rafmacalaba/data-use-mentions-extended huggingface dataset:
Advanced EMDEs<br>economies<br><!-- End of picture text --> D. Impact of one-unit increase in financial openness on sensitivity of inflation expectations <!-- Start of picture text --> Percentage points<br>1.8 Financial openness<br>1.4 Financial openness ×<br>1.0 exchange rate regime<br>0.6<br>0.2<br>-0.2<br>-0.6<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> F. Impact of 10 percentage point increase in public debt-to-GDP ratio on sensitivity of inflation expectations <!-- Start of picture text --> Percentage points<br>0.4 Group-mean FMOLS<br>Group-mean DOLS<br>0.2<br>0.0<br>-0.2<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> Sources: Chinn and Ito 2017; Dincer and Eichengreen 2014; International Monetary Fund; Shambaugh 2004; World Bank.

Note: DOLS = dynamic ordinary least squares; EMDEs = emerging market and developing economies; FMOLS = fully modified ordinary least squares; GDP = gross domestic product.

A.-D. Inflation expectations are five-year-ahead expectations of annual inflation. Bars denote coefficients of panel regressions of 24 advanced economies and 23 EMDEs using annual data for 1995-2016, as described in Appendix 3. Vertical lines denote 90 percent confidence intervals.

D. Financial openness x exchange rate regime is the interaction of these two explanatory variables.

E.F. Bars denote coefficients of group mean panel FMOLS and group mean DOLS regressions of 24 advanced economies and 23 EMDEs using annual data for 1995-2016, as described in Appendix 3. Vertical lines denote 90 percent confidence intervals.

23
a change in the policy environment, which then leads to changes in tax compliance.

Torgler conducted a field experiment among ordinary citizens in Costa Rica to side-step the criticism that too many lab experiments rely on college students. Torgler found evidence that participants were influenced by the decisions of other participants. “It is interesting to notice that taxpayers do not behave as free-riders, but are willing to comply. Thus, it seems that _tax morale_ is important for a satisfactory explanation of tax paying behaviour” (Torgler 2003: 43). This finding suggests that citizens look to their fellow citizens for cues regarding the extent to which they should comply with the existing tax regime. Casal et al. (2016) also found that the “availability of voice reduces the frequency of the implementation of the exit option” in a similar lab experiment. Feld and Tyran argue that “(d)irect voter participation in the political decision making process in the form of referenda might be a beneficial condition increasing tax compliance by citizens” (Feld and Tyran 2002: 218). Lamberton, De Neve, and Norton also find that providing individuals with the ability to express policy preferences makes them much less likely to underpay their taxes or to use a questionable loophole in another experiment (2014). Wahl et al. find that giving respondents greater voice in the decision-making process leads to a greater willingness to support higher cooperation and taxation (2010). Finally, in an online survey experiment spanning 50-countries, Sjoberg et al. (2019) finds sizeable improvements in tax morale when the salience of anti-corruption efforts is increased and when citizens are able to state their expenditure preferences to the government.

Several recent studies, including RCTs, provide support for the idea of reciprocity in tax 5
may be more premature deindustrialization (Rodrik, 2015) rather than an endogenous transition to a more advanced stage of development.

# **References**

Barefoot, Kevin and Jennifer Koncz-Bruner (2012): “A Profile of U.S. Exporters and Importers of Services – Evidence From New Linked Data on International Trade in Services and Operations of Multinational Companies”, Survey of Current Business, June 2012, Bureau of Economic Analysis Bloom, Nicholas and Draca, Mirko and Van Reenen, John Michael. 2011. Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, it and Productivity. CEPR Discussion Paper No. DP8236.

Bloom, Nicholas, Raffaella Sadun, and John Van Reenen. 2010. "Does Product Market Competition Lead Firms to Decentralize?" American Economic Review, 100 (2): 434-38.

