rafmacalaba/datause-encoder-data huggingface dataset:
_Epistemology, RCT methodology, and research transparency._ Researchers have begun to examine large sets of studies to understand the “political economy” of designing, conducting, and publishing experiments (e.g., Andrews and Oster (2019) on external validity bias; Gechter and Meager (2022) on the role of pre-existing infrastructure for site selection; H ̈offler (2017) on how data publication influences citation rates; Anderson and Magruder (2017) on how to reduce the number of false discoveries; or Christensen and Miguel (2018) on the adherence to transparent research practices).

_Summary statistics._ RCT data can help both researchers and the broader public to better understand underrepresented populations or get an overview of the body of experimental evidence. Especially in lowincome contexts, RCT data delivers a detailed picture of populations that are typically not well-represented in available data – neither government data, such as (formal) labor market statistics, nor private data, such as bank records. RCT data can be used to extract stylized facts, conduct exploratory research or power calculations, and more. For example, the non-profit AidGrade (2019) compiled a database of standardized effect sizes and standard errors to facilitate simple forms of comparative analysis (e.g. Vivalt, 2015, 2019). The metadata schema is designed to benefit all these different applications by

- enabling filtering datasets on criteria such as unit of randomization or intervention assignment strategy;

- collecting information specific to RCTs such as interventions, treatment arms, or treatment compliance;

- facilitating the combination of multiple RCT datasets by documenting features such as available covariates, time period covered in the data, or inclusion/exclusion criteria;

- recording external resources such as registry entries, ethics review protocols, and academic publications, and documenting information such as whether a pre-analysis plan exists or who funded or partnered in the implementation of the RCT.

# **3 Creating the Metadata Schema**

We followed the process outlined in ISO (2021) in creating the metadata schema. Throughout, we were advised by a group of data scientists and data librarians from Harvard Dataverse, Harvard Medical School, Harvard Business School, MIT, ISPS/Yale, DDI, Vivli and the World Bank Microdata Catalog.

We started by reviewing the types of research that use RCT and experimental data (as summarized in section 2), conducting a survey with researchers who have re-used RCT data or expressed interest in methods research,<sup>5</sup> and researching existing metadata schemata for social science and experimental data. The four main sources of metadata fields we ended up using host some of the largest collections of information on existing RCTs and RCT data (at the time of writing).

> 5Expressions of interest were collected through the Research Methods Initiative of Innovations for Poverty Action (IPA) and the Global Poverty Research Lab, or through surveys of affiliated researchers of the Abdul Latif Jameel Poverty Action Lab (J-PAL).

5
**Appendix table 3: Parcel attributes of lease transactions**

||**Total**|**By**|**type**|**w. tenant ch**|**aracteristics**|**w. registry**|
|---|---|---|---|---|---|---|
|||**Comm.**|**Personal **|**Comm.**|**Personal **|**Comm**|
|**Panel A: Distances**|||||||
|Tertiary road|2.09|2.08|2.12|2.05|2.06|2.01|
|Secondary road|7.04|7.21|6.57|7.14|6.47|7.06|
|Primary road(km)|12.11|12.00|12.42|12.35|12.17|11.55|
|Motorway|26.95|27.73|24.81|25.74|23.86|27.59|
|Railway|9.93|9.65|10.69|9.74|10.43|9.83|
|Port|408.29|397.94|436.64|392.97|441.97|408.69|
|Grain elevator|12.95|12.74|13.52|12.59|12.77|11.70|
|Nearest city|54.54|54.69|54.13|55.13|53.96|55.24|
|**Panel B: Area shares**|||||||
|Share cultivated area|0.774|0.803|0.694|0.804|0.704|0.902|
|Share grassland|0.198|0.170|0.275|0.168|0.264|0.086|
|Share forested area|0.021|0.021|0.022|0.021|0.023|0.009|
|Share built-up area|0.002|0.002|0.002|0.002|0.002|0.001|
|Share other land|0.005|0.005|0.007|0.005|0.007|0.002|
|**Panel C: Soil indicators**|||||||
|Bulk density (g/cm^3)|1.310|1.311|1.306|1.308|1.306|1.296|
|Total nitrogen (g/kg)|4.145|4.074|4.342|4.083|4.340|4.137|
|Soil pH)|6.770|6.762|6.791|6.764|6.786|6.772|
|Organic carbon (g/kg)|5.674|5.710|5.575|5.707|5.551|5.743|
|Number of observations|884,936|648,292|236,644|382,653|151,474|79,316|

_Source:_ Own computation based on land monitoring data reported by SGC and complemented by administrative and remotely sensed data as discussed in the text.

