rafmacalaba/datause-encoder-data huggingface dataset:
underlying environment.

The key variables are _FemHead_ and its interactions with elements of the underlying environment. In our base specification, we allow it to affect the intercept. That is, it captures the average differences in female-headed firms with male-headed firms. In light of our hypotheses discussed above, we also allow _FemHead_ to interact with some aspects of our environmental indicators, in particular, those related to individualism, the rule of law, corruption, competition, and the importance of social/personal network.

We now discuss what _FemHead_ represents. It is not the classical sense of causal effects—which would represent context-invariant effects. The effects of _FemHead_ simply represent how female-headed firms differ from male-headed firms after conditioning on exogenous controls. It is clearly context-varying, would depend on a society’s institutions, culture, norms, which change over time. For instance, recent research shows that children’s competitiveness does not show gender disparity in matrilineal society but does in patriarchal society (Anderson et al. 2013). This is why we allow its effect to depend on the underlying environment. What we could do is to ensure its robustness, and that the differences are not due to important omitted variables. We thus control for basic firm characteristics, which may be correlated with both productivity growth and FemHead. Since the literature on gender and firms argue that industry and firm sizes are two key factors on which female-headed firms differ, we also control for industry dummies, and size dummies. As such, we also estimate the association between the gender of the head of the firm and productivity outcomes for observationally identical firms using propensity score matching. An advantage of our study is that all firms are selected from the same industries. Furthermore, by examining the growth of productivity, selection of femaleheaded firms in terms of static characteristics are eliminated, and the effects are more likely to reflect the “true” conditional disparity between male- and female-headed firms.

# **Measurements of the environmental variables**

The business environment variables are measured at the city-industry-year level. While firm-level business environmental indicators are available from WBES, we do not directly use individual answers because they reflect choices made by firms and that are, therefore, endogenous. We instead follow the literature by using the local average across firms of the 14
PPP for China from non-ICP price data.<sup>32</sup> On looking more closely at how China’s price surveys were done for the 2005 ICP, Chen and Ravallion (2010b) point to sampling biases that would lead to an overestimation of the level of prices. However, their proposed correction still implies a large increase in China’s price level between 1993 and 2005.<sup>33</sup> The correction for that bias proposed by Chen and Ravallion (2010b), using non-ICP data on rural prices, brought China’s (expenditure-weighted) price index for consumption (rather than GDP as a whole) down from 52% to about 45%, though still considerably higher than the prior estimates of around 25% based on the 1993 ICP (19% for GDP). Assuming a similar correction for the GDP price index, the combined effect with the DPE (given China’s high growth rate), leaves the doubling of China’s price index almost fully explained.

# **6. Implications for estimating PPPs for non-benchmark years**

As noted in the introduction, the most widely-used method for extrapolating and updating PPPs relies solely on the inflation rate in the country in question, relative to the US. I find that the inflation rate is a strong predictor of the proportionate changes in PPPs; the regression coefficient of ln( _PPP_ 05 _i_ /<sup>_PPP_</sup> 93 _i_ ) on ln( _DEF_ 05 _i_ /<sup>_DEF_</sup> 93 _i_ ) is 0.981 (s.e.=0.044), with R<sup>2</sup> =0.958. (Note that the US inflation rate is a constant and so drops out.) However, in a nested test, the inflation-adjustment method is clearly outperformed by a model incorporating the DPE. This is evident is one adds a term in ln( _DEF_ 05 _i_ /<sup>_DEF_</sup> 93 _i_ )<sup>to equation (4); its coefficient is 0.094, with a</sup> standard error of 0.102, while other coefficients and their standard errors change little. So one cannot reject the null hypothesis that the inflation rate has no effect, once one controls for the DPE and the change in market exchange rates.

