rafmacalaba/data-use-mentions-extended huggingface dataset:
topic by highlighting nuances associated with social norms under various circumstances faced by married young women in Nepal.

Given the prevalence of outmigration in Nepal, the paper also explores the flexibility of norms among households with migrant spouses. Male migration not only changes intrahousehold power allocations and dynamics, as well as upholding of norms (Ahmed, 2020; Ram Mohan et al., 2023), but also local labor markets. However, the impacts of male out-migration on labor supply of non-migrating members of the household, such as wives, remain ambiguous. On the one hand, receiving remittances could increase members’ consumption of leisure and decrease their labor supply (an income effect). On the other hand, a large outflow of migration could lead to a reduction in aggregate labor supply, which would in turn increase aggregate wages in the local labor market, thereby increasing the opportunity cost of not participating in the labor market (substitution effect). Empirical evidence from Nepal shows support for both hypotheses. Shrestha (2017), using data on large-scale male migration between 2001 and 2011, finds that a rise of 1 percentage point (p.p.) in the share of migrants in the population at the village level increases wages by 2.3 percent and increases LFP by 0.4 p.p. in non-migrant households, driven largely by increased female participation and wages in nonagricultural employment. The LFP impact for households with a migrant is lower, at 0.2 p.p., suggesting some degree of an income effect. Similarly, Lokshin and Glinskaya (2009) and Raju et al. (2018) find that FLFP falls for households with a migrant due to an income effect from receiving remittances. Phadera (2016) finds that male migration either increases women's workload, potentially overburdening them, or forces them to realign their priorities and fill home production and family farm roles vacated by men. This is consistent with findings from Ghimire et al. (2021), who find that wives of migrant workers
marriage-only penalty are quantitatively similar, and widest in Nepal and Maldives. As expected, the estimates for the “ignore child” sample are larger than the no child sample for all countries. Still, the relatively small gap in these estimates implies that the reduction in female labor force participation around the time of marriage is driven specifically by – – employment restrictions placed on married women likely due to social norms rather than their childcare burdens.

In contrast, men enjoy a marriage premium in South Asia. Table 1, Panel B, shows the estimates for the men’s sample. The estimated premium for men ranges from 13.2 to 17 p.p., with an aggregate effect size of 12.8 p.p. These impacts are consistent with the extensive literature on the marriage earnings premium in the US (Hersch and Stratton, 2000). As with women, the difference in the estimates between the no-child and ignore-child samples is small. Taken together, the findings in Table 1 demonstrate a marriage penalty for women and marriage premium for men in South Asia. These effects are driven primarily by the act of marriage itself, rather than the time and financial costs associated with childrearing.

# **3.2 Event-studies**

Figure 1 plots event-study coefficients on indicators for leads and lags of the marriage year, relative to _t_ = _−_ 1, by country. The pre-marriage event coefficients are all precisely estimated zeroes across each country, suggesting no trends in married women’s labor force participation leading up to marriage, and no evidence of anticipation effects. The reduction in labor force participation kicks in immediately in all countries, with a _t_ = 0 reduction in the nochild sample ranging from 7.3 to 13.2 p.p. across the region (dynamic point estimates are available in Table A4 and Table A5). As expected, the no-child dynamic estimates in Figure 1 are almost always above the ignore child sample estimates. Still, the confidence intervals typically overlap, and the
A related question concerns the availability of conflict resolution mechanisms within the river basin. The irrigation communities have irrigation courts to resolve disagreements among water users, but no comparable forum exists on the river basin scale. Water users who wish to challenge a decision taken by CH Guadalquivir can go to the constitutional court, but as mentioned earlier such a challenge can address only whether the CH has exceeded its granted authority, not disagreements over policy direction, resource allocation, and so on.