31
income has increased and declined when growth has declined. Yet, on the other hand the data suggest that they may have not benefited as much as the non poor.

Trends, of course, have varied by country as the variance in Figure 2 indicates. In particular, while during the 1990s the Gini has increased in about two-thirds of the countries (especially in some of the previously less unequal countries, such as Argentina or Costa Rica), there are also countries that have experienced a marked decline in inequality. For example, in Brazil (one of the most unequal countries of the region) the Gini coefficient fell by 3 percentage points between 1990 and 2003. The country with the most dramatic decline in inequality over this period was Mexico: between 1992 and 2002 the Gini coefficient declined by 4 percentage points.

**Figure 3. Latin America: Change in Gini coefficients in the 90’s (%)**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>COL ARG CRI VEN URY BOL ECU HND PAN PER PRY JAM CHL SLV NIC BRA MEX<br><!-- End of picture text --> Source: own calculations using data in Gasparini et al. (2007)

# _II.3 How have the poor really fared over the past years? A look at price levels_

When we compute the evolution of income for a particular group of the population (as in figure 2 above) there is a need to deflate the nominal income levels of each individual, typically using the consumer price index or other suitable deflator. If all the households in the economy faced the same inflation levels, this action should not introduce any bias in the analysis.

However, rich and poor families consume different baskets of goods and the inflation rates of these baskets can differ greatly. Goñi, Lopez, and Serven (2005) and Perry et al (2006) show that using the aggregate CPI can greatly mislead actual trends and policies. First, tax brackets, pensions, social transfers, and minimum wages are often indexed to the CPI and using an inappropriate aggregate index can lead to engineering real transfers among income classes that were not intended. Further, our picture of the evolution of inequality (and hence poverty) can be sharply distorted by assuming that deflators are similar across income classes, either by working with un-deflated nominal baskets of 6
**Table 6. Discrete time hazard analysis for both genders to test separately migration to Italy and Greece and beyond Italy and Greece using logistic regression on both sexes, baseline models,  Albania LSMS 2005**

||**Hazard f**|**or First Mig**|**ration to**|**Hazard f**|**or First Mi**|**gration**|
|---|---|---|---|---|---|---|
||**Ita**|**ly and Gree**|**ce**|**Beyond**|**Italy and G**|**reece**|
|**First‐Migration**|**OR**|**SE(b)**|**P>|z|**|**OR**|**SE(b)**|**P>|z|**|
|**Female**|0.258|0.013|0.000|0.338|0.034|0.000|
|**1991**|1.018|0.150|0.905|1.024|0.359|0.946|
|**1992**|1.030|0.151|0.842|0.517|0.216|0.115|
|**1993**|0.886|0.134|0.426|0.773|0.284|0.484|
|**1994**|1.165|0.165|0.282|1.014|0.346|0.967|
|**1995**|0.978|0.144|0.878|0.772|0.280|0.475|
|**1996**|1.667|0.220|0.000|1.396|0.441|0.290|
|**1997**|2.161|0.274|0.000|2.322|0.677|0.004|
|**1998**|1.653|0.218|0.000|2.983|0.840|0.000|
|**1999**|2.349|0.295|0.000|2.172|0.632|0.008|
|**2000**|2.086|0.267|0.000|2.113|0.612|0.010|
|**2001**|2.063|0.264|0.000|1.791|0.525|0.047|
|**2002**|1.929|0.249|0.000|1.188|0.365|0.575|
|**2003**|1.606|0.213|0.000|1.078|0.335|0.809|
|**Age 20‐25**|1.325|0.078|0.000|1.196|0.147|0.146|
|**Age 25‐30**|1.161|0.076|0.022|0.768|0.116|0.080|
|**Age 30‐35**|0.652|0.055|0.000|0.703|0.116|0.032|
|**Age 35‐40**|0.526|0.054|0.000|0.430|0.096|0.000|
|**Age 40‐45**|0.409|0.057|0.000|0.306|0.097|0.000|
|**Age 45+**|0.115|0.032|0.000|0.189|0.086|0.000|
|**Wealth Index**|1.058|0.014|0.000|1.016|0.027|0.561|
|**At least some HS**|1.173|0.058|0.001|1.578|0.184|0.000|
|**At least some Univ**|0.623|0.061|0.000|1.905|0.269|0.000|
|**Coastal Urban**|2.690|0.262|0.000|0.603|0.103|0.003|
|**Coastal Rural**|2.064|0.191|0.000|0.301|0.061|0.000|
|**Central Urban**|1.926|0.198|0.000|0.567|0.094|0.001|
|**Central Rural**|1.586|0.157|0.000|0.412|0.073|0.000|
|**Mountain Urban**|1.043|0.118|0.710|0.470|0.084|0.000|
|**Mountain Rural**|0.877|0.087|0.185|0.408|0.062|0.000|
|**Number of Cases**||**60356**|||**50373**||
|**Log Pseudo likelihood**||**‐8274.1**|||**‐2300.4**||

