rafmacalaba/datause-encoder-data huggingface dataset:
power of the elite is minimized and that of the non-elite is maxiniized," suggests that democracy's two main components are popular sovereignty and political liberties. The first of these concerns the extent to which the elite of a country is accountable to the non-elite. The second dimension concerns institutions through which the non-elite can influence the decisions of the elite, including free speech, a free press, and freedom of opposition. Bollen then combines three indicators of popular sovereignty and three of political liberties into a single index.

In parallel, other related measures have been developed by several authors. All these measures rely invariably on subjective rankings and are based on aspects of civil liberties and political rights. One commonly used such index is drawn from Freedom House publications and is derived from the work of Gastil and co-workers. Finally, there exists the Polity m index of democracy generated by Jaggers and Gurr, 1995. Despite the fact that all these indices are derived from subjective valuations and sometimes yield very different rankings for individual countries, they exhibit a relatively high correlation between them, of over 0.80.

# _B. Selection bias_

This refers to the bias of including some countries in a cross section analysis while excluding others. Typically, those included are more advanced countries with much better developed data collection procedures than the average. Early work, in particular, was usually based on quite small samples of countries, because of data availability. It is unclear how much bias this introduces into the results; however, our survey of East European countries below - usually excluded from most analyses - suggests that these omissions might be too important to neglect.

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# TABLE 4 : Replanting Equation with Subsidy Variable

Dependent variable Is RPBARR2 18 observations used for estimation from 1968 to 1985 Regressor Coefficient T-Ratio CONS .0069743 3 1.1821 ARBVPROD(-1) .0828x 0<sup>3</sup> 2.9185 AREVPROD(-2) -.0057x 0-2 -.8038 RSUBRATE .1194xlO 4.1312 PRICOCO(-1) -.0366xlO -1.8451 OLDPROD(-1) -.9990X10<sup>2</sup> -1.0819 R-Squared .9532 F-statistic F( 5, 12) 48.9066 R-Bar-Squared .9337 S.E. of Regression .0024 3 Residual Sum of Squares .0694xlO Mean of Dependent Variable .0092 S.D. of Dependent Variable .00934 DW-statistic 2.7985

# TABLE 5 : Tests for Mon-Nested Regression **Models**

Dependent variable is RPBAHR2 Regressors for model MI: CONS, AREVPROD(-1), AREVPROD(-2), RSUBRATE, PRICOCO(-1), OLDPROD(-1) Regressors for model K2: CONS, AREVPROD(-1), AREVPROD(-2), REALINT(-1), PRICOCO(-1), OLDPROD(-1)

|Test Statistic<br>MI|against K2|K2 against MN|
|---|---|---|
|COX-PESARAN TEST|-4.0023|-1.3238|
|ADJUSTED COX-PESARAN|-3.0145|-. 9773|
|DAVIDSON-MACKINNON<br>J-Test|2.3284|.8935|
|ADJUSTED J-Test|2.3284|.8935|

Model MI: DW 2.7985 ;R-Bar-Squared .9337 Model M2: DW 2.1109 ;R-Bar-Squared .9524 Akaike's Information Criterion of MI versus M2= -2.9756 favours M2
**Table 3. Base Specification**

||**Lgrow**<br>||**LPgrow**<br>|
|---|---|---|---|
||**coef**|**se**|**coef**<br>**se**|
|Foreign|0.020**|0.003|0.054**<br>0.007|
|OwnLargest|-0.012**|0.003|-0.026**<br>0.006|
|L0: 20-100|0.026**|0.004|0.036**<br>0.004|
|L0: 100+|0.087**|0.008|0.046**<br>0.005|
|Age 6-10|-0.027**|0.003|-0.007<br>0.005|
|Age 10+|-0.041**|0.003|-0.006<br>0.005|
|Corruption Obstaclec|-0.016*|0.006|0.017<br>0.013|
|Security Costc|-0.078|0.049|-0.130<br>0.111|
|Outagec|-0.001|0.007|-0.002<br>0.013|
|Webc|0.017**|0.006|0.035**<br>0.013|
|Skill Obstaclec|0.005|0.006|-0.010<br>0.013|
|Overdraft Facilityc|0.014*|0.006|0.033*<br>0.014|
|Trade Creditc|-0.008|0.008|0.034*<br>0.016|
|L Regu Obstaclec|0.002|0.007|0.003<br>0.015|
|Tax Rate Obstaclec|-0.007|0.007|0.008<br>0.015|
|Land Access Obstaclec|-0.007|0.007|-0.004<br>0.014|
|In Big City|0.006*|0.003|-0.000<br>0.005|
|Capacity Agglomerationc|0.046**|0.015|0.015<br>0.026|
|Inf Competitionc|-0.012*|0.005|0.006<br>0.011|
|Country, industry, year FEs|Yes||Yes|
|N|57,455||44,279|