Monitoring of water deliveries, water use, and water quality occurs in the basin, with water quality monitoring performed principally by CH Guadalquivir and by urban water suppliers. Water use by license holders is monitored inconsistently since the water user registry is not complete, and most of the individuals interviewed for this study acknowledged a substantial problem with illegal water uses continuing in the basin. With respect to irrigation water use, the CH monitors what goes into and comes out of irrigation communities, but monitoring of use by individual irrigators occurs within the irrigation communities and it is impossible to draw sound conclusions across the hundreds of communities about how effectively this is done. With respect to urban water use, the larger suppliers (such as those in the Seville metropolitan area) meter water usage, but in many multi-family buildings such metering does not go all the way down to the individual water user level.

# **10. Concluding Remarks**

In some respects, Spain is no different from any other country—integrated river basin management in the new water policy environment is genuinely difficult, and it is not clear that any country is doing it especially well. Shortcomings observed in Guadalquivir are witnessed in other places too. In many ways, Spain is further along than almost anyone else in pursuing IWRM with a river basin focus, but Spain’s experience also demonstrates that the establishment of river basin authorities in and of itself is not a solution. Even under such favorable organizational circumstances, political, economic, legal, and historical factors complicate the development and implementation of
big jump in the series in the year 2010. We hence restrict our sample to start in January 2010 when using the ACLED data set.<sup>2</sup>

**Figure 1: Evolution of the Total Number of Protests**

**Panel A: Protests in levels Panel B: Protests in per capita**

<!-- Start of picture text --> 1995m1 2000m1 2005m1 2010m1 2015m1 2020m1 1995m1 2000m1 2005m1 2010m1 2015m1 2020m1<br>Sources: Authors' calculation using ACLED dataset Sources: Authors' calculation using ACLED dataset<br>1.2<br>6000<br>1<br>.8<br>4000<br>.6<br>.4<br>2000<br>Monthly number of protests<br>.2<br>Monthly number of protests (per million persons)<br>0 0<br><!-- End of picture text --> 2 Note that for countries added to the data set after 2010, we consider these country-year pairs as missing observations (and not zeros) before they are added to the data set. However, our results remain robust when using data from ACLED starting in January 1997.

3
The eastern regions appear to have enjoyed the largest poverty reduction impact from growth in the services sector. In the 3-region classification (columns 5 and 6), Mediterranean regions are included in R1 “rest of the country”, which experienced a boom in hotels and was one of the only regions to weather the financial crisis. By sector, employment growth in the services sector has been the strongest, with positive growth in all years. Additionally, the services sector employs the most people, about half of all workers. Moreover, growth and improvements in infrastructure were catalysts to growth, creating stronger connectivity throughout the country.

Employment in industry shrank during the crisis (2008-2009), and in recent years, employment in agriculture has been declining as well. However, just as in gross value added, there is strong regional variations in sectoral employment.

As a robustness check, when restricting the sample to the same time period in which GVA is available (2007-2011), the impact from growth in employment in services is only present in the West (Table 25). Any poverty reduction impacts from services is not present in the Eastern regions.

# **<u>Fiscal</u>**

Next, fiscal variables are included into the specification. Government expenditures can be poverty reducing when correctly targeted. In the case of Brazil, social spending was found to be the saving grace and primary driver of poverty reduction in the absence of economic growth (Ferreira, Leite, and Ravallion, 2010). Equation (3) extends the specification of equation (2) with the inclusion of average central or local expenditures in log 2005 PPP per capita values.

Fiscal variables are collected from the Ministry of Finance, General Directory of Accounts and are available from 2007 to 2013. Fiscal expenditures are categorized as either expenditures disbursed by the central government or local government. Central and local spending cannot be combined since part of central spending includes transfers to local governments but this amount is not clearly categorized using available data; transfers
of investing in their own capacity or, more generally, copying project LGAs’ practices. Addressing the first point, we show that that the project’s own scoring of local government performance does not indicate that all improvement across the project life-cycle happened before our baseline survey. Furthermore, approximately half of the total disbursement of the project happened between baseline (or first round of survey) and endline (or second round), so the period between our surveys covers a substantial amount of the project cycle. Addressing the second point is more difficult–there may have been learning between LGAs that we cannot capture in our data. However, this interpretation would cast doubt on the incentive design of the project as the driver of improvements in state capacity, since the non-project LGAs did not receive these incentives but nevertheless improved over time.

