rafmacalaba/data-use-mentions-extended huggingface dataset:
LSMS surveys are both smaller-about 3,500 households each, in 199 districts (in 1994) and 228 districts (in 1997)-but they have the advantage that a very similar questionnaire was applied in both years. The LSMS includes a panel covering just over a quarter of the households in the two samples.

The LSMS and INEI surveys are useful for the analysis of targeting and impact in two ways. All of the surveys include district-level identifiers for every household. Household-level information from the surveys can therefore be linked to geographic information on allocations and expenditures kept by FONCODES. In addition, the 1996 INEI survey includes questions about benefits from various social programs. Specifically, households who had at least one member attending a public school were asked about recent improvements in school facilities, and whether these had been carried out by (separately) FONCODES, the educational infrastructure program INFES, or the local parents' committee. Respondents in the 1996 INEI survey were also asked whether any household member currently benefited from a feeding or nutrition program carried out by one of 14 organizations, including FONCODES' school breakfast program.

We use the household surveys to construct various education outcome measures, including school attendance rates, measures of the number of years of schooling passed for a given age, and the average amount of time it takes children in a household to get to school, as well as householdlevel covariates, such as the age, gender, ethnicity, and education of other household members. Because these surveys were not designed specifically for an evaluation of FONCODES, however, they also have shortcomings for our analysis. Three limitations are worth noting. First, none of the surveys collected information on the quality of education, including measures such as the amount of time spent in school, pupil-teacher ratios, and scholastic achievement. Second, there appears to be a large amount of measurement error in the FONCODES "treatment" variable in the 1996 INEI survey. Third, questions about benefits from FONCODES programs in the 1996 INEI survey were asked only of families
# **What Determines the Extent of Fiscal Decentralization? The Russian Paradox**

# **I. Introduction**

Why are some countries highly decentralized while others are not? And in particular, what determines the degree of fiscal decentralization, i.e., the decentralization of government spending? The benefits and perils of decentralization have been actively studied both theoretically and empirically; however, few studies have attempted to construct and empirically test the positive theory of fiscal decentralization. A general consensus that emerged from several cross-country empirical studies (most notably Oates, 1972, Pommerehne, 1977, and Panizza, 1999) pointed toward four main determinants of fiscal decentralization: larger size, higher income per capita, higher ethnolinguistic fractionalization, and higher level of democracy.

Large federations (such as the United States, Brazil, India or Russia) provide a valuable opportunity to analyze the determinants of fiscal decentralization at the regional level<sup>4</sup> . Such analysis is in many ways similar to a cross-country analysis since regions in large federations are often as large as independent states; they enjoy sufficiently high fiscal autonomy and arguably are under the influence of the same decentralization drivers as independent states.

This paper tests the hypotheses about the determinants of fiscal decentralization using data on regions in Russia from the 1990s to the early 2000s. Russian regions constitute an interesting sample for studying the determinants of decentralization for four main reasons. Firstly, cross-country studies of the determinants of decentralization inevitably run into difficulties when trying to control for unobserved heterogeneity. Moreover, the choice of a specific sample of countries often influences the results and data are not necessarily consistent across countries since the methods and standards of data collection vary. In the case of Russia, though present, unobserved heterogeneity is arguably less of a problem, the sample choice is well defined and the data are collected and reported using the same methods and standards. Secondly, Russian regions

> 4 Fiscal decentralization at the regional level is conventionally measured by the ratio of municipal government expenditure to
funds), including foreign R&D, varies within strategic sectors, from 18 percent in living systems to 48 percent and 63 percent in rational use of natural resources and energy and energy efficiency, respectively (see Table 1).

