rafmacalaba/data-use-mentions-extended huggingface dataset:
recruit workers with the appropriate experience and pay. According to Ortega and Griffin (2009) IPAs need both management and staff members who have appropriate private sector experience, service skills, and deep business knowledge (including understanding of investor needs, motivations, challenges, and concerns but also sector terminology and trends). Nelson (2009) further calls for staff with international exposure, that would have the appropriate language and cultural skills to foster translational learning. They further find that traditional civil service recruitment and pay policies hamper IPAs potential to recruit such qualified, specialized staff. More effective IPAs therefore have the operational independence and financial ability to recruit such staff and provide them with adequate compensation (often in line with the private sector).

# **Investor Service Delivery**

A fourth area of IPA effectiveness relates to their ability to provide high-quality services to increase investor satisfaction, notably in areas considered most relevant to meet sectoral strategic priorities. As an example, Harding and Javorcik (2012) find that better information services (such as website information and inquiry handling) are positively correlated with FDI inflows. According to the World Bank’s 2017 GIC Survey, IPA services were often most appreciated by investors during the establishment, retention, and expansion stages of investment. This included both hands-on assistance with issues during registration and business establishment, but also advocacy to improve the business environment (World Bank 2018).

IPAs with quality services also tend to adopt a series of tools, systems and monitoring and evaluation (M&E) framework that help identify investor priorities and strengthen their service delivery. Examples of this include development of standard operating procedures, a customer relationship management (CRM) system or adopting a system for gathering investor complaints or disputes. Using digitalization and emerging technologies was considered an important attribute by IPAs to help reach target investors more efficiently (DCI 2017; WAIPA 2019). The United Kingdom reported several improvements in the functioning of the different IPAs in its union because of a new M&E framework (DIT 2018
**Table 2: Summary of empirical research on supercycles**

|**Author(s)**|**Data**|**Main finding**|
|---|---|---|
|Cuddington and Jer-<br>rett (2008)|Six base metals, 1850-2006, de-<br>flated by the U.S. CPI and PPI|Four supercycles (the last was ongoing):<br>1890-1930, 1930-1962, 1962-1998, 1998-|
|Jerrett and Cudding-<br>ton (2008)|Three metals, 1850-2006, deflated<br>by the U.S. CPI|Three supercycles: 1850-1925, 1925-1998,<br>1998-|
|Cuddington and<br>Zellou (2012)|Crude oil, 1861-2010, deflated by<br>the U.S. CPI and PPI|Three supercycles after WWII which<br>comove with metals: 1861-1884, 1966-<br>1996, 1996-|
|Erten and Ocampo<br>(2013)|Prices of 24 commodities and indi-<br>ces, 1865-2010, deflated by the U.S.<br>CPI and MUV index|Four supercycles consistent with the<br>Prebisch-Singer hypothesis: 1890-1930,<br>1930-1965, 1970-1998, 1998-|
|Rossen (2015)|Prices of 20 metals, 1910-2011,<br>monthly, deflated by the U.S. CPI|Four supercycles: 1910-1930, 1930-1965,<br>1970-1998, 1998-|
|Erdem and Ünalmış<br>(2016)|Crude oil, 1861-2014, deflated by<br>the U.S. CPI|Three supercycles: 1861-1882, 1966-1996,<br>1996-|
|Buyuksahin, Mo, and<br>Zmitrowicz (2016)|Bank of Canada commodity index,<br>1899-2015, deflated by the U.S. PPI|Four supercycles
|1.8|0.0|-0.5|**-3.5**|**-2.4**|-1.5|-2.2|-1.5|
|Nonresident-held government debt<br>(Percent of government debt)|-2.7|-3.3|**-4.3**|**-4.2**|**-5.8**|**-4.7**|**-3.2**|-2.5|-1.0|

Note: Coefficients of estimations in which each indicator of balance sheet composition is regressed on a series of dummy variables for government debt booms started in year t, based on data for all countries. The numbers in bold indicate that the coefficients are statistically significant at least at the 10 percent level.

