rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Annex 2. Public vs. Private Sector Compensation across working categories**

An alternative approach to investigate the existence of compensation differentials between the public and the formal private sector is to use ENAHO data to distinguish employees based on their responsibilities and the service provided and verify the existence of compensation differential within each group. In particular, compensation differentials in Peru can be analyzed after dividing employees into the following categories: professionals, technical experts and so-called “auxiliaries” (i.e., support staff or ‘auxiliaries’) (SERVIR, 2010).

In order to test the robustness of the results obtained by this study, we used the same approach followed by SERVIR (2010) to disaggregate the data sample and estimated the log-wage equation (Rees and Shah, 1995) for each occupational group. Table A2.1 shows some descriptive statistics useful to contextualize the analysis. The majority of respondents belong to the Technical experts’ category. As for professionals, the large part of respondents is employed in the public sector (with a proportion close to 4:1) whereas the majority of technical experts and auxiliaries work in the formal private sector. Average values for total compensation are consistent with our expectations. When we focus on total compensation, we notice that compensation is significantly for professionals and, generally, for those one working in the formal private sector.

# **Table A2.1. Descriptive statistics**

|**Population Characteristics**|**Professionals**|**Technical**<br>**Experts**|**Support**<br>**staff**<br>**(****_auxiliares_)**|
|---|---|---|---|
|Number of observations|1633|2178|1892|
|Number of observations (sub-sample: public sector)|1282|783|505|
|Number of observations (sub-sample: private sector)
# **<mark>Appendix B: Further data description</mark>**

The Vietnam Household Living Standard Surveys (VHLSS) are conducted biennially, covering approximately 45,000 households in each survey round. The VHLSSs are designed to be representative at the provincial level (63 provinces). Within the VHLSSs, a sub-sample of around 9,000 households is selected to collect expenditure data. However, this smaller sample is only representative at the regional level (6 regions). To obtain estimates that are representative at the provincial level, the full sample of 45,000 households should be utilized. Thus, we estimate per capita expenditure of households in the sample of 36,000 households using the “poverty mapping” imputation method (Elbers _et al.,_ 2003). Estimating per capita expenditure consists of two steps. First, we estimate an expenditure model using the small-sample VHLSs (9,000 households). The dependent variable is the per capita expenditure, and the explanatory variables consist of household characteristics including demographics, ethnicity, education of household heads and household members, durables, housing conditions, and region dummies. We estimate separate models for urban and rural areas. The variables are selected using stepwise regressions. Only variables that are statistically significant at the 1% level and demonstrate reasonable signs are used in the final models. Second, we apply this expenditure model to the sample of 36,000 households (using the same variables that were employed in the expenditure model based on the small-sample VHLSSs) and predict per capita expenditure for these households. As a result, we have per capita expenditure data for the full sample of 45,000 households, and we use this data to estimate the per capita expenditure of provinces.

54
related to both the capability of the owner to exert influence on firm policy and the type of restructuring it can implement once gaining control.

# **II.2 Data**

For the job flow and productivity of job flow analysis, the paper uses annual industrial registries provided by the State Committees for Statistics in Russia (the Goskomstat) and Ukraine (the Derzhkomstat). In Soviet Russia, the data include the universe of civilian industrial enterprises, while after 1991 the coverage is supposed to be all industrial firms with more than 100 employees plus those that are more than 25 percent owned by the state and/or by legal entities that are themselves included in the registry. The firms in the Russian registry accounted for 90.5 percent of officially reported total industrial employment in 1992, while the Ukrainian registry covered 94.1 percent. The coverage rate in relation to official employment declined somewhat thereafter, falling by the year 2000 to 69.8 percent in Russia and 85.2 percent in Ukraine. No doubt the decline is due partially to the entrance of new small firms owned by individuals, since the registries do not include such entities. Our analysis includes cases of entry and exit, probably due in most cases to reorganization rather than genuine startup or shutdown.<sup>15</sup> The worker flow and worker flow relationships with job flows analysis is based on a survey of 530 industrial firms, selected through national probability sampling in 32 Russian _oblasts_ , with a probability proportional to employment size. The data permit us to estimate annual rates of both job flows and worker flows for a consistent set of firms for the period 1990-1999 and to relate these flows to detailed information on firm characteristics.

A 2001 survey of 297 new small enterprises in Romania is used to investigate the determinants of employment growth in the new private sector. The data contain detailed measures of loans, tax breaks, technical assistance, managerial and employee human capital, and the business environment, as well as employment in 1992-2001.