*, ** represent statistical significance at the 5 and 1 percent levels.  Heteroskedasticity-corrected standard errors clustered at the country level in columns indicated by “se”.

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as roads to enhance job creation and employment in developing countries, and documents how the benefits of this complementarity vary across individuals and societies. It does so using both individual and firm-level data from various sources and employing an identification strategy that involves a new instrumental variable based on the arbitrary ethnic composition and political history of African countries. Our analysis adds to the recent literature on the impacts of internet access on economic development. Hjort and Tian (2021) provide an overview of the nascent yet already sizeable empirical body of research on the economic impact of internet connectivity in developing countries. Theoretically, internet access can drive economic development through its impacts on both the supply-side and the demand-side of an economy. In many contexts, internet access appears to improve the productivity of workers and firms (Draca et al., 2006; Khanna and Sharma, 2018; Goldfarb and Tucker, 2019; Houngbonon et al., 2022). Research findings show that labor market outcomes for female workers especially improve when firms use more ICT (Dutz et al., 2017), but the findings are mixed on whether internet connectivity plays a role in building skills. Demand-side forces focus on the expansion of market access for firms, workers, and/or consumers, and the reduction of information frictions (Allen and Arkolakis, 2014; Fan et al., 2018; Couture et al., 2021). Internet access has also been shown to improve health-related human capital by reducing HIV/AIDS prevalence (Abbasi and Pongou, 2022). In contributing to this literature, our work breaks new ground by analyzing not just the effect of the internet in isolation, but also the additional impacts that arise from the complementarity between internet and road infrastructure investments on employment opportunities and outcomes.

Our paper also adds to the literature on the impacts of transport infrastructure expansion on economic growth and development. Several papers have shown that the net welfare gains of highways are large (Aschauer, 1989; Shatz et al., 2011; Duranton and Turner, 2012; Faber, 2013; Allen and Arkolakis, 2014; Ejaz Ghani, 2016; He et al., 2020). Others have focused on the impacts of better accessibility to roads or railroads on GDP per capita (Atack et al., 2009; Donaldson, 2010; Duflo 5
firm-level, nationally representative data, which also allows for comparisons across firm size, sector and region.

The remainder of the paper is structured as follows. Section 2 reviews the existing literature on gender differences in labor productivity and sets the stage for the empirical analysis. Section 3 describes the data and presents the base regression results. The decomposition methodology is detailed in section 4, while its results are presented in section 5. Section 6 presents additional robustness checks and section 7 concludes with policy recommendations.

# **2. Literature Review**

Gender equality is an important policy objective and there are both efficiency and human rights arguments for advancing equality between men and women (Duflo 2010). A large literature points to the interrelationships between gender inequalities and economic development. This includes, for instance, the association between economic development and women’s labor force participation (Goldin 1994; Tam 2011; Gaddis and Klasen 2014), the impact of gender gaps in education on economic growth (Hill and King 1995; Dollar and Gatti 1999; Klasen 2002), and implications of gender disparities in wages and earnings (Tzannatos 1999; Seguino 2000).

