rafmacalaba/data-use-mentions-extended huggingface dataset:
</sup> measures of the number of workers ( _NRoW_<sup>_m_) and farmers (</sup><sup>_N F_</sup> _RoW_<sup>) described in Appendix A.K. All</sup> monetary variables expressed in USD are converted into LKR using the 2019 exchange rate. The international price of fertilizer, _p_<sup>_f_</sup> _RoW_<sup>=0.31 USD/kg,from our trade data (see Appendix A.B), is</sup> converted to LKR using the 2019 exchange rate.<sup>119</sup>

# **F Equivalent Variation**

Since we are interested in the welfare effects of the fertilizer import ban, we use our estimated QSM of trade and agriculture to estimate such effects in Section VII. However, to do that we must first address some difficulties that emerge when using PIGL preferences. Careful inspection of equation (12) shows that indirect utility can be negative for some combinations of prices and income.<sup>120</sup> As a result, it is not straightforward to express utility as “real income” (i.e. nominal income divided by a price index), as it is often done for other classes of preferences such as CES. Therefore, we propose an alternative welfare metric to measure changes in welfare between any two equilibria, namely, the EV.

Suppose we are comparing a given initial equilibrium to a given final equilibrium. Starting from the initial equilibrium, the EV for a given agent is defined as the change in income that would leave that agent with the same level of utility as she has in the final equilibrium if prices were held constant at their initial levels. Formally, the EV for that hypothetical agent living in region _n_ is given by: where _v_ 1 is the agent’s utility in the final equilibrium, _y_ 0 is her income in the initial equilibrium, ( _Pn,
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Smith, J., McArdle, J. J.
14 members. In addition, two other statistics are given. First, the 1988 regional orientation index is shown along with the 1988-94 index change. The intention here is to indicate the extent to which a reorientation of trade toward the region contributed to each product's dynamism. Second an estimate of each product's _relative_ labor (capital) intensity in production is also given.'<sup>0</sup> This index was drawn from a World Bank study and takes a value of 100 for products whose labor intensity is average relative to all manufacturing activity, while increasing values above this level identify goods that are more capital intensive in production (conversely, the index will decline below 100 for products that are increasingly labor intensive). Since economic theory holds that developing countries do not have a comparative advantage in capital intensive goods the table attempts to determine whether trade has been distorted in this direction. it

# '°The factor intensity for industry j (Lj) is defned as,

(4) _L)j_ = (Vj - Nj) - (Vt _+Nt)-_ 100 where Vj and V, represent value added in industry j and all US manufacturing respectively, while Nj and N, represent the number of workers in the industry and all manufacturing activity. Two points should be noted. First, studies by the National Bureau of Economic Research (Lary 1968) show that products manufactured by labor intensive processes in the U.S. are also manufactured by relatively labor intensive processes in other countries, although, quite clearly, the levels of use may differ. On this basis Lary justified the use of US Census of Manufactures data for computation of the index. Second, the reader should note that there is an inverse relation between the numeric value of the index and the labor intensity of a given product. That is, the lower the numeric value the higher the labor intensity. See Yeats (1989) for details concerning the computation of the index and the results reported
Productivity: Evidence from Large Coffee Producers in Guatemala.” Working Paper R-473. Inter American Development Bank, Washington, D.C.

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- World Bank. (2004b). How well do SME Programs Work? Evaluating Mexico’s SME Programs Using Panel Firm Data. Washington DC: The World Bank. Mimeo.

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33
access to a city of more than 50,000 inhabitants, temperature, and elevation. _FE_ is the year and country fixed-effects. The coefficients _β_ capture the correlation between access to a type of infrastructure on the different sectoral employment shares, while the coefficients _γ_ capture the infrastructure interaction terms.

We identify several identification challenges. Infrastructure investments are likely endogenously allocated with respect to the outcomes of interest. Given the high cost of infrastructure investments, conscious allocation decisions are to be expected (by, for example, targeting high growth potential locations or politically demanded locations). Measurement error in the righthand side variables may lead to attenuation bias (from, for example, inaccurate information on the timing of infrastructure expansion or imprecise historic road and grid maps). Measurement errors, which are expected to be large in this case, lead to an OLS estimate that is biased toward zero.

We report results for the unbalanced panel of districts that include at least one EA. We first estimate local average associations of the three infrastructure investments—roads, electricity, and Internet—and the interaction between these investments on sectoral employment at the district-year level. Then we analyze within-country heterogeneity in structural transformation across districts. All regressions exclude the Somali region of Ethiopia, because coverage of this region in the DHS data is poor.

