rafmacalaba/data-use-mentions-extended huggingface dataset:
For the case of those who agree that men should have more right to a job, according to data from the World Values Survey combined with other demographic characteristics from the LFS, we find that men’s labor force status is not very sensitive to any of the covariates, relative to the effect on women’s, which we interpret as male labor force supply being inelastic. The effect of the number of children in the household is found counterintuitive and positively related with FLFP (maybe due to the need of more income) when using LFS, but negative when using the World Values Survey, which reinforces the self-reported reason of childcare as a cause to remain out of the labor force but is not conclusive on our analysis. Finally, and in line with the literature on the topic, higher educational attainment in women increases their probability to participate in the labor force.

37
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coordination in the management of assets and liabilities across institutions while maintaining each institution’s independence. Full implementation of ALM in the public sector or for the sovereign is rare but a number of countries have been implementing a partial approach.

The findings of this paper are based on a survey conducted in 2017 by the World Bank in collaboration with the OECD and the IMF, among 28 countries.<sup>5</sup> The survey comprised 6 sections and focused on the various approaches underpinning the management of sovereign assets and liabilities.<sup>6</sup> It assessed how integrated and operational balance sheet frameworks are applied across countries, looking at the production of an actual balance sheet as well as at the objectives, priority areas and challenges of the management of the balance sheet.

This paper is organized as follows. Section A presents the participants’ responses to the survey in regard to the production, coverage, periodicity and publication of sovereign balance sheets. Section B examines the SALM practices, the entities in charge, the objectives of management, the reporting mechanisms, as well as the risk framework under which the respondents operate. Section C examines how the respondents have been able to implement such an integrated approach to managing sovereign assets and liabilities, the challenges they are facing and the plans they have for the future.

As indicated above, 28 countries responded to the questionnaire,<sup>7</sup> most of them high‐income economies that are members of the OECD (80 percent) with a smaller share of middle‐income countries (20 percent). In terms of geography, 50 percent of the respondents are from an EU member country. The income level of the respondents implies that the countries producing a sovereign balance sheet and fully or partially implementing a SALM framework have well developed governance and institutional structures. As indicated by the survey respondents, countries face a number of challenges in the implementation of a SALM framework and production of a sovereign balance sheet, chief among them the
gross fixed capital formation|-|9.842***|-|7.658***|
|Log exports of goods and services|-|1.344|-|3.123**|
|Log REER|-|0.764|-|1.458|
|World oil price index|-|0.078***|-|0.055***|
|World metal price index|-|-0.015|-|0.003|
|Gini index|-|-1.877***|-|-2.073***|
|Observations|873|873|873|873|
|R2|0.113|0.245|0.901|0.799|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Overall, these results indicate that a positive variation in the dependencies is empirically associated with a negative variation in both GDP per capita and the Gini index. In other words, resource dependence is associated with lower levels of GDP per capita, but also with lower income inequality. These results are consistent with the literature on Dutch disease, which explains them as an effect of the diversion of labor and capital from the industrial sector to the natural resource sector, combined with the negative effect of real exchange-rate appreciation on the tradable sector. However, the model’s findings suggest that, on average, the inter-sectoral shift in labor patterns—which affects the distribution of income through rising wage differentials between sectors—is offset by the investment of additional fiscal revenue in pro-poor policy interventions. The literature highlights that the sign of the relationship between resource dependence and inequality is likely to depend on each country’s institutional framework and the quality of its policy interventions. These factors determine whether—and how—natural resource revenues support rising productivity and consumption among lower-income households. The model
leads to a smaller number of clusters. In particular, the 506 villages in the experiment belong to 358 clusters of partially overlapping five-kilometer neighborhoods—the 320 treatment villages belong to 249 such clusters—and this number reduces to 180 when considering clusters formed by overlapping ten-kilometer neighborhoods and 45 with 20kilometer ones.

# _Main Results_

Tables 3 and 4 report the OLS estimates of the spillover effects of the program on eligible children’s school participation decisions. The estimates are obtained using the data for the post-treatment period (October 1998 and November 1999).

