rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2. Democracy trends by region, 1960-2005**

<!-- Start of picture text --> OECD<br>Latin America<br>EE/FSU<br>E/S Asia<br>Africa<br>MENA<br>1960 1970 1980 1990 2000 2010<br>year<br>10<br>5<br>0<br>-5<br><!-- End of picture text --> _Source:_ calculated from Marshall and Jaggers (2007) 12. Figure 3 plots MENA country scores for democracy against the log of GDP per capita. While democracy scores, like many other governance indicators, typically show a positive relationship with income, MENA observations clearly underperform. Elsewhere, Gelb and Grassman (2010) has shown than MENA countries, and oil producers in general, underperform on the Kaufman-Kraay Accountability and Voice Indicators.<sup>5</sup> Thus, we may not find the recent emergence of popular democratic movements across the region so surprising given that public opinion surveys demonstrate strong support in the MENA region for more democratic governance (Tessler and Jamal 2008).

> 5 Haber and Menaldo (2011), using a long-run panel of 162 countries from 1800-2006, contest the notion that natural resource reliance mechanistically leads to a higher propensity for authoritarianism.  We concur with the premise that resource endowments do not mechanistically lead to particular development outcomes, but are contingent on the particular institutional preconditions and state-society make-ups of a given country‘s development trajectory.  We do, however, contend that resources will condition the particular strategies and policies by which governments leverage these resources to rule, and in turn the implications these have for longer run development trajectories, including the depth of civil society and private sector development.

6
these potential biases in enrollment by constructing an alternative indicator of enrollment using administrative data based on attendance the day of the application of the ENLACE tests.

There are very few quantitative studies of Prospera on achievement test impacts, mainly because achievement tests were not part of the main data collection in the initial experimental evaluation. In 2003, a version of the Woodcock Johnson tests in mathematics and Spanish was applied. Comparing the test scores of the original treatment and control groups, Behrman, Parker and Todd (2009) find no impacts on achievement tests, despite significant impacts on increasing schooling attainment between the two groups.

Several reviews examine the evidence on how other CCT programs impact school achievement. An early study by Schady and Fizbein (2009) summarizes the effects of CCTs on achievement tests and argues that results have been disappointingly small. Since then, several additional studies show mixed effects. Baird et al. (2014) review education effects of both conditional and unconditional transfers and find that effect on achievement are “small, at best.” Snilstveit et al. 2015 carry out a meta-analysis review of educational interventions, studying both conditional and unconditional cash transfer programs. They conclude that, while cash transfers have been effective at increasing school enrollment, overall, they have low effects on achievement.

One caveat to these conclusions is there are far fewer studies on the effects on achievement as there are on the effects on enrollment. Snilstveit et al. note the number of studies of achievement is much lower than that for enrollment, in particular they review studies from 38 programs of the effects of transfers on enrollment, but only 11 programs have evaluation studies of their achievement impacts. Within these studies, however, there are examples of transfer programs that significantly affect attendance. For instance, Baird, McIntosh, and Ozler (2010) show significant impacts of 0.14 standard deviations on English achievement tests and 0.12 on math achievement tests after two years of operation of a CCT program in Malawi.

Our study builds on two recent studies
for the purpose of our analysis. By exploiting these variations, the paper shows how difficult it can be to obtain key labor market indicators and identify their trend over time using household survey data in SSA. We show that it is possible that different users of the same data set may pick up questions from different parts to construct their key variables, possibly coming up with different results from the same data on the basic employment outcomes even if they are only doing simple tabulations. By implication, these results also apply to country comparisons when different countries use different instruments. The analysis suggests that we may actually know very little about employment outcomes in SSA, and how relationship to economic policy or household welfare.

We start the paper by reviewing the definitions of the main employment outcomes, using the framework established by the UN SNA for defining employment and the conventions established by labor statisticians through the International Labor Organization (ILO) for the measurement of these concepts. We discuss the issues to be faced when applying these definitions in a low-income setting, and review the survey research that is available to guide the measurement of these concepts. The section summarizes recommendations from past literature on questions and techniques most appropriate for measuring employment outcomes in a low-income setting. In Section 3, we present the questionnaires for the Uganda National Household Survey (UNHS ) used by UBOS from 1992-2010, which contain both good and not-sogood practice techniques, and estimate the effects that different questionnaire designs have had on nonsampling error in the measurement of employment outcomes. We conclude the paper with suggestions for SSA statistical offices on collecting employment data, for researchers on possible approaches to adjust for known measurement biases in data collected in other SSA countries, and for future survey research on measuring employment outcomes in low-income countries.