Another way to assess these methods is to compare their errors in predicting the 2005 PPP. Figure 8 plots the empirical (normal kernel) density functions of those errors (log of PPP minus its predicted value). One density in Figure 8 is for the errors obtained using the inflationadjustment method (based on the GDP deflator) while another is for the residuals from a regression of ln _PPP_ 05 _i_<sup>on</sup> ln _PPP_ 93 _i_ , ln( _Y_ 05 _i_ /<sup>_Y_</sup> 93 _i_ ) and ln( _E_ 05 _i_ /<sup>_E_</sup> 93 _i_ )<sup>(which naturally have zero</sup>

> 32 More precisely, the previous PPPs were derived using a bilateral comparison of 1986 prices between the United States and China as documented in Ruoen and Kai (1995).

> 33 The ICP aims to collect prices from a representative sample of outlets in each country. However, this was not possible in China and the ICP only covered 11 cities.

17
**Table 1  Construction time of nuclear power plants worldwide**

|Period of|Number of|Average construction|
|---|---|---|
|reference|Reactors|time (months)|
|1965-1970|48|60|
|1971-1976|112|66|
|1977-1982|109|80|
|1983-1988|151|98|
|1995-2000|28|116|
|2001-2005|18|82|

_Source_ : Clerici (2006).

The increase in the quantity of materials and labor required for nuclear plants and the time required for their construction, led to a pronounced escalation in construction costs. According to figure 5, cost escalation was especially dramatic after the TMI accident. It appears that the much-anticipated reduction in unit costs due to learning-by-doing and increasing scale did not materialize. They were more than offset by the higher costs induced by increasing regulation and the longer construction times associated with large plants. Regulatory ratcheting in particular and the continuous reassessment of safety that led to frequent and costly upgrading of the technical designs of plants under construction played a major role.<sup>8</sup> Moreover, the technical complexity and site-specific nature of large plants inhibited their standardization and the consequent reduction in units costs from learning by doing (Cooper, 2010). Thus, costs escalated as the industry scaled up the size of new build. Indeed, there is some evidence to suggest that the industry has attempted to put together plants that were too large and complex to be efficiently managed by the constructors (Cantor and Hewlett, 1988).

> 8 The findings from several studies (Mooz, 1979; Paik and Schriver, 1979;  Komanoff, 1981; Zimmerman, 1982; Cantor and Hewlett, 1988; MaCabe, 1996; Canterbery, 1996)  suggest that increasing regulation in the United States led to a yearly increase of approximately 15 percent in plant costs during the 1970s and 1980s.  It should be noted that these percentages probably capture the influence of factors other than regulation (e.g. the tendency of the industry to build larger and more complex plants during a period when the technology was not sufficiently mature (UofC, 2004).

15
Life Index, the United Nations Human Development Index, the World Bank Human Capital Index, the World Press Freedom Index, and Yale University’s Environmental Performance Index.

# **Potential mechanisms**

While the different components of each tool form an integral whole, the self-reinforcing five SPI pillars help showcase a virtuous data cycle (Figure A.1 in Dang _et al._ (2023)). For specific illustration, Figure 6 suggests a theory of change, where the five SPI pillars together result in greater data production transparency that enables more data investment and analysis capability and facilitates data flows to key users. The key users—which consist of the government, international organizations, civil society, academia, and the private sector—ensure greater data accountability, provide better policy making and service delivery, and obtain more business efficiency. A better functioning economy, in turns, can contribute more resources to strengthen the country’s statistical capacity. This whole process leads to better development outcomes. Conceptually, this process applies not just to the SPI, but other assessment tools as well. More generally, this theory of change is consistent with the economic theory of asymmetric information (information economics), whereby better information flows among different stakeholders in the economy (including firms, workers, investors, and traders) produce more economic benefits and enable the economy to operate more smoothly (Stiglitz, 2002).

This process is broadly consistent with the cumulating evidence from a growing literature in economics that investigates related data topics. These include the studies that analyze the SCI (Appendix A, Table S1). Other recent studies suggest that increased data transparency is found to have beneficial effects on GDP and economic growth (Arezki _et al._ , 2020; Islam and Lederman, 2020), external borrowing costs (Kubota and Zeufack, 2020) as well as a positive relationship with more democracy (Janus, 2022). More data dissemination, as measured by the amount of data that countries reported to the World Development Indicators (WDI) database maintained by the World Bank, could also help raise economic growth (Hodelin, 2022). The beneficial impacts of improved data transparency are also well-documented in other disciplines such as political science (e.g., Kelly and Simmons (2014)) and natural sciences (e.g., Nagaraj _et al._ (2020)). As a recent example, Canergie _et al._ (2021) observe that populist governments report less data to the WDI compared to non-populist governments.