The estimates in table 3 correspond to the model in equation (1) with continuous variables for the numbers of beneficiary, _N_<sup>_B_</sup> , and potential beneficiaries, _N_<sup>_P_</sup> . The estimates are obtained with two alternative measures of program frequency _N_<sup>_B_</sup> in the areas surrounding the evaluation villages: the models in columns 1–3 use the numbers of villages treated in a five-kilometer radius, while those in columns 4–6 instead use the numbers of eligible households within the same radius. This second definition takes into account the variations in population density across neighborhoods and, hence, possibly better captures the extent of potential interactions among program beneficiaries. We report and discuss only the estimates of the parameters _α_ 1 and _α_ 2 but, as explained above, the regressions further include controls for the numbers of potential beneficiaries and for baseline characteristics observed in October 1997, notably baseline school enrollment.<sup>16</sup> Column 1 of table 3 reports the estimates for the baseline model in equation (1) when measuring program frequency by the numbers of villages. It indicates that when considering the entire sample of children in treatment and control villages while living in 16. The last rows of table 3 report the estimated coefficients of the conditioning term NP in equation (2), split into its two components, NE and NNE. Those are, in general, negative and
the income, consumption and asset management behavior (Khandker 2012; Khandker and Mahmud 2012; Pitt and Khandker 2002).

# **3.   Seasonality in household borrowing and income: Descriptive evidence**

Using Household Income and Expenditure Surveys (HIES) of 2000 and 2005, Khandker (2012) demonstrates that seasonality is pronounced in household consumption and income, and that income seasonality affects significantly the seasonality of consumption and poverty beyond and above seasonal variations in consumption and poverty. This phenomenon is most acute in the Northwest region of Bangladesh (Khandker and Mahmud 2012). Findings also indicate that seasonal variations severely affect poor people who mainly live on agricultural income, and microcredit has so far played a good role in mitigating seasonal hardship. Seasonality still matters, however. Using the 2010 HIES data, Khandker and Samad (2016) show that although the severity of seasonal hunger has declined, consumption and poverty are still influenced by the seasonality of income caused by the agriculture cycle.

In order to explore the seasonality of household borrowing and income, we use panel data collected by the World Bank with help of Bangladesh Institute of Development Studies (BIDS) in 1998/99 and Institute of Microfinance (InM) in 2010/11 (see Khandker and Samad 2014 for a detailed description of the data set).<sup>2</sup> Using these data we can divide the borrowing sources into three categories (microcredit, formal or commercial, and informal) and the whole year into four seasons ( _Boro_ , _Aus_ , _Monga_ and _Aman_ ). Figure 2 shows the trend in the amount of loans from each source and aggregate loans from all sources combined across four seasons during 1998/99. As the figure shows, the aggregate loans from all sources is the lowest during the _Monga_ season. As for the individual sources, the pattern is more pronounced for microcredit and informal sources than for formal sources.<sup>3</sup> As shown in Figure 3, the

> 2 While the panel surveys
-6 -

for 1996 and 1997 have been estimated with "shuttle trade" already being taken into account in merchandise exports. Therefore there could be substantial amount of short-term flows which were captured by the official statistics on "short-term" capital inflows.

In terms of the volatility of the flows, it is noteworthy that, during 1989-1997, foreign direct investments had the lowest volatility in all category of flows, whereas net errors and omissions had the highest volatility. Loans, deposits and trade credits were more volatile than portfolio investments, but most interestingly, there was no substantial difference in volatility between short-term loans and deposits and long-term loans and deposits.

**Table 4**

||**Turk**|**ey: Vol**<br>(Coe|**atility of**<br>fficient|**New C**<br>of Varia|**apital Fl**<br>tion)|**ows**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||1989|1990|1991|1992|1993|1994|1995|1996|1997|89-97|
|Capital Account Balance|4.2|0.5|-1.6|1.0|1.0|-1.8|1.9|0.9|0.9|2.1|
|Foreign Direct Investment|0.6|0.5|0.5|0.5|0.6|0.6|1.2|0.9|0.9|0.8|
|Portfolio Investment|0.9|1.6|2.4|1.0|1.4|3.2|19.5
Which activities are considered employment and work is far from straightforward as illustrated by the periodic changes adopted by the International Conference of Labour Statisticians (ICLS) in 2008 and 2013.<sup>6</sup> There is an important distinction between work and employment (Annex 1 details the ICLS 19 categories of work). The currently accepted international definition of employed people is “all those of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit in cash or in kind” (ICLS 19).<sup>7</sup> It is important to note that the threshold for inclusion in terms of amount of time spent working is very low: one only needs to have worked in this type of activity for one hour during the reference period to be considered employed.<sup>8</sup> The phrase “for pay or profit in cash or in kind” is critical, as it excludes activities that were previously included in the definition of employment, including activities for own-consumption.

