rafmacalaba/datause-encoder-data huggingface dataset:
Table 5 reports the regression results for equation 3, separately for light manufacturing, heavy manufacturing, “other industry,” WRT services, and “other services.” An increase in the size of employment in an average firm in heavy manufacturing if it is located in a mining district in the south (compared to firms in other districts) is higher by 23.2 percent, followed by 13.7 percent higher in “other industry,” 12.1 percent higher in “other services” and 6.2 percent higher in WRT services. The result is statistically significant only for heavy manufacturing and “other services.” Finally, we extend our model in equation 3 to include the effect of changes in the mining employment shock over time, as follows: Equation 4 estimates the difference in average employment of a firm in the manufacturing sector if it is located in a mining district in the south compared to other manufacturing firms between 2003 and 2013. Since we use both 2003 NIC and 2014 IBES data, to estimate δ _DDD_ , the sample is restricted to only manufacturing firms. _Xi_ , _t_ in equation 4 controls for various firm-level characteristics other than employment.

Table 6 reports the results of the regression results for equation 4 separately for light and heavy manufacturing. We do not find a significant difference in employment in light manufacturing firms that are located in mining districts in the south and light manufacturing firms located in other districts between 2003 and 2013. Among light manufacturing industries, average firm employment in the food industry grew by almost 26 percent in mining districts in the south over this period compared to those in other districts. Meanwhile, average firm employment in the paper industry fell by 22 percent in mining districts in the south compared to those in other districts during the same period.

Average employment size in heavy manufacturing firms in mining districts in the south grew by 31 percent compared to firms in other districts between 2003 and 2013. The firm-level evidence suggests that firm growth in heavy manufacturing over this period is primarily driven by chemical firms, registering a growth of 61 percent in average firm employment size, followed by nonmetal (30 percent), machinery (28 percent), and metal (23 percent). Overall, the growth in average employment size in heavy manufacturing firms is more than three times larger than that for light manufacturing firms in mining districts in the south between 2003 and 2013.

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challenge of being able to rapidly adjust the tax policy response to a changing environment. Indeed, tax policy reforms which could be considered today pro-poor and welfare improving could turn the opposite if the current evolution of commodity prices was reversed.

The results described above rely to some extent on the assumption of competitive transmission of prices from producers to consumers. However, the possible existence of market power in the trade and transport sectors could alter such transmission. In Tanzania, there is no obvious regulation candidate to generate monopolistic situations in these sectors. Rather, the absence of competition in intermediary sectors is often linked to the lack of adequate public infrastructure combined with fixed costs in the transport sector.<sup>27</sup> Another uncertainty lies in Tanzania’s ability to benefit from its new potential comparative advantages, in oil crops notably. According to land surveys, Tanzania still disposes of a huge reservoir of uncultivated arable land (for oil crops in particular, but for sugar cane too<sup>28</sup> ), and World Bank analysis suggests that land extension significantly contributed to agricultural output growth in the past. But land extension might not be immediate, as it would depend on the ability of people to migrate to low density zones, secure farmland and use labor saving technologies in these areas. The adequate provision of infrastructure and public services in these new areas might also be an important factor in this regard. Besides, the impact on rural poverty would depend to a large extent on who is able to claim new lands. In the model, though, land extension – as any other capital accumulation – only results from past investment expenditure, and it would take specific efforts to model correctly this dimension.

> 27 See Nyange, D. (2005).

> 28 In spite of higher demand for bio-fuels, international prices for sugar cane have not gone up so far. But it is likely that they will if the high demand for bio fuels is accompanied with a decrease in subsidies granted to producers of corn-based ethanol.  This reduction in subsidies is justified on several grounds: for the global environment, as bio-fuels produced with sugar cane in developing countries is far less Co2intensive than that produced with grains in developed countries; for food consumers as increased demand for grains to produce bio-fuels exert upward pressure on food prices. Furthermore, relying on sugar cane ethanol imported from developing countries rather than only ethanol from corn would be likely to increase energy security. This is because the year-to-year variation of corn yields is very high (higher according to some calculations than the variation in oil prices), and because exporters of sugar ethanol would have a vital interest in exporting continuously.

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of economic uncertainty (Altig et al., 2020, Baker et al., 2021), utility from weather (Baylis, 2020), political polling data (Beauchamp, 2017), Arab spring protests (Acemoglu, Hassan and Tahoun, 2018), and which politicians are influential (Mankad and Michailidis, 2015).<sup>2</sup> Accordingly, social scientists frequently use social media to assemble data more quickly and at a higher frequency than is typically available in standard datasets.