**Table 1: Gross domestic expenditure on R&D in priority areas of science, technology, and engineering by** **<u>source of funding, 2007 (thousand US$)</u>**

||Total|ICT|Nano-<br>technology<br>and<br>materials|Life<br>sciences|Natural<br>resources|Energy<br>and<br>energy<br>efficiency|Transport,<br>aviation,<br>and space<br>systems|
|---|---|---|---|---|---|---|---|
|Gross domestic expenditure<br>on R&D in priority areas of<br>Science, Technology and<br>engineering|6,570,375|1,026,233|465,476|279,025|520,033|533,353|3,392,642|
|_Sources of funding_||||||||
|Federal budget|3,584,383|591,761|362,885|217,878|246,696|192,164|1,784,651|
|Regional and local budgets|147,631|18,984|6,596|9,785|23,946|3,286|86,617|
|Extra budgetary funds|2,838,361|415,487|95,995|51,362|249,391|337,903|1,521,374|

Source: Indicators of Science (2009) – MoES, ROSSTAT, and the Higher
were able to work from home were more likely to keep their jobs.

Concerning the effect of the internet on women’s labor force participation, Dettling (2017) finds a positive impact of high-speed internet use on the labor supply decisions of married women by about 4.1 percentage points. This conclusion does not hold for men or single women, for whom high-speed internet does not have an impact on their labor supply decisions.

Beyond the use of telework to refer to the decision of where and how to work; there is a much longer literature that considers to costs and benefits of participating in the labor market as the basis for women’s decision to work. For instance, Cogan (1981) analyzes the labor supply factors to explain the desired number of hours a person will choose to work. He shows that the fixed costs of working are why people choose to work very few hours. Using data from the 1967 National Longitudinal Survey on Mature Women, he considered married women between 30 to 34 years old and considered variables such as the number of young children, husband’s earnings, and individual characteristics such as the level of education and age. He finds the presence of children raises fixed costs by about one-third.

Cogan (1981) ’s original idea of fixed costs has been modified by Edwards and Field-Hendrey (2002) to distinguish home-based work. They use data from the Public Use Microdata Samples (PUMS) to model women’s decisions on labor force participation and the workplace in the US, taking into account specific worker types, such as self-employed and employees. They find that women who have a high cost of working (i.e., living in a rural area, having young children, etc.) have a higher probability of working from home and being self-employed.

In this paper, we use Edwards and Field-Hendrey’s (2002) framework to study the characteristics of workers who were able to telework in Mexico during the COVID-19 pandemic. From an efficiency point of view
# **_Further observations on regulatory comparisons_**

Regardless of one’s take on the outcomes discussed in the preceding sections, the results demonstrate the value of _ex post_ analysis. Why, then, is it so rare, especially when so many close observers claim to be in favor of it? One answer is that it depends on what sort of _ex post_ analysis you mean. There are plenty of _ex post_ studies of policy effectiveness and overall progress at achieving policy goals. In the environmental sphere EPA closely monitors air and water quality, among other things. They also routinely conduct studies of the overall benefits and costs of programs, often retrospective studies like the benefits and costs of a clean environment, which EPA is mandated to prepare and send to Congress periodically. Some analyses are also carried out by academic researchers, such as an oft-cited paper by Greenstone (2002) on the effect of nonattainment designation on regional economic performance and air quality. Still other researchers carry out studies of the overall costs of regulations, attempting to assess the importance of regulation of various kinds relative to the economy as a whole. But these studies do not compare costs and benefits of individual regulations. Yet, it is the such uncommon _ex post_ studies that are particularly valuable for assessing the quality of regulatory processes.