Breinlich, Holger, Anson Soderbery, Greg C. Wright, 2014. "From Selling Goods to Selling Services: Firm Responses to Trade Liberalization," CEP Discussion Papers dp1303, Centre for Economic Performance, LSE.

Benedettini, Ornella, Ben Clegg, Mario Kafouros, and Andy Neely. (2010). The ten myths of manufacturing: what does the future hold for UK manufacturing? Operations & Information Management 18
different elasticity assumptions. Assuming a 25 or 50 percent price increase (Panels A and B), the effect is still generally negative and somewhat regressive. Though the magnitude and regressive pattern is more moderate than under direct

> 17 The national estimate of smoking-attributable medical expenses (derived from Goodchild et al. 2018) and described above is adjusted by the factor of 56 percent of average out-of-pocket expenditure in medical bills.

17
2 change in inequality. In Dar es Salaam, per capita consumption increased by 43%, headcount poverty dropped by a third, and inequality as measured by the Gini coefficient jumped from 0.30 to 0.34. All the changes in Dar are significant at the 95% confidence level.

**Table 1: Consumption Data from Household Budget Survey Data**

||**Mainland**<br>**Tanzania**|**Dar es**<br>**Salaam**|**Other**<br>**urban**<br>**areas**|**Rural**<br>**areas**|
|---|---|---|---|---|
|**% Below Basic Needs Pov. Line**|||||
|**1991/92**|38.6|28.1|28.7|40.8|
||(2.1)|(2.8)|(5.0)|(2.4)|
|**2000/01**|35.6|17.6|25.8|38.6|
||(1.6)|(2.7)|(2.2)|(2.0)|
|**Consumption per Adult Equivalent**|||||
|**Mean, 1991/92**|10223|11161|12445|9820|
||(290)|(410)|(842)|(326)|
|**Mean, 2000/01**|10880|15944|13536|10060|
||(250)|(779)|(487)|(273)|
|**Ratio (00/01) to (91/92)**|
Growth in Ethiopia_**

Research on Ethiopia has focused on both internal and external firm-level drivers of growth. Among these, firm size is critical. Evidence from Ethiopia does not support the view that growth is a stochastic process as implied by Gibrat’s law. Studies using formal manufacturing data, in fact suggest a negative association between firm size and growth (Bigsten and Soderbom, 2006).<sup>17</sup> By comparison, using the World Banks’s Enterprise Survey data on nine African countries (including Ethiopia) Van Biesebroeck (2005) finds that conditional on other covariates, large firms grow the fastest.<sup>18</sup> Using data from the Ethiopian Rural Investment Climate Survey (RICS) and the Ethiopian Enterprise Survey (EES) in 2007, Rijkers et. al. (2010) support the view that size is positively correlated with success. Larger firms have higher (i) likelihood of survival (ii) productivity (iii) wages for workers, and (iv) probability of breaking into export markets. The latter findings contradict the theory of a negative relationship between firm size and subsequent growth, as well as the view that small firms have suboptimal initial size and therefore, grow quickly to reach efficient size. Nonetheless, large firms could possibly grow more because of their ability

> 15 The industrial policy was more concretized into action by various sub-sector strategies and by the successive development plans such as Sustainable Development and Poverty Reduction Program (SDPRP) 2002/03-2004/05, the Plan of Action for Sustainable Development and Eradication of Poverty (PASDEP) 2005/06-2009/10, and the Growth and Transformation Plan (GTP) 2010/112014/15. The export promotion strategy was targeted at high value agricultural exports (e.g. horticulture products and meat) and labor-intensive manufacturing products such as clothing, textile, leather and leather products.

> 16 Gebreeyesus (2013) provides an extensive review of the Ethiopian reform process in the 1990s as well as industrial
Column (5) of Table 4 investigates whether this particular form of conditional independence is a reasonable assumption. It turns out that our findings are robust to excluding all spells other than the first, ruling out dependency among recurring slumps.