Most research on gender differences in entrepreneurship was initially focused on industrialized countries. A prominent finding that emerged from (especially early) studies in this field is that male entrepreneurs outperform female entrepreneurs on a range of quantifiable performance measures, such as sales or turnover, profits, productivity, etc. (for the US: Loscocco and Robinson 1991; Chaganti and Parasuraman 1997; for the UK: Rosa et al. 1994; for Germany: Gottschalk and Niefert 2011). These gender gaps in enterprise performance often appeared to be mediated by differences in enterprise characteristics, with women-owned or -managed enterprises often being smaller, less capitalized and more strongly concentrated in low productivity sectors than enterprises owned or managed by men (see Klapper and Parker 2010; Minniti and Naudé 2010; OECD 2012). Empirical evidence, however, remains more mixed, about whether 4
one may be worried about very lopsided distributions where most people benefit modestly, tive provides evidence on this issue. While moments a eus lly easy to estimate by their sample analog, sO we Cannot estima its ome direc y. However, we can calculate e residuals rom partial regressio , the residual from a partial regression of for eatm nt gro p observations and tions.

treatment effect Ww ca S t artial residuals: rst two ter c e d t rtia residuals and is ju age treatment effect. Thus, each of these expectati ns ca mated par Is. This is possible because we have assumed that This is just a conditional version of the assumption hat that we u tify the variance of treatment effects in our exampl abo T s of the distributio y: Once we know the ance of treatment effects, ly unknown term in this equation is . To esti it from rtial regression residuals, we need to extend our assump that terms h do no p dsolely on the marginal distributions of potential outcom re zero to . This is a restriction on how the varition, the third moment h fe) p iven by: Thus, we can estimate S e f ent effects from moments of our partial residuals. Appendix 3 shows how to use the bino mula to solve for In theory, we can use this method to estimate moments that are not determined by the marginal distributions to . Of course, es mating infinitely many moments is impractical. Thus, one may pref timate the first moments and select one of the (usually many) distributions they are consistent with. For e ample, in Section 7.2, we estimate the entire distribution of treatment effects by assumin they are normally distributed and by plugging in mean and variance estimates. Wu and Perloff (2006) estimate the first four moments and recommend selecting the distribution according to the Principle of Maximum Entropy. This distribution is unique and is “maximally noncommittal with regard to missing information” (Jaynes, 1957; Wu and Perloff, 2006). See
(iv) This last point remains true even if moving costs for workers are zero on average.

(v) The economy adjusts only gradually to changes in policy. This is consistent with findings by Topel (1986), Blanchard & Katz (1992), and others. This complicates empirical work on trade and wages, however, because changes in wages and labor allocation will continue long after the change in policy has occurred.

(vi) The economy begins to adjust to a policy change as soon as it is anticipated. This complicates empirical work even more, because it means that adjustment begins _before_ the policy change takes effect, and in fact changes in wages can reverse their direction when the policy change actually is executed.

(vii) The incidence of trade policy needs to be analyzed on the basis of lifetime utility, taking worker mobility and option value into account, and not simply on the basis of wage levels. We show how to do this. The correction is important: It is theoretically possible, for example, for a policy change to lower real wages in a sector both in the short run and in the long run, and yet for the workers in the sector to be better off as a result of it, because it raises those workers’ option value. This is far from being only a theoretical possibility, as Artu ̧c, Chaudhuri and McLaren (2010) show using US worker data.

(viii) Announcing a trade liberalization in advance tends to reduce the difference between its effect on export-sector workers and its effect on import-competing workers. This is because it causes workers to begin to leave the liberalizing sector in advance, pushing up wages for those who remain, and pushing down wages for workers in the export sector. We say that this process confers ‘anticipation rents’ on workers in the liberalizing sector. We show that this leads to worker unanimity in the limit, meaning that all workers agree on the desirability of the liberalization if it is announced far enough in advance.<sup>2</sup> However, it is

> 2Note that there are three separate reasons that delayed liberalization attenuates losses for importcompeting workers. The first and least interesting is simply that if losses from liberalization are postponed, with time discounting the effect on present value of utility will be diminished. This will of course not change the _sign_ of the effect on those workers’ welfare, but the other two reasons can. The second reason is that postponement can give a worker a chance to time her switch to another sector optimally before the change takes place, lowering adjustment costs; this is a benefit that results only in a model such as ours, where time-varying idiosyncratic shocks create option value for workers’ reallocation decisions. The third, the anticipation rent, is a much more subtle effect that results from general-equilibrium forces. We offer empirical evidence for such rents in Section 6.