One reason why there are so few _ex post_ studies of individual regulations is that there is no obvious or consistent source of funding. _Ex post_ studies are not routinely performed by regulatory agencies in the U.S. Rather, they generally appear for idiosyncratic reasons, largely dependent on the initiative of individual researchers. _Ex post_ studies also must contend, typically, with serious problems of data acquisition and interpretation. Compliance cost data are likely to be considered business confidential by regulated firms, whose participation in cost studies is often voluntary anyway. And even if the data are available, there are likely to be difficult issues of joint cost allocation. OMB’s work collecting the available _ex post_ studies is valuable, and it is
25,724|21,807|5,253|4,430|1,404|1,282|

Notes: Weighted to national level with weights provided by the Nepal DHS in each year. Standard errors, in parentheses, are clustered by regionyear. The notation<sup>***</sup> is p<0.01,<sup>**</sup> is p<0.05,<sup>*</sup> is p<0.10. In each regression the key difference-in-difference terms are the binary variable for moreor less-forested interacted with the year dummies. The Widow/Sep/Div/HH Head sub-sample excludes women whose husbands have migrated. Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

38
<!-- Start of picture text --> Figure 14. Country correlations for commodity<br>exporters CE and law and order<br>Low Law & Order index High Law & Order index<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2 Low L&O index = 0.50***<br>High L&O index = 0.28***<br>-0.4<br>-0.6<br>-0.8<br>AzerbaijanYemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep.NicaraguaEcuador UruguayGuyanaLiberia Argentina<br>Brazil Namibia NigerBolivia KenyaZimbabwe AngolaQatar<br>Sudan SenegalMali Burkina Faso<br>Ghana Saudi Arabia MozambiqueThe Gambia Colombia Tanzania LibyaGabon Iceland EthiopiaMongolia<br>AlgeriaUzbekistan MyanmarSouth Africa Paraguay<br>Iran, Rep. of Madagascar<br>UgandaCameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>NigeriaPeru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>New Zealand<br>NorwayMalawi<br>Australia Guatemala<br>Brunei Darussalam United Arab Emirates<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br>Canada<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high (low) law and order, defined as law and order above (below) the sample median. The difference between the two averages is significant at the 5 percent level. *, **, and *** denote significance at the 10
force participation determinants. We use annual aggregate data from 1960 to 2018.

The literature on female labor participation, drawing from both cross-country comparisons and individual country analyses, has produced valuable insights.<sup>2</sup> Among OECD countries, several studies have shed light on the determinants of female labor participation, with Cavalcanti and Tavares (2008) highlighting the impact of increased availability and lower prices of domestic appliances, Mishra and Smyth (2010) noting a negative correlation with fertility rates, and Gonzales, Chandra,

> 1 This trend was interrupted by the Covid-19 pandemic in 2020. Since the beginning of 2022, the participation of women started to recover. For more details, see, for example, García-Rojas, Herrera-Idárraga, Morales, Ramírez-Bustamante, and Tribín-Uribe (2020).

> 2 Another strand of the literature uses individual datasets from population surveys and censuses. See, for example, Blau and Kahn (2007), Fernández (2013), Gangl and Ziefle (2009), Gasparini and Marchionni (2015, 2017), Goldin (1988, 2006), Goldin and Mitchell (2017), Olivetti (2014).

2
%|
|Excise Duty|3579.663|16.60%|253.2|7.07%|4.12%|0.50%|
|<br>Import Duty|562.563|2.61%|1265.3|224.92%|0.65%|2.30%|
|<br>Other Indirect Tax|366.673|1.70%|||||
|Green Tax|909.652|4.22%|774|85.09%|1.05%|1.40%|

_Source:_ Annual Financial Statement of the Royal Government of Bhutan, FY2016-2017. Ministry of Finance.

Key features of taxes and contributions in Bhutan are briefly presented next. The description in this section includes features of the prevailing fiscal system as of 2017 which is the year of the last household survey on which the analysis is based. Changes to the system after 2020 are discussed in section 5 along with results from a simulation exercise that estimates the ex-ante distributional impact of those reforms.

# **_Direct and indirect taxes_**<sup>**_5_**</sup>

- Personal income tax (PIT): All citizens and residents that earn more than Nu. 200,000 per year are liable to pay PIT. Income sources that are subject to the PIT include income from salary, property, dividends, interests, income from cash crops, income from hire of privately-owned vehicles; plant and machinery and from intellectual property rights.<sup>6</sup>

4 The ratio of total consumption in the survey to expenditure of households and nonprofit institutions serving households (NPISHs) in the national accounts is 76%.