Column (6) allows for institutions to change endogenously during the decline phase (meaning we replace _INS_<sup>�</sup> 0 by _INS_<sup>�</sup> _t_ ). We showed earlier that constraints on the executive and a number of other institutional indicators improve in the aftermath of a crisis. So far, we have excluded this endogenous adjustment for two reasons: ( _i_ ) it could bias the estimation in favor of our hypothesis (if institutions both improve as a response and then shorten the remaining decline phase), and ( _ii_ ) elites might find it difficult to credibly promise to bind their hands and not renege later on (Acemoglu and Robinson, 2006). In any case, these concerns are not borne out empirically. The results remain quantitatively and qualitatively similar to our baseline.<sup>20</sup> A serious issue in the cross-country growth literature is the fragility of regression estimates with respect to different GDP series. Johnson et al. (2013) show that new renditions of the Penn World Tables (PWT) incorporating more recent PPP benchmarks can imply vastly different historical growth rates. Worse still, they point out that the findings of several prominent studies, including Ramey and Ramey (1995) and Hausmann et al. (2005) cited in the introduction, are not robust to different versions of the PWT.<sup>21</sup> In fact, this has prompted major changes in how the PWT data are prepared and made available to the public. Starting from version 8 different time series are reported for level and growth rate comparisons.<sup>22</sup> We take these concerns seriously and repeat the restricted structural break search using two very different data sets (in addition to
Africa. For example, when the MVP was just beginning, Sachs (2005: 236) called for foreign aid to Kenya to increase fifteen-fold in order to provide $1.5 billion per year for Millennium Village interventions in that country alone. Before any evaluation had been published, MVP (2008b) applauded plans to expand the MVP in several countries and concluded, “The MVP has therefore created a powerful pressure to expand as a result of its notable successes.” At the same time, the MVP has received criticism similar to that of earlier model villages: Carr (2008) decries “absence of critical thought” in the design of the short-term intervention and fears that “human well-being in the Millennium Villages is ... likely to rely on a constant flow of aid money in the foreseeable future.”

# **4 Estimated Effects Depend Critically on the Evaluation Method**

We begin by taking a critical look at the mid-term evaluation results reported by the MVP for five village clusters. The design of the project makes it impossible to carry out a truly rigorous assessment of the project’s effects. Our goal here is not to perform such an assessment, but to demonstrate the importance of careful evaluation by showing that the estimated effects of the project depend crucially on the point of comparison for the experience of the treated village clusters.

# _The Mid-term MVP Evaluation Report_

In June 2010, the project released its first public evaluation of the effects of the intervention on the Millennium Villages (MVP 2010c). The report describes the interventions and compares baseline values to those three years into the MVP for several indicators, using surveys from five Millennium Village sites: Sauri, Kenya; Ruhiira, Uganda; Pampaida, Nigeria; Bonsaaso, Ghana; and Mwandama, Malawi. This before-and-after comparison follows the now-outdated evaluation design described by the MVP three years earlier in the _Proceedings of the National Academy of Sciences_ : “project impact is assessed by rigorous before-and-after comparisons and detailed studies by sector” (Sanchez et al. 2007
starting point for our panel dataset is the Global Power Plant Database detailed in Byers et al. (2018).<sup>1</sup> This database provides detailed information on the geolocation, capacity, fuel type, and commissioning year for all power plants with generators above 1 megawatt (MW). This feature of our dataset is important because little is known about the pollution burden created by plants with smaller 1https://datasets.wri.org/dataset/globalpowerplantdatabase, Dataset Last Updated June 2, 2021.

