rafmacalaba/datause-encoder-data huggingface dataset:
to acceptance of violence, with a slightly increasing trend in justification of IPV. While attitudes are incomplete and noisy proxies for social norms, this stable trend in conservative attitudes is indicative of the persistence of regressive gender norms in the region. _Fourth_ , a negative relationship exists between these conservative gender attitudes and their respective gender outcomes. Although this is not a causal relationship, their interdependence is important to note, given the difficulty in explaining the lack of progress in gender outcomes in the region.

The remainder of the paper is organized as follows. Section 2 discusses the related literature on social norms and women’s outcomes focusing on South Asia. Section 3 describes the secondary data sources used to explore these trends in the region, while Section 4 outlines the methods used to analyze them. In Section 5, we present the four stylized facts and discuss their implications. Finally, Section 6 concludes.

# **2. Literature Review**

Women’s economic participation may be influenced by several factors operating at different levels -from the macro to the individual. While there are both demand and supply-side factors that may predict and determine women’s labor force participation and employment outcomes, we focus primarily on barriers on the supply-side, especially those that interact strongly with social norms to influence FLFP.<sup>4</sup> Many studies argue that gender norms are behind the low levels of women’s economic participation (Heintz, Kabeer, and Mahmud, 2017; Bridges, Lawson, and Begum, 2011; Luci, Jütting, and Morrisson, 2012, Blau et al., 2013; Fernandez 2013, Fernandez and Fogli, 2009, Giuliano, 2017).<sup>5</sup> In some South Asian countries, economic growth has reshaped long-standing norms, as in Bangladesh, where women’s employment and mobility in cities have risen due to the growth of the garment sector (Heath and Mobarak, 2015). Nevertheless, many norms remain unchanged or have regressed along with this progress; for example, child marriage remains a widespread practice in Bangladesh despite legal frameworks against it (Male and Wodon, 2017). Advancement in some areas and lack of progress in others illustrate the complex links between norms, gender disparities, and economic development.

While studies in the region are relatively small-scale and country-specific, the literature does converge on social norms being one of the key drivers of low FLFP in the region. In India, Deshpande and Kabeer (2021) find that detrimental social norms that reinforce expectations of women being primarily responsible for household chores lower their probability of employment. Analysis of national household survey data in Bangladesh reveals a strong correlation between a conservative outlook (as indicated by wearing a _burqa_

> 4 For a discussion on demand-side factors see for example, for the case of India, Deshpande and Singh (2021), Klasen and Pieters (2015), Chatterjee et al. (2015), Mehrotra and Parida (2017).

> 5 The reverse is also true, female labor force participation and employment has a transformative potential on gender norms (Seguino 2007).

4
Nevertheless, we perform additional robustness checks using the financial development index compiled by Sahay et al. (2015) which incorporates measures for depth, access and efficiency of financial institutions and markets. It is noteworthy that this composite index suggests that financial development in sub-Saharan Africa has been mediocre over the past three decades, and trails other regions, although there has been some modest improvement over the last 15 years (Mlachila et al., 2016). The results, which are presented in Table 7, show the interaction term for the exchange rate regime and financial development indexes bears a positive coefficient. Moreover, the effect of an increase in exchange rate flexibility estimated at the mean level of financial development is consistent with prior estimates; the coefficient for the nonlinear combination of the standalone effect of exchange rate regime and the interaction with financial development being positive and statistically significant in columns (3) and (6). These estimates substantiate the initial finding that an increase in financial development facilitates a positive impact of exchange rate flexibility on economic growth.

In general, the observed impact of financial development on the dynamics between exchange rate flexibity and growth is consistent with Aghion et al. (2009), yet there are some notable observations. Aghion et al. (2009) show that for a sample that includes both developed and developing economies, there is a threshold level of financial development beyond which exchange rate flexibility has a positive effect on productivity growth. In this study, the estimated nonlinear effect at the 75th percentile of financial development for instance, is negative but of a significantly smaller magnitude in comparison to the standalone effect of exchange rate flexibility. This suggests that for African countries, the level of financial development as captured by traditional measures is quite low, such that even in countries with the highest level of financial development in the region, the best outcome for a further deepening of the financial system would be to neutralize the negative effect of exchange rate flexibility on growth. Nevertheless, when financial development is measured using the indicator by Sahay et al. (2015), a positive impact of exchange rate flexibility is realized at the mean level of financial development for the countries in the sample.

