rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2: Actual versus Fiscal Gap Formula-based DAU 2006 Allocations**

<!-- Start of picture text --> 1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>1 217 433<br>Districts<br>DAU06 Actual DAU06 Pure FG<br>DAU 2006 in billion Rp<br><!-- End of picture text --> Source: Author’s calculation using MoF data The extent of the trade-off will vary by individual districts. Figure 2 ranks districts DAUs from low to high based on a presumed formula base allocation. For example, some districts on the left would have presumably received no formula-based DAU transfers. Of the 15 districts that would have received no DAU transfers, eleven of them are located in natural resource rich provinces such as Riau and East Kalimantan. What districts received in reality, denoted by the scatted dots, differed quite significantly in many cases from their intended allocations. The advantage of this approach is that it also allows us to pin-point the apparent degree of over and under-financing of a particular locality, potentially allowing for a more systematic follow-up analysis.

15
2011). As a result, econometric estimations of the effects of IFFs on tax revenues could be criticized for their lack of external validity related to the contingency of IFFs data on a given definition and/or methodology. Second—from a methodological perspective—the effect of IFFs on tax revenues may be subject to endogeneity issues arising (in addition to measurement errors related to the mere IFFs’ definition) from reversed causality. Indeed, poor fiscal institutions can result—aside from low collecting rates and therefore low tax revenues—into poor fiscal control and enforcement that may ultimately favor IFFs.

Addressing these issues looks like an unfeasible task, given that it requires finding timevarying reliable instruments for a variable whose mere measurement may be unreliable. Consequently, in this paper we adopt a different perspective that exploits the fact that combating IFFs requires compliance with international standards, as defined by the Financial Action Task Force (FATF) recommendations on domestic tax revenue mobilization.<sup>**††**</sup> The FATF recommendations formulate a comprehensive and consistent regulatory framework that has been endorsed and recognized since 2003 by more than 180 countries as the international standard for anti-money laundering (AML) and countering the financing of terrorism (CFT). Conversely, the lack of compliance with these recommendations is considered as a serious signal for not combating IFFs, and is made visible through the inclusion of such countries on the FATF “blacklist”. Therefore, we develop an impact assessment analysis in which the presence of a country on the FATF list is considered as a “treatment” variable signaling the presence of major IFFs, i.e. a Non-Cooperative country, as opposed to Cooperative countries that are not on the FATF list. Of course, since being on the FATF list can hardly be seen as a random event, we draw upon appropriate techniques in order to make this event exogenous conditional upon a wide set of covariates that may influence the decision of becoming a listed country.

Our results are as follows. First,
They find that compared to individuals in never-reserved councils, individuals in reserved councils were more likely to perceive women as being effective leaders, and less likely to hold on to stereotypes about gender roles.

In a follow-up study, Beamen et al. (2012) use the same data set and similar empirical strategy to estimate the impact of the reservations on the gender gap in aspirations and educational attainment for adolescents. They find that compared to never-reserved councils, adolescent girls in twicereserved councils saw a larger increase in aspirations than their male counterparts. Parents in twicereserved councils also saw a larger increase in aspirations for daughters compared to sons. Adolescent girls in twice-reserved councils saw a larger increase in their educational attainment compared to adolescent boys, and adolescent girls spent less time on household work.

Kalsi (2017) exploits the same natural experiment to find that the infant mortality rate of girls (versus boys) at higher birth orders declined and the percentage of surviving boys (versus surviving girls) at higher birth orders declined following reservations in rural India. She argues that the likely channel for this was normative change that accompanied exposure to female leaders.

The presence of female politicians can also lead to investment in programs preferred by or strongly needed by the female population. Chattopadhyay and Duflo (2004) find evidence of this using household survey data from West Bengal described above, and from Udaipur district in Rajasthan, India. Note however, that the authors do not estimate whether female politicians increase expenditure on programs specifically targeted at female children.

Overall, the studies from the Indian context strongly suggest that the presence of female politicians can lead to a narrowing of gender gaps across various dimensions. There are studies in the context of developed countries that also provide evidence of this, namely the positive role model effect of female politicians.<sup>18</sup>

# _Education and employment opportunities_

See Table 4 for an overview of the studies described below.

An expansion of educational opportunities (Pande & Astone 2007; Murthi et al. 1995; Ren 1995
climate in (mostly) developing countries and to understand the determinants of firm performance. The richest country in the sample is Sweden with a GDP per capita of US$46,000, and the poorest countries (i.e., Burundi, Liberia, Malawi, Ethiopia) have a GDP per capita around US$200. In each country, the survey is based on the universe of eligible non-agricultural firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the nonagricultural private economy in the country.<sup>2</sup> Stratification is based on two criteria: the sector of activity and firm size. Typically, the stratified sampling yields between 100 and 1,000 firms per country, with 108 firms for the median city. Industries range from manufacturing and construction to services and retail and wholesale trade,<sup>3</sup> and some industries such as mining and financial services are excluded in the survey. Each survey is largely a cross-sectional data set.

