rafmacalaba/data-use-mentions-extended huggingface dataset:
*|17|17|17|17|
|**(Pseudo Log)-Likelihood**|-7.18|-7.23|-4.40|-4.42|

Robust standard errors in parentheses. * Denotes significant at the 10% level, ** significant at the 5% level, ***significant at the 1% level. See Appendix A for details of variable descriptions. _Source: The Author_

# **Conclusion**

The aim of this paper was to contribute to understanding the determinants of rent extraction, especially in high-income democracies in which variations in rent extraction persist but cannot be explained by the lack of either long-established democratic institutions or socio-economic development. Utilizing the insights of recent developments in the economic voting literature, specifically the substantive DuchStevenson model, in conjunction with a game-theoretic, career concerned modeling framework, it was possible to derive new hypotheses regarding the determinants of rent extraction. Specifically, the model anticipated that a higher EDD/NEDD ratio in democratic contexts would be associated with less rent extraction as electorally accountable and career-concerned office holders would be incentivized to minimize their short-term rent extraction and voters would be able to re-elect only high competency incumbents.

Consistent with these theoretical expectations the EDD/NEDD ratio was associated with: (1) lower levels of rent extraction in the sub-sample of countries identified as high-income democracies; and (2) in the pooled dataset, only associated with lower levels of rent extraction as part of an interaction effect with high-income democracies. Thus, the EDD/NEDD ratio is robustly associated with variations in rent extraction for the sub-sample of countries that are considered high-income democracies. While future research needs to focus on examining the micro-level empirical implications of the model in more detail, the fact that the macro-level results obtained above are consistent with the insights of both: (1) the economic voting literature; and (2) the empirical finding that a high-income democratic context is associated with less rent-extraction; provides a critical first step in justifying
consumption measures being statistically significant. Karshenas (2003) finds a corresponding difference of only approximately 5 percent using a slightly different assessment method.<sup>22</sup> While our estimates differ substantially from those of Deaton and Ravallion, we find this difference disappears when we compare our estimates over similar time periods. The Deaton analysis includes data from 1979-2000, while the Ravallion analysis covers 1985-1998. Our estimated overall difference between household survey means (pooling consumption and income) and HFCE for the 1980s and 1990s are 15 percent. These estimates are similar to Ravallion’s 17 percent (95% confidence interval of approximately 9 to 25 percent) and Deaton’s 12 percent (95% confidence interval of approximately 6 to 18 percent). One inference to be drawn from this analysis is that our estimated gaps are substantially larger from those estimated separately by Deaton and Ravallion, but this is not due to differences in methods or assumptions, but due primarily to our data including more recent observations. We estimate the overall average gap during the 2010 decade to be 75 percent greater in absolute magnitude relative to the average gap during the 1990s. For a more detailed comparison of our findings with those of Deaton (2005) and Ravallion (2003), see Appendix 1.

Across all sub-samples by geography and income groups, the gap is negative and statistically significant (Table 1). In Panel A of Table 1, we compare all survey measures (overall, consumption, income) with HFCE. There is a marked pattern of the ratio and country income levels, with the gap being largest among middle-income countries (23 percent for lower middle-income countries and 33 percent for upper middle-income countries). The gap is much smaller for low- and high-income countries, but still statistically significantly different from zero. The pattern across levels of economic development is shown in greater detail in Figure 3, which plots all gaps (and highlights the most recent observations) against economic development, as measured by GNI per capita. The relationship between
ments to estimate travel demand and supply. Second, counterfactual trip information extracted from Google Maps allows comparison of similar trips under different traffic scenarios, which helps attenuate endogeneity issues by breaking any correlation between the dependent variable and the error term.<sup>2</sup> The authors find a negligible welfare loss associated with traffic congestion, estimated to be less than 1 percent of the daily wage in Colombia. This result is consistent with mobility patterns in urban India, where the examination of traffic data collected via Google Maps (Akbar et al., 2018) or smartphone applications (Kreindler, 2018) also points to small welfare effects of congestion.

In a recent research, Yang et al. (2020) combine nearly 400 million real-time observations of speed and traffic volume data with a plausibly exogenous policy shock inducing a variation in traffic congestion (driving restrictions based on the last digit of the license plate number) to estimate the marginal external congestion cost in Beijing. Using traffic microwave sensors, they collected real-time traffic flows at two-minute intervals for the city’s major roads in 2014. The authors find that the marginal external congestion cost can rise up to $0.30 per vehicle-km while the deadweight loss of road congestion represents about half percent of Beijing’s GDP. The authors find that this welfare loss would increase about three fold if social and environmental externalities such as accidents and air pollution were included in the calculation.