**Average Effects in Ethiopia and Kenya** We start with a regression that includes Ethiopia and Kenya, and then compare the results by country. Throughout, standard errors are clustered at the district level, the level of treatment. Access to roads, especially when complemented with access to electricity, is associated with a transformation away from agriculture in the Horn of Africa. Table 2 reports the results of pooling data from Ethiopia and Kenya for all years. Having access to a paved road at the district level is associated with a 4 percentage-point reduction in the employment share of agriculture and a 2 percentage-point increase in the employment share of manufacturing. Combined investments in roads and electricity are associated with a major shift from agriculture to
.g., Albouy and Lequien 2009; Gathmann et al. 2015), health (e.g., Kemptner et al. 2011), crime (e.g., Bell et al. 2016), religion (e.g., Hungerman 2014), preferences (e.g., Yang 2021), and immigration (e.g., Cavaille and Marshall 2018).</mark>

# **<mark>3.4. Use of Compulsory Schooling Laws to Study the Impact on Environmental Behaviors</mark>**

<mark>There has recently been some interest in using regression discontinuity design with changes in compulsory schooling laws to analyze whether more schooling improves climate change literacy and pro-environmental attitudes and behaviors later in life. Using the raising of school leaving age (ROSLA) law from September 1972 which increased school leaving age from 15 to 16 years in England as a natural experiment, Powdthavee (2021) shows that remaining in school because of the reform causally reduces people’s unwillingness to change their behaviors for the environment and their perception that climate change is too far in the future to worry. However, Powdthavee finds little evidence that more education improves the pro-environmental behaviors of those who were affected by the reform. This raises an important question of whether policies aimed at improving climate change awareness through education can effectively produce long-lasting</mark> changes in pro-environmental behaviors.

<mark>For Europe as a region, using a regression discontinuity design to instrument for educational attainment, Meyer (2015) uses changes in compulsory education laws in the 20</mark><sup>th</sup> <mark>century as a source of exogenous variation. Meyer finds strong evidence of a positive average treatment effect of increased education on pro-environmental behavior. Using two waves of Eurobarometer surveys, he finds a positive local average treatment effect for 7 of 8 pro-environmental behaviors. An analysis of related questions on the survey supports the notion that education causes individuals to
area of ocean and time period.

Estimating the causal effect of the temporary spatial closures policy requires counterfactual areas and times that could have been closed and are comparable to closures declared by the regulator. To address this challenge, I generate “potential closures” by creating an algorithm that mimics the regulator’s closure rule and takes as its input the same data the

> 1In my regressions, I correct for misreporting to the regulator by matching vessel-reported data to percentage juvenile measured by third-party inspectors. Third-party inspector data is not used by the regulator to determine closures.

3
for the full sample and 3.4 percentage points for the restricted sample.

Data from the exit interviews conducted at the health facilities also contain some information on the rationales for the different choices made by women of different socioeconomic status. During the exit interviews, the women were asked the primary reason they chose the health facility where they received care. Proximity of the facility to their residence was the most frequent answer given by women on both sides of the wealth median. However, women above median wealth were four percentage points more likely to respond that the reason was the high quality of services and eight percentage points more likely to say they chose the facility because of the confidence they had in the staff. There is also a statistically significant relationship between wealth and using some mode of transportation to get to the consultation. Twelve percent of women above median wealth reported using transportation, while 5 percent of those below did so. This suggests that, at least to an extent, the choice of facilities by

> 14 Some villages were closer to a health post or center in a neighboring catchment area.

12
affects the probability a child is underweight. As an indicator of household food insecurity, the coefficient on the BMI variable suggests that children in a household where the mother has a low BMI are about 6 percent more likely to be underweight. Children living in a female headed household are also 3 to 4 percent also more likely to be malnourished. The coefficient on household size is not significant in any of the regressions. The number of children under 5 years of age, however, has an adverse impact on the probability of a child being stunted but not underweight. It increases the probability of a child being stunted by 2.5 percent.

> 14 Sahn and Stifel (2002) examine whether mother’s schooling has a higher impact on daughter’s than son’s nutrition and whether father’s education favors son’s nutrition using DHS data for 14 African countries. 15 The generalized Hausman test does not reject the consistency of the estimated model which includes the mother’s BMI.