13
-22-

intemational rate of interest were reduced by half, the fiscal adjustment required to restore solvency and sustainability would be reduced to 9.2 percent and 20.7 percent of GDP.

**Table 3: Zambia: Alternative Scenarios for Fiscal Adjustment (percentage of GDP)**

|Base Case|**1997**|
|---|---|
|Quasi Public Sector Solvency Adjustment|16.1|
|Quasi Public Sector Sustainability Adjustment|39.0|
|Reduced Export Growth (1.9 percent instead of 3.8 percent)||
|Quasi Public Sector Solvency Adjustment|18.5|
|-Quasi Public Sector Sustainability Adjustment|41.2|
|Reduced External Debt Burden (50 percent debt burden)||
|Quasi Public Sector Solvency Adjustment|9.2|
|Quasi Public Sector Sustainability Adjustment|20.7|

_Source:_ Zambia Fiscal Management Report, World Bank Report 18552ZA _56._ The government could take a number of urgent actions to help bring the non-financial public sector deficit and the quasi-fiscal deficit under control. These actions include the privatization of the remaining parastatals; use of privatization proceeds to reduce the public sector liabilities; freezing of arrears to all public entities and requiring all public agencies to come up with plans for speedy resolution of these arrears before new funds are released to them in the next budget year, setting out a medium-term program to reduce the non-financial public sector deficit and the quasi-fiscal deficit to sustainable levels; designing a plan to restore the solvency of the central bank. and other public sector financial institutions; and improving the management of expenditures, especially the cash budget'<sup>9</sup> , to attain the medium-term fiscal targets.

57. The discussion of Zambia's fiscal solvency and sustainability points out several issues. First, it is important to distinguish fiscal performance at different levels of the government. While it is important to tackle the central government budget deficit, as Zambia has done until now, overall macroeconomic stability can only be achieved when the overall fiscal position of the country (including the fiscal positions of other levels of the government) has reached a sustainable level. Second, in a heavily indebted, low income country such as Zambia, debt relief, in both stock and flow terms, is an important tool for fiscal solvency and sustainability. Third, a real devaluation can carry heavy losses to the budget and makes it 19 Since 1993, Zambia has used a cash budget system, under which expenditures are authorized only when revenues are received by the Ministry of Finance and Economic Development (MOFED).
# **Table 5c: OLS Regressions of Student Learning Gains for Kiswahili on variables selected by CIF & LASSO** **<u>(Post-CIF & Post-LASSO; N=336)</u>**

|Variable|Variables selected by<br>CIF<br>(A)|Variables selected by CIF<br>and LASSO<br>(B)|Variables selected by either<br>CIF or LASSO<br>(AUB)|
|---|---|---|---|
|1)**Baseline Kiswahili Score**|-0.491***<br>(0.0624)|-0.491***<br>(0.0541)|-0.489***<br>(0.0621)|
|2) Student Age|-0.0303<br>(0.0450)||-0.0283<br>(0.0447)|
|3)**Are any actions taken in case of poor teacher**<br>**performance?**|-0.115**<br>(0.0539)|-0.120**<br>(0.0518)|-0.107*<br>(0.0545)|
|4)Haveyou received trainingin 3Rs?|0.0154<br>(0.0471)||0.0205<br>(0.0465)|
|5)**Your Kiswahili teacher offers extra help to students who**<br>**find the subject difficult**|0.139***<br>(0.0471)|0.138***<br>(0.0458)|0.130***<br>(0.0475)|
|6) Are you satisfied by the support you get from the school<br>administration?<br>(= 1 if Teacher says ‘Satisfied’; = 0 if Teacher says ‘Not<br>Satisfied’)|0.1875<br>(0.134)||0.151<br>(0.133)|
|7) It is okay to be absent as long as I: complete the curriculum<br>OR leave students with work OR doing something useful for the<br>community|0.0657<br>(0.0448)||0.0649<br>(0.0448)|
|8)Lesson Facilitation(Classroom Observation)|0.0727<br>(0.0454)||0.0746*<br>(0.0446)|
|9) Pupil Teacher Ratio|-0.0492<br>(0.0499)||-0.0478<br>(0.0492)|
|10)**Relationships Index**|0.114***<br>(0.0381)|0.104***<br>(0.0374)|0.115***<br>(0.0382)|
|11) Are you satisfied by the support you get from the<br>Government?||0.0805<br>(0.0514)|0.0813<br>(0.0511)|
|R-squared|0.315|0.307|0.321|