# **2. Some basics on collecting data on employment and labor force participation**<sup>_4_</sup>

The concepts used by social scientists in the analysis of labor force status and employment outcomes were initially defined in microeconomic and specifically labor economic
with the residency definition only in specific cases when information is available, as acknowledged in the LIC DSF Guidance Note.<sup>32</sup> In fact, debt denominated in foreign currency is still used as a proxy of external debt in 43 percent of the 2019 DSAs.

# _4.3. Recording errors_

Data collected in over 20 WB-led missions on Medium-Term Debt Management Strategy (MTDS)<sup>33</sup> in LIDCs between 2010 and 2020 reveal severe recording mistakes in approximately 30 percent of debt databases. The quality of DRS data assessed by the WB on an annual basis confirms these findings: of 74 LIDCs, 15 (20%) do not submit comprehensive reporting templates or, if they do, the quality is not deemed satisfactory. Among those who provide a complete template, 28 percent are affected by at least one type of recording mistake (Table 2).

**Table 2. Assessment of the 2020 reports by IDA countries to DRS**

||**No / partial /**<br>**unsatisfactory**<br>**reporting**||**Complete reporting**||
|---|---|---|---|---|
|**Number**<br>**of**<br>**countries(%)**|20%|of which 28% af|80%<br>fected byone or more of t|he errors below|
|**Error types**||_stocks/flows_<br>_inconsistencies_|_missing information_<br>_on new commitments_|_transactions not_<br>_reported_|
|**Frequency (%)**||15%|17%|17%|

Source: WB Development Data Group. Notes: author’s elaboration These problems are mostly driven by the debt recording process itself, which is still a very manual exercise involving
# **4.  HETEROGENEITY AND IMPERFECT COMPETITION**

Wacziarg and Wallack’s results (2004) are also consistent with more recent findings for developed countries. Thus, Bernard et al. (2003), using the US Census of Manufactures, conclude that liberalization had a significant impact on aggregate trade, but that this was not accompanied with sectoral reallocations. Although Wacziarg and Wallack and similar findings appear to discount large-scale intersectoral movements of labor, they do not preclude the existence of significant _intra_ sectoral effects. Indeed, micro-econometric analyses that use firmlevel data conclude that there is significant turnover of firms within industries. The implication is that intrasectoral firm heterogeneity may be more important than intersectoral differences when discussing the effects of trade liberalization.

Although the majority view is that SBTC explains the lion’s share of observed reduction in the relative return to low-skilled labor—and increases in unemployment in countries where wages are rigid—e.g., in Germany) (Heitger and Stehn 2003), the factor-content studies noted above established a presumption that labor markets outcomes are affected by international trade, although it is left unclear what the channels are through which this occurs (Greenaway and Nelson 2001, Francois 2004).<sup>12</sup> Recent papers increasingly conclude that (the threat of) competition drives enterprises to improve productivity and that quality of output is likely to have an important role in determining labor market effects. The simple Heckscher-Ohlin prediction that trade results in a redistribution of employment away from the import substituting towards export-oriented production assumes a world of homogenous firms/products and inter-industry specialization/trade. In practice most trade is of the intraindustry type, reflecting the exchange of differentiated products between countries with very

> 12 Neary (2001) notes that it is not clear how compelling the SBTC finding is either in explaining the stylized facts. He argues that in a competitive HOS type setting this should benefit disproportionately the unskilled labor-intensive (import competing) sector and reduce the skill premium, which is not observed. SBTC while
|absolute change w.r.t original income, p.p.|-14.9|-14.1|
|---|---|---|
|relative change w.r.t. original income, percent|-30.0|-28.5|

Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

Among the components of the tax-benefit system, the most important one is pensions **.** The marginal contribution<sup>7</sup> of pensions to poverty reduction is almost 26 percentage points, meaning that poverty more than doubles if pensions are excluded from income (Table 2).<sup>8</sup> The Gini index in this case almost returns to the level recorded at the _original_ income. Other components have much smaller effect on poverty and inequality. The largest among these other components in terms of marginal contribution to poverty are social insurance contributions: poverty would have been lower by 4 percentage points if employees were able to retain their contributions. The second biggest marginal impact on inequality is coming from non-means-tested social assistance benefits.