20
the second war from 1983 to 1987. Atlas and Licklider (1999) depict the first war as a “blackwhite civil war” in which the coalition of two guerrilla forces maintained cohesion against a common white enemy and the second war as “a black-on-black armed conflict between former civil war allies” who “had fought to bring Ian Smith’s Rhodesia to an end” (Atlas and Licklider 1999, 40). However, Atlas and Licklider also note that the relationship between the two guerrilla parties and their leaders was fraught even during the first war. Atlas and Licklider’s efforts to justify separating the two wars they identify for Zimbabwe actually point to the close connections and inseparability of the two wars. More important, the preoccupation with death counts comes at the expense of understanding the history of how the two violent episodes are connected (Kriger 2003).

A number of general points can be made based on further examination of the PRIO/Uppsala data presented in table 1. Points 1–4 relate to nonrandom errors in the classification of the dependent variable, and point 5 addresses the question of autocorrelation.

1. The strictest rule for coding a civil war that seems applicable to the PRIO/Uppsala data is “internal armed conflict” (type 3, denoted by X and O) and “war” intensity (the threshold based on 1,000 battle deaths in any one year, reported in row 1 for each country that has two rows). Countries with at least one civil war with this criterion are Algeria, Angola, Burundi, Chad, Congo-Kinshasa, Ethiopia, Liberia, Morocco, Mozambique, Nigeria, Rwanda, Sierra Leone, Somalia, South Africa, Sudan, Uganda, and Zimbabwe. These instances of civil war are far fewer than suggested by a cursory reading of the narrative literature on Africa. Cameroon, Central African Republic, Congo-Brazzaville, Côte d’Ivoire, Kenya (Shifta War, 1963 to 1967), Mali, Niger, and Senegal are all plausible cases of civil
# **4.2 Country‐Specific Results**

This section presents the regression findings for the country‐specific cases of Malaysia, Vietnam and China. Further details of the survey questions and variables covered in the regressions, as well as some summary statistics for each of the specific country data sets for Malaysia, Vietnam and China, are provided in Annexes B, C and D, respectively.

# **4.2.1 Malaysia**

For Malaysia, we have data on the extent to which each firm has purchased, used and produced intangible capital such as patent, goodwill, work in progress (including imports of both new and used intangible assets), and to the amount of R&D spending for every firm. Used assets are the purchases of assets previously used in Malaysia including those reconditioned or modified before acquisition in the country. Purchased assets are newly bought assets and finally, produced assets are assets produced by the establishment in Malaysia for its own use. Data only span the years after 2008, because of its use of the MSIC 2008 classification which neatly corresponds to ISIC Rev. 4. However, this leaves us with only two years for the analysis, namely 2010 and 2015, which is demanding if we apply year fixed effects. All variables are transformed in a binary mode so that positive values of greater than 0 will be assigned a 1 and 0 otherwise.

Before turning to the regression results, Figure 7 provides a descriptive examination of the main variables used for the empirical specification. The graph plots the IO coefficients of ICT‐services inputs for Malaysia on the horizontal axis against a composite indicator of all the four firm‐level innovation variables from our Malaysian data set, which we call innovation score, and which is summarized into the INNO term. This Innovation Score is then computed as ∑INNO�� ��N� where _N_ is the total number of questions. The innovation score is averaged by sector and year. In this graph, the fitted values line
**Table 4: Indices of Inequality for Consumption, by Urban and Rural Areas (2005-2008)**

||**Lower H**<br>**Distri**|**alf of the**<br>**bution**|**Upper H**<br>**Distri**|**alf of the**<br>**bution**|**Tails**||
|---|---|---|---|---|---|---|
||**p25/p10**|**p50/p25**|**p75/p50**|**p90/p50**|**p90/p10**|**Gini**|
|**Total**|||||||
|2005|1.26|1.33|1.36|1.95|3.27|28.67|
|2008|1.32|1.33|1.38|1.98|3.48|30.46|
|**Urban**|||||||
|2005|1.33|1.41|1.38|2.04|3.84|30.23|
|2008|1.33|1.37|1.42|2.13|3.87|32.34|
|**Rural**|||||||
|2005|1.23|1.27|1.26|1.56|2.42|20.43|
|2008|1.29|1.31|1.28|1.59|2.68|21.99|

_Source: own estimates based on panel HIECPS survey 2005-2008._ The widening
**Figure 11: The marriage market: age and schooling differences between partners, by birth cohort of the wife. Cambodia, Demographic and Health Survey 2000.**