Note: We report robust standard errors clustered at the School level in parenthesis | Variables in Bold have been selected as important by both CIF & LASSO | Variables have been standardized with Mean = 0 and Std. Dev. = 1 | ***, **, and * indicate significance at the 1, 5, and 10 critical level, respectively

37
Citi (2021). Global Asset Allocation: How Much Crypto? Citi Research Viewpoint.

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Global Financial Markets Association (GFMA), Financial Services Forum (FSF), Futures Industry Association (FIA), Institute of International Finance (IIF), International Swaps and Derivatives Association (ISDA) and Chamber of Digital Commerce (CDC) (2021). Comments in Response to the Consultative Document on the Prudential Treatment of Crypto-Asset Exposures.

Graf von Luckner, C., C. Reinhart, and K. Rogoff (2021). Decrypting New Age International Capital Flows. NBER Working Paper 29337.

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Goldman Sachs (2021b). Digital Assets: Beauty is not in the Eye of the Beholder. Investment Strategy Group Insight, June.

38
After the September 2001 terrorist attacks, the United States launched the Operation Enduring Freedom, whose objective was to break apart the Taliban and Al Qaeda groups in Afghanistan. The operation encompassed two decades of intense conflict between the U.S. allies and the Taliban militias until 2021, when the U.S. government withdrew its troops from Afghanistan. Seizing this opportunity, the Taliban abruptly regained complete territorial control of Afghanistan in 2021. Their rule, once again, restricted access to information, and women’s access to education and labor markets (Amnesty International 2023, United Nations 2023a, United Nations 2023b).

To examine the impacts of extremism exposure on refugees’ integration, we utilize a unique census of Afghan refugees living in Tajikistan in 2023.<sup>3</sup> Tajikistan has an open border with Afghanistan, and has accommodated nearly 9,532 Afghan refugees and asylumseekers after the events of 2021 (UNHCR 2023). For this purpose, the census interviewed all Afghan refugee households registered with the United Nations Refugee Agency office in Tajikistan.<sup>4</sup> Our empirical strategy compares the integration outcomes of refugees who experienced to extremism the and sudden dramatic shift varying exposure generated by exogenous in Taliban’s territorial control in their province of birth between 2017 and 2021. During this period, the Taliban’s territorial control abruptly shifted from 19 to 100 percent of the we the outcomes of Afghan territory. Specifically, compare integration refugees living in Tajikistan in 2023, that had similar characteristics before the migration episode, but who were born in provinces that experienced diverse shocks in Taliban territorial control between 2017 and 2021. To do this, our main specification controls for a rich

> 3The census was collected by the project Enhancing Government capacity in hosting temporarily Afghan refugees with focus on socio-economic resilience and gender issues, under the Rapid Social Response Program (RSR), multi-donor Trust Fund of the World Bank. The objective of that project was to identify opportunities for refugee integration in Tajikistan. The census was collected with the objective of understanding the profile of Afghan refugees, their current socio-economic outcomes, as well as their needs and potential vulnerabilities to support their integration.

> 4After their arrival, refugees are entitled to primary and secondary education, employment on legal basis, healthcare, and residence in Tajikistan until the final consideration of their migration status case.

