rafmacalaba/datause-encoder-data huggingface dataset:
“stereotypical” paired test, a respondent sees the same stimuli, but the categories in which to order them are presented together. The pairs are made to follow the stereotype; women with home and men with career. Stimuli still always fall only in one of the four categories. In the final “nonstereotypical” round, the categories are presented in pairs that are not stereotypically associated (women and career; men and home). This approach assumes that respondents who have a stronger association between the two stereotypical categories (women are associated with home, while men are associated with career) will find it easier to group stimuli in the stereotypical round, compared to the non-stereotypical round. The IAT D-score is the normalized difference in average response times between the “stereotypical” and “nonstereotypical” paired tests (Greenwald et al., 2003).

For this study we developed two sets of IATs to test for associations between the two car types and “safety” and “provokes advances”. Table E1 gives an overview of the IATs. We used an identical set of photographs of the women-reserved and public space in both IATs. We selected sets of words for the two tasks that had a similar number of elements, similar length and were all in common daily use in the Rio context; we piloted these with native speakers to eliminate any words that were difficult or ambiguous from either set. We also translated a widely used standard IAT of gender and career IAT as a benchmark.

Each participant who consented to the IAT completed the safety, provocation and career instruments. The order in which a respondent takes the three different IATs is randomized. We implemented the IAT instruments with the software developed by Meade (2009), which calculates the main outcome of interest, the D-score, following the standard methodology in Greenwald et al. (2003).
**Table 1:** Summary Statistics of selected variables in ERIVaS sample

|Variable|Mean|Std.Dev|Min|Median|Max|Obs.|
|---|---|---|---|---|---|---|
|**Plot Characteristics**|||||||
|Slope (percent)|1.31|0.70|1.00|1.00|8.00|577|
|Elevation (m)|335.31|22.21|240.00|333.00|487.00|577|
|Potential Wetness Index|16.24|4.95|12.00|15.00|36.00|577|
|Plot has erosion problems<sup>_†_</sup><br>|0.20|0.40|0.00|0.00|1.00|577|
|Euclidean distance to dwelling (km)|2.11|2.01|0.00|1.66|18.39|577|
|Self-reported distance to market (km)<br>|7.55|5.40|0.00|7.00|30.00|577|
|HH owns the plot<sup>_†_</sup>|0.95|0.21|0.00|1.00|1.00|577|
|**Plot Manager Characteristics**|||||||
|Plot manager is female|0.01|0.08|0.00|0.00|1.00|577|
|Age of plot manager|52.81|14.24|15.00|53.00|97.00|577|
|Plot manager is literate|0.10|0.30|0.00|0.00|1.00|577|
|**Household Characteristics**|||||||
|HH size|19.14|11.79|2.00|16.00|60.00|577|
|Dependency ratio|1.15|0.53|0.00|1.12|3.50|577|
|Head of HH is female|0.01|0.08|0.00|0.00|1.00|577|
|Age of head of HH|55.96|14.52|18.00|57.00|97.00|577|
|Head of HH is literate|0.08|0.28|0.00|0.00|1.00|577|
|Number of plots cultivated by HH|5.26|2.33|1.00|5.00|18.00|577|
|Number of Sorghum plots cultivated by HH|1.50|0.81|1.00|1.00|7.00|577|
|Number of Cotton plots cultivated by HH|1.34|0.82|0.00|1.00|7.00|577|
|**Inputs Usage on plot**|||||||
|GPS measured area (ha)|1.56|1.25|0.05|1.20|12.02|577|
|Quantity of Seeds used (kg)|8.30|5.90|0.56|6.56|51.02|577|
|Household labor on plot (persons-days)|159.30|267.37|8.36|76.40|2711.45|577|
|Hired labor<sup>_†_</sup>|0.33|0.47|0.00|0.00|1.00|577|
|Hired labor (persons-days)|15.23|180.21|0.00|0.00|4195.02|577|
|Organic fertilizer<sup>_†_</sup>|0.24|0.43|0.00|0.00|1.00|577|
|Mineral/Chemical fertilizer<sup>_†_</sup>|0.30|0.46|0.00|0.00|1.00|577|
|Mineral/Chemical fertilizer (kg)|25.74|59.85|0.00|0.00|750.00|577|
|Pesticides<sup>_†_</sup>|0.50|0.50|0.00|1.00|1.00|577|
|**Yields (kg/ha)**|||||||
|Self-reported|636.9|822.3|0.0|400.0|8139.5|577|
|Crop-cut 8m x 8m|500.7|380.1|0.0|423.4|2472.5|577|
|Crop-cut 4m x 4m|501.0|402.4|0.0|419.4|2690.0|577|
|**Harvest (kg)**|||||||
|Self-reported (plots without crop-cut)|738.7|1000.2|0.0|438.5|10000.0|305|
|Self-reported|837.3|1147.3|0.0|488.7|9773.3|577|
|Crop-cut 8m x 8m|794.4|959.8|0.0|515.9|9253.2|577|
|Crop-cut 4m x 4m|782.2|980.0|0.0|463.3|9312.2|577|