Note: Reference categories are 1990, Age 15‐20, 0‐8 years schooling, and Tirana 28
quality matters for millions of commuters in urban Moscow.<sup>22</sup> The quick repair of each pothole – that is presumably noted by hundreds and thousands of Moscovites who pass them on their walk, run or ride to work, etc. – represents an important signal that the city has its _khozyain_ (an effective administrator) who cares and who can ensure the timely fixing of city problems. Therefore, each pothole complaint provides the city government with an opportunity to demonstrate _effective performance_ to hundreds and, in some cases, thousands of city residents. Such demonstrated performance, we argue, produces electoral support and works to legitimize the incumbent city authorities.

Sergei Sobyanin has invested massive resources into Moscow’s transport and other city infrastructure aimed at improving urban life. Besides transport and roads, the city invests in parks, sidewalks, bicycle lanes, housing renovation, and other city ‘beautification’ projects. Over the last decade, the city spent as much money on urban renewal as the rest of the country combined.<sup>23</sup> Our findings reveal that these investments appear to have important electoral implications for Moscow’s mayor.

But can Moscow’s experience be transferred to other Russian regions or even to the national scale? Arguably, the most important condition for making this governance mechanism work is Moscow’s affluence. As noted by Natalia Zubarevich, a highly respected expert on Russian regions, ‘Moscow is not just rich; it is filthy rich!” (Zubarevich 2019). Moscow is unique in Russia because it benefits from the profit tax revenues of the majority of Russia’s large banks and state-owned corporations. The highest paying jobs and, therefore, the personal income tax revenues are also the biggest in Moscow. No other federal subject in the Russian Federation has such enormous financial resources to invest in transport infrastructure and road maintenance at such a scale. Nonetheless, there are signs that other regional governments in Russia are aware of the political dividends of investing in digital and physical infrastructure and lobby the federal government for additional resources to capture those dividends (Sharafutdinova 2013). In his 2020 annual address to the Federal Assembly, the Russian President Vladimir Putin had also highlighted the investments in transport infrastructure nationwide as one of the federal government’s priorities.<sup>24</sup>

> 22 According to a recent poll by one of Russia’s leading polling agencies, Levada Center, roads and transportation (congestion, parking, and repairs), along with migration and health care were the most important issues of concern to Moscow residents, see https://www.kommersant.ru/doc/3984374 [last accessed: September 2020].

> 23 https://www.themoscowtimes.com/2019/12/13/moscows-urban-renewal-budget-equals-rest-russias-a68613 [last accessed: September 2020]. For alternative arguments on the underlying motives, see Trubina (2020) .

> 24 <u>http://kremlin.ru/events/president/news/62582</u> [last accessed: September 2020].

26
# 7 Conclusion

We provide a simple way of estimating the structural parameters of a heterogeneous ...rm model. One of the advantages of our approach is that it uses only cross sectional data to recover all the structural parameters of the model, including ...xed costs at di¤erent levels. These include entry costs at the industry and market levels as well as ...xed costs of production and documentation costs needed to obtain preferences. Moreover, all our estimates seem reasonable and roughly in line with previous work.

The policy implications inherent in our counterfactual simulations are as follows. We think of these as making a case for “trade as aid.”Recently, there have been serious doubts cast on the e¢ cacy of direct aid. It may be diverted to the pockets of those in power or used ine¤ectively. Giving aid and having it be e¤ective in terms of growth or a reduction in poverty are two very di¤erent things. For example, governments may cut back their own support for the poor as aid grows. In contrast, “trade aid”works through market forces. For example, in our application, preferences given by the EU are responsible for a huge increase in export ‡ows from Bangladesh to the EU and to the US, rather than diverting trade away from the US market to the EU market if there had been no US quotas (exogenous quota price). In this manner, trade preferences or other forms of trade facilitation by one country can have a powerful e¤ect on exports to all markets and on output, exports, and employment in the recipient developing country but this e¤ect is sharply attenuated by the presence of quotas in other importing countries. Our counterfactuals also suggest where subsidies might be the most e¤ective in increasing exports. The rule seems to be to subsidize late in the process so that subsidies are not wasted on failed ...rms and to subsidize where existing market share is low so that there is more room to poach from foreign competitors.