16
implies that estimates of the population of informal businesses and their activity will be missing or incomplete; and sampling frames for developing high-quality, representative survey data will be unavailable. In turn, descriptions of the characteristics of many economies will be lacking information on a sector that is a ubiquitous source of jobs and business activity.

This paper lays out a methodology for conducting an enumeration exercise that generates a representative sample of informal businesses for a subsequent survey; it does so by utilizing an established geographic sampling method, Adaptive Cluster Sampling (ACS). ACS assumes that informal business activity is relatively clustered in certain areas and, in turn, it takes advantage of the information from the discovery of some informal businesses to alter the probability of selection of primary sampling units (PSUs) to capture concentrations of additional informal businesses more efficiently. ACS accounts for this altered probability of PSU selection and is able to provide estimates of the population totals of informal businesses, allowing any subsequent survey of those businesses to be considered representative. In the process, ACS frequently allows for reductions in fieldwork effort without an increase (and often a decrease) in the variance of these population estimates, if they were done repeatedly—a point which is later illustrated in this paper through simulation. The methodology is designed to be implemented in diverse situations, across different countries, while maintaining comparability.

Informal businesses may operate completely outside all or some legal channels (without a tax or business registration or license), or they may include businesses that operate informally by non-compliant behavior, such as employing workers ‘off-the-books’ (Ulyssea, 2018). Operationalizing data collection first requires a clear and functional definition of the underlying population of interest. A useful and basic typology of informality is the following: informality includes partial or full non-compliance with registration and licensing (legal informality), on employment (labor informality), or tax non- or under-payment (fiscal informality) (World Bank, 2020). This paper focuses on businesses’ activities that are _legally_ informal: that is, lacking one or all of the registration or licensing requirements to operate. This definition must be anchored to the local context of implementation but generally consists of those businesses that lack a business license, do not exist on a business registry, and/or are not registered with the relevant tax authority.

In addition to the operational aspect of this definition, there are important policy implications for surveying unregistered businesses. First, such businesses are prevalent and may 3
35 data sources. We recognize that there are conceptual and statistical problems with these measures. Research is needed to design and construct more accurate measures of fiscal, monetary, trade and financial policies. Also, since the listed indicators are commonly used, it is useful to determine whether these variables reliably predict growth. If these policy indicators are useful in explaining growth, then they may be useful target indicators in formalizing policy reform packages.

Empirical implementation should update earlier datasets. For example, the compilation of census data on educational attainment by Psacharopoulos and Arriagada (1986) could be updated with 1990 census observations in some countries.

From the WDR 1991 database, one could use the following variables:

1. Estimates of factor inputs (capital stock, labor force, arable land, aid mean education of the working population for 68 countries). Some of the specifications that will be tested include factor accumulation on the right hand side so as to test for the effect of policies on efficiency. The factor input data will be useful in this regard.

2. Welfare indicators (infant mortality, female/male ratios, mean index of civil and political liberties). These and other indicators will be useful to examine the correlation between growth and more general welfare improvement.

3. Data on project economic rates of return used in a recent paper by Kaufmann (1991).

4. Government expenditures for health and education (original source is IMF and UNESCO); private spending on health (available for 19 countries, original source is United Nations). The government spending data will be important for the analysis of fiscal policies; the private health spending will be useful in measuring factor inputs.

5. Trade policy indicators (average tariff rates, nontariff barriers (UNCTAD), index of trade liberatlization -- 1960-84 (Papageorgiou, Choksi, Michaely), index of trade liberalization -- 1978-88 (Halevi, Thomas), price distortion index (modified version of Dollar (1991).

6. Direct foreign investment flows (55 countries, 1970-89).

7. Political indicators (irregular executive transfers).

8. Measures of inequality (35 countries).
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|---|---|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Second Integrated Sanitation and Sewerage Project - EG|Mouhamed Fadel Ndaw|
|Capacitybuildingfor the use of sectoral Medium Term Expenditure|Jean-Pascal Nganou|
|Capacitybuildingfor the use of sectoral Medium Term Expenditure|Jean-Pascal Nganou|
|StrengtheningSocial Resilience to Climate Change|Rodrigo Serrano-Berthet|
|StrengtheningSocial Resilience to Climate Change|Rodrigo Serrano-Berthet|
|EncouragingMore Resilient Communities in Conflict-affected Areas|Matthew Stephens|
|EncouragingMore Resilient Communities in Conflict-affected Areas|Matthew Stephens|
|EncouragingMore Resilient Communities in Conflict-affected Areas|Matthew Stephens|
|EncouragingMore Resilient Communities in Conflict-affected Areas|Matthew Stephens|
|Cabo Verde Statistical CapacityBuilding|Djibril Ndoye|
|Cabo Verde Statistical CapacityBuilding|Djibril Ndoye|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|Norte Grande Road Infrastructure|Veronica Ines Raffo|
|StrengtheningStatistical Infomation in Yucatan|Indu John-Abraham|
**Table 13. Features of approaches**