These results raise further questions. What is driving the changes observed in state capacity in local governments that did not receive the project? What can we learn from this about how state capacity evolves over time? What is the role of external partners in the process? What can external partners do differently than the project we have evaluated to bring additional value to within-country processes of endogenous change? The rich survey data we gathered allows us to offer some answers to these questions, along with ideas for how external partners might innovate for greater impact.

Prior research steers us towards looking at the role of citizens for an explanation of how improvements came about in non-project local government areas. Available research has argued 3
# **1. Introduction**

Measuring work accurately is crucial for policy making, especially in low- and middle-income countries where the employment gaps between women and men as well as youth and adults are particularly large.<sup>7</sup> Even though the gender- and age-based employment gaps can be explained by a variety of reasons, including structural changes exacerbated by the COVID-19 pandemic and persistent gender norms (Klasen, 2019; Goldin & Mitchell, 2017; Goldin et al., 2017); these gaps might also reflect, in part, the quality and composition of the underlying data, specifically the possible undermeasurement of women’s and youths’ labor market outcomes. In fact, existing evidence suggests that standard survey methods traditionally used to collect labor data may not adequately elicit accurate responses from some groups (Ambler et al., 2021; Bardasi et al., 2011; Arthi et al., 2018; Dillon, 2012; Discenza et al., 2021). For example, as evidence shows that women tend to underreport their work activities—due to social norms (Franck and Olsson, 2014), misunderstanding of housework and outside employment (Muller and Sousa, 2020), or the structure of labor modules (Discenza et al., 2021)—official labor data may reflect inaccurate measurements of their work.

There are at least two reasons that may explain why standard survey methods used to collect data on labor indicators potentially undermeasure women’s and youths’ labor market outcomes. First, standard labor modules included in household surveys are often better at collecting information on formal or regular activities and may fail to properly capture the informal and casual activities that are classified as work under current international guidelines. Respondents may not consider their informal activities, such as preparing food to sell or helping in a family-owned business, as work. Second, standard data collection protocols for household surveys allow for proxy respondents to provide responses on behalf of other household members. This practice may result in biased
of trade performance, because insecurity may act as a hidden tax on trade (Anderson and Marcouiller, 2002).

Another layer in the drive towards greater investment and export capacity is a macroeconomic environment characterized by stable and competitive exchange rates, stable prices and low levels of debts. Poorly managed exchange rates can have unfavorable outcomes by limiting investment and export opportunities (Rodrik, 2008). Moreover, the package of formal and informal labor market and wage bargaining institutions matters in the effort to attract investment and expand export capacity (Aidt and Tzannatos, 2008). Though there is some understanding that all factors including institutions, regulatory frameworks and infrastructure are critical; it is essential for policy makers to identify priorities. The policy implication is that reform would have a greater impact if it focused on a few priorities.

Using the estimated trade effects of AGOA, we provide a simple test to identify fundamental characteristics of countries in the effort to evaluate the heterogeneity in the effects. The goal is to better understand which of these particular factors are more important in explaining the variation in the impact of AGOA after controlling for basic country characteristics. We control for specific features of countries that could determine their participation in trade with the US in particular. Using country fixed effects might help account for these time-fixed variations across countries. One needs to exercise caution in considering the results as robust causal mechanisms since most determinants are correlated and endogenous. However, similarities in the countries considered suggest that any significant difference in the determinants could be very useful in understanding the heterogeneity in exploiting export opportunities as a result of AGOA and other export opportunities.

35
latest population estimate for Nepal is 28.2 million, a 50 percent increase since 1990. The projection that Nepal’s population would double between 1990 and 2015 turned out to be unfounded.

**Figure 10: Population Growth in Nepal and Selected Countries**

<!-- Start of picture text --> 3.5%<br>3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Nepal Bangladesh<br>India Sub‐Saharan Africa<br>World<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Source: World Development Indicators (World Bank 2016b). Sub-Saharan Africa includes only low and middle income countries as of July 1st 2015.