**Table 2. Marginal contributions of key tax/benefit programs to disposable income, 2017**

||Disposable<br>Income(DPI)|DPI plus<br>SIC|DPI plus<br>direct taxes|DPI minus<br>pensions|DPI minus non-<br>means-tested SA|DPI minus<br>means-tested SA|
|---|---|---|---|---|---|---|
|**Poverty rate, percent**|14.7|11.0|12.6|39.3|16.8|15.0|
|absolute change w.r.t original income, p.p.||-3.7|-2.1
Making creative use of the Adelman-Taft (AM) index of socio-economic development, Temple and Johnson (1998) demonstrate that social capability measured in the 1960s predicted more accurately than conventional indicators which countries would grow in the 1980s. They conclude that over a period of 25 years, a one standard deviation increase in this variable would raise the annual growth rate by 1.8 percentage points. As an illustrative example, if India had achieved the same level of social development on this measure as South Korea by 1960, then its income per capita would have grown at just about three percent a year instead of 1.3 percent. India's income per capita in 1985 would then have been almost 60 percent higher.

These findings have been criticized because the AM index included some economic indicators rather than pure social indicators. For further analysis, Temple and Johnson use four social indicators, to capture underlying social arrangements. They are: kinship (dominance of the immediate family over the extended family or clan and tribal allegiances); modernization of outlook (which included an assessment of social and political participation through voluntary associations and also the support for political and economic modernization); the extent of mass communications (newspaper circulation and number of radios per head); and the importance of an indigenous middle class (proportion of men employed in the professions in contrast to number of expatriates in these professions). They run regressions using these four components.

All the social variables except outlook have statistically significant relations with growth. The relationship between mass communications and growth remains robust even after controlling for a range of human capital, trade policy, political stability and ethnic fragmentation. The authors conclude by suggesting that the extent of mass communication maybe a good proxy for the strength of civic communities, as reflected in trust and membership of associations.

Temple and Johnson (1998) also examine the relationship between measures of trust and associational activity as measured in the World Values Survey and their social indicators. Although the overlap in countries from the two data sets was
who belong predominantly to lower-middle wealth classes at the expense of the richest 20 percent, who grants the usufruct.

<!-- Start of picture text --> Figure 1 - Gini concentration index - Adjusted and<br>unadjusted net wealth<br>0.650<br>0.640 Unadjusted<br>H1<br>0.630<br>H2<br>0.620<br>H3<br>0.610<br>0.600<br>0.590<br>0.580<br>0.570<br>1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->

# **5. Free residential houses in HFCS data**

The Household Finance and Consumption Survey (HFCS) is a comprehensive and harmonized survey conducted at the national level within the Eurozone. It aims to collect detailed data on household finances and consumption. The SHIW survey is the Italian part of the HFCS.

According to this source, the share of main residences used for free is a significant issue in several countries, including Austria, Croatia, Cyprus, Estonia, Greece, Poland, Portugal, Slovenia, and Spain, where the share of households is around 10 percent, close to or higher than in Italy (Table A6).

By examining the age of the reference person, we can gain evidence of the relative importance of this tenure mainly for the young and the old. In many countries (Austria, Cyprus, Czechia, Estonia, Germany, Hungary, Latvia, Poland, and the Slovak Republic), the age profile shows a U shape, and the shares of households whose reference person is below 30 years or above 65 years are the highest. In many other countries (Italy, Croatia, France, Greece, Lithuania, Portugal, Slovenia, and Spain) the share is generally decreasing with age.

Unfortunately, the harmonized survey's questionnaire collects information
# **4.2 Empirical strategy**

We rely on the latest empirical gravity literature developments to specify our econometric model as follows:<sup>11</sup> Here, _Xij_ , _t_ denotes bilateral trade flows from country _i_ to country _j_ at time _t_ in levels. Importantly, _Xij_ , _t_ includes both international and intra-national trade flows, which is critical to get theory-consistent estimates (Yotov, 2022; Larch and Yotov, 2023). Our analysis uses trade data in total goods (agriculture and manufacturing sectors) as a baseline.<sup>12</sup> We also use sector-specific data for a more disaggregated analysis.