Russian
>.6<br>Child care attendance<br>.55<br>.5<br><!-- End of picture text --> <mark>Note: The vertical axis gives the proportion of children attending child care, and the horizontal axis presents the birth months of children with contiguous birth months. Children born in December are 1 month older than those born in January.</mark> <mark>Sources: Authors’ estimation from VHLSSs 2010, 2012, 2014 and 2016.</mark> 29
## **5.4 A Control Function Approach**

The dependent variables in our analysis are discrete indicating the probability of participation in nonagricultural activities. This requires an approach to instrument variable (IV) estimation which differs from the standard 2SLS regression. To correct for the omitted variable bias, we implement the control function approach developed for the discrete choice models (Blundell and Powell (2001), and Petrin and Train (2009) for multinomial logit model, and Rivers and Voung (1988) for probit model). According to this approach, the discrete choice instrumental variables model is estimated in two steps. First, the endogenous variable is regressed on exogenous instrument(s) and all other regressors in the model. Second, the multinomial logit/probit regression is then estimated by including the residual from the above first-stage regression as an extra variable (i.e., the control function term). To account for any non-linearity in the distribution, we follow the literature and include higher order terms of the residual from the first stage regression.

# **4 Data**

The main data source for the estimation of employment choices is the Household Income and Expenditure Survey, 2002 (HIES, 2002). The survey collected information from a nationally representative sample of 16,924 households drawn from 1913 primary sampling units. The survey covered 17 of Sri Lanka’s 25 districts, and 249 of its 322 Divisional Secretariat Divisions (DSDs).<sup>32</sup> The DSD identifier in the HIES (2002) allows us to examine the employment and wages pattern at a much disaggregated geographical level. To define our sample, we use three criteria: (i) we exclude age groups which may have been exposed to historical malaria that afflicted Sri Lanka before 1947; and (ii) we focus on the rural sample where at least 0.1 percent of total land are under restrictions; and (iii) we focus on the male sample to avoid complications from changes in
Our results indicate that migration in Libya can be characterized as forced migration: conflict intensity is the main determinant of the decisions to relocate from one province to another. The analysis of migration flows across provinces indicates a dense network of connections; for each province, there is not a unique migration route from or to any other province, suggesting that individual-level characteristics play an important role in choice of migration route. Comparing 2017 and 2018, we identified a change in the migration pattern between Libyan provinces; whereas in 2017, the main corridors were routes connecting Alkufra, Ejdabia, and Benghazi (Eastern route); Murzuq and Tripoli (Central route), and the corridor connecting Ghat to Tripoli (Western route), in 2018, because of the reduction in number of migrants, paths became much more blurred. Our analysis also identified five clusters of provinces that were the preferred destinations of migrants from the same countries of origin. These findings show that migrants from the same country of origin, moving in the same direction, sorted themselves into contiguous routes following similar paths. As for the evolution of international migration flows into and out of Libya, from 2017 to 2018, the number of origin and destination countries decreased. In 2018, each of the origin and destination countries had fewer connections than in 2017, and the network was sparser. Finally, our analysis indicates that the location and number of refugees and IDPs across Libyan provinces did not change much between 2017 and 2018.

The present paper has provided a first general assessment of migration flows in Libya from 2017 to 2018 using a rich yet much-unexplored data set on migrant presence in the country. As a first piece of analytical work that is part of a broader research project on migration in Libya, this paper documented the spatial distribution of migrants at the most disaggregated level (i.e. provinces), identified the network of human corridors connecting Libyan provinces, and described the migration pattern between origin and destination countries. Expanding on these findings, further research will focus on the
_Table 6 Budget Share of Categories Not Routed Through FABS_

||**Transfers/Loans**|**Debt Service**|**Development spending**|
|---|---|---|---|
|Punjab|10.8%|2.9%|38.0%|
|Khyber Pakhtunkhwa|2.6%|1.8%|38.1%|
|Sindh|7.2%|3.9%|36.8%|
|Baluchistan|5.3%|1.3%|51.1%|

_Source:_ Authors.

# The Problem of Capturing Transactions Ex‐Post

The Government of Pakistan informed while some transactions (as outlined below) may be out of the normal workflow, monthly data on them is still being captured into the SAP data-warehouse of FABS and a dashboard report produced for the Secretary Finance Division. These include (i) Domestic Debt figures are captured on a weekly basis from Central Directorate of National Savings (the data from saving centers across the country is entered into the central system at CDNS on weekly basis and is pulled-in into the IFMIS through an automated solution).

(ii) External debt data is being captured into FMIS at the same time as it is updated in DMFAS, through entry into a SAP terminal made available at Economic Affairs Division (this arrangement has been implemented as an alternative to interface with DMFAS).

(iii) Data on tax receipts from Federal Board of Revenue on a daily basis through optic-fiber linkage with database of FBR.

Daily expenditure data from SAEs like National Highway Authority is also being captured into system ex-post as cheques of NHA come to AGPR for endorsement and data is entered into the SAP system. Daily expenditure statement of Pakistan Public Works department is entered into the FMIS ex-post from its 58 formations. Similar steps are being taken to extend this process to capturing daily expenditure data ex-post from Pakistan Post.