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then this is counted as two legal disparities as it applies to both married and unmarried women. Thus the value of a disparity is either 1 if it only applies to married, and 2 if it applies to both married and unmarried women. There is only one specific situation where the value of the disparity is not assigned a score of 1 or 2 – differences in paternity and maternity leave. In this case the disparity is measured as (1-(P/M)), where P is length of paternity leave and M is maternity leave. A value of 0 would indicate parity in paternity and maternity leave. A value of 1 would indicate the highest degree of disparity as length of paternity leave would essentially be zero. Thus the greater the length of maternity leave relative to paternity leave, the greater the gender disparity (paternity leave does not tend to be greater than maternity leave). Since the disparity applies to both married men and women, it is multiplied by 2. The rationale behind having a continuous measure for the disparity in paternity and maternity leave is to capture the heterogeneity in the amount of leave allowed across economies. A full listing of legal gender disparities and the respective score assignment are presented in table A1in the appendix by subcategory. Data on legal disparities are available for 167 economies based on data collected for the 2016 _Women, Business and Law_ report.

# **3. Economy Rankings and Regional Patterns**

The ordering of economies from the lowest to the highest number of legal gender disparities is presented in the appendix in table A2. Overall the top 5 in decreasing order are as follows, with the number of legal gender disparities indicated in brackets: Slovak Republic (2), Portugal (3), Australia (4) , Spain (5.77) and Mexico (5.88); all are high income OECD economies with the exception of Mexico.<sup>2</sup> At the other end, the bottom five (in order of lowest to highest) ranked

> 2 The details of the legal disparities in the top 5 economies are as follows. For the Slovak Republic, there is a legal disparity in the amount of paternity and maternity leave in the Getting a Job subcategory. For Portugal, in the

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treatment effect heterogeneity can be effectively leveraged to fine-tune optimal policy targeting.

Several caveats are in order regarding the limitations of this work. <mark>The most important one is the use of a broad definition of migration for our migration outcome variable, which is driven by limitations in the existing data, as discussed in Section 3. To overcome this constraint, we stress the need for longitudinal household surveys to collect more comprehensive migration data. This includes details about the nature and duration of migration episodes, as well as the characteristics, history, and purposes of migrant individuals. Achieving this goal requires equipping household surveys with improved migrant tracking tools and protocols. Ideally, efforts to enhance the design of longitudinal surveys should be also harmonized and standardized across countries to ensure comparability of migration data and enable researchers to study potential differences in countryspecific relationships.</mark> At the same time, for half of the migrant-sending households, we observe more than one household member migrating across waves, so even if it were possible to distinguish different types of migration, accounting for intra-household heterogeneity in the estimation approach would not be straightforward. A complementary strategy to investigate resourceconstrained immobility would be to look at the dynamics of migration intentions and aspirations rather than actual migration, but the available information on these variables is limited to crosssectional surveys (e.g., Gallup World Poll) which come with their related econometric challenges. Regarding internal versus international migration, while our results apply predominantly to internal migration, we suspect that external validity with respect to international migration is high, as the key finding regarding the importance of endowments and liquidity constraints should hold even truer when it comes to costlier cross-border movements.

Secondly, it must be emphasized that conducting this type of research is not possible without leveraging panel surveys that are multi-topic and interoperable in nature. Unfortunately, such surveys remain uncommon in many developing contexts, which hinders the possibility of studying climate migration dynamics in those regions. Relatedly, even in countries where panel data are available, these are often of limited length, preventing the analysis of longer-term dynamics and impacts. This is also the case of this paper, where we can only provide short-run evidence in support of our conceptual framework. Having longer panels would allow going a step further and establishing whether the short-run relationships detected ultimately lead to permanent immobility 29
included in the framework, as well as status of study (completed or on-going). Depending on the purpose of the gap map and the interest of the researchers/ commissioners, other categories may also be relevant, including geographical scope of the evidence, inclusion criteria (PICOs) of systematic reviews, information about intervention effectiveness and the design of primary studies.

We assess the quality of systematic reviews using a standardized critical appraisal tool, giving reviews an overall rating of high, medium or low in terms of the confidence with which their findings can be assured. The checklist is adapted from the checklist developed by the SURE collaboration (2011) and is included in Appendix 2. The critical appraisal is conducted by two people, with a final review by a third researcher with expertise in systematic reviews.

# _Producing user-friendly summaries, presentations, and analysis_

A key feature of the gap map is that it provides direct access to user-friendly summaries. For 3ie evidence gap maps we use the same format as the summaries we produce for our evidence databases, including information about geographical location, background, methods and main findings. The summaries are peer reviewed, edited and uploaded to the 3ie evidence database. Once studies have been uploaded on the database the cells in the gap map are populated with links to the summaries of studies covering corresponding interventions and outcomes.