Moreover, Twitter does not only reflect offline attitudes, there is also evidence that exposure to certain material on Twitter can affect offline behavior. Alatas et al. (2019) worked with celebrities in Indonesia willing to endorse vaccines; they find that a Twitter endorsement affects vaccine takeup and knowledge of study participants’ social networks. Donald Trump’s tweets about the federal reserve affected the interest rates and other macroeconomic variables (Bianchi et al., 2023). This influence is not limited to material directly attributable to politicians and celebrities; quasi-random exposure to Twitter (driven by the home counties of South by Southwest festival attendees in 2007) led to anti-Muslim hate crimes during Donald Trump’s campaign (M ̈uller and Schwarz, 2020).

This potential to influence offline behavior underscores the importance of understanding the determinants of participation in political and social discussions on Twitter. Indeed, there is evidence that women use social media differently than men. Men are followed and retweeted by both men and women in greater numbers in medicine (Zhu et al., 2019) and journalism (Usher, Holcomb and Littman, 2018). Bamman, Eisenstein and Schnoebelen (2014) find that men and women use different linguistic styles on Twitter; men were more likely to use numbers, quantifiers and technology words while women were more likely to use pronouns, emotional terms and family terms. Gender

> 2Though this finding is not universal; Zeitzoff, Kelly and Lotan (2015) study discussion on the Iran-Israel conflict on Twitter. They find that English and Farsi discussion correlates with offline behavior, but Hebrew discussion does not.

5
report zero visitation in the past year. Among those who do choose to participate (see _mean model_ ), increases in income and household size increase trip demand, while increases in travel costs and education decrease trip demand.

For the on-site survey, first an LR test between a truncated Poisson and truncated negative binomial (TRNB) was rejected indicating that over-dispersion in visitation is significant, leading to us to favor the TRNB specification. Second, the TRNBES model was estimated to see whether higher trip frequencies have any systematic association with an individual’s characteristics. Estimation results for both TRNB and TRNBES show that increases in travel costs, age and education decrease visitation, whereas increases household size increase trip demand. In the TRNB model, estimated coefficients and standard errors are higher leading to a lower significance across each explanatory variable. By correcting for ES, the magnitude of estimated coefficients falls, and standard errors fall by a greater extent such that significance rises among the major determinants of visitation. In the next section, we explore the consequences of these differences on expected trip demand as well as the implications on welfare estimates.

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facilities such as grain elevators. Also starting in 1992, privatizations in the energy sector began to take off through the sale of 10 power utilities for another $1.4 billion. in June 1993, a consortium including three electric utilities from the U.K. and the U.S. won a 95year concession to own and operate Argentina's high voltage transmission system for $230 million.

# **Brazil**

During the military regime, Brazil established a large number of SOEs, reaching 700 in 1985.34 In March 1990, President Fernando Collor announced a sweeping privatization program, to be headed by the Social and Economic Development Bank (BNDES), and in August, 57 companies were earmarked for privatization despite strong political opposition. In 1991, the first five companies were sold off, among them the steel producer Usiminas, which with $1.5 billion at present represents the largest privatization transaction in Brazil. During the early part of 1992, the privatization program picked up pace and another 13 companies were auctioned off, with the largest transactions occurring in the steel and petroleum sector.

With the ouster of President Collor in late 1992, the program began to stall and not until recently has his successor President Itamar Franco decided to revive the proaram. In total, a revenue of $4.3 billion has been generated during 1988-92, but foreign participation was, at best, marginal. Currently, Brazil is not a very attractive option for international investors, given the general political and economic uncertainty, reflected in a high rate of inflation. This is exacerbated by the fact that the country still has not been able to conclude a Brady deal as well as the legal restriction that foreigners cannot acquire a share larger than 40 percent through privatizations, except through direct congressional authorization.

However, the privatization program might gain speed again with the announcement by President Franco to intensify privatization efforts. In early 1993, two major trarsactions have been concluded, involving the sale of 31.5 percent of Poliolefinas, a petrochemical company, for $86.1 million, as well as the country's largest steel producer, CSN, which was auctioned off for $1.06 billion.

# **Chile**

Chile certainly has the richest history in terms of privatization compared to all other developing countries, having privatized around 400 enterprises since 1973 for a total of about $2.5 billion. After Patricio Aylwin replaced General Pinochet as the first democratically elected president in March of 1990, however, the privatization process has been reduced significantly. Only six transactions were completed since then, with the largest ones in mining and energy. Besides the still state-owned copper producer CODELCO as well as CORFO, a major financial institution, most of the state-held assets

> 34 Privatization International, Yearbook 1991, p.138.

34
SDD2: This assumes that only the data on outcomes are missing. Thus standard corrections can be made for selection bias based on other observables at the baseline.

