rafmacalaba/data-use-mentions-extended huggingface dataset:
come without costs in terms of loss of productivity (Bedi et al., 2021).

# **7 Conclusions**

In this paper we have explored the impact of severe drought in the local labor markets of Moroccan provinces during the period 2000-2009. We used nationally representative labor force surveys and the SPEI, a state-of-the-art climate indicator, to estimate the causal effects of severe drought shocks on labor market dynamics by observing the employment rate in agriculture and in other less-exposed sectors and the unemployment rate. Our staggered diffin-diff estimates shed light on the effects occurring in Morocco, which is considered a climate hotspot and for which little evidence is available. Importantly, Morocco is a good case study to investigate the interacted effects of climate change on the local labor markets in a socioeconomic context characterized by high demographic expansion, a growing labor force and structural market frictions.

We found that in provinces that experienced a severe and prolonged drought, measured by a 12-month SPEI value lower than -2 s.d., the share of employment in agriculture decreased by about 4.3 p.p. on average. The effect took place in the aftermath of the shock and was long-lasting: the coefficients remain significant and negative up to 5 years after the drought occurred. Moreover, we observed a contemporaneous and significant increase in the unemployment rate, which signals that those shocked workers found difficulty in accessing alternative jobs in less climate-exposed sectors. These figures point to general reallocation failure as a consequence of severe drought shocks, a failure which is more pronounced for the most vulnerable workers. This generates concerns about an exacerbation of climate injustice in the future, if we also consider the historical trend in MENA countries characterized by large unemployment among youth and females (Alfani et al., 2020). However, our study needs to be hedged with some caveats. First, the results rely on a time period (2000-2009) that is only relatively recent, because of missing information on workers’ location in the 20
goods (column 1b). These results further support that the principal channel through which EIAs and distance reinforce one another is through two-way trade in inputs, which likely are less differentiated and of more similar quality than other types of goods. Moreover, our variable of interest is both positive and statistically significant for other goods categories, lending evidence that EIAs help trading partners overcome the hurdle of distance when it comes to final goods for which the quality and variety is of paramount importance to consumers.

In terms of the coefficients on the controls for depth, we observe that—very much like in Figure 2—they tend to increase in magnitude until roughly four provisions, after which they decline to levels similar to zero provisions. This affirms our prior that certain provisions included in the DESTA depth index deter trade flows, and also that there might be an ordering to which provisions are added to agreements among proximate countries (i.e. agreements among proximate countries may include fewer provisions that negatively impact trade flows).

10
Given our use of US import trade data, we also exclude exports to North America (Canada, Mexico and the United States) for the construction of the instrument.

# **_ii. Instrument relevance_**

The first stage results in Table A3 in the Appendix suggest that our instrument, the diffusion of robotics among pre-existing foreign customers is a strong predictor for robot adoption of the home country. The first stage results of our instrumental variable, used throughout the paper, is reported in model 1. The size of the first-stage F statistic (115.8) demonstrates our instrument has strong predictive power for robot adoption in the home country, despite the inclusion of a range of restrictive country-product and country-year fixed effects. A 10% increase in robot use in export customers leads to a 7% increase in robot use amongst their foreign suppliers.

In models 2 to 6, we investigate alternative identification strategies that we not do not employ in the rest of the paper. In model 2, we calculate an alternative instrument, using robot diffusion in (initial) _developed_ country export partners. Trade connections to advanced markets may in principle have greater potential for technology spillovers, because of the wider variety or more sophisticated technologies used there. However, we do not find any additional predictive power of developed country export partners, over export partners more generally, and including this additional instrument halves the first stage F statistic.