5
# Methodology

## Distribution Based on Rural Population

The methodology for distributing rural population used in this analysis is relatively straightforward. First, as with all the methodologies compared here, the agricultural GDP is aggregated from the municipio level up to the microregion level. Then, the rural population grid was produced using the Gridded Population of the World (GPW) version 4 (CIESIN 2017) for 2010 adjusted to the United Nation’s World Population Prospects (UN WPP), and masking out urban area using the Global Human Settlement grid for 2015 (Pesaresi and Freire 2016).<sup>7</sup> Once these steps are done, it is straightforward to aggregate population to the 5 arc minute pixel used by all three methodologies, then for each microregion, distribute the agricultural GDP per rural person to each pixel based on the rural population in each pixel.

## Spatial Allocation Using Cross‐Entropy

<mark>The methodological framework is a spatial allocation model that estimates the subnational agricultural GDP to 5 arc minute pixels (or any other spatial resolution desired). The probabilistic model includes the control (constraint) of national or subnational agricultural GDP available from various sources of national ministries by country or reports. The five components of agricultural GDP (crops, livestock, fisheries, timber, and non‐timber forest products) are used to provide a prior estimate of agricultural</mark> GDP at the pixel level.

<mark>We define our spatial AgGDP allocation problem in a cross‐entropy framework following You et al. (2014). The first step is to transform all real‐value parameters into a corresponding probability form. Let S</mark> _i_ <mark>be the percentage of the total agricultural GDP allocated to pixel</mark> _<mark>i</mark>_ <mark>within a certain country (e.g. Country X) except the crop value for that pixel.</mark> _<mark>AgGDP</mark
- Pitts, Melinda. 2021. “Where Are They Now? Workers with Young Children during COVID19.” _Federal Reserve Bank of Atlanta Policy Hub Papers_ .

- Sevilla, Almudena and Sarah Smith. 2020. “Baby steps: the gender division of childcare during the COVID-19 pandemic.” _Oxford Review of Economic Policy_ 36(1):169–186.

- Th ́ebaud, Sarah. 2015. “Business as plan B: Institutional foundations of gender inequality in entrepreneurship across 24 industrialized countries.” _Administrative Science Quarterly_ 60(4):671–711.

- Torres, Jesica, Franklin Maduko, Isis Gaddis, Leonardo Iacovone and Kathleen Beegle. 2021. “The Impact of the COVID-19 Pandemic on Women-Led Businesses.” _World Bank Policy Research Working Paper_ .

- UNESCO. 2020 and 2021. “Global monitoring of school closures.” _2020 school closure data retrieved in November 2020 and 2021 school closure data retrieved in November 2021 from: https://en.unesco.org/covid19/educationresponseschoolclosure_ .

- Vaillant, J, Koussoub ́e E, Roth D, Pierotti R, Hossain M and Falb KL. 2020. “Engaging men to transform inequitable gender attitudes and prevent intimate partner violence: a cluster randomised controlled trial in North and South Kivu, Democratic Republic of Congo.” _BMJ Glob Health_ 5(5).

- World Bank. 2019. “Profiting from Parity : Unlocking the Potential of Women’s Business in Africa.” _World Bank Report_ .

- World Bank. 2020. “Women entrepreneurs needed—stat!” _World Bank Data Blog https://blogs.worldbank.org/opendata/women-entrepreneurs-needed-stat_ .

35
for government contracts. All regressions include country, sector, and year fixed effects given that some missing data on corruption and tax evasion seems to be specific to the country, the sector, and the year of the survey. As seen in panel A, there is no strong evidence that responders and non-responders are statistically different in their observable characteristics. A limited number of variables are barely significant at the 10% level and not robust across specifications. Panel B of Table .8 considers three additional key characteristics of firms: capital stock, investments, and profits.<sup>22</sup> The findings show that responders and non-responders do not differ significantly relative to any of these aspects. Thus, there is no observable evidence that potential selection bias drives our results.

> 22We did not employ these variables in our main analysis because of missing data.

18
Bank (2008b) that reached 5% penetration in developing countries, only 6 reached a 50% market share. Similarly, Winkler _et al_ . (2011) note that simply providing access to grid electricity is not sufficient to ensure its use in low-income countries. Affordability is an important constraint. Even after on-grid infrastructure is in place, poor households may be unable to afford appliances that use electricity. Thus, income disparities within countries will lead to uneven diffusion of new technologies.