We include data collected after 2006, although some enterprise surveys were conducted earlier. Prior to that year, there was considerable heterogeneity across countries in terms of the questionnaire format, sectors covered, and sampling methodology. Moreover, the samples for surveys conducted before 2006 were not generally representative. After dropping observations that have missing values for whether the firm is headed by a female manager, our final sample consists of more than 80,000 firm-year observations covering 567 cities in 117 countries.<sup>4</sup> A complete list of variables and data sources is shown in Table 2; the summary statistics for our key variables used in regressions are in Table 3.

# **4. Basic patterns for the relationship between FDI and female corporate leadership**

How do we define female entrepreneurship? Ideally, we want to capture firms with women being the primary owner and running the firm; that is, women act as both owners and chief
computes the cumulative probability or nonexceedance probability of the Gamma distribution<sup>8</sup> given shape and scale parameters of the distribution computed from the _L-moments_ parameters.<sup>9</sup> In addition, the ―lmomco‖ package is used to create 0.90 confidence intervals for BWI derived from a Monte Carlo simulation. This PDF gives estimates for both low- and high-flow.

# **Risks of being below treaty supply parameters**

Applying the probability density function, a theoretical estimation of runoff can provide a probability of low flow, and, in the case with specific water requirements, this probability can be extended to estimate the probability of treaty non-compliance.

# **ASSESSING THE PERFORMANCE OF THE MODEL**

# **Basin selection**

One of the leading databases on international river basins, Transboundary Freshwater Dispute Database (TFDD), reports on 276 international river basins. The foundation of a defensible model has several necessary requirements that apply to the selection of gauge and satellite data (Table 1 includes a summary of these criteria and references) in order to select the basins. Natural (unimpeded) flow is a fundamental assumption of the model. Therefore regulated rivers are identified through GIS survey of TFDD international river basin. ESRI ArcGIS defined watersheds from 3535 gauging stations (GRDC) within international river basins derived from the HydroSHEDS database.<sup>10</sup> The universe of unregulated multilateral basins with available

> 8 There is much evidence that the distributions of hydrologic variables are heavy tailed (Katz et al. 2002). This is even more pronounced in arid and semi-arid environments (Anderson et al. 1998; and Farquharson, Meigh, and Sutcliffe 1992).

where F(x) is the nonexceedance probability for the quantile x. The parameters have the following interpretation in the R syntax; α is a shape parameter and β is a scale parameter.

> 10 A small correction factor of up to approximately 2 Km is used to snap the location of
2009), but it has a number of innovative aspects. First, unlike most NEG applications, including applications to China (see, for example, Au and Henderson, 2006; Moreno-Monroy, 2008; Bosker _et al_ , 2010; Hering and Poncet, 2010a, 2010b), it allows sectoral disaggregation; in our case, we disaggregate the regions of analysis into urban and rural sectors. Second, this paper is one of the first to use the full set of structural equations from an NEG model for the explicit purpose of applied policy evaluation (for a similar application to the reform of China’s _Hukou_ system see Bosker _et al_ , 2010). Third, while most empirical NEG applications use a simplified measure of transport costs – such as straight line distance – we utilize detailed GIS-derived data on a country's road network to derive travel time estimates that can be shown to relate closely with transport costs (see Combes and Lafourcade, 2005; Bröcker, 2002). Our travel time estimates are taken from a detailed digital representation of China's road network with and without the NEN.<sup>5</sup> Building on earlier work by Fujita _et al_ . (1999, chp. 7) and Fingleton (2005b, 2007), we develop a two-sector, multi-region, NEG model of the Chinese economy based on 331 prefectural level regions (prefectures for brevity) spread over the entire territory of China.<sup>6</sup> Prefectures are typically large and varied, so that each contains both an urban and a rural economy. The urban-rural distinction in China is fundamental to understanding the Chinese economy and this distinction is reflected in our impact measures, which are aggregate real income together with real urban wages and real rural wages. As noted above, an important variable in the set of equations leading to the short-run equilibrium of the NEG model is the concept of "market access" or "market potential." A prefecture with strong market access will, typically, possess
tell the full story. Indeed, women's share of waged non-agricultural employment has increased in the last decade but only marginally; the share of women in the agricultural labor force exceeds 45 percent in SubSaharan Africa and South East Asia and the Pacific (Figure 1). In most regions female employment remains concentrated in either services or agriculture, with fewer women than men employed in industry (ranging from 7 to 23 percent, compared to 12 to 34 percent for men). The only region where men and women have similar patterns of employment by sector, both in relative composition and in trend, is East Asia and Pacific. There is also considerable variation in the gap between the proportions of males and females participating in the labor market in some regions, especially Asia and Sub-Saharan Africa (Figure 2).