In models 3 and 4, we find that our instrument works well as a predictor of robot adoption in both developed and developing countries. The instrument also predictors robot diffusion if we restrict the sample to developing (Model 3) and developed economies (Model 4) however the strength of the instruments are lower than in the pooled sample, reflected in the F statistics of 27.2 and 44.7 respectively. For either sub-sample we do not find any additional predictive power of developed export partners, over export partners more generally. Note we do not split the sample in our main regressions, but rather pool
|||
|ln(GDP p.c. _i)|1.098***|1.807***|||
||(0.233)|(0.318)|||
|ln(GDP p.c. _j)|-0.0776|-1.107|||
||(0.552)|(0.823)|||
|Constant|-6.072|-4.780|-0.410|-1.726**|
||(6.006)|(8.651)|(0.652)|(0.791)|
|Observations|26,160|26,160|19,676|20,574|
|Pseudo R2|0.958|0.918|0.978|0.956|
|Sending FE|Yes|Yes|No|No|
|Receiving FE|Yes|Yes|No|No|
|Year FE|Yes|Yes|No|No|
|Sending FE*Time FE|No|No|Yes|Yes|
|Receiving FE*Time FE|No|No|Yes|Yes|
|LogLik|-18492.28|-23746.92|-16226.61|-20463.96|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand
variables are included (hence, excluding 2007-2008 datasets because of the lack of the distance variables).

Columns (3) and (4) report the probability of working in seasonal employment given different regional road quality leading to towns and markets. Respondents who report average and poor road quality in their regions are, respectively, 1,5 and 5.2 percentage points less likely to work in seasonal employment. Considering that 66% of the respondents employed in agriculture report working whole year round and only 1,3% of them or 370 people report having a second job, this could mean that people living in areas with poor roads are less likely to take seasonal jobs during low agricultural seasons. Lastly, column (5) shows that households are less likely to report any household member working outside of the village if they report having poor or average road quality leading to towns and markets.<sup>17</sup> Table 3 reports the relationship between hours worked, agricultural employment, and reported road quality. The first column shows that people who work in agriculture tend to work less, estimated at 16.5 less hours worked the week before the survey, holding all other variables constant and controlling for the survey month. Descriptive statistics show that non-agricultural workers had worked on average 23 hours the week before the interview, while the non-agricultural workers had worked on average 41 hours. This result is in line with the study done by McCullough (2017). By analyzing the Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) for four African countries - Ethiopia, Malawi, Tanzania and Uganda - she found that each agricultural worker was working on average 700 hours per year compared to the 1850 hours per non-agricultural worker. Our estimates are higher than those of McCullough (2017) but still show significantly large differences between the working hours of agricultural and non-agricultural workers. Column (2) shows that individuals reporting average or poor quality of roads leading to towns and markets, work slightly less than people reporting having
s specialization profile, as captured by export specialization (ES) indices,<sup>17</sup> has remained unchanged during transition. Bulgaria has had “revealed comparative advantage” in unskilled labor and natural resource intensive products in EU markets. Except for natural resource intensive products in 1996-97 and 2001, the values of ES for both groups have been above unity in 1991-2003 (Table 24). However, the intensity of specialization in unskilled labor intensive products in EU markets has been very strong, whereas that in natural resource intensive products has been much weaker and declining.

The changes in the shares of factor intensity groups in EU external imports over 19912003 further corroborate these observations. Bulgarian producers of unskilled intensive products have consistently outperformed their competitors from other countries and seen their share in EU imports growing almost each year over 1991-2003. Performance of natural intensive producers in Bulgarian was rather lackluster. Their share in EU external imports exceeded its 1995 peak level of the 1990’s only in 2002-03 and only by three percentage points. Producers of both skilled labor 17 The export specialization index (ESij) for a product _j_ of country _i_ , is specified here as follows: ESij = (xij/Xi) /(mj/M), where: xij is country i’s exports of product j to the EU; Xi = Σ j xij is country i’s total exports to the EU; Mj = Σ j xij is EU’s total ‘external’ imports of a product _j_ ; M = Σ i Σ j xij is EU’s total external imports. A value for this index below unity indicates a comparative disadvantage. If the index takes a value greater than unity, the country is considered to have a "revealed" comparative advantage in the product. In this particular case, Bulgaria has a revealed comparative advantage in a product if its export of that item as a share of its total exports exceeds the EU imports of the item as a share of EU total imports.
**Table 6. Cash Stock Regressions, Listed Firms data**