It is worth emphasizing that trade preferences are a form of aid that can create a scenario where most participants win, which is much easier to sell to all parties concerned. The developed country giving preferences wins as its consumers face lower prices. Other developed countries also stand to gain as entry reduces the price of the goods they import. In addition, the developing country gets to increase its exports, earning foreign exchange and employing its labor force. The losers would be developing countries, who do not get these preferences 42
progressions (Rosabeth Kanter 1977, Blum, Fields and Goodman 1994; Kimberly E. O’Brien, Andrew Biga, Stacey R. Kessler and Tammy D. Allen 2010; Mingzhu Wang and Elisabeth Kelan 2013). However, in developing economies, where access to education is a crucial element for career development, it is difficult for women to even reach the position where they qualify for important positions in a firm, irrespective of the prevailing culture and social attitudes. Of course, this does not mean that culture and social attitude towards women managers is not important in developing countries (see for example, Pak-Wai Liu, Xin Meng and Junsen Zhang 2000, Andrew T. Newell and Barry Reilly 2001; Luigi Guiso, Paola Sapienza and Luigi Zingales 2003, Mandana Hajj and Ugo Panizza 2009;). The present study aims to explore the extent to which these other factors render education important for the cause of women managers.

In addition, we also explore heterogeneity in the relationship between access to education and women’s employment in managerial positions along various dimensions, such as firm-size (small and medium firms vs. large firms), which has been the focus of many gender related studies (Candida G. Brush, Nancy M. Carter, Elizabeth .J. Gatewood, Patricia G. Greene and Myra M. Hart, 2006; Sara Carter and Eleanor Shaw 2006, Susan Coleman 2007; Shwetlana Sabarwal and Katherine Terrel 2008).

To investigate the link between women’s access to education and the likelihood of female top managers in the country, we use data on the gender of the top manager in 16,000 manufacturing firms spread across 73 developing countries. We use a relative measure for access to education - the average of women’s to men’s enrollment rates for primary, secondary and tertiary levels of schooling (henceforth, education enrollment or access to education). We find that an increase in access to education for women in a country has a positive and highly significant relationship with the likelihood of (firms that have) female top managers. Our most conservative estimate shows that moving from the country with the lowest ratio of women’s education enrollment in our sample (Chad) to the highest (Dominica) is associated with an increase of 16 percentage points in the 4
incidence of the VAT, similar to the analysis in Devarajan and Hossain who analyze sales taxes rather than a VAT. We also describe how effective the VAT is at generating government revenue and compare it to other tax instruments. To further evaluate the current tax structure, we describe the marginal cost of funds (MCF), computed as the change in welfare per unit of tax revenue collected from each household, for the VAT, tariffs, and indirect taxes.

Next, we describe the trade-off between a VAT and income tax adjustments to high-income households. We reduce the VAT rates by 25% (50%, 75%) and show the necessary increases in the income tax rate for households in the 10<sup>th</sup> decile.

Finally, we consider changes in the structure of VAT rates. We remove the VAT for agriculture, food, and beverages, commodities on which low-income households spend a large portion of their incomes, and replace the lost revenue with (1) a uniform adjustment to all direct taxes on households and firms; (2) a uniform adjustment to income taxes for households in the 10<sup>th</sup> decile; and (3) a uniform adjustment to other VAT rates.

# **5.1 Remove VAT with Tax Replacement**

When we remove the VAT and replace lost revenue with a proportional increase in direct taxes, we can compute the incidence of the VAT. As noted in Devarajan and Hossain, this allows us to examine the “true” price-distorting effect of the tax; we avoid price changes generated by macroeconomic imbalances as government revenue declines. In addition to a proportional increase in direct taxes, we also consider a proportional increase in commodity taxes and a proportional increase in income taxes on rich households only.<sup>13</sup> As seen in Figure 3, each change reduces the tax burden of low-income households.

_Figure 3: Tax Burden when VAT is removed, with tax replacement_ <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>h_d0 h_d1 h_d2 h_d3 h_d4 h_d5 h_d6 h_d7 h_d8 h_d91 h_d921 h_d922 h_d923 h_d924<br>BASE COMMODITY DIRTAX RICH HH<br><!-- End of picture text --> Source: South Africa CGE model simulations When the tax revenue is replaced by a tax increase on high-income households, the overall tax system becomes even more progressive as the tax burden on rich households actually increases.

> 13 We also considered raising tariffs to replace tax revenue. As seen in Table 2, tariffs contribute a small share of total government revenue; given this low tax base, it is not feasible to increase tariffs to replace the lost tax revenue when the VAT is removed.