|**Forecasting approach**|**RMSE and bias**|**Appropriate for**<br>**scenario analysis**|**Data requirements**|
|---|---|---|---|
|Futures|For<br>most<br>commodities<br>(except<br>aluminum), lowest RMSEs or bias for<br>forecasts up to four quarters but reliable<br>data unavailable for longer horizons|No|Low|
|Consensus forecasts|For most commodities (except tin and<br>zinc), lowest RMSEs or bias for<br>forecasts of more than a year but poorer<br>short-term performance|No|Low|
|Bivariate correlations|For metal commodities, lowest RMSE<br>or bias but only at the very shortest<br>horizon|No|Medium|
|BVAR|For all commodities at all horizons,<br>higher bias and RMSE than other<br>approaches|Yes|Medium|
|OEM|For all commodities, lowest RMSEs or<br>bias for forecast horizons above one<br>year|Yes|High|
|Machine learning<br>techniques|For all commodities (except for nickel),<br>intermediate bias and RMSEs.|No|Medium|

_Note_ : BVAR (Bayesian Vector Autoregression); OEM (Oxford Economic Model); RMSE (Root Mean Squared Error).

# **References**

Abosedra, S. 2006. “Futures versus Univariate Forecast of Crude Oil Prices.” _OPEC Review_ 29 (4): 231–241.

60
therefore, substantial explanatory power.<sup>40</sup> A final potential explanation is that the BERI and ICRG variables do not sufficiently capture institutional characteristics that have implications for banking performance.<sup>4</sup> ' Better indicators might focus on the autonomy and quality of the central bank or other proxies for the quality of bank supervision (e.s supervisors' salaries). Unfortunately, no cross-country database exists that adequately captures salient institutional features of the banking sector.<sup>42</sup> In short, that these broad institutional indices do not perform as expected may come as little surprise to many. The inconclusive results, moreover, speak to a need for more data on institutional features of the banking sector if the impact of interventions is to be well measured.43 The institutional variables that described the intervention provided substantially more explanatory power than did the BERI and ICRG indices. The coefficient for the number of reform areas covered in the intervention was negative and that for the FSAL 40 However, ICRG/BERI growth results are typically robust to sample selection (e.L. OECD vs. non-OECD countries). Of course, the regressions here employ only a small subset of non-OECD countries.

41 Of course, if the BERI and ICRG variables are robustly correlated with growth, and if the Gertler-Rose hypothesis is correct, there should be a positive partial correlation between these indicators and the size of the financial sector. That we don't find one suggests we have either measurement problems (as alluded to above) or, perhaps, that these indicators are not exogenous, and thus not truly correlated with growth.

42 OED is, however, striving to create one. Unfortunately, comparability of data across countries and a general lack of comprehensive data collection in many countries make this task especially arduous. Initial collection results are positive for some time series on banking indicators, but the series are not sufficiently complete for enough countries to permit their inclusion in the econometric analysis. On central bank independence, Cukierman and Webb (1993) also offer some indicators.

43 Indeed, this data could also provide us with more direct ways to measure the success of an intervention rather than analyzing changes in M2/GDP. For example, data on loan classification or credit allocation by sector may tell us more about the success of an effort to reform directed credit or to privatize banks than would M2/GDP.

32
To illustrate these challenges, Figure 1 shows that countries with higher vulnerability to climate change also face higher sovereign risks. The climate vulnerability index comes from the new IMF Climate Change Dashboard and comprises climate-driven hazard and exposure, vulnerability, and lack of coping capacity. The EIU Country Risk Index indicates sovereign debt risks, based on more than 50 institutional, macrofinancial and structural variables. Climate-fiscal nexus due to the simultaneous exposure to both risks suggests that emerging market and developing economies (EMDEs) are more sensitive to physical risks of climate change and feature tighter fiscal space. When natural disasters hit an economy, a fiscal response to address economic and distributional effects may lead to a debt increase which carries downside risks of its own. Potential debt distress and limited access to international financial markets could reduce the intended fiscal support and exacerbate growth and fiscal prospects.