|LABELS|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Log of net sales|-0.00<br>(0.822)||||||
|Volatility of Sales||0.06<br>(0.202)|||||
|Total Debt to Assets|||-0.17***<br>(0.003)||||
|Interest Rate||||0.19**<br>(0.012)|||
|Gross Margin|||||0.04<br>(0.557)||
|Net Income||||||0.49***<br>(0.005)|
|year==2002|-0.03**|-0.02|-0.00|-0.01|-0.01|-0.01|
||(0.033)|(0.214)|(0.904)|(0.808)|(0.779)|(0.804)|
|year==2003|-0.03*|-0.02|-0.00|-0.01|-0.01|-0.01|
||(0.067)|(0.424)|(0.958)|(0.878)|(0.714)|(0.736)|
|year==2004|-0.02|-0.02|0.00|-0.00|-0.01|-0.02
.3).

After weighting our random respondent estimates using the re-calibrated phone survey weights (see Section 3), the gap between survey data and administrative records remains substantial in all cases (11 percentage points or 46% of administrative coverage in Burkina Faso, 19.5 percentage points or 76% in Malawi, and 8.9 percentage points or 13% in Uganda; Table 4).<sup>17</sup> Estimates are closest to the survey data when using (proxy-reported) data for all household members. However, differences remain statistically significant in all countries and non-negligible in Burkina Faso (5.7 percentage points or 42% of the administrative coverage rate) and Malawi (5.4 percentage points or 36%).

# **4.3 Measurement errors**

# **Survey mode**

We argue that if phone survey mode effects were driving the misalignment between administrative records and survey estimates of vaccine uptake, there should be no misalignment between administrative records and _in-person_ survey estimates. We test this by comparing the administrative data to the sample of Ethiopia phone survey respondents who were also interviewed in person in the Ethiopia Socioeconomic Survey (ESS). We find a significant discrepancy of 12.6 percentage points (or 38%) between administrative records (33.4% vaccine coverage)<sup>18</sup> and inperson survey estimates (46.0% coverage) in this sample (Table 5). This difference remains substantial at 10.4 percentage points (or 52%) and statistically significant when estimating vaccine coverage based on the full ESS sample, which is representative of the general population. Survey

> 17 At the time of data collection, Malawi and Uganda had already been vaccinating children under the age of 16 for over 8 and over 13 months, respectively. This means that our assumption of zero vaccinated children under the age of 15 that we have to make for comparability between survey data estimates for the adult population (15+)
relatively high contributions to national FDI inflows from the EPZ programs, despite low absolute levels of investment in the EPZs. This perhaps highlights that the relative failure of African EPZ programs to attract investment may be more attributable to a poor investment environment overall than to the failure of the zone programs per se.

**Table 2: EPZ investment statistics**<sup>**9**</sup>

|||**FDI statistics**||
|---|---|---|---|
||**Total EPZ FDI**<br>**stock (2008)**<br>**(US$m)**|**EPZ FDI per**<br>**capita (US$)**<br>**(2000‐08)**|**EPZ FDI as % of**<br>**total national**<br>**(2000‐08)**|
|**Bangladesh**|1,435|6|30|
|**DR**|2,611|141|18|
|**Vietnam**|36,760|325|100|
|**Ghana(Tema)**|68|3|48|
|**Ghana(single units)**|2,806|120||
|**Kenya (EPZs)**|162|||
|**Kenya (single units)**|155|6|20|
|**Nigeria**|N/A|<1|<1|
|**Tanzania**<sup>**10**</sup>|210|5|18|

Source: EPZ FDI author’s compilation from individual country EPZ authorities; national FDI data from UNCTAD How does this investment translate into actual firms operating on
typically been higher.

> 13 In this paper, a syndicated loan is categorized as “European” if at least one European bank is involved in the deal and “non-European” otherwise. Purely domestic deals and deals with only one lender are excluded. Deals with supra-national agency involvement are included.

> 14 This is consistent with a flight home effect in syndicated markets during financial crises, as documented in Gianetti and Laeven (2012). This effect is distinct from a flight to quality effect which is also active during crises. 15 In 2012, lenders from the Asia-Pacific area were the most active project financiers. Bank of Tokyo led the market with a share of 8 per cent, followed by Sumitomo Mitsui Banking Corporation.