> 8Studies based on data pertaining to both developed and developing countries suggest the existence of a U-shape relationship between labor force participation and the level of income per capita, with participation being the highest in the poorest and richest countries (see Mammen and Paxson (2000)). Box 4 discusses a possible rationale for this pattern, based on Galor and Weil (1996).

9
to members|611.210***|584.225***|
||_(149.176)_|_(114.571)_|
|Memo items|||
|Number of observations|2,102|2,343|
|R2|0.164|0.160|

Sources: Village Fund Survey 2010, and SES 2009. Robust standard errors in parentheses. Stepwise regression uses p=0.2 cutoff for dropping variables. * p<0.05, ** p<0.01, *** p<0.001.

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range exceeds that of the IPCC SREX report. Intensities range from the historical baseline to a 60% increases in intensity, as shown in Table A.4

**Table A.4. Range of intensities (mm) for 3-hour rainfalls with 2, 5, and 10-year return periods.**

|**Return Period**|**Recent(mm)**|**60% Increase(mm)**|
|---|---|---|
|2-Year|83|133|
|5-Year|104.1|167|
|10-Year|118|189|
the applicable vintage of the WEO database. Hence, for example, the projection for outturns for Dominica before it was struck by hurricane Erika at the end of August 2015 are taken from the April 2015 WEO database. For each variable, we show the deviation of the outturn, after the disaster, from the projection made just before the disaster. As a control group, we use the similar deviations of outturns from projections for the other SIDS in the same region.<sup>11</sup> For Caribbean countries, cyclone disasters all occurred towards the end of the third quarter of the calendar year. As we are using annual data, we construct outturn variables composed of one quarter of the value of the variable in the calendar year in which the disaster occurred and three quarters of the value of the variable in the following year and similarly for the subsequent year. Cyclone Evan struck Samoa in December 2012, so we examine the economic outturns in the 2013 and 2014 calendar years. Cyclone Pam struck Vanuatu in March 2015, so we construct 12-month outturns for 2015/16 and 2016/17 from the calendar year data.

Table 3.A shows the impact of hurricane Erika, which struck Dominica in August 2015 and caused damages estimated at 89 percent of GDP (table 2). Compared to pre-disaster projections, real GDP growth was slightly lower in the first 12 months after the disaster and substantially lower in the second 12 months. There was a large increase in government expenditure in both periods (a combined 30 percent of GDP) presumably to pay for the costs of repair and reconstruction, but this was more than offset by even higher government revenues and, as a result, fiscal balances improved and gross public debt fell in the first 12 months, although it was at a roughly similar level by the end of the second 12 months to that which had been

> 10 We do not include hurricane Dorian in the Bahamas in 2019 or cyclone Harold in Tonga in 2020 because the impact of these disasters is
# _Box 1: Grouped data poverty estimates_

<mark>Grouped data are consumption expenditure or income organized in intervals or bins, such as deciles or</mark> percentiles. These bins are used to derive a continuous Lorenz curve, which plots the cumulative welfare share (on the y-axis) against the cumulative population share (on the x-axis). Together with information about mean welfare, the Lorenz curve can be used to construct a full distribution. Two approaches are used to derive a Lorenz function, the general quadratic (GQ) Lorenz function and the Beta Lorenz function (Datt 1998). Both functions are parameterized and estimated. The function that provides the best fit is selected for poverty and distributional statistics, conditional on passing normality and validity tests. The GQ Lorenz function is estimated using the following specification: _L_ (1− _L_ )= _a_ ( _p_<sup>_2_</sup> _−L_ )+ _bL_ ( _p_ −1)+ _c_ ( _p−L_ ), where _p_ is the cumulative proportion of the population, L is the cumulative proportion of consumption expenditure or income, and a, b, c are parameter estimates. Poverty and inequality measures are based on the parameter estimates.

The Beta Lorenz function is estimated using the following specification: L(p)=p−θp<sup>γ</sup> (1−p)<sup>δ</sup> , where θ, γ, and δ are parameter estimates.