36
1 In the developing world internal and international migration is often caused by individuals seeking better economic opportunities for themselves and their families.

Internal migrants, coming from rural areas, stream into Third World cities seeking higher incomes in the industrial and service sectors of the economy. Responding to similar stimuli, international migrants seek improved earnings in employment located outside of their home countries.

Both internal and international migrants tend to remit or send a sizeable portion of their increased earnings to families back home. Despite the ever-increasing size of these internal and international remittances,<sup>1</sup> very little attention has been paid to analyzing the impact of these financial transfers on poverty. Two factors seem to be responsible for this lacuna. The first is a lack of poverty data: it is quite difficult to estimate accurate and meaningful poverty levels in developing countries. The second is an absence of remittances data: few household surveys collect useful data on the size and volume of remittance transfers to households in origin communities.

As a result of these problems, there is little agreement in the literature concerning the impact of migration and remittances on poverty in the developing world. Stahl, for example, writes that “migration, particularly international migration, can be an expensive venture. Clearly it is going to be the better-off households which will be more capable of (producing migrants) (1982: 883).” Other analysts, however, maintain that the poor can and do benefit from migration and remittances. For example, Stark finds that in rural Mexico “relatively deprived” households are more likely to engage in international migration than are “better off” households (1991: 140). Similarly, Adams finds that in
Table C.12: Effect of Years of Schooling on Sector of Employment: Job Different than Husband

||Secto|r of Current|Work|
|---|---|---|---|
||Skilled /<br>Professional|Service<br>/ Sales|Agriculture<br>/ Unskilled<br>Manual|
||(1)|(2)|(3)|
||A. No|n-MTI Region|s Only|
|�<br>Years of Schoolingizy|0.058|0.062|-0.027|
||(0.023)|(0.034)|(0.019)|
||[0.011]|[0.071]|[0.250]|
|First Stage F-Statistic|12.48|12.48|12.48|
|N|11,870|11,870|11,870|
||B. National|Estimates (F|PE + MTI)|
|�<br>Years of Schoolingizy|0.035|0.014|0.004|
||(0.015)|(0.024)|(0.029)|
||[0.019]|[0.569]|[0.879]|
|First Stage F-Statistic|8.22|8.22|8.22|
|N|21,242|21,242|21,242|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : MTI = mother tongue instruction; FPE = free primary education. The dependent variable is an indicator that equals 1 if employed in the denoted sec� tor and 0 otherwise. In panel A, Years of Schooling _izy_ is the predicted level of schooling, instrumented with only the FPE intensity
practical implementation level is concerned, considering the Uganda case to be concrete, we can give the following indication, defined to change the current process as little as possible: - 1 3 5. The 2 is not explicitly stored, since a non-respondent in the original sample (wave

- 1) is not clearly distinguished from a new individual that was not included in the original According to Table 5.1, Uganda panel data stores the variables, vv , vv and  vv sample.  variable  vv

- - 6 , using other stored variables and making some accurate assumptions. The variable should be directly asked We artificially created the population membership variable, vv

32
% of the poverty line were divided into just four groups, along similar lines, while those with prepayment incomes in excess of 200% of the poverty line were divided into just three groups. To put this into perspective, nearly 60% of households fell below the poverty line in 1993, and nearly 40% did in 1998. Obviously, other groupings of prepayment equals are possible. If this were intended as anything other than an illustration, one would want to assess the sensitivity of the results to alternative groupings, though it seems likely that in this case as in other cases where the Aronson et al. decomposition has been used, widening the bandwidth would be likely to reduce H, increase R, but probably leave their sum relatively unaffected. With groups of prepayment equals defined, it is straightforward to compute _Cp_ on the grouped data, and to form the ranking variable to compute _Cxp._ Using the former and _Gpre,_ one can compute K, and using the latter and _Gpost_ one can compute R. This leaves H, which can be computed as a residual.