_Notes:_ Dependent ratio is the ratio of number of members in the household less than 15 and more than 70 years old over the members between 15 and 70 years old. HH refers to household.<sup>_†_</sup> denotes a dummy variable.

7
percent a year. By the end of the century, the total number of persons in this age group will have virtually stabilized.

Within the Mexican education system, basic education is the government’s highest priority. The basic education system consists of: (a) early childhood education (or pre-school), which is optional for children 3 to 5 years old; (b) mandatory primary education, ideally from ages 6 to 12, but due to late enrollment and grade repetition targeted to ages 6 to 14, and (c) mandatory lower secondary school, consisting of a 3-year cycle, and intended for children ages 12 to 16.

This system has become highly centralized in the hands of the Federal Government. This centralization is reflected by the growing share of Federal schools in total enrollment, which rose from 64 percent in 1970 to 72 percent in 1990. In May 1992, however, the states, the federal government structures, and the National Union of Workers in Education ( _Sindicato Nacional de Trabajadores de la Educación_ , SNTE) signed the National Agreement for the Modernization of Basic Education ( _Acuerdo Nacional para la Modernización de la Educación Básica_ , ANMEB). This agreement was created in response to demand for a decentralized educational system. This agreement should allow states to have more participation. Previous attempts to decentralize the educational system have failed due to constraints on states and federal government structures and to the opposition of the SNTE. ANMEB is part of a long process that yielded satisfactory results through May 1992, when the Federal Government, State Governors, Federal agencies and the SNTE signed the agreement.

In this context, the federal government modified its educational discourse, placing more emphasis on the quality of educational content instead of the previous focus on educational coverage. _Carrera Magisterial_ was created as part of the ANMEB in 1992.<sup>2</sup> It was aimed to raise the quality of basic public education through: (a) teachers’ professional training; (b) new learning presence in schools; and (c) improving working conditions. It represents an effort on the part of the government to provide better support for and recognition of the valuable work of teachers.<sup>3</sup> One component of _Carrera Magisterial_ is the training of teachers; another is a merit payment system in which professional staff on a voluntary basis are evaluated and rewarded with

> 2 The impact of Carrera Magisterial is examined in Lopez-Acevedo and Salinas (2000a) Professional Development and Incentives for Teacher Performance in Schools in Mexico. The World Bank Mimeo. Lopez-Acevedo and Salinas (2000b) Factors that Affect Learning Achievement in Mexico: The Case of Mexico D.F., Nuevo Leon and Tabasco. The World Bank. Mimeo.

> 3 The _Carrera Magisterial_ Program, which has several parts, is governed by the Comisión Nacional Mixta consisting of officials of the Ministry of Education ( _Secretaría de Educación Pública_ , SEP) and SNTE.

4
exacerbate daughter aversion and may fuel sex-selective abortion and female infanticide (Srinivasan and Bedi, 2007). Evidence also indicates that the implementation of the Protection of Women from Domestic Violence Act in 2005 has significantly reduced dowry violence, while at the same time reporting of such violence has increased in north and central India (Das, 2015). Research also finds that the effectiveness of anti-dowry laws may be limited without additional strategies that mobilize women, families, and communities to challenge the widespread acceptance of dowry and to promote gender equity (Rocca et al., 2009). Finally, a recent study (Shakya et al., 2017) finds that since 2012, reporting of intimate partner violence has increased significantly in the state of Maharashtra, while the acceptance of existence of such violence has increased by both men and women, possibly as a result of the prominent Delhi rape case at the end of that year.