_RTAij_ , _t_ captures the average world effect of trade agreements and will serve as a reference point to the effect of CAFTA-DR. Equation (4), as is standard in the literature, uses exporter-time fixed effects _γi_ , _t_ and importer-time fixed effects _ωj_ , _t_ to control for the unobservable exporter and importer multilateral resistances. These fixed effects will also absorb/control for any other country-time-specific characteristics that may impact bilateral trade on the exporter and importer sides. In addition, following the recommendations of Baier and Bergstrand (2007), we also employ country-pair fixed effects ( _μij_ ), which control for all time-invariant bilateral trade costs and will mitigate endogeneity concerns with respect to our trade policy variables of interest. Finally, we follow Bergstrand et al. (2015) and account for common globalization effects that could bias our trade agreements variables, with a set of time-varying border dummy variables _BRDRij_ , _t_ , for which a parameter _βt_ is estimated for each year _t_ separately. We estimate our model using the Poisson pseudo maximum likelihood (PPML) estimator, which allows accounting for heteroscedasticity in trade data and takes advantage of the information that is contained in zero trade flows (Santos Silva and Tenreyro, 2006).

Our primary purpose is to estimate the effects of the CAFTA-DR agreement. For this, we introduce the variables _CAFTAij_ , _t_ to capture the specific impact of this trade
from 22.7 percent in 2002 to 4.1 percent in 2016. Due to these major demographic and structural changes, and to incorporate areas of the country not covered in the earlier work, this paper presents a PMT formula based on data from the 2016 round of the Household Income and Expenditure Survey (HIES). We benchmark the PMT to the targeting performance of the PMT for the Samurdhi program, which currently determines eligibility solely based on self‐reported income. We find that switching to a PMT could significantly improve the targeting performance of Samurdhi.

This paper examines only the PMT method, although we recognize that this is one of many targeting methods used around the world (Alatas et al., 2012; Alkire and Seth, 2013; Klasen and Lange, 2015; Brown et al., 2016; Diamond et al., 2016; Sabates‐Wheeler et al. 2015; Del Ninno and Mills, 2015; Kydd and Wylde, 2011). This is because our goal is to inform the efforts of the Sri Lankan government, which already distributes Samurdhi transfers based on self‐reported income and is in the process of updating this system, a component of which is to replace self‐reported income with a vector of household attributes to identify beneficiaries (World Bank, 2016d).

> 2 The Samurdhi program provides monthly cash benefits to almost 1.5 million families, and accounts for over 80% of Sri Lanka’s cash transfer spending. The program also provides other assistance to its beneficiaries, including microfinance, housing, and banking services.

> 3 For instance, the disability benefit would need to include criteria relating to severity of disability.

> 4 Although the survey was nationally representative, data for the Northern and Eastern regions, affected by conflict at the time, were excluded for the purposes of the paper due to concerns about data quality.

2
The main distinction lies in the fact that these studies primarily concentrated on specific, more easily identifiable activities, such as walking, running, driving, and sleeping. In contrast, our work, focuses on non-specific, broadly defined time allocation activities, like farming, taking care of children, working for wage, and collecting foodstuff.

# **3.1 Dataset merging**

Building a supervised machine learning model requires data on observations and relevant predictor variables. Data containing known outcomes, serving as dependent variables, are essential for model building and evaluation. We utilize the reported time use activities collected through the time use survey module as our dependent variable.

The first step in preparing the data for machine learning modeling involved merging the time use survey data with the physical activity sensor data. The sensor-based physical activity tracking study spanned over two consecutive weeks. During this period, participants were surveyed twice with 24hour recall-based surveys to gain insight into their time use activities. The amalgamation of the datasets took place on the participant level for these two days. The aggregated dataset was further augmented with demographic information of the participants as well as meta information derived from the timestamp.

Harmonizing the data registration time intervals was crucial. The physical activity sensor data were recorded at one-minute intervals, whereas the time-use data were recorded at 15-minute increments. To synchronize the datasets at a consistent granularity, the time-use data were restructured to align with the one-minute interval of the sensor data.

# **3.2 Dataset cleaning**

The combined dataset underwent cleaning, which involved discarding data points with missing or unclear information on time use activities. Subsequently, data points indicating the accelerometer was not being worn were also removed. The wearable device registers inactivity when detached from the body, such as during bathing, and the corresponding time stamps registered by the device as non-wear were marked and discarded accordingly. One additional consideration was that approximately 10 percent of the entries had reported secondary activities simultaneous with the primary activities. From a modeling standpoint, these dual activities
Press. <u>https://doi.org/10.25546/102924</u>

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