# _RESULTS_

Using the definitions indicated above, the values of the WHO FFC index for out-ofpocket payments in Vietnam in 1993 and 1998 are 0.9557 and 0.9617 respectively (cf. Table 1). These are different from the number published in the WHR, in part because the FFC index here is computed only for out-of-pocket payments (the WHR claims to allocate health revenues from _all_ sources) and in part because food consumption has not been netted out from total consumption in this exercise for the reasons indicated above. The change in the FFC index suggests a move towards greater fairness in the out-of-pocket payment component of the

> 5 Recall that the Kakwani index in eqn (2) needs to be computed on the assumption that everyone at each level of prepayment income pays the same amount in health care. Hence the need for groups of prepayment equals even in the computation of _K._
reliable than reported consumption in LMICs, where self-employment and/or agriculture predominate, and which makes accurate reporting of incomes difficult (Deaton, 1997). The concerns about the reliability of income data collected in poor countries pertain mainly to measuring poverty. It is possible that income data is more reliable at the top end than at the bottom because agricultural income is likely to be less important at the top. However, self-employment income is still likely to be important, although the extent to which more accurate record keeping in formal or at least larger businesses translates into better income data in surveys is unclear.

# _Lack of common support_

All the challenges discussed thus far can limit the number of observations with incomes close to the top of the true income distribution. At worst, this can result in a lack of common support between the top of the distribution in the household survey and the true distribution, meaning that there are simply no individuals in the surveys with reported incomes near the top of the true income distribution (Lustig, (2020), Ravallion, (2022)). This implies the surveys will not accurately represent the top of the income distribution. But it is also a concern because some of the solutions to the measurement challenges that we discuss below require at least some individuals in the surveys at the top of the distribution.

Table 1 summarizes the challenges described above, in a rough order of their importance when measuring the top tail of the income or wealth distribution. However, we should stress that the order is likely to vary in different countries and/or time periods and depend on the level of development of the country concerned. In the section below outlining the research agenda, we further discuss which challenges are more important and deserve further investigation.

Table 1: Measurement Challenges for the Top tail of the Income and wealth distributions

|**Challenge**|**Description**|
|---|---|
|Unit non-|High Income or Wealth Individuals or Households
This version: 15 Feb 2024 64 15 years prices have tended to increase more rapidly in heavily-urbanized Selangor than they have in Sabah or Terengganu. Lastly, CPI omits investment outlays, including home purchases, a key component of living costs, especially in Malaysia where three-quarters of the population are owner-occupiers.

To this end, **Figure B4** reports trends in standard CPI vis-à-vis two additional indices. The Everyday Price Index (EPI) uses a reference basket consisting only of items purchased at least once a month, based on research showing that the prices of frequently-purchased items have a greater weight in consumers’ perceptions of inflation than items purchased infrequently. The PePI (Perceptions Price Index) includes the same frequently purchased items as the EPI, but only counts price increases because it has been shown that consumers tend to remember price increases more than decreases, such that they perceive inflation to be higher than it really is. A number of patterns are noteworthy. First, based on the standard CPI, the rate of inflation has been moderate during 2016-2021. Second, this is true even if we use EPI as an alternative measure of inflation. In Figure B4, the movement in CPI and EPI data are highly correlated with little indication of systematic downward bias in CPI when compared to EPI. Third, although not reported here, there is no evidence that poorer households experience an inflation rate that is significantly higher than the standard CPI. For the years 2016-2019, trends in the CPI measure specific to consumption patterns of lower-income households (i.e., households with a monthly earning less than RM 3,000) does not show significant differences with the overall inflation rate based on standard CPI (World Bank, 2023). Fourth, in contrast to CPI and EPI, data on 64
than 30 years. On the other hand, income inequality has steadily increased over this period, as the Gini coefficient climbed from 0.42 in 1985 to 0.51 in 2000.

In 1994, the base year the household survey underlying our analysis, about 41 percent of the population of 67 million was below the poverty threshold. Generally, rural households, which represent roughly half the population, are substantially poorer than urban households. Whether in 10
graduates continue to come from urban centers and nuclear families. Most of the graduates have been able to escape from poverty in the past although recently this trend has been changing.