> 16 Following the definition by data provider Dealogic, the trade finance data include structured commodity finance, export-credit agency financing, trade flows, trade financing, and supply chain finance.

> 17 These include project, export, structured commodity, and ship and aviation finance.

11
# **Trade Policy and Access to Intermediate Inputs: Quantifying the Welfare Costs of a Fertilizer Shortage**<sup>*</sup>

## Devaki Ghose<sup>†</sup> Eduardo Fraga<sup>‡</sup> Ana Fernandes<sup>§</sup>

Originally published in the Policy Research Working Paper Series in _December 2023_ . This version is updated in _April 2026_ .

To obtain the originally published version, please email prwp@worldbank.org.

_JEL Codes:_ D58; F13; F14; O13; Q17.

_Keywords:_ Trade, industrial policy, import ban, non-tariff measures, fertilizer, and agriculture.

*Eduardo is extremely grateful to Costas Arkolakis, Sam Kortum, Lorenzo Caliendo, and Ana Cecilia Fieler for their continuous support and guidance. We also thank Doug Gollin, Sebastian Sotelo, Heitor Pellegrina, Michael Peters, Bob Rijkers, Nuno Limao, Lauren Falcao Bergquist, Florence Kondylis, Treb Allen, David McKenzie, John Keyser, Gonzalo Varela, Athula Senaratne, Sheu Salau, Manoj Thibbotuwawa, Camille Reverdy, Daan van Soest, Kari Heerman, and Alejandro Forero for helpful discussions. We thank Mutlu Ozdogan for his work on the remote sensing estimates and Yewon Choi for assistance with Global Trade Alert data. The authors gratefully acknowledge support by the “Whole of Economy” Program, a Climate Support facility administered by the World Bank. We also thank the Umbrella Facility for Trade trust fund (financed by the governments of the Netherlands, Norway, Sweden, Switzerland, and the United Kingdom) and the World Bank’s Research Support Budget for financial support. The findings, interpretations, and conclusions expressed in this paper are solely those of the authors and do not necessarily represent the views of the World Bank, its affiliated organizations, or the Executive Directors or the countries they represent. All errors are our responsibility.

> †Corresponding author, World Bank, DECRG. Email: dghose@worldbank.org

> ‡London Business School.
Policy Research Working Paper 8783

# **Abstract**

An expanding body of literature has shown that better management practices can offer significant boosts to firms’ productivity; this research illustrates that firms in South America are no exception. Using recent Enterprise Survey data from seven countries in South America (Argentina, Bolivia, Colombia, Ecuador, Paraguay, Peru, and Uruguay), the paper explores the various dimensions and drivers of management practices and analyzes how they are related to productivity. This is an important topic to investigate, given the lagging levels of productivity growth in the region. If management practices can boost firms’ productivity, this may be a cost-effective way to accelerate economic growth. The results show that improved management practices are associated with higher levels of productivity in all countries, and it is the impact of improved management specifically in larger firms that is driving the overall results. Indeed, in some countries, specifically Argentina, Paraguay, and Peru, it is only among larger firms that there is a positive link between management practices and productivity.

This paper is a product of the Global Indicators Group, Development Economics. 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/research. The authors may be contacted at mhyland@worldbank.org.

_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
|16,831|53.4|1985|
|Lebanon|20,338|18.1|-|-|70,510|39.0|1984|
|(iar|-|-|779|57.4|3,894|62.7|1984|
|Saudi Arabia|3,625|33|26,437|20.1|<br>79,356|35.8|1983|
|Syria|32,653|<br>16.8|65,348|22A|123,735|<br>29.7|1983|
|u.4.|-|-|-|-|6,326|549|1984|
|Yemaen,<br>AR.|_-_|_-_|2,408|102|4,519|<br>112|1980|
|Yemen, vmD.R|<br>-|_|934b|182|3,645|523|1983**c**|

**a** Excuding Al-Azhar Univerity. b Figures for 1974.

**C** Ibrd set of fips for anl leIels of higher education.

Souw" UNESCO **_Statii YeVbooks_** 1977, 1986 _&8 cULmS Sws and Reearch in Satiaics_ (CSR-B-54, Januaiy, 1987).