3
engaged in farming in both survey rounds, it could also reflect changes in sample composition. Table 2 shows start-to-end transition matrixes in the intended use of farmers’ output, restricting the analysis to individuals who were engaged in farming in both survey rounds.<sup>23</sup> In all three countries, farmers are more likely to report producing only or mainly for sale at the end rather than at the start of the season, which confirms that the over-time variation highlighted in Table 1 reflects changes in the self-reported intended use of output and is not primarily driven by sample composition effects. To put it differently, farmers are more likely to respond yes on producing for sale after harvest, rather than when they are beginning to plant (Ghana, Malawi) or just finished planting (Nigeria). If these patterns are generalizable to other contexts, they suggest that rural employment ratios and labor force participation rates may be highly sensitive to the timing of data collection under the revised standards, even in periods of high agricultural activity.

**Table 2: Start- to end-of-season transitions in intended use of farmers’ output**

|**Ghana**<br>_Start of season:_|_End of season:_<br>For sale|For own use|Total|
|---|---|---|---|
|For sale|62.4|13.6|76.0|
|For own use|18.0|6.0|24.0|
|Total|80.4|19.6|100 (N=1015)|
|**Malawi**|_End of season:_|||
|_Start of season:_|For sale|For own use|Total|
|For sale|16.0|13.6|29.6|
|For own use|34.3|36.1|70.4|
|Total|50.3|49.7|100 (N=169)|
|**Nigeria**|_End of season:_|||
|_Start of season:_|For sale|For own use|Total|
|For sale|20.5|9.5|30.0|
|For own use|20.8|49.2|70.0|
|Total|41.3|58.7|100(N=3492)|

_Notes:_ The estimates for Ghana refer to individuals whose main occupation is in agriculture at the start and at the end of the season. The estimates for Malawi and Nigeria refer to individuals who worked at least one hour in agriculture at the start and end of the season. Based on the Ghana and Malawi Agricultural Labor Surveys and Nigeria GHS Panel Wave 3.

Beyond these changes in aggregate employment, there is even more churning at the individual level. Almost a third of farmers in Ghana and Nigeria, and half of the farmers in Malawi, ‘update’ their reports about the intended use of output between the start and the end of the season, and these transitions go in both directions (see Table 2, cells shaded in grey). In other words, even though there is a general trend of farmers being more likely to report producing for sale at the end than at the beginning of the season, a significant

> 23 Due to this sample restriction, the number of observations is smaller in Table 2 than in Table 1.

15
<!-- Start of picture text --> 0.8<br>0.7 public<br>0.6<br>industry<br>0.5<br>0.4<br>4 occupation<br>0.3 categories<br>0.2 9 occupation<br>categories<br>0.1<br>299 occupation<br>0<br>categories<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

# **IV. Methodology**

Hence, we establish that the degree of industrial and occupational segregation in Georgia is considerable. For our purposes, a key issue is whether and how much industrial and occupational segregation have contributed to the presence of the gender wage gap and its decline in Georgia between 2004 and 2015, especially in the context of the relatively stable picture in terms of industrial and occupational employment patterns. We examine this question by using the approach proposed in Brown, Moon and Zoloth (1980), hereafter BMZ. This approach breaks down the gender wage gap into the intra-category and inter-category components. For example, considering the case of industrial segregation, the gender wage gap is decomposed into the component that is due to the presence of the gap within each industry (intra-industry component) and the component due to gender differences in industry employment shares (inter-industry component). The second component can be viewed as the contribution of industrial segregation to the gender wage gap whose role we are particularly interested in quantifying.

Intuitively, the BMZ approach enables us to see whether the gender wage gap is present due to the wide gap within sectors or due to the high degree of segregation, which relegates women to lowly remunerated industries and occupations. If the culprit is the wide gap within sectors, the overall gender wage gap will be present even if women and men are equally represented in all sectors of the economy, i.e. if there is no industrial or occupational segregation. On the other hand, the overall gender wage gap will also be present if the gap within sectors is very small, but there is high degree of industrial or occupational segregation, in which women are concentrated in industries and occupations with relatively low wages.

To obtain the decomposition in Brown et al. (1980), first consider the Mincerian earnings equation for male and female employees, 12