13
**Table 10: Average Treatment Effect on the Treated of the Government Support**

|||El Sa<br>|lvador<br>|Geo<br>|rgia<br>|
|---|---|---|---|---|---|
|||Kernel<br>Matching|5:1 NN<br>Matching|Kernel<br>Matching|5:1 NN<br>Matching|
|Percentage change|Propensity Score|0.094|0.095|0.087|0.041|
|of permanent full-<br>||(0.128)|(0.127)|(0.068)|(0.059)|
|time employees|Mahalanobis|||||
|since December<br>|Distance|0.179*|0.120|0.099|0.141|
|2019||(0.095)|(0.089)|(0.074)|(0.104)|
|Probabilit of|Propensity Score|0.150|0.107|0.034|0.065|
|y<br>decreasing total<br>b f|Mahalanobis|(0.136)|(0.138)|(0.101)|(0.102)|
|numer o<br>temporary workers|Distance|-0.252|-0.164|0.122|0.070|
|||(0.267)|(0.295)|(0.110)|(0.118)|
|Probabilit of|Propensity Score|0.069|0.030|0.020|0.057|
|y<br>decreasing the total<br>|Mahalanobis|(0.158)|(0.160)|(0.092)|(0.103)|
|hours of operation<br>per week|<br>Distance|0.043|0.268|0.012|0.096|
|||(0.206)|(0.192)|(0.137)|(0.079)|
|Probability of<br>|Propensity Score|0.079|0.038|-0.079|-0.060|
|anticipating falling<br>in arrears on<br>|Mahalanobis|(0.113)|(0.132)|(0.085)|(0.094)|
|outstanding<br>liabilities within|Distance|0.246|0.246|0.070|0.047|
|next 6 months||(0.198)|(0.194)|(0.084)|(0.094)|

Note: This table reports the average treatment effect on the treated of the government support for all outcome variables in both countries. Kernel matching columns are for the firms that are matched according to the kernel matching algorithm. 5:1 NN Matching columns are for the firms that are matched according to the 5 to 1 nearest neighbor matching algorithm. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 5 for more details.

43
# **Table A3:  Afrobarometer (AFR)**

**Data Provider** Michigan State University; Institute for Democracy (South Africa); Centre for Democracy and Development (Ghana). **Description** U.S-based university and African non-governmental organization **Website** www.afrobarometer.org **Data Source** Afrobarometer surveys **Type** Survey **Respondents** Households **Frequency** Approximately every three years since 1999 **Coverage** African countries **Public Access** Country level aggregates are publicly available through afrobarometer website.  Record-level data is released with some lag through the Inter-University Consortium for Political and Social Research (www.icpsr.org). **Description** This household survey is designed to collect data on attitudes towards democracy and government in a sample of African countries.  We do not use data from the 1999 survey as the questionnaire from this year differs substantially from subsequent years.

|**Voice and Accountability**|2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|How much do you trust the parliament?|X|X|X|X|X|X|X|..|..|..|
|Overall, how satisfied are you with the way democracy works in your country?|X|X|X|X|X|X|X|..|..|..|
|Free and fair elections|X|X|X|X|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||||
|Government handling of public services (roads, health, education, electricity, ecc..)?|X|X|X|X|X|X|X|..|..|..|
|**Regulatory Quality**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||||
|Over the past year, how often have you or anyone in your family feared crime in your<br>own home?|X|X|X|X|X|X|X|..|..|..|
|Over the past year, how often have you or anyone in your family had something<br>stolen from your house?|X|X|X|X|X|X|X|..|..|..|
|Over the past year, how often have you or anyone in your family been physically<br>attacked?|X|X|X|X|X|X|X|..|..|..|
|How much do you trust the courts of law?|X|X|X|X|X|X|X|..|..|..|
|Trust in police|X|X|X|X|X|X|X|..|..|..|
|**Control of Corruption**|||||||||||
|How many elected leaders (parliamentarians) do you think are involved in<br>corruption?|X|X|X|X|X|X|X|..|..|..|
|How many judges and magistrates do you think are involved in corruption?|X|X|X|X|X|X|X|..|..|..|
|How many government officials do you think are involved in corruption?|X|X|X|X|X|X|X|..|..|..|
|How many border/tax officials do you think are involved in corruption?|X|X|X|X|X|X|X|..|..|..|
|**Country Coverage**|**19**|**18**|**18**|**18**|**15**|**15**|**15**|**..**|**..**|**..**|
|**Year of Publication**|**2008**|**2005**|**2005**|**2005**|**2002**|**2002**|**2002**|**..**|**..**|**..**|

42