Note that the difference is in whether an allowance is made for selection on observables. If the recall of changes since the introduction of the project works well then both SDD1 and SDD2 will be able to address selection based on (time-invariant) unobserved factors.<sup>10</sup> Importantly, the shoestring evaluations were ―tacked onto‖ a full scale evaluation. This was for a large antipoverty program in poor areas of rural China, and the results are reported in Ravallion and Chen (2005) and Chen, Mu and Ravallion (2009). The paper is thus able to compare SDD1 and SDD2 to the ―actual‖ DD, as estimated from high-quality, comprehensive and contemporaneous baseline and follow-up surveys. The implications for the structure of recall errors are also examined.

The findings from this case study suggest that SDD methods are vulnerable to biases that confound identification. Respondents’ perceptions of how their living standards have changed provide a weak and biased signal of consumption changes measured from contemporaneous surveys. There are also signs of ―false positives‖ stemming from the weak ability of retrospective recall of welfare changes to neutralize selection bias based on unobserved initial conditions influencing program placement.

The following section describes the project and data. Section 3 presents the impact estimates, while section 4 explores the responses on recall further to help understand the results in section 3. Section 5 concludes.

# **2. Setting, data and methods**

The project being evaluated is the World Bank’s Southwest China Poverty Reduction Project—the Southwest Program (SWP) for short. This comprised a package of multi-sectoral interventions targeted to poor villages using community-based participant and activity selection. The aim was to achieve a large and sustainable reduction in poverty. The project was implemented in selected poor villages in the designated poor counties of Guangxi, Guizhou and

> 10 On the distinction between selection bias based on observables and that based on unobserved factors see Heckman et al. (1998).

6
reaction function and identify 2002 as a turning point in Sub-Saharan Africa. Similar test was conducted for Latin America and Developing Asia. For these regions, the time break is 2005 and 1999, respectively.

All developing regions, including Sub-Saharan Africa, have made some progress towards more acyclical (and countercyclical in some cases) fiscal policies after the breaking point (year). In fact, there were no countries with negative and statistically significant correlation coefficients (countercyclical fiscal policies) in the pre-break period in all regions (Figure 3). However, after the breaking year, the proportion of countries with countercyclical fiscal policies have increased to 8% in Sub-Saharan Africa and 10% in both Latin America and developing Asia. At the same time, the proportion of countries with positive and statistically significant correlation coefficient (procyclical fiscal policies) have decreased considerably.

**Figure 3** . Cyclical properties of Government Consumption Expenditure by region

<!-- Start of picture text --> 100%<br>8% 10% 10%<br>30%<br>80%<br>55%<br>68%<br>60%<br>84% 85% 75%<br>40%<br>70%<br>20% 45%<br>32%<br>15%<br>0% 8% 5%<br>1970-2001 2002-2014 1970-2004 2005-2014 1970-1998 1999-2014<br>SUB-SAHARAN AFRICA (2002) LATIN AMERICA (2005) DEVELOPING ASIA (1999)<br>Procyclical Acyclical Countercyclical<br><!-- End of picture text --> _Note: The government final consumption expenditure and GDP are expressed in local currency at constant prices (source: UN-Statistics database). Their cyclical components are obtained using the Hodrick-Prescott filter. Countercyclical government consumption means that the correlation coefficient is negative and statistically significant at 5% level. It is procyclical when it is positive and significant, and acyclical when the coefficient is not statistically different from zero._ The analysis of the correlation coefficient between the cyclical components of government consumption and GDP in Sub-Saharan African countries in the sub-periods of 1970-2001 and 2002-2014 suggests that the region still has considerable margin to improve in terms of the cyclical stance of fiscal policy. Figure 4 shows that only 9 out of 37 countries in the region have moved from a procyclical stance (positive correlation) to a more countercyclical one (negative correlation).<sup>14</sup> Only 4 countries (Guinea, Malawi, Zambia, and Zimbabwe) have registered a negative correlation between the cyclical components of government consumption and GDP in both periods. Government consumption in the majority of countries (24) continue to exhibit a procyclical behavior. As was noted before, regardless of the dynamic between periods, most of region’s countries are still classified as having acyclical fiscal policies in the recent period 2002-2014, since their correlation coefficients are not statistically different from zero.

> 14 These countries are: Angola, Cabo Verde, Chad, Kenya, Liberia, Mauritius, Nigeria, Rwanda, and South Africa. However, only Angola, Kenya, and Mauritius have exhibited negative and statistically significant correlation coefficients in 2002-2014.