# B. Mobility, Employment and Productivity

There is an established literature that uses traditional data (mainly travel surveys) to the links between and economic outcomes. For exam- analyze reciprocal mobility ple, Bhat and Koppelman (1993) use the Dutch Mobility Panel to show that employment and household income have a on car significant influence ownership, trip generation,

> 2 Potential identification challenges may arise from trip selection and the simultaneous determination of trip cost and flows. To overcome these issues, the authors combine a number of empirical strategies, including controlling for weather shocks, enlarging the study area
strongly with formal ones. Finally, informality seems to pose macroeconomic challenges as tax efforts and financial sector depth in Indonesia remain below the EMDE averages.

5. **The paper makes several contributions to the existing literature.** It is the first study on informality in Indonesia that uses a multidimensional approach adapted to the country’s specific conditions. Following Elgin et al. (2021a), the paper looks at output, employment, and firm informality, and utilizes all existing data series that can be used to capture the extent of informality in Indonesia. Second, it is the first to estimate informality for Indonesia at the regional, provincial, and sectoral levels. No existing study has attempted to understand the informal sector in Indonesia at the sub-national level. Third, it is also the first paper that utilizes the various labor force surveys (SAKERNAS surveys) to estimate both output and employment informality. Lastly, it provides a systematic examination of the features of the informal sector in Indonesia, highlighting what is also commonly seen in other EMDEs (or documented in the literature) and what is not.

6. **The paper has limitations as many questions about the informal sector in Indonesia remain unanswered** . Limited studies have looked at the informal sector systematically in Indonesia. The existing ones tend to focus on one or two dimensions of informality. While this paper helps deepen the understanding of the informal sector in Indonesia via a multidimensional approach, it does not provide additional information on the drivers of informality, nor does it try to establish any causality relationships, nor provide policy recommendations.

7. **The paper is divided into five sections** . Section 2 highlights the literature behind the multiple informality analytical frameworks and discusses definitions and data used in this study. Section 3 depicts the levels and trends of informality in EMDEs (as well as the EAP region) and Indonesia. Section 4 summarizes the correlates of informality documented in the literature and other EMDEs and examines whether those correlates of informality also exist in
of public transit|
|Beaudoin &<br>Lin-Lawell<br>(2017)<br>**_Restrictions on D_**|United States<br>**_riving and Veh_**|1991-2011<br>**_icle Purchas_**|Air quality data, traffic<br>congestion and transit<br>capacity<br>**_e_**|Using political voting<br>records and the level of<br>Federal funds provided<br>for transit in the region<br>in the prior year as an<br>IV|Increase in US public transit<br>supply led to a small<br>deterioration in overall air<br>quality|
|Wollf (2014)|Germany|2008-2010|Air quality (half-hourly,<br>hourly, daily), location<br>information of stations, and<br>low emission zones|Difference-in-<br>differences|Low Emission Zones<br>decrease air pollution by<br>around nine percent in<br>urban traffic centers while|

46
responsibilities are frequently attributed as a reason for women’s absence from the labor force. This trend has remained mostly unchanged in the 15-year period since 1999 (Amir et al., 2018).

Women’s ability and voice in decision-making within the household, and the social norms, such as expected gender roles, that regulate it, have implications beyond childbearing. These include women’s mobility (Jejeebhoy, and Sathar, 2001, Kantor 2002) when norms bound women to limited access to means of transport, or limited ability to leave their homes; their ability to build their capabilities and access economic opportunities (Afridi, et al., 2018; Rahman and Islam 2013; Kabeer, 1999), and their experience of IPV when participating in the labor market or earning an income (Krishnan et al. 2010; Weitzman 2014; Raj et al. 2018).

# **3. Data**

To explore the role and evolution of social norms in women’s outcomes in South Asia, we searched for sources of data on both gender outcomes and their related attitude indicators that were available for more than a single period. While most countries in South Asia have several waves of comprehensive household surveys, the combination of outcome and attitude variables is not common in any single survey. Surveys were mapped based on these criteria, and several potential secondary data sources were identified.<sup>7</sup> In our analysis, we use nationally representative data from the Demographic and Health Survey (DHS) and the World Value Survey (WVS) surveys. Among the secondary data sources considered, DHS provided the most comprehensive combination of gender-relevant outcome variables across all countries and time periods. However, one limitation of the DHS sample is that it is restricted to women of childbearing age (and in some countries, it covers only women who have married or live in a consensual union). WVS, though not available for all countries in the region, was selected due to the availability of gender
and were now better able to recognize the number and cost of the tools and inputs they needed. However, group comparisons show that group C was the only group experiencing a significantly larger impact in any of the capital outcomes relative to the other treatment groups.