The rate of return to schooling increases with higher education and job experience. It is observable that in the wage sector primary education without skills has not been very rewarding. The return to an additional year of schooling has been varying between 4-6 percent in Ghana which is quite high for a Sub-Saharan African country. However, it is very important to note that the sectoral wage differences and public sector does reward formal sector better on average. Also, there has been the concern that the quality of education is declining. This is an issue which needs further investigation and might be useful in understanding the relevance of present education system for emerging new employment opportunities in Ghana. The returns to education analysis based on wage workers show that private and social returns to education are higher for primary than secondary or post-secondary. However, it is worth noting that higher education carries the larger amount of public subsidization compared to primary and might be useful for designing educational reform and policies in Ghana. This again is an issue which needs further investigation. Also, the rate of returns to education from selfemployment might have a different story to tell and one needs to explore this before making broad inferences about education-employment mismatch (Vijverberg, 1995).

The present overview paper has elaborated some interesting hypothesis and broad relationships which can be further analyzed and used for policy design and sector investment. The current state of knowledge although useful does not give robust results to make causal links. Also, the GLSS data itself does not lend itself to meaningful analysis and one needs to see other sources of information and methodologies. Female education, female employment and self-employment sector are currently very weak in terms of causal links and until one has some firm findings it is not easy to be useful for policy design or even poverty alleviation in this country. A country with more than one third of its population below poverty
ing, educational upgrading and international labor mobility. While we assume a world where technology and institutions are not changing in our main analysis, we relax these assumptions and explore how they interact with demographic changes.

The second strand of the literature pertains to the impact of immigration on the labor market outcomes of natives. Papers examining the impact of immigration on the wages of natives using a framework similar to ours include Ottaviano and Peri (2012) in the context of the USA, and Manacorda et al (2012) and Dustmann et al (2013) in the case of the UK. These papers only focus on immigration, although emigration is particularly high in some OECD countries such as Ireland and Poland. Docquier et al. (2014), referred to as DOP hereafter and the closest study to ours, fills this gap by looking at the joint effect of emigration and immigration on the labor market outcomes of high ~~-s~~ killed and low ~~-s~~ killed natives in OECD countries between 1990 and 2000.

Our study differs from DOP along several dimensions. Whereas their focus is entirely on the labor market changes due to immigration and emigration, we analyze the effects of ageing, educational improvements, emigration and immigration jointly. For this purpose, we introduce the age dimension into the model via imperfect substitution between the young and old workers in production. We also exploit recent data from the 2000 and 2010 censuses which contain detailed information on the age ~~-~~ education ~~-n~~ ativity distribution of the workforce across OECD countries.

The rest of the paper is organized as follows. Section 2 presents the aggregate pro ~~-~~ duction and labor supply framework, and analytically derives the wage effects of ageing, education and migration shocks. Section 3 describes the construction of our data set and provides summary statistics of the labor force, migrant data, and changes in their age and educational composition. This section also presents the discussion of the main parameter values used
θ_<sup>_k_</sup> be the true best arm, and _k_<sup>_∗_</sup> the arm that is chosen. We call the loss (per unit) from implementing a suboptimal arm _k_ the policy regret, ∆<sup>_k_</sup> = _θ_<sup>_k_(1)</sup> _− θ_<sup>_k_</sup> . Ex post, the policy maker will select the arm _k_<sup>_∗_</sup> that has the highest average outcome, or lowest policy regret, based on the observed data.

It is assumed that the outcomes of the experimental units are observed at the end of each period _t_ . This means we can learn from wave _t_ and adjust the allocation of units to treatment arms in wave _t_ + 1, i.e. use adaptive sampling. In the policy choice problem, the policymaker’s wants to implement an adaptive sampling strategy that maximizes welfare, that is, minimizes the expected policy regret from the final choice given the true (unobserved) vector of average outcomes: _E_ [∆<sup>_