Research results on risk and protective factors for violence are mixed. The long-practiced tradition of dowry unequivocally increases the risk of violence. Other forms of contribution to household wealth, such as working outside the home, are not accepted in many communities in the country, although engagement in paid work by women has led to declines in marital violence in Uttar Pradesh over time (Bhattacharya et al., 2011). Chin (2012) finds similar results in other states. However, other studies find that community and institutional acceptance of violence against women negates the positive impact of women’s education and working outside the home (Boyle et al., 2009), and a study in southern India produced diametrically opposite results compared to the study in Uttar Pradesh (Krishnan et al., 2010).

Kerala is an interesting case for analyzing the prevalence of violence against women and girls in that it performs better than other Indian states on a host of human development indicators for women. It has a favorable female sex ratio<sup>6</sup> of 1,058 females to 1,000 males compared to India’s 933, a high female literacy rate of 92.1 percent compared to India’s 65.5 percent, and a high female life expectancy rate of 77 years compared to the national 67.7 years (Sakhi, 2004). At the same time, economic impoverishment, malnourishment, low political participation rates, and low labor force participation rates are prevalent

> 6 The Merriam-Webster Medical Dictionary defines _sex ratio_ as “the proportion of males to females in a population as expressed by the number of males per hundred females” (http://www.merriam-webster.com/medical/sex%20ratio), accessed on September 29, 2016.

6
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implication may be that non-OECD economies have far greater needs for safeguards in the public procurement system due to weak other institutions. Improvements in public procurement regulation may have more influence on road quality in non-OECD economies than OECD economies.

In some OECD countries, compensatory mechanisms exist to ensure that the execution of procurement contracts is done in a satisfactory manner. In Australia, for example, standard procurement contracts are used and include remedies available to the purchasing entity if the supplier fails to perform its obligations under the contract. Contract managers are often tasked with rating performance and delivery of services against contract milestones. Similarly, in Ireland, key performance indicators and payment deduction mechanisms are typical instruments used to ensure satisfactory performance by suppliers. The purchasing entity takes responsibility for monitoring the supplier's performance and for implementing the compensatory mechanisms in accordance with the procurement contract.

# **4. Conclusions**

We use a newly constructed database to evaluate public procurement regulation in 142 countries across the world. A public procurement score is constructed based on 3 key areas of the public procurement process in the eyes of potential suppliers, namely the bidding process, the content and management of the contract, and the payment of suppliers involved in public procurement. We find that the quality of public procurement regulation is correlated positively with road quality outcomes. The higher the overall score for the public procurement index, the better the road quality outcomes. Specific aspects of the public procurement –bid preparation, bid and contract management, and payment of suppliers - are also found to be strongly correlated with greater road 14
nor do they account for counterfactual forest loss in the absence of the refugee population’s arrival. Two previous studies estimate camp impacts in Sub-Saharan Africa using a quasiexperimental approach. First, Maystadt, Mueller, Van Den Hoek, & Van Weezel (2020) examine the land characteristics of large grid-cells that contain camp populations over the subcontinent. They find that larger camp populations increase the probability that the grid-cell experiences both forest loss and agricultural expansion, but they also find that larger camp populations lead to slightly higher vegetation density. Building on this study, Salemi (2021) examines higherresolution grid-cells around refugee camps and finds that forest losses in response to camp openings are statistically significant but extremely small in magnitude. Our paper builds on this recent discourse by examining a case study with characteristics quite different from Sub-Saharan Africa, where land is relatively abundant and population densities are low. The environmental impact of camps may be quite different in Bangladesh, which is characterized by high population densities and low levels of in-tact natural lands.

Our case study focuses on the most recent arrival and encampment of Rohingya refugees in Cox’s Bazar, Bangladesh. In late 2017, more than 740,000 Rohingya citizens of Myanmar fled violent persecution in their home villages and crossed into the Cox’s Bazar region. Upon arrival, UNHCR, the Bangladeshi government, and their partners constructed and began servicing several large refugee camps, all within the southern half of Cox’s Bazar. Some of the unanswered questions regarding the impact of this migration and the establishment of the refugee camps are: how much forest cover was lost within and outside the refugee camps? To what extent is the forest cover change around refugee camps comparable to other locations across Cox’s Bazar district? What are the primary drivers of deforestation near the camps? Are Rohingya refugees directly causing forest cover loss in the region? Or has increasing economic activity associated with the refugee camps’ presence driven forest change?