# c. Relative effectiveness of various intervention elements

# _Treatment group comparison_

Many studies of training programs face difficulty in determining which training elements are most effective in promoting employment and entrepreneurship because various types of content are mixed together in most experiments (McKenzie and Woodruff 2014). This study aims to inform this discussion by examining the relative effectiveness of providing youth with the following: (i) occupation‐specific skills through technical training, plus on‐the‐job training (treatment A); (ii) business skills training and facilitation of access to finance (treatment B); and (iii) a package combining the two (treatment C). However, given that Ebola effectively nullified the capital intervention among groups B and C by rendering the microfinance element infeasible, the comparison involves a technical skills package with on‐the‐job‐training, a business skills package, and a combination of both.

The expectation based on the program design was that larger effects would be found in treatment group C, which combined all aspects of the intervention, but this is not borne out by the data. There is no evidence that the various training elements are additive: the econometric results undermine the hypothesis that the impacts of receiving treatment C are either greater than or equal to the combined impacts of receiving treatments A and B; nor are the vast majority of the impacts statistically distinguishable across the groups (appendix A, table A7). There is thus insufficient evidence to conclude that any one package is more effective at increasing the employability of youth and promoting youth‐owned household enterprises.

The absence of statistically significant differences across the groups may partly reflect the evaluation timeline and the consequences of the Ebola crisis. The endline survey was collected less than a year after the
by smallholders using low-tech methods of production. The domestic sector produces an import competing good with capital intensive methods. The elasticity of substitution between imports and the domestic good is assumed to be 0.5 for the intermediate good and 2.0 for the final good.

Table 2.2 Calibrated Parameters for the Two-Sector Model

||αl|αk|A|βM|βD|B|
|---|---|---|---|---|---|---|
|Export|0.80|0.20|1.98||||
|Domestic|0.40|0.60|1.98||||
|Final||||0.38|0.62|1.89|
|Intermediate||||0.69|0.31|1.92|

Source: Author’s calculations As far as the functional distribution of income is concerned, it is assumed that the rural household owns 70 percent of labor and 10 percent of capital. Therefore the urban household owns 30 percent of labor and 90 percent of capital. Furthermore, when we introduce the government, we assume that the rural population gets 60 percent of transfers from the public budget. The urban households receive 40 percent. In this simple framework, the role of the government is solely to redistribute its revenue from taxes to households. Finally, we make the assumption that rural households receive 20 percent of the trade balance and the urban folks 80 percent. In the base case, the balance of trade is equal to zero.

# _Lorenz Parameter Estimation_

When household data are available, either in the form of unit records or aggregated in income or expenditure classes, one can use regression analysis to fit the data to a model such as the General Quadratic model. We follow the procedure described in Datt(1992, 1998). This calls for regressing **[L(1-L)]
ployment size threshold at which IDA Chapter V-B starts to apply to a factory was raised from 100 to 300 workers. The amendments also imposed more stringent minimum membership size requirements on unions. Furthermore, they exempted factories with fewer than 50 contract workers from the Contract Labor Act (CLA), the law which regulates the hiring of contract workers.

We first conduct a comparative case study of the amendments using the synthetic control method. This technique is suited for studying the aggregate impacts of changes that effect a small set of large economic units. It uses a weighted combination of potential comparison units -in our case, Indian states other than Rajasthan- as the control group, with the choice of weights being data-driven (Abadie and Gardeazabal 2003).<sup>6</sup> For this exercise, we use official state-level estimates of the formal manufacturing sector for the 1980-2018 period. The availability of 35 years of pre-treatment data is advantageous as it potentially improves the quality of the synthetic control.

The results suggest that contrary to expectations, the amendments did not have a sizable aggregate impact on employment and output in the formal manufacturing sector. There is some evidence of a positive impact on output, but it is not robust to standard checks.<sup>7</sup> Next, we use factory level panel data from 2010 to 2018 to conduct a difference-in-differences analysis of the main policy change, the repeal of IDA Chapter V-B. This strategy exploits the fact that the amendment affected factories with 100-300 workers.<sup>8</sup> We compare outcomes before and after the amendment across factories with 100-300 workers and those with more than 300 workers, across Rajasthan and other states. This comparison is important as there may have been nationwide shocks that varied systematically by firm size.