The methodology to estimate poverty starting from grouped data is applied on grouped data information on per household consumption by decile of the welfare distribution in 2007 and in 2009 HIES available from the CBS website. Information on average household size by decile, which is needed to convert household expenditure in per capita terms, was obtained from Prof. Heba El-Laithy, one of the authors of the Abu-Ismail et al. (2011) study who was in possession of 2007 HIES microdata. Through this consultancy, grouped data poverty estimates for 2007
–15).<sup>5</sup> At each point in time, the survey is a random sample of the corresponding census sample frame.<sup>6</sup> This allows strict comparability of the distributions of a given variable between both data sources. Even in pairings where gaps exist, that is, 1990–92 and 2014–15, it is possible to identify common sets of covariates _Xhm_ that satisfy the equality criterion, mainly because

> 4These exercises were conducted by CONEVAL by simulating income in the population censuses of 2000 and 2005. Though new rounds of census data were available in 2010 and 2015, the estimates of poverty followed a multidimensional approach based on a different methodology. Thus, a longer series of comparable income-based poverty data across municipalities is not officially available.

> 5The census data correspond to the general census of population and housing for the years ending in zero; for 2005, the data are taken from the population and housing count; and, for 2015, they are taken from the intercensal survey. Unless otherwise stated, from here onward, the term census refers indistinguishably to these three data sources.

> 6In 2014–15, both the ENIGH and the intercensal survey represented random samples of the 2010 general census sample frame.

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/58, International Monetary Fund.

Abiad, A., P. Mishra, and P. Topalova (2014). How does trade evolve in the aftermath of financial crises? IMF Economic Review 62(2), 213–247.

Abiad, A., E. Detragiache, and T. Tressel (2008). A new database of financial reforms. IMF Working Papers 08/266, International Monetary Fund.

Ahn, J.B. (2011). A theory of domestic and international trade finance. IMF Working Paper 11/262.

Allen, F. and D. Gale (2001). Comparing Financial Systems. MIT Press.

Amiti, M. and D. Weinstein (2011). Exports and financial shocks. Quarterly Journal of Economics 126(4), 1841–1877.

Araujo, L., G. Mion, and E. Ornelas (2016). Institutions and export dynamics. Journal of International Economics 98, 2-20.

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# **I. Introduction**

During the twentieth century, one of the most important achievements for Colombian women was the remarkable increase in their labor force participation, especially during the second half of the century. Women’s participation increased from 18% in 1951 to 26% in 1978, 47% in 1992, 55% in 2000, and 60% in 2018.<sup>1</sup> The upward trend in female labor participation can be attributed to the remarkable advancements in education, fertility, sanitation, civil rights, and political engagement that women experienced during this period. The contribution of married women to the growth of labor participation was a key driving force behind this significant improvement. They seemed to be more responsive to economic cycles and to the legislation introduced in the first half of the twentieth century, which allowed married women to enter the workforce.

This paper contributes to identifying the main drivers of women's participation in the labor market since the second half of the 20th century, tracing the transformation of women's roles and opportunities. The changing nature of female labor force participation and its determinants highlights the importance of considering the historical moment and context in which these variables are examined. We present evidence that the effects of these variables have changed over time. To this end, we follow a two-step approach in which, first, we implement a principal component dimension reduction to summarize the whole set of variables (fertility, education, mortality, marriage, labor force participation, violence, female political participation, and some macroeconomic variables) with a few components. Second, we employ the Time-Varying Effect Modeling (TVEM) method to find the dynamic pattern evolution for the coefficients over time under different specifications on the female labor 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
as both merely ‘latched’ ointo restaurants’ incumbent existing system and automatically relayed taxable sales records to local tax authorities. With such minimum required adjustment, deviances were less likely to occur, therefore manifested in better compliance in general in the form of boosted tax receipts upon TMD and WS installation.

Third, we may need to highlight the centrality of business size in influencing our results. Generally, less noncompliance observed in larger businesses (Slemrod, 2007). Likewise, we found that bigger hotels and restaurants tend to be more tax compliant. That is particularly visible among West Manggarai’s restaurant observation, where TMD and WS recipients were mostly larger businesses (Figure A.1). Consistent with the AllinghamSandmo (1972) and Naritomi (2019) tax evasion framework, this compliance is because they—particularly those that are part of a restaurant/hotel chain—have higher perceived probability of detection stakes to underreport due to their visibility and number of thirdparty stakeholders able to attest to their report credibility. This high probability of being caught evading partly contributes to the upward impact of TMD and WS observed in our baseline estimation.

On the other hand, smaller restaurants receiving RPOS normally have more leeway to underreport as they are generally part of the informal sector. Their scales of business enables them to lay low and alter their transaction reports with minimum risks. Due to the cash-based nature of the informal sector, it is simpler for these businesses to avoid having any third-party data associated with them, making it difficult for the tax authority to confirm their transactions.

In Gorontalo, TMD’s muted effect on both restaurant and hotel tax compliance could be explained by the businesses’ relatively high compliance—as measured in their tax payment—prior to receiving TMD units. As the provincial capital, the city of Gorontalo is bigger and more densely populated compared to West Manggarai, and with such different characteristics we may expect better technology sophistication among bigger businesses. With more avid use of electronic payment technology even before the acquisition of TMD devices