Remote-sensing data opens up new opportunities to investigate whether the areas near camps face higher rates of natural capital losses and whether camp residents primarily drive such transitions. Using the US Geological Survey’s Landsat satellite imagery at 30-meter resolution, we conducted an LULC change detection using data from 2010, 2014, 2017, and 2020. We first provide a full accounting of forest loss and expansion of other land cover types following the camp openings. Our descriptive analysis reveals forest cover reductions and settlement 3
# their competitive position in external markets.

This work can be considered industrial policy because it requires collecting information about opportunities and constraints in specific sectors, and targeting interventions towards those sectors. An older generation of studies suggested that export promotion agencies had been ineffective (for example, Hogan et al. 1991), but recent research has called this blanket conclusion into question. The Martincus and Carballo (2008) study of Peru’s PROMPEX shows services from the agency helped exporters grow, using a matched difference-in-differences strategy with firm fixed effects. The effects were greater on the extensive margin, where firms increased the number of products exported and the number of countries served, rather than the intensive margin, where firms increased sales of existing products to their current markets. In a cross-country analysis, Lederman, Olarreaga, and Payton (2010) use instrumental variables to show that export promotion agency budgets cause increases in exports.<sup>4</sup> Randomized experiments have evaluated the effects of specific interventions that can be implemented by these agencies. In a study of a nongovernmental organization providing new export orders to rug manufacturers in the Arab Republic of Egypt, Atkin, Khandelwal, and Osman (2017) find that the new orders led to increased exports and productivity after three years, with some evidence of learning-by-doing. In a study of providing training and consulting in digital marketing to firms in the Balkans, including search engine optimization and improved Facebook content, Cusolito, Darova, and McKenzie (2023) find a significant increase in the intensive margin of export sales in a destination-product market. Interventions to help firms identify new international input suppliers could also have benefits, though more research is needed.

_3) Quality certification and standards._ As tariffs have fallen, quality regulation has become a prominent constraint on trade. Countries set minimum quality standards for a good to be imported, which is permitted under trade rules if standards are applied both to foreign and domestic firms. Such standards often apply in health-related sectors like food and pharmaceuticals, but also apply more broadly. For example, units of measure reported by machinery and equipment must be verified for accuracy before sale and labeling requirements can require the verified energy consumption of a product to be displayed on packaging. Most

> 4 Contemporary research has found positive effects of export promotion agencies in specific high-income contexts: Belgium (Broocks and Van Biesebroeck 2017), Canada (Van Biesebroeck, Yu, and Chen, 2015), Denmark (Munch and Schaur 2018), and the United States. (Kabir et al.2024).

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the dummy for the small Caribbean countries (when included) was statistically insignificant.<sup>18</sup>

# _Human capital_

The lower panel of Table 6 shows the results when we include human capital ( _Skill_ ) as an additional control. The variable is taken from Barro and Lee dataset and is the (logged) per capita stock of the tertiary educated in 1995<sup>19</sup> and instrumented using the 1985 values. We note that the sample size of these regressions is smaller (71 countries) due to missing data on the _Skill_ measure. The base regression (using _LAC_ and _Law_ as the only explanatory variables) yields a value of .280 for the _Law_ coefficient (significant at less than 1% level). The corresponding coefficient value with the _Skill_ variable included was .291 (less than 1% significant).<sup>20</sup> In all regressions the Hansen test statistics and the _Skill_ coefficients were insignificant at 10% or less.

Summarizing our results so far, we found that _Law_ has a positive and statistically significant impact on the performance of the service sector relative to the rest of the economy. Overidentification and robustness tests show that it is highly unlikely that our results suffer from endogeneity related problems. The estimated coefficients are

> 18 As with _LAC_ , the endogeneity problem disappeared when we controlled for rest of _LAC_ .  However, using the dummy for small Caribbean countries alone or dropping these small countries from the sample did not remove the endogeneity problem discussed above.  Thus, the results reported for the _LAC_ dummy above are primarily driven by the specifics of the larger countries in the group and not by those of the small Caribbean countries.

> 19 We use the 1995 value of _Skill_ to allow for any gestation lag between schooling and entry into the labor market. However, our results are almost unchanged if we use the 2000 values as these are highly correlated. Further, we treat _Skill_ as potentially endogenous (just like the other controls) and instrument it using its value in 1985.

> 20 With _Skill_ and all the other controls in the Table included the coefficient of _Law_ equalled .312 (p value of .006).

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