble government. If used well, the country’s deep-water oil reserves should boost not only oil exports but also a range of associated services and industries at home.

As for Botswana, its story is similar to Brazil’s, in that the share of employment in agriculture fell dramatically between 1970 and 1990. But unlike Brazil, the decline in agriculture’s share of employment was almost entirely matched by an increase in the share of the labor force in services. Moreover, numerous government efforts to industrialize never
<!-- Start of picture text --> low−income country<br>middle−income country<br>high−income country<br>country industrialized in 1985<br>−2 −1 0 1 2<br>Coefficient on Interaction With Drought<br><!-- End of picture text --> Figure 3: **Heterogeneity of the Observed Effect by Country-Level Income** . The whisker plot displays the coefficients on interactions between a given country characteristic and the and measure of that we use in our main spatially temporally lagged drought analyses.

interactions, high-income nations as well as those that were already industrialized in the starting year of our dataset experience a comparatively diminished impact of drought on urban growth.

# **6.2 Push vs. Pull Factors**

Given that the bulk of the effect of drought on urban growth is clearly concentrated among developing countries, we next evaluate more specific causal mechanisms within the sample of low- and middle-income countries. The first three whiskers in Figure 4 display the characteristics of the source cells (cells surrounding the cell in which builtup pixels are measured), thus focusing on a set of attributes conveniently labeled as factors. The of these factors is the share of a cell that is as “push” first given classified agriculturally cultivated land. This measure of coverage of agricultural areas in each cell comes from the Globcover 2009 dataset v.2.3. We create a simple indicator that specifies whether a given cell lies above the developing world median in terms of agricultural land. We find that among developing countries, greater reliance on agriculture does indeed amplify the effect of drought on urban growth. This is in line with the reasoning 19
2008, indicating wider and better internet access. This suggests that consumers in local markets are able to conveniently check and compare prices online instead of having to visit local vendors. The HCES data set shows that the average real term expenditure on mobile cards in 2004-0 5 was 907.4 birr whereas it surged to 1,436.2 birr in 2010-11.

By using the expenditure levels, we postulate that households in a woreda would have access to mobile internet once the average household expenditures on mobile cards exceed certain points. As the 3G rollout in Ethiopia started in 2008, we compare changes in the expenditure levels between 2004-05 and 2010-11 and assign treated and untreated woredas. Specifically, if changes in real term average expenditure from 2004 to 2010 in a woreda exceeds the median of the distribution, the woreda is coded as treated (1 in the treatment variable); 0 otherwise. Given the limited publicly available data on internet access in Ethiopia at the woreda level, these binary measures of internet access are the best indicators this study can currently use.

# **3.2 Firm Performance Measures**

In this paper, several firm performance measures are used to estimate the impact of local market’s mobile internet access status. The focus is on firm markups and productivity measures. Our measurement of TFP relied upon the methodology developed by Ackerberg et al. (2015) using a structural approach, and the estimation of markups was done without making any assumptions beyond cost-minimization behavior by firms using the econometric framework by DeLoecker et al. (2012 and 2018).

13
climate change (IPCC, 2014) and the area where LSMS-ISA surveys (which we use as benchmark in the assessment of data gaps in household survey data to understand the climatemigration nexus) are currently implemented.

A work by Lilleør and Van den Broeck (2011) in the northern highlands of Ethiopia revealed that environmental stress shapes migration primarily through impacts on household production. Di Falco, Veronesi and Yesuf (2011) carry out a study based on a survey conducted on 1,000 farm households located within the Nile Basin of Ethiopia in 2005. They find that about 58% and 42% of farm households had used no adaptation strategies in response to long-term changes in temperature and rainfall, respectively, and that migration is one among many adaptation strategies, adopted by less than 5% of

> 6 For a recent review of the literature on population exposure to sea level rise and migration, see <mark>McMichael et al. (2020).</mark>

13
## subcategories alongside each other.

The stock of trees on farms identified in our study likely represents a lower bound. First, home gardens are plausibly underreported as plots (and thus also trees in home gardens) and trees with no immediate productive function may have been left out of household questionnaires. Second, respondents may not recall all trees on their lands or may be hesitant to report them where, for example, colonial legacies of state control of tree resources persist (Ribot, 1999). Lastly, the study was unable to classify a few species for which only the local name was available (Table A.1). Yet, such omissions would especially affect the number of trees reported, and not so much their incidence or the share of land allocated to trees (for each plot it is recorded whether trees are present or not). Consequently, this paper focuses on analyzing the prevalence of trees on farms and the share of land allocated to trees as opposed to the number of trees per se.

# 2.2 Contribution to Household Income and Welfare

To examine the contribution of trees on farms to farmers’ livelihoods, three indicators are examined: 1) how tree products are used (as a source of cash or mainly for own use or consumption); 2) their share in household crop and income portfolios (as an indication of their direct economic importance) and 3) the consumption levels among farmers with and without trees on farms. As a broader reflection of overall wealth, the last of these indicators also captures some of the more indirect contributions of trees on farms such as soil conservation, nitrogen fixing, water regulation, and carbon sequestration (Booth and Wickens, 1988; Nair, 2007; Place and Garrity, 2015) or their use as fodder for livestock or provider of organic fertilizer. None of these indirect aspects is typically addressed in the LSMS-ISA surveys (or household surveys more generally).

Information on the harvested amounts of the tree products and their different uses (sale,
trade remedy could be particularly reliant on the United States for bilateral assistance. Therefore, we expect the larger is the share of total aid received by the foreign country that derives from the U.S., the less likely is that country to initiate a dispute against the U.S. The bilateral aid data is derived from OECD (2001). In order to implement this, in specification (6) we also _drop_ the foreign exporter’s diversification variable, in case this is highly collinear with either the aid relationship or legal capacity variables so as to confound their estimated impact. While the results do indicate a negative relationship between the foreign country’s reliance on the U.S. for bilateral aid and its dispute settlement decision, the size of the estimate is not statistically significant at conventional levels. Furthermore, the parameter estimates for the variables on legal capacity in specification (6) are also virtually unchanged, and the log likelihood is substantially higher than in the other specifications when the diversity variable is included. Thus, the lack of empirical importance of the proxies for the litigation costs and the political economy relationship stands in contrast to the evidence presented in the related research of Bown (2004b), which investigates the participation decisions of exporting countries adversely affected 21