<!-- Start of picture text --> 4.5 2.4<br>4<br>2.2<br>3.5<br>2<br>3<br>1.8<br>2.5<br>2<br>1.6<br>1.5<br>1.4<br>1<br>1.2<br>0.5<br>0 1<br>29-25 24-20 19-15 14-10 9 to 5 4 to 0<br>Age in december 1979<br>Mean age difference with partner Mean education difference with partner<br>Mean age difference with partner<br>Mean schooling difference with partner<br><!-- End of picture text --> 31
# **Table 8. Guatemala: Robustness of Results—Alternative Data Sources**

|||Depe<br>|ndent variable: Per<br>|cent change of GDP/<br> <br>|worker<br>||
|---|---|---|---|---|---|---|
||Includes time<br>trends starting in<br>1985 and 1999|4 percent<br>depreciation of<br>capital stock|<br>Disaggregated<br>capital stock<br>estimate<sup>b/</sup>|<br> <br>Barro and Lee<br>(2001) education<br>data<sup>c/</sup>|<br>Cohen and Soto<br>(2001) education<br>data<sup>c/</sup>|Population 15-64<br>instead of labor<br>force data|
||IV<br>1951-02|IV<br>1951-02|OLS<br>1971-02|IV<br>1951-00|IV<br>1961-02|OLS<br>1951-02|
|Explanatory variables|(1)|(2)|(3)|(4)|(5)|(6)|
|Constant|-0.071**|-0.075**|-0.042*|-0.073**|-0.061**|<br>-0.013|
||(-4.05)|(-3.46)|(-2.57)|(-4.31)|(-4.43)|(0.66)|
|Percent change of capital/worker|0.865**|0.780**|0.827**|
fill the demand from rising exports.<sup>4</sup> Third, firms in capital-intensive sectors may expand production using either new machinery or extending the run-time of existing machinery. Finally, firm-level production may simply shift from the domestic to the international market, without expanding production or employment.

To evaluate these possible explanations, we build upon Robertson et al. (2021) by turning to the World Bank Enterprise Surveys (ES) for Egypt, a small panel of firms. Specifically, this analysis will focus on a panel of 198 firms in 2013, 2016, and 2020.

The remainder of the paper is organized as follows: Section 2 presents the Egyptian context. Section 3 discusses the conceptual framework underlying our empirical analysis. Section 4 details our data sources and discusses descriptive statistics. Section 5 lays out the empirical framework and discusses the results. Finally, Section 6 summarizes the conclusions and offers policy recommendations.

# **2. Egyptian Context**

To better understand why rising exports did not directly increase employment in Egypt, it is informative to review recent history of the country’s trade liberalization, macroeconomic stabilization, and overall structure of its export sector.

Beginning in the early 1990s, Egypt began implementing policies designed to boost trade. The first of these, the Economic Reform and Structural Adjustment Program (ERSAP), aimed at rectifying the country’s macroeconomic imbalances, including its chronic balance of payment deficits and high inflation (Korayem 1997). As part of this liberalization, the country reduced its maximum tariff from 110 percent in the 1980s to 40 percent by the end of the 1990s. This was

> 4 Selwaness and Zaki (2019) demonstrate that labor market rigidities in MENA reduce the positive impact of exports on employment.

5
# **3. Data and Methodology**

## **3.1 Data**

The DHS is a nationally representative population-based household survey that has been conducted since 1984. The DHS includes data on family planning, maternal and child care, gender, fertility, and nutrition and has been collected in over 90 countries. The DHS also asks married women aged 15-49 about their decision-making roles (“who usually makes decisions over [X]”) across different areas, including family planning, use of one’s own earnings, the respondent’s own health care, ability to visit family and friends, as well as major household purchases.

Our sample consists of married couples in all Sub-Saharan African countries covered by the DHS for which both husband and wife answered the question “ _who usually makes decisions about making major household purchases_ ?” The question is asked about large purchases overall and is not obtained by aggregating different survey questions on decision-making over individual asset purchases. It was introduced in the questionnaire administered to husbands in 2004. The last two phases of the DHS thus include a consistent question on decision-making for both women and men in married couples over making large household purchases. Response options were (a) respondent, (b) husband/wife, (c) respondent and husband/wife jointly, (d) someone else, (e) other.

Women having a say over household assets is closely related to their ability to expand their resource base, and thus empowerment. For this reason, this decision-making variable has frequently been used in the literature (Connelly et al., 2010; Doss et al., 2008; Ting et al., 2014). We use the last available survey round for each of the 23 countries where the decision-making 9