18
<!-- Start of picture text --> oO<br>=<br>o* g ° ~ ¢@ Pand a tee a<br>Sso *. x *.* ° * ne a Pos oar’Sa a oo 9, , °<br>S~x Fe°ot T+ oy+eet4O 6 x HO° .++ o, 0%°ofwoeox” ate.¢ te: x ye *<br>a + + + % o«% x % MF + x<br>ro et TH Pepe Pg xx tele<br>(0) +4, «4 x + to x ¥ y + ¢ + +)<br>re eet Ne et OF . * x +.<br>iPe x 2 + teh« ¢ x tta Pa nn$ey+,+ .<br>iva)©‘.++*4 * +t x<br>oOo<br>=1<br>0 20 40 60 80<br>Area<br>+ H3-CensusEB * H3-CBEB (old bootstrap)<br>« ELL with context<br>oO<br>oO<br>co a x oa ol<br>So=} 4 Pad a4 °<br>= o ° o + 6<br>x * °<br>~— x * x a<br>5— ° ° xe +<br>Woan x + +x x oot x «?¢ ° o + . °<br>S . « ° x + ee x xe oe x<br>+ _* o ote + © ex :<br>+ +ete. a+Ba* | Te‘ , ++TO e+ag* Se+ te Hyat+<br>+ +4 t oy oy xt + +. x<br>o xNee+ FOO¢° + xe +.
_nil_ percent). Again, these differences are statistically significant at the 1 percent level. In general, the picture that emerges from Table 2 is that in Bhutan wages, jobs and workers all differ considerably between the public and the private sector.

Fringe benefits that are paid in addition to the base salary are an important aspect of overall worker compensation for which the BLFS 2014 contains relatively detailed information. According to Bhutan’s Labor and Employment Act, the majority of workers should _de jure_ be entitled to the following eight distinct fringe benefits: gratuity (not in the sense of tips but as an additional retirement benefit after a certain length of service), provident fund payments (another retirement benefit), overtime payments, paid annual leave, paid sick leave, paid casual leave, paid maternity/paternity leave

> 8 Administrative data from the MoLHR and the World Bank’s Bhutan Enterprise Survey 2015 document much higher proportions of non-Bhutanese workers than the BLFS 2014. This hints at a general under-reporting of nonBhutanese individuals in the BLFS 2014.

- 16 -
The paper is organized as follows. The next section illustrates the data and methods used for the analysis. The paper then provides a comparative distributional analysis of subsidies and simulates subsidy reforms comparing the outcomes across countries.

# Data and Analytical Approach

In the following sections, we describe the microdata used for the analysis and the baseline prices (subsidized products and unit subsidies) as of 2014, our baseline year. The updates were made using published IMF macroindicators for inflation and gross domestic product (GDP) per capita as well as population statistics (see Tables B.1 and B.2). The exercise that follows estimates the distribution of subsidies and provides new simulations of subsidy reforms using the primary data files for each country and transforming expenditure into U.S. dollars ($) at purchasing power parity (PPP). This allows comparing subsidies and the outcome of subsidy reforms using a common currency.

## Microdata

Table 1 shows the population statistics estimated directly from the surveys. These numbers are not identical to all country-specific population estimates, but they are very close. We can see that the sample of countries considered amounts to a total population of almost 130 million people, approximately 34 percent of the population in the MENA region in 2014. The total household expenditure for the countries considered is approximately $0.63 trillion-PPP per year, which amounts to $3,913-PPP per capita, per year, and $17,381-PPP per household, per year. This average hides differences across countries. The Islamic Republic of Iran is by far the country with the highest per capita expenditure ($7,477-PPP). Morocco, and Tunisia follow with approximately $4,000-PPP, and Libya and Djibouti come last with approximately $2,000-PPP. The sample of countries we have is representative of three groups of countries at different levels of economic development. We also have oil-producing countries and net exporters of oil, such as the Islamic Republic of Iran and Libya; non-oil-producing countries with some natural resources, such as
||Scenario A|0.06|0.39|1.02|2.02|3.73|5.42|8.02|13.03|20|46.35|100|
|PIT|Scenario B|0.06|0.43|1.14|2.21|3.95|5.71|8.23|13.02|19.72|45.56|100|
||Scenario C|0.06|0.43|1.15|2.19|3.79|5.51|8.03|12.82|19.57|46.46|100|

_Source:_ Authors’ estimation using data from HIES 2014.