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**Table 12:  Robustness Check on Autocorrelation. Ackerberg et al. TFP Measure**

|Banking Break 2001|2.859***<br>(0.686)|||||
|---|---|---|---|---|---|
|Rajan-Zingales Break 2001||0.412***<br>(0.061)||||
|Telecom Break 2002|||30.678***<br>(2.411)|||
|Insurance Break 2002||||15.203***<br>(2.219)||
|Transport Break 1997|||||-1.453***|
||||||(0.512)|
|Observations|6,142|6,142|6,059|6,059|5,440|
|R-squared|0.003|0.007|0.026|0.008|0.001|
|Number of firms|3771|3771|3771|3771|3771|

Notes: The dependent variable is the log TFP estimated using the Ackerberg et al. method for each of the 11 industries listed in Table 2. *** denotes significant at the 1 percent level, ** at the 5 percent level, * at the 10 percent level 47
1.14 Drawing on nonrepresentative case studies invites criticisms of external validity – namely that findings from one context cannot naturally be extrapolated to another. To address external validity concerns, an effort was made to triangulate information from the literature, World Bank experience and case studies to assess convergence of findings and provide important contextual factors that may inform policy makers on the relevance of the findings to their specific situation. While context plays an important role, focusing on mechanisms (as this report does) and then judging whether a mechanism is likely to apply in a new setting has a number of practical advantages for policy making. The interested reader is referred to Bates and Glennerster (2017), which provides a four-step schematic on how to assess whether particular findings can be applied to a specific setting.

- 1.15 It is important to note the following limitations to this study:

- The report relies extensively on World Bank evaluations and self-evaluations to draw lessons on what has worked. Any assessment of effectiveness and associated lessons is a function of the project as a whole to which FMIS usually only partially contributes. If no explicit effort is made to document the FMIS experience explicitly (as was done for the case studies), lessons on attribution of FMIS to project outcomes can be unclear, as they are mixed with other interventions.

- Case studies are not representative of the entire portfolio and do not test hypotheses or derive causal effects. While an effort has been taken to make a case for external validity through the use of axioms and convergence of findings across data sources, the practitioner is still urged to exercise caution when applying findings to his/her specific context. The relevance of findings will still likely vary by whether countries have an anglophone or francophone history. It will also vary by type, sophistication, and application of FMIS solution. As such, it is important to reflect on ‘under what conditions’ what has worked to better inform the relevance of lessons to the specific need. For this purpose, a reference to the countries with hyperlinks to the relevant project documentation is provided in appendix A.<sup>8</sup>

- An effort of triangulation of data was made, but this was at times difficult given that the literature is sparse and that lessons in ICRs and ICR reviews are at times excessively generic.

1.16 The underlying project data were drawn from the Community of Practice that captures FMIS projects in investment operations. The data do not however capture

> 8 Appendix A can be accessed at this link.

**17**

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tion against a male politician. Similarly, the estimates in Panel B indicate that district characteristics, including the shares of the district population that are female, urban, or members of scheduled castes or scheduled tribes (SC/ST), and the female and male literacy rates, do not significantly vary with respect to the gender of the winner in close elections.

# **4 Results**

Nearly 25 percent of women in our sample reported experiencing physical IPV at some point during their marriage, and 22 percent reported being physically abused by their husbands during the last 12 months. Below, we first present the estimates for the impact of exposure to female leaders on female constituents’ self-reported experience of IPV. Thereafter, we delve into the mechanisms underlying these effects.

## **4.1 Intimate Partner Violence**

Table 1 reports estimates from equations (1) and (2). In Panel A, the OLS estimates show that there is a significant and positive relationship between the fraction of seats held by women at the district level and female constituents’ risk of experiencing physical IPV. However, the OLS estimates are likely to underestimate the true effect if women are more likely to be elected in districts with a lower acceptability of violence against women. Indeed, the 2SLS estimates for the full sample are positive, significant, and larger in magnitude than the OLS estimates. Column 6 indicates that increasing the share of seats held by women at the district level by one s.d. (i.e., by 0.13) increases a female constituent’s experience of physical IPV during the last year by 0.05 s.d.<sup>12</sup> This also implies that electing one additional female politician in a district increases a female constituent’s experience of physical IPV by 0.07 s.d.<sup>13</sup> The reduced-form results

> 12The magnitude of the estimated effect is similar to the effect sizes estimated in studies that examine the impacts of other policy changes on IPV indices. For example, Erten and Keskin (2018) find that an additional year of schooling in Turkey increased the reporting of psychological violence by rural women by 0.06 s.d. Erten and Keskin (2021a) show that a one s.d. increase in the Syrian refugee share in a Turkish province resulted in a 0.05 s.d. decline in the experience of physical violence reported by Turkish women.

> 13Since the median district has six constituencies and the median share of seats held by women is zero, election of an additional female politician increases the median number of seats from zero to one, with the share increasing from zero to 1/6 or 0.17. Thus, an increase in the share of female politicians by 0.17 percentage points corresponds to a 0.07 s.d. increase in physical IPV reported by the woman.

8