However, while
improve the well-being of the Irish in any meaningful way, nor did it increase economic activity, but was simply a tax-avoidance measure that resulted in distorting economic growth as measured by GDP. Damgaard et al. (2019) estimate that up to 40 percent of global foreign direct investment is phantom investment with no tangible links to the local economy.

> 19 Household surveys sometimes conduct survey-to-survey imputations when the methodology changes. This can yield a more comparable series over time, despite changes in methodology.

13
# **1. Introduction**

While extensive theoretical and empirical literature has studied the size and composition of government expenditure (see for example Shelton, 2007 for a review of the literature), less is known about the determinants of public employment. Two notable exceptions are Rodrik (2000) and Alesina, et al. (2000) who developed theoretical models to explain the behavior of public employment. The first one shows that public employment could play a welfare-enhancing social insurance role in an economy buffeted by external shocks, and presents evidence of a positive association between the exposure to external risk and the share of public employment across countries. The second one motivates public employment as a redistributive tool to circumvent opposition to explicit tax-transfer schemes, predicting a positive relationship between the size of public employment and inequality or fractionalization, and provide empirical evidence of cities in the United States.

Public employment and its determinants is an important topic because it affects not only the size of expenditure, but it also affects its composition due to the rigidity of the wage bill (Vegh, et al., 2017). Recent empirical literature that uses expenditure-based measures of government size explores the relationship between trade openness and size, but its counterpart with employment measures has received less attention. The robustness in a panel data context over a period that goes beyond the 1990s has not been tested, in part because data on public employment is much more scarse than data on government expenditure. In the same vein, there is not much evidence about how alternative hypotheses to explain the size of the government can help to explain the size of public employment. This paper attempts to fill this gap in the empirical literature.

# **2. Data, Stylized Facts, and Methodology**

## **i. Data**

The paper uses three measures of public employment from the International Labor Organization Statistics (ILO Stats): total public-sector employment, general government employment, and central government employment. The coverage of each employment aggregate is the standard one.
., 2018; Clement et al., 2021) and to pay more attention to the potential of climate change in preventing voluntary migration and trapping more vulnerable and poorer populations in immobility.</mark> <mark>By analyzing this flourishing strand of the literature, we flesh out some thematic issues that we see as pivotal for the subsequent analysis of the empirical issues and data gaps. S</mark> pecifically, we look at recent insights into five key facets of the climate–migration relationship as they have emerged in the literature: slow- vs fast-onset events (United Nations Framework Convention on Climate Change, 2012; BohraMishra, Oppenheimer, and Hsiang, 2014); direct and indirect links (Bardsley and Hugo, 2010); internal

> 3 Our survey is a data-oriented synthesis of selected recent findings relevant for the study of the potential of household survey data. For general and more comprehensive reviews and meta-analyses of the literature on the climate-migration relationship, we recommend readers to consult Cattaneo et al. (2019), Kaczan and <mark>Orgill-Meyer (2020), and Hoffmann et al. (2020).</mark>

4
............... 8|
|**2.1**<br>**Post-conflict estimates based on nowcasting methodologies**................................................... 8|
|**2.2**<br>**Post-conflict estimates based on HNAP 2021 Demographic and WASH survey**...................... 10|
|2.2.1<br>Measure of welfare used in HNAP analysis ........................................................................ 11|
|2.2.2<br>Poverty line used in HNAP analysis ..................................................................................... 13|
|**3**<br>**Poverty estimation using the HNAP 2022 Demographic and WASH Household Survey**................. 13|
|**3.1**<
of ordinal poverty measures. Within our framework, different degrees of poverty aversion merely require setting different restrictions on the ordering weights, preserving the measures’ additive decomposability property.

Third, since each of our classes and subclasses admits a large number of poverty measures, we develop related _stochastic dominance conditions_ whose fulfilment guarantees the robustness of poverty comparisons to alternative functional forms and measurement parameters. Although some of these conditions turn out to be the ordinal-variable analogue of existing dominance conditions for cardinal variables (Foster and Shorrocks, 1988b), several others are novel methodological contributions to the literature on stochastic dominance with ordinal variables (to the best of our knowledge).

To demonstrate the efficacy of our approach, we present an empirical illustration studying the evolution of sanitation deprivation in Bangladesh using Demographic Health Survey datasets. Interestingly, our measures are able to discern the instances where the improvements in overall sanitation deprivation did not necessarily include the poorest. Furthermore, we apply the stochastic dominance conditions to test the robustness of poverty comparisons over time.