_Notes:_ a. MY, GY, GN & BY, and PIT denote market income, gross income, wage and salary and business income, and personal income tax, respectively. b. GE denotes the gross revenue effect of TIETP. TF denotes tax revenue forgone for TIETP. NE denotes net effect of TIETP.

38
.368]|[0.429]|||
|Concentration 3|||-0.521***||
||||[0.166]||
|Herfindahl index (HI)||||-0.677***|
|||||[0.158]|
|Financial development|-0.160|-0.047|-0.223|0.130|
||[0.226]|[0.171]|[0.151]|[0.088]|
|LernerFinancial development|1.943**|1.779**|||
||[0.777]|[0.696]|||
|Concentration 3Financial development|||0.898***<br>[0.177]||
|HI IndexFinancial development||||0.993***<br>[0.197]|
|Log firm size|0.086***|0.088***|0.086***|0.086***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Manufacturing|0.027**|0.027**|0.029***|0.029***|
||[0.010]|[0.011]|[0.011]|[0.011]|
|Exporter|0.033***|0.030***|0.032***|0.031***|
||[0.008]|[0.010]|[0.008]|[0.008]|
|Foreign-owned|-0.078***|
lowers growth (Campos et al., 1999). Corruption may also reduce parental investments in human capital (Varvarigos and Arsenis, 2015; Brunetti et al., 1998). Islam and Lederman (2020) find a statistically significant impact on transitional growth to a higher potential level of gross domestic product per capita. The estimates indicate an elasticity of the magnitude of 0.03 percent per year, which is much larger than the elasticity of trade openness and schooling in their estimation sample. Thus, there is some evidence that better data ecosystems can have far-reaching economic benefits.

# **Measuring the Performance of Data Systems**

The ideal data transparency measure covers the capacity of the data system to produce timely and good quality data that is widely accessible. Additional elements include the legal framework surrounding data and the features or services data systems provide. There are three main measures that are widely used: (i) the Statistical Capacity Indicator (SCI) from the World Bank (that is to be discontinued), (ii) the Statistical Performance Indicator (SPI) also from the World Bank released as part of WDR 2021, and (iii) the Open Data Inventory (ODIN) from the Open Data Network.<sup>2</sup> The SCI is a composite score assessing the capacity of a country’s statistical system. It is based on a diagnostic framework assessing the following areas: methodology; data sources; and periodicity and timeliness. Countries are scored against 25 criteria in these areas, using publicly available information and/or country input. The SCI score is then calculated as the simple average of all three area scores on a scale of 0-100. The SCI only covers developing economies but has the advantage of going back historically to 2004/2005, allowing for tracking the evolution of data systems over time. See Table A2 in the annex for definitions of subcomponents.

The SPI, which is the successor to the SCI, measures the capacity and maturity of national statistical systems by assessing
in average population density from 64 persons per km<sup>2</sup> in 1980 to 124 in 2002 (Uganda Bureau of Statistics 2005). As more than 85% of the population live in rural areas and agriculture accounts for 77% of employment and 50% of total output (Belshaw _et al._ 1999), land has enormous socio-economic significance as a key productive asset and source of livelihood. Household surveys indicate that land makes up more than 50% of the average household’s asset endowment and that 74% of households use agricultural land for subsistence (Deininger and Okidi 2003).

Elimination of implicit and explicit taxation of the agricultural sector in the early 1990s, together with comparatively high levels of economic growth created favorable conditions for agricultural productivity growth (Blake _et al._ 2002). However, the extent of investment and associated diversification of the productive sector remained limited and productivity failed to increase as expected (Belshaw _et al._ 1999). As a result, most of the increments in production achieved were due to expansion of cultivated area at the expense of woodlots, wetlands and natural grazing areas (Place and Otsuka 2000, Pender 2004) that can not be sustained in the future. There is consensus that higher investment to enhance land productivity, 8