Two studies by Allan Blackman illustrate differences in adoption of green technologies between developing and developed countries. Blackman and Kildegaard (2003) study the adoption of three clean leather tanning technologies in Mexico. They use original survey data on a cluster of small- and medium-scale leather tanneries in León, Guanajuato, noting that small and medium scale enterprises often dominate pollution intensive industries in developing countries. To explain the adoption of each tanning technique, they estimate a system of multivariate probit models. They find that a firm’s human capital and stock of technical information influence adoption. They also find that private-sector trade associations and input suppliers are important sources of technical information about clean technologies. In contrast to results typically found in developed countries, neither firm size nor regulatory pressure is correlated with adoption. In addition to economic incentives, direct regulation, and information provision, some research has emphasized the role that “informal regulation” or community pressure can play in encouraging the adoption of environmentally clean technologies. For example, in an analysis of fuel adoption decisions for traditional brick kilns in Mexico, Blackman and Bannister (1998) suggest that community pressure applied by competing firms and local non-governmental organizations was associated with increased adoption of cleaner fuels, even when those fuels had relatively high variable costs.

30
# **GENDER EQUALITY AND ECONOMIC GROWTH IN BRAZIL: A LONG-RUN ANALYSIS**

## **Pierre-Richard Agénor***

Pierre-richard.agenor@manchester.ac.uk

## **Otaviano Canuto****

ocanuto@worldbank.org

**Keywords** : gender equality; economic growth; overlapping generations models; public capital

**JEL Classification Numbers** : I15, I25, J16, O41

### World Bank Policy Research Working Paper

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or any of the governments that they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ __________________________________________________________________________________________

> *Hallsworth Professor of International Macroeconomics and Development Economics, University of Manchester, and co-Director, Centre for Growth and Business Cycle Research; and **Vice President, World Bank. We would like to thank Lim King Yoong and Baris Alpaslan for research assistance, Lilian Lopes Ribeiro for helping us to identify the PNAD data used for calibration, and Pablo Fajnzylber for helpful comments. The Appendix is available upon request.
services and endogenous productivity effects. These larger estimated welfare gains are consistent with the econometric literature on the productivity impacts of services liberalization. Given the crucial importance of productivity impacts to our modeling framework, I begin with a survey of the literature on the impact of services liberalization on economy-wide productivity and the impact of increased access to intermediate goods on productivity.

# **The Impact of Services Availability and Services Liberalization on Productivity**<sup>**5**</sup>

First, several studies using firm level data have established a link between increased access to services and increases in productivity. Using panel level data and controlling for FDI endogeneity, Arnold, Mattoo and Javorcik (2011) for the Czech Republic, Fernandes and Paunov (2012) for Chile, Shepotylo and Vakhitov (2012) for Ukraine and Arnold, Javorcik, Lipscomb and Mattoo (2012) for India find a significant positive relationship between FDI in services following liberalization (especially allowing foreign entry) and the performance of downstream domestic firms in manufacturing. Arnold, Mattoo and Narciso (2008) find a statistically significant positive relationship between productivity in a sample of over 1000 firms in ten SubSaharan countries and the performance of the three service industries for which they collected data. Several papers have shown, e.g., Fink, Mattoo and Neagu (2005), that openness in a range of producer or intermediate service sectors is linked to increased export competitiveness and performance for high-technology sectors, for which services tend to be an important element of total cost.

Second, using cross-country growth regressions, several studies have found that open services regimes induce greater growth rates. Mattoo, Rathindran and Subramanian (2006) find

> 5 This section draws on the survey paper by Francois and Hoekman (2010).  The reader should consult that source for a more complete review of the literature on the impact of services availability and services liberalization on productivity and growth.

4