This definition of employment is relatively new and has not yet been fully operationalized. Rather, as of 2019, most countries’ labor force statistics are based on the previous definition of employment (ICLS 13) which, in addition to work “for pay or profit”, included production activities for own-consumption such as subsistence agriculture as well as volunteer work. Within this definition, family members who work on the family farm were included as unpaid workers. In other words, under ICLS 13, all working age people engaging in at least one hour of work for monetary remuneration (pay or profit) or to generate goods for own-consumption are considered employed. Both the ICLS 13 and ICLS 19 definitions are considered for the purposes of this study as it was undertaken during a period in which Honduras was making the transition between the two.

In general, women are more likely to be involved in informal or irregular activities for pay or profit (
i.e., the intensive margin) and the increase in the number of products and varieties for each product (i.e., the extensive margin).

Figure 1 shows average annual imports as shares of GDP between 1995 and 2021. The imports share of GDP greatly varies among the countries. For instance, Japan has an import

> 2BACI relies on data from the United Nations Comtrade - International Trade Statistics database

> 3Note that not all 5,383 products were imported to each country. On average, 4,537 products were imported by one country.

4
Congestion Study: Final Report_ . Washington DC.

World Bank (2018a) _Doing Business 2018 ‐ Reforming to Create Jobs_ . Washington DC: The World Bank. World Bank (2018b) _Enterprise Surveys_ .

World Bank (2018c) ‘National Accounts Data’. Available at: https://data.worldbank.org/indicator/ny.gdp.mktp.cd?end=2014&start=2013.

World Bank (2019) ‘Electricity Uptake for Economic Transformation in Sub‐Saharan Africa’.

Zhang, F. (2019) _In the dark: How Much Do Power Sector Distortions Cost South Asia?_ Washington, DC. doi: doi:10.1596/978‐1‐4648‐1154‐8.

# 6. Annex

## **a. Impact of the current level of power supply quality on firm productivity**

|**Country**|**Type**<br>**of**<br>**firms**|**Power**<br>**quality **|**supply**<br>**variable**|**Method**|**Effect**<br>**of**<br>**low**<br>**electricity**<br>**supply quality on firms**|**Source**|
|---|---|---|---|---|---|---|
|Ghana|Small<br>and<br>medium‐<br>sized<br>manufacturi<br>ngfirms|Number<br>with<br>outage|of days<br>power<br>|Regression|Major power outages in<br>Ghana between 2012 and<br>2015<br>decreased<br>firms’<br>average monthly productivity<
Adjusting for the panel design and for over and above CPI increases in the cost of poverty basket result in less impressive growth. Table 2 shows that moving to the panel constant weights and the welfare index instead of per capita consumption (reflecting real growth rates relative to the cost of the poverty basket) welfare growth was close to 2 percent per year (6.85 percent over 2005-2008 period), and not 4 percent per year as implied by the overall CPI index. Table 5 also shows how standard errors estimated with classical assumptions are different from correct standard errors which take into consideration survey sample design (clustering) and panel properties. It means for example that for rural areas growth rate in average consumption is not statistically different from zero (at 95 percent confidence level the range around the estimate is plus minus 6 percentage points – larger that the mean estimate itself of 5.6 percent).<sup>15</sup>

**Table 2: Growth rate of the welfare index and standard errors**

||**Growth rate of the**||**Standard errors, pe**|**rcent**|
|---|---|---|---|---|
||**welfare index,%**|**Classic**|**Allowing for clustering**|**Panel (boostraped)**|
|Urban|7.83|0.48|3.75|3.59|
|Rural|5.66|0.33|3.13|3.44|
|**Total Egypt**|**6.85**|**0.31**|**2.50**|**2.57**|

_Source: Authors estimates based on HIECPS 2005-2008 and regional CPI indice). Note: Table uses fixed household size to construct constant panel weights._ This
a straightforward estimation of _λ_ . A key disadvantage of tax record data is that it is often difficult to obtain access to them. Moreover, they are more likely to be available in developed countries with good quality data systems in place, and less likely to be available in developing countries.