This paper examines to what extent the impacts on economic growth after natural disasters are accompanied by widening fiscal deficits and corresponding pressures on public debt. It relies on the ifo GAME database (Felbermayr and Groeschl, 2014), which contains exogenous measures of physical disaster intensity, well suited for causal analysis. Regression results suggest that natural disasters lead not only to output losses, but also to further deterioration of countries’ fiscal positions. A disaster of one standard deviation above the disaster index mean leads to 1.32 percent lower output per capita growth, 1.58 percent higher government debt, and 0.88 percent increase in fiscal deficit. Local projection estimates indicate that the adverse effects persist in the years after the disaster, not only on impact. Finally, the effects are significant in the subsample of EMDEs, while the advanced economies remain stable after disaster episodes.

In addition, I provide a general equilibrium interpretation of the empirical results using a Dynamic stochastic general equilibrium (DSGE) model. The model explains the propagation mechanism of a natural disaster shock that affects agricultural productivity. Based on Gallic and Vermandel (2020), it features farmers endowed with land with timevarying productivity subject to economic and weather conditions. The model is extended with a more detailed government sector, which allows considering the fiscal implications. Last but not least, counterfactual simulations highlight the role of structural resilience in limiting the impact of disasters.

# **2 Related Literature**

Empirical literature remains inconclusive on the macro-fiscal effects of natural disasters. When it comes to the impact on economic activity, Rasmussen (2004) suggests that natural disasters cause a 2.2 percent median reduction of the same-year real GDP growth rate. In a similar vein, Loayza et al. (2012) and Fomby et al. (2013) use a large panel of countries to conclude that severe disasters carry much worse effects than moderate ones, especially in developing economies. Klomp and Valckx (2014) conduct a meta-analysis based on 25 2
(and in some cases, data on hate crimes) to explore how migrants’ presence affects attitudes and behaviors toward migrants and migration policies. Since little public opinion and granular election data is available for developing countries, and given that policy makers in democracies are generally more attuned to their constituents’ preferences, such studies have focused almost exclusively on high- and upper-middle-income countries.

With few exceptions (e.g., Vertier and Viskanic, 2020), these studies have found that migrants’ presence—usually measured as the share of migrants (or refugees) in an area—substantially increases support for anti-immigration policy positions in Germany (Schaub, Gereke and Baldassarri, 2021; Otto and Steinhardt, 2014), Italy (Barone et al., 2016), Switzerland (Brunner and Kuhn, 2018), France (Edo et al., 2019), Denmark (Dustmann, Vasiljeva and Piil Damm, 2019), Austria (Halla, Wagner and Zweimüller, 2017; Steinmayr, 2021), Greece (Hangartner et al., 2019), and Colombia (Rozo and Vargas, 2021). As mentioned above, some of the factors that drive these negative effects may be less relevant in low-income countries. We contribute to this literature by exploring the effect of refugees’ proximity on the migration policy preferences of host community populations in a low-income country.<sup>5</sup> Focused almost exclusively on the Global South, the second strand of research explores whether the presence of refugees is associated with a greater risk of conflict (Jacobsen, 2002). Earlier studies highlighted possible tensions with local citizens that are exacerbated by resource competition or ethnic rivalry (Salehyan and Gleditsch, 2006; Rüegger, 2017). Much of this scholarship recognizes that refugees are often victims of conflict (Onoma, 2013; Fisk, 2018; Böhmelt, Bove and Gleditsch, 2019; Savun and Gineste, 2019). Recent research suggests that conflict between host communities and refugees may be avoided if refugees’ presence attracts aid and economic activity that benefits both (Lehmann and Masterson, 2020). We complement this literature on the refugee–conflict nexus, which thus far has relied on cross-country analysis, by exploiting—following Zhou and Shaver (2021)—within-country variation in exposure to refugee settlements.

The third strand explores the welfare consequences of refugees’ presence on host communities in developing countries. However, without auxiliary data (such as survey data on policy preferences regarding refugee policies), these studies cannot tell us how the economic consequences of refugee hosting affect social cohesion (if at all). Moreover, almost all studies in this research domain strand of the literature concentrate on a single domain, such as labor market outcomes (Fallah, Krafft and

> 5See also Zhou (2018), which examines how the presence of refugees can change local citizens’ opposition to citizenship inclusion in sub-Saharan Africa.

6