5 Bhutan implemented the Sales tax, Customs and Excise Rules in 2001 and amended it several times, most recently in 2014. As of 2017, the sales tax was levied on the following items: alcohol; packaged fruit juices; spare parts
3 presents our baseline regression. Regarding our control variables, larger, older firms, located in the main city and having a higher share of government ownership are more likely to be owned by females for all the regions (Table 3a) and the MENA region (Table 3b). Larger firms are generally better performing, listed and might have a diverse board. Li and Chen (2018), using a panel data from listed non-financial firms in China, find that gender diversity in the board has a positive impact on firm performance. Similar results are confirmed by Said et al. (2021) for Egyptian firms. As per the sector of operation, a higher share of government ownership increases the likelihood of female ownership. Generally, the public sector remains a larger employer of women than the private one. In addition, females also enjoy a higher wage premium in the public sector compared to those employed in the private sector.

As per our variable of interest, Tables 3a (overall) and 3b (MENA region) shows that the least restrictive definition of GVC is positively associated with a higher probability of having a women as the owner of the firm or with the share of female employees (especially production ones) whereas a deeper integration into GVC (GVC4) reduces the probability of female ownership. In addition, GVC integration exerts a negative impact on the likelihood of a women being the top manager of the firm (for GVC1) and an insignificant impact with the stricter definition (GVC4). Three remarks are worth mentioning. First, there are fundamental differences between female owner and manager given that an owner physically owns the business, while the manager is an employee of the business and works for the owner. In addition, the owner is more affected by

> 5 The minimum Eigenvalue is higher than all the critical values at 10% and the p-value is significant at 1%. Therefore, we reject the null-hypothesis according to which the instruments are weak.

> 6 Regressions for Non-Production Workers are presented in Table
. These are only as accurate as the source data—the census—and hence their accuracy declines as the census itself ages. We propose a “bottom-up” technique by pairing survey data with satellite imagery using Poisson regressions employing variables selected based on LASSO regularization. This model can yield population density predictions in areas where the survey was not conducted, and can be repeated frequently using periodic survey and satellite data to frequently track population changes. We apply the method in the context of Sri Lanka to predict population density at the lowest administrative level, the Gram Niladhari (GN) division (village-level). Our model uses the Sri Lankan Household Income and Expenditure Survey (HIES), and predicts density out-of-sample in the non-HIES villages.

Two aspects of our results stand out. First, our performance is better than the only

> 23 The relative error with respect to the census is the same for population density and population count because the only difference between the two is the multiplicative factor, village-area. 24 We conducted a similar exercise using population count as the dependent variable in the model, from which we directly obtained population predictions. The accuracies of these predictions are reported in Table S10. Examining the _R_<sup>2</sup> , SRC, and Mean RE, it is evident that the population predictions derived indirectly using a density-based model by multiplying density predictions with village-area (as in Table 9) performs much better than obtaining population count predictions from a population-based model.

> 25 The CV of 45.2% was obtained by manually calculating the mean CVs from a synthetic 80% sample of the census.

13
restrictions were lifted, India‘s manufacturing has seen large changes in spatial locations and agglomerations (e.g., Fernandes and Sharma 2011).

These changes provide much greater longitudinal variation than could typically be exploited. Micro-data for India‘s organized manufacturing sector extend back to 1989. We prepare our metrics for 1989 similar to those used in 2005. We restrict our sample to districtindustry observations present in both periods. Table 8 estimates a panel specification of the form: ln(Entrydit) = ηit + δdt + πdi + γ∙ln(Incumbent Employmentdit) + γI∙Inputdit + γO∙Outputdit + γL∙Labordit + εdit.