Studies may be repeated in several places in the gap map if they cover several interventions or outcomes. We use a traffic light color coding to indicate the quality of the systematic review evidence in the gap map so that users can visually assess the state of the evidence in the field. Lum et al. (2011) use symbols and color coding to indicate the statistical significance and direction of effects of studies in their matrix. A similar approach can also be adopted in evidence gap maps, allowing users to easily identify which interventions work or not.

evidence base is nascent and there is a lack of systematic reviews, but there is a strong demand for evidence to inform decision making researchers may decide to also highlight lessons learnt from existing impact evaluations. In such cases researchers should assess the quality of relevant impact evaluations using a recognised tool for critical appraisal.

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# **APPENDIX 2: DEFINITION OF VARIABLES USED IN THE REGRESSION ANALYSES**

|**Variabl**|**e**|**Description**|**Unit of Measurement**|
|---|---|---|---|
|_Ease of Doing_|_EDB_|Ease of doing business, a relative ranking|Smaller value indicates less|
|<br>_Business_||of the countries reflecting the state of<br>business regulation|favorable business environment|
|_CDM Incidence_|_CDMI_|A dichotomous variable with 0 if there<br>are no, and 1 if there are any number of<br>CDM dyad projects.|0 or 1|
|_Avr Annual Energy_<br>_Use_|_ENR_AVG_|<br>Average annual energy use over t he<br>period 1960-2003|Million tons of oil equivalent|
|_Annual Per Capita_<br>_Energy Use_|_ENR_PC_|Annual per capita energy consumption|Tons of oil equivalent|
|<br>_GDP_|_GDP_TOT_|Total gross domestic product for the<br>period 1960-2003.|Million US $ (constant 2000)|
|_GDP per Capita_|_GDP_PC_|<br>Gross domestic product per capita for the<br>period 1960-2003.|Thousand US $ (constant 2000)|
|_Average GDP_|_GDP_AVG_|<br>Average annual gross domestic product<br>for the period 1960-2003|Million US $ (constant 2000)|
|_Governance_|_GOVR_|<br>Indicator reflecting the overall<br>governance level, a principal component<br>product of 6 governance indicators|Smaller value indicates poorer<br>level of governance|
|_Impact_|_IVUL_|Impact vulnerability index (reflecting|Scale of 1 to 100 (1= lowest,|
|_Vulnerability_||country vulnerability in terms of various<br>impacts of climate change)|100=highest)|
|_Number of Project_|_NPRJ_|<br>Number of CDM projects||
|_Nonrenewable_<br>_Energy_|_NREN_|Non-renewable energy resources (total)<br>available per capita|Million tons of oil equivalent|
|_Renewable Energy_|_REN_|Renewable energy resources (annual)<br>available per capita.|Million tons of oil equivalent|
|_Total CO2_<br>_Abatement_|_TCO2_TOT_|<br>Total amount of CO2 abatement (CERs)<br>until 2012.|Kiloton of oil equivalent|
|_Annual CO2_|_TCO2_YR_|Annual amount of CO2 abatement|Kiloton of oil equivalent|
|_Abatement_||(CERs) until 2012.||
|_Trade1_|_TRD_1|<br>Total trade (the sum of the volume of<br>bilateral imports and exports) between<br>the host and investor countries as a<br>fraction of the sum of the countries’<br>GDPs|Share|
|_Trade2_|_TRD2 _|Total trade (the sum of the volume of<br>bilateral imports and exports) between<br>the host and investor countries as a<br>fraction of their trade with the rest of the<br>world|Share|
|_Volume of_<br>_Investment_|_VINV_|.<br>Volume of investment in CDM projects:<br>TOT_reflecting total investment and<br>_YR_reflecting annual investment|US $ (constant 2000)|
|_hst_*_|_hst_*_|_ <br>Variable related to the host country|* represents the variables<br>described above|
|_inv_*_|_inv_*_|Variable related to the investor country|* represents the variables<br>described above|
|_hst_inv_*_|_hst_inv_*_|An interaction term|* represents the variables<br>described above|

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