Cairo, Ain Shams, Alexandria and Assiut universities in Egypt grew from 109,700 in 1963-64 (Qubain, p. 71) to about 405,000 around 1984. Adding to this the enrollment in the seven other Egyptian universities established in the 1970s and the figures increase appreciably.

The past two decades witnessed heavy demand for entry to universities, coupled with socio-political pressure to meet the demand. There has been strong parental motivation to educate children at higher levels, especially sons. Table 4 provides enrollment figures in universities and equivalent institutions (excluding the non-degree colleges and institutes, unless otherwise specified) of sixteen countries during the
analysis that compares the difference in child sex ratios between Maharashtra and its neighboring states before and after the passage of the PNDT Act.

The key assumptions of this empirical strategy are that (a) Maharashtra and its neighboring states were on similar trajectories before 1988, i.e. they had parallel trends in child sex ratios before 1988; (b) couples in Maharashtra did not go to villages in neighboring states seeking sex selective abortions between 1988 and 1994 , i.e. there were no spillover effects; (c) the national ban did not affect enforcement in Maharashtra; and (d) the ban was similarly implemented in Maharashtra and its neighboring states.

The data for this study came from village and town level longitudinal data from the 1991 and 2001 Indian censuses. To account for spillover effects, the authors compare villages in Maharashtra to villages in neighboring states that are not located along the border of Maharashtra. This way, the newly treated villages are culturally similar to Maharashtrian villages but are geographically distant enough to prevent spillovers. The evaluation results suggest that after controlling for individual and community level factors, and spillover possibilities, there was a 14 to 26 percentage point increase in the female-to-male child sex ratio. The ban resulted in 106,000 more surviving girls in the 0-6 age groups in the newly treated rural areas.

While these results suggest that the ban was effective in achieving a less imbalanced sex ratio, caution must be used when interpreting them. First, the outcome variable in this study is the child sex ratio (which is affected by prenatal and postnatal sex discrimination), so the authors cannot isolate the effect of the ban on prenatal sex selection or SRB. Second, as the authors argue themselves, evidence on parallel trends in sex ratios before 1988 is not conclusive. Third, the available data do not allow the authors to test the assumption that the Maharashtra ban and the national ban were similarly implemented, or that the national ban did not affect enforcement in Maharashtra.

# _Republic of Korea_

In Korea
**Data Collection Timeline and Sample** : Figure 1 shows the timeline for data collection. Between January and September 2015, we located 1,104 facilities in the three counties and completed the baseline in 1,027 for a response rate of 93%. Following a delay of 15 months between the completion of the baseline and the start of the intervention we updated the census between October and November 2016, increasing the number of facilities to 1,258. For this update we collected basic characteristics such as ownership, level and location, but did not complete a full baseline survey. These are the facilities we used for the randomization.

The intervention then took place between November 2016 and December 2017 and the endline was completed between March and August 2018. The average time elapsed between the last inspection or closure visit, and the endline for all facilities was 7 months, although this varied from 4 to 18 months, a variation that we exploit when we examine the impact of program duration on impact.

During the endline survey we counted 1,322 facilities and completed the endline in 1,285 facilities for a 97% response rate.<sup>17</sup> Of these, 173 were new facilities which we allocated to existing markets using a nearest-neighbor algorithm and 90 were facilities that had been missed previously, with 4.5% market share at endline.<sup>18</sup> For the treatment impacts, we always use the 1,285 facilities surveyed at endline. When we examine impacts on facilities that were open at the baseline, we use the 1,258 facilities we located at baseline or during the pre-randomization update plus the 90 missed facilities, for a total of 1,348 facilities. When we estimate impacts on exit/entry, we use all facilities operational at randomization (1,348) and/or endline (1,319) regardless of whether they have a completed survey.

# **III.3 Data Sources and Description of Main Outcomes