Consistent with the synthetic control results, the panel data analysis indicates that the IDA amendment did not affect total employment, output, capital and output per worker in treated firms.<sup>9</sup> But,
Policy Research Working Paper 9863

# **Abstract**

In recent decades, hurricane frequency and intensity have increased in the Caribbean Basin. From 2000 to 2012, more than 100 hurricanes impacted lives, infrastructure, and economic activity along the region’s shorelines. Studies suggest that mangrove forests’ dense root systems might mitigate the impact of hurricanes, which would help stabilize the coastline and prevent erosion from waves and storms. Although many tropical mangroves are found on Caribbean coasts, climatic and anthropogenic events have been clearing these wetland ecosystems at an annual rate of 1 percent since the 1990s. This study quantifies the effects of hurricane windstorms on economic activity using nightlight as a proxy at the highest spatial resolution data available (1 square kilometer). Using different widths of the mangrove belt, it measures levels of mangrove natural protection against the impact of hurricanes and studies the broader socioeconomic and environmental effects of this protection. The results suggest that while major hurricanes reduce nightlight by approximately 2 percent and up to 16 percent in storm surge prone areas, the presence of mangroves on the coast mitigates the impact of hurricanes, reducing nightlight by 1–6 percent.

This paper is a product of the Environment, Natural Resources and Blue Economy Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jjmiranda@worldbank.org.

_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 views
Better Life Index (BLI), and so on.

What is common among these indices is that they attempt to enrich the development debate. Along those lines, it is worth exploring the creation of a Cultural Trade Index (CTI), an index that would gather data to show and rank how countries perform in aspects of international cultural trade. The prime aim is to inform policies that promote cultural trade for development. As will be shown later, a Cultural Exchange Index (CEI) that shows cultural exchanges between countries could compliment the CTI. Like other indices, the CTI would not account for the true cultural contribution to development — especially the non-instrumental aspects, which enrich human welfare in countless ways. It could, however, be useful in evaluating global cultural trade, and thereby stimulate debate, enrich public knowledge, and inform decision-making on the diverse role the arts can contribute to sustainable development.

_Creative or Cultural Economy? A Word on Definitions_ With respect to definitions, a 2008 United Nations creative economy report has this to say: “There is no single definition of the creative economy nor is there a consensus as to the set of knowledgebased economic activities on which the creative industries are based.” With that in mind, the terms “creative” and “cultural” economy here mean the same thing, as are the terms cultural or creative trade index. “At the heart of the creative economy lie the creative industries. Loosely defined, the creative industries are at the crossroads of the arts, culture, business and technology. In other

> 1 Workman 2015.

> 2 The following notes are intended for non-specialists: “Gross domestic product (GDP) is the monetary value of all the finished goods and services produced within a country's borders in a specific time period. Though GDP is usually calculated on an annual basis, it can be calculated on a quarterly basis as well. GDP includes all private and public consumption, government outlays, investments and exports minus imports that occur within a defined territory
12.3 percent of children are in the expected grade by age 12. On the other hand, the lack of effects on primary enrollment could have been expected given that the overall baseline coverage is high. Still, if improvements in adequate progression are signaling that children are better prepared to the challenges faced in primary education, the intervention may produce changes in drop-out in lower grades.

The positive effects on adequate progression are in line with Berlinski et al. (2009) who document sizable effects of pre-primary attendance on test scores in primary school in Argentina. In Uruguay, attending preschool was associated with a large increase in the probability of attending school at age 15 of 27 percentage points (Berlinski et al., 2008). However, as mentioned above, extrapolating these positive findings to rural contexts in poorer countries such as Guatemala is not straightforward. First, stimulation of skills conducive to high academic achievement is likely to be significantly worse in Guatemala’s rural households than in those of Argentina and Uruguay. For example, average mothers’ education in the Uruguay study amounted to 9.8 years, whereas in the communities included in our study average education for women aged 20 to 40 is only 3.2 years. Second, reports from several sources suggest that the quality of pre-primary education in Guatemala is low, and this could attenuate any positive benefits related to attending a pre-primary (UNICEF, 1996; Rubio et al., 2001). Finally, Guatemala has one of the highest levels of child chronic malnutrition in Latin America (Pan American Health Organization, 2008), which could potentially offset any positive effects associated to pre-primary attendance.

# **5.2 Further results**

Figure 2 presents point estimates and 95% confidence intervals for the effects of preprimary access at different ages. Results suggest negative effects of pre-primary access on primary enrollment at age 7 (p-value 0.082). This is consistent with evidence from Berlinski et al. (2008) for Uruguay. It can be the case