We now include a vector of district-industry fixed effects πdi that control for fixed differences across district-industries; we also extend our earlier fixed effects to be district-year and industry-year controls. These specifications thus employ panel variation: how much of the growth in district-industry entrepreneurship can we explain through changes in incumbent local conditions that are especially suitable for particular industries? By including district-year and industry-year fixed effects, we measure this effect after controlling for general district and industry development between 1989 and 2005.

Table 8 provides strong confirmation for our basic findings. In the first column, growth in general incumbent employment over the 16 years is linked to higher entrepreneurship. The elasticity is half the size estimated in the cross-section work. The elasticity decline may indicate that an upward bias existed in the cross-sectional estimations (e.g., due to an omitted factor), but is also possible that the differencing exacerbated measurement error and its downward bias. In the second column, we also find support for the Marshallian metrics related to input and output markets. The coefficients are slightly larger than in the cross-sectional estimations and precisely estimated. Interestingly, labor conditions do not find support in the panel setting.<sup>15</sup> Changes in the Chinitz metric yielded implausibly large coefficients values due to outliers, and we do not report them. Of our metrics, the Chinitz effect is the most sensitive due
large and diverse sample.<sup>5</sup> Earlier studies have tended to focus on the experience of individual countries, and often on responses of IPV to an _individual_ woman working,(e.g. Bowlus and Seitz, 2006; Chin, 2012; Tertilt and Van den Berg, 2015) and these studies are mostly set in developed countries.<sup>6</sup> In contrast, this study considers macroeconomic shocks in a cross-section of countries over time.<sup>7,8</sup> Closely related to our approach are two recent studies, set in the US and the UK respectively, which consider how macroeconomic changes in relative male and female economic opportunities influence IPV.<sup>9</sup> Using demand driven variation in female relative to male wages at the county level in the United States, Aizer (2010) shows that IPV is decreasing in women’s relative wages. Anderberg et al. (2016) similarly find that improvements in the relative employment rates of women in the UK are associated with lower rates of IPV, and that male unemployment acts to inhibit IPV.<sup>10</sup> These findings can be rationalized with reference to a household bargaining model in which better labour market prospects for women relative to men increase their bargaining power by improving their outside options. The higher the threat point of women, the more averse men are to committing violence that may enhance the risk of marital dissolution (Farmer and Tiefenthaler, 1997). This study is able to

> 5 As discussed below, recent country specific studies instrument unemployment rates with a Bartik-style instrument. This is not possible in the sample in this paper since it would be necessary to gather data on industrial composition for every country in the sample. In contrast, by virtue of using data from several countries, the results have wider scope. The study is also able to investigate the role of barriers to divorce.

> 6 Understanding domestic violence in developing countries
# **Figure 1 Uncertainty and Balance of Risks for Risk Factors**

**_Uncertainty._** _The implied current-year volatility of equity price options and next-year volatility of term-spread forecasts have increased since the second half of 2015 but remain near their historical medians. In contrast, the implied volatility of oil price futures widened to post-crisis highs, pointing to increased uncertainty._ **_Balance of risks._** _The distribution of future equity prices is increasingly tilted to the downside while that of oil price futures is tilted to the upside. Movements in the skewness of term spread forecasts are mixed. Together, these developments suggests high uncertainty and rising downside risk to global growth for 2016 and 2017 ._

## **A. S&P500: Degree of Uncertainty**

<!-- Start of picture text --> Implied volatility<br>Current year Next year<br>50 Current year median Next year median<br>40<br>30<br>20<br>10<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

## **C. Term Spread: Degree of Uncertainty**

<!-- Start of picture text --> Standard deviation<br>Current year Next year<br>Current year median Next year median<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>E. Oil Price: Degree of Uncertainty<br>Implied volatility<br>Current year Next year<br>Current year median Next year median<br>60<br>50<br>40<br>30<br>20<br>10<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>2006 2007 2008