This paper explores the feasibility of using an alternative to tax record data that are more readily available. The empirical application presented in Section 4 considers data on house prices compiled from publicly available real estate property listings as the alternative.<sup>7</sup> The advantage of these data is that their availability extends to developing countries. The flip-side is that they also introduce a number of key methodological challenges due to the fact that the alternative database (a) observes predictors of income, not actual incomes, and (b) need not constitute a proper sample, so that it is unclear what population is being represented by the data.

The following two subsections aim to provide workable solutions to these two challenges that will hopefully contribute to a wider application of this approach.

# **2.2.1 A database of predictors of top incomes**

Let us first focus on the challenge posed by observing a predictor of household income rather than actual income. Consider the following assumption.

**Assumption 1** _Suppose that household income per capita can be described by:_

_where xh denotes the predictor of household income, εh denotes a zero expectation error term, subscript h indicates the household, and where β denotes a vector of model parameters._ The assumption of a log-linear model is motivated by ease of exposition and by the fact that it fits our empirical data remarkably well. This assumption can be relaxed however by accommodating flexible functional forms for _m_ ( _xh_ ; _β_ ) if the data call for it. In our application the value of the household’s house (or rental value) will serve as the predictor _xh_ .

To obtain some intuition for the implications of Assumption 1 it may be helpful
activity and the level of development. Specifically, we see that discovery activity is low among the poorest countries, but interestingly is also low among wealthy industrialized countries. At the 4-digit level, there were no discoveries within both the Central African Republic and the United States, while there were 26 discoveries in Romania and 29 in Indonesia. The frequency of discoveries appears to be a nonlinear function of the level of development. This apparent relationship may be the result of economic discoveries being driven by broad economic changes that occur as countries become richer. One potential explanation for this observation was identified in section 2, namely the process of productive diversification, which we now evaluate.

# **4. Discovery and the process of productive diversification**

As discussed in section 2, recent work has found a robust relationship between economywide diversification and levels of development. Imbs and Wacziarg (2003) find a persistent pattern of increasing diversification until relatively high levels of development, (GDP per capita between $9,000 and $10,000 1985 US dollars) followed by increased specialization.

Before investigating if discovery is driven by these stages of diversification, we first expand on the findings of Imbs and Wacziarg (2003) by considering diversification of the export basket. This is because Imbs and Wacziarg (2003) used labor data in their analysis of the stages of diversification, while we use export data to identify discoveries. It may not be true that the export basket follows a similar pattern of diversification as the national production basket. To determine whether these same stages of diversification exist in export data, we construct a 19
from the control group in

> 8 The comparison is only indicative as the employment indicators are measured slightly differently. Still, approximately 70 percent of graduates in the control group were unemployed or inactive (63 percent of males and 74 percent of females), with approximately 28 percent employed (37 percent of males and 27 percent of females). There is a slightly higher unemployment and inactivity rate (73 percent) and a lower employment rate (26 percent) in the overall population of young graduates in 2012. This is consistent with previous graduate tracer studies showing that graduates’ labor-market insertion takes significant time. Another way to illustrate this is that, whereas 28 percent of the control group was employed in 2011, 49 percent was employed in 2014 at the time of the endline survey. Similarly, unemployment declined by 10 percentage points from 48 percent in 2011 to 37 percent in 2014. Looking at the composition of employment, the national population is slightly more concentrated in wage jobs compared to the control group, with 90 percent (83 percent of males and 96 percent of females) compared to 76 percent (68 percent of males and 83 percent of females) of employed individuals. In contrast, the national population is slightly less concentrated in self-employment: 5 percent (8 percent of males and 2 percent of females) compared to 16 percent (24 percent of males and 10 percent of females) among the control group in 2011.

10
national scale?. Viewed in historical perspective, a major shift had already occurred the

> 6 It is not appropriate to compare agricultural employment growth rates in the early 1980s with the second half of the decade owing to different procedures adopted for enumerating female unpaid family workers in the agricultural in the 1985 intercensal population survey. Among males, however, there was a noticeable decline in jobs taken up in agriculture in the second half of the decade.

7 The extent of these trends need to be confirmed from further survey since a significant part of the decline occurred in 1994 when one annual National Labour Force Survey was undertaken, in contrast to quarterly surveys conducted in previous years. The quarterly surveys had shown little seasonal variation in agricultural employment for several years. It therefore seems unlikely that seasonal effects alone can account for the sharp decline in 1994.

8 According to the Population Census data, agricultural employment grew by over three per cent per annum in the Outer Islands during the 1980s.
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