The rest of the paper proceeds as follows. After providing the notation, we present and then axiomatically characterise the class of depth sensitive ordinal poverty measures in Section 2. Section 3 introduces the concept of precedence to poorer people, states the properties related to the degrees of poverty aversion, and characterises the subclass of relevant poverty indices. Then section 4 develops stochastic dominance conditions for the characterised class and subclasses of poverty measures. Section 5 provides an empirical illustration analysing sanitation deprivation in Bangladesh. Section 6 concludes.

6
was to be measured in three ways: a significant and growing number of countries seriously addressing key governance impediments to development effectiveness and poverty reduction; Bank projects and programs increasingly addressing impediments to better governance; and countries and global partners valuing and respecting the Bank’s capacity in this area. It was envisaged that these objectives would eventually be reflected empirically in improvements in country governance performance.

Several initiatives—comprising joint workshops, peer-to-peer learning events, clinics, and upstream assessment activities—were launched in 27 countries to help Bank teams systematically diagnose governance challenges and identify ways of addressing them through the Country Assistance Strategy (CAS), sector strategies, and project preparation. They were intended to deepen the Bank’s understanding of what can be done to identify governance entry points (for example, core public management and accountability institutions, private sector engagement, and demand-side capacities and frameworks). Following these initiatives, a more targeted effort involving 18 countries sought to enhance Governance and Anti-Corruption (GAC) responsiveness with the help of considerable support provided under the Governance Partnership Facility (GPF).

There was an effort to strengthen staff sensitivity to governance issues and an increase of the use of political economy analysis, actionable governance indicators, and demand-side measures. Guidance notes and toolkits were designed to advise Bank teams on how to address GAC issues in the sectors and to support cross-cutting concerns, such as social accountability. They also included handbooks, tools, and training to support efforts to prevent fraud and corruption in projects. A 2009 Quality Assurance Group (QAG) survey of projects approved in FY08 aimed to establish a baseline for incorporation of generic GAC elements in projects. The Bank increased its involvement in collaborative governance initiatives, such as the Extractive Industries Transparency Initiative (EITI), and in legal conventions, such as the Stolen Asset Recovery (StAR) Initiative, Medicines Transparency Alliance (MeTA), and Construction Sector Transparency (CoST) Initiative. Finally, internal reforms were also carried out (Volcker Panel strengthening the Integrity Vice Presidency; new
</sup> This is an advantage in our context as current migrants in 2016 are likely to be more comparable to the return migrants captured in 2019 by the BRMS. Indeed, among the BRMS respondents, the median returned migrant had been back in Bangladesh for two years. Ahmed, Ahmed, Bossavie, Ozden,<sup> ̈</sup> and Wang (2020) show that the sample composition of temporary migrants in the HIES and the BRMS are very similar, and that the characteristics and destinations of migrants in the two surveys are also aligned with those in administrative data covering the entire population of temporary migrants from Bangladesh.<sup>15</sup>

> 14The Bangladesh HIES was carried between April 2016 and March 2017.

> 15The Bureau of Manpower, Employment and Training in Bangladesh publishes aggregate data on the number, composition and destinations of legal migrants from Bangladesh by year of departure.

11
# **1. Introduction**

Household survey data commonly exhibit considerable nonclassical measurement error (NCME) (Bound et al., 2001). This is true even for key assets and factors of production, like agricultural land, that are readily measurable and observable to the survey respondent, and that heavily affect farmers' incomes and livelihood. The measurement error literature largely treats NCME as an econometric challenge to overcome. This would be appropriate if NCME arises purely due to respondents misreporting their true, accurate beliefs about land holdings and other key variables. In that case, NCME carries no implications for behavior and respondents’ decision-making processes; it is merely a statistical nuisance. Abay et al. (2021) show, however, that a large share of NCME in plot sizes reported by farmers in four African countries appears to reflect farmers' reporting mistaken beliefs, not misreporting of accurate beliefs.<sup>1</sup> Such findings suggest that NCME in household survey data not only matters for statistical inference but can also shed light on respondents’ decision-making processes and behaviors in ways that may reveal actionable information, consistent with a longstanding behavioral economics literature that routinely finds that people often act upon mistaken beliefs or misperceptions (Tversky and Kahneman, 1973; Kahneman and Tversky, 1984; Angner and Loewenstein, 2012).

If at least some NCME reflects mistaken beliefs, then uncovering the nature and sources of NCME and their prospective implications for measurement and policy design becomes important for multiple reasons. First, mistaken beliefs might reveal smallholder farmer behavioral phenomena that drive these errors, such as inattention, self-esteem, or confirmation bias. As a burgeoning literature on “choice architecture” underscores, organizations worldwide increasingly design policies around behavioral anomalies (Thaler and Sunstein, 2009; Ariely, 2016), but the agricultural development community has been slow to do so.

Second, we know little about whether and how farmers incorporate objective information to correct mistaken beliefs, although this surely matters for information-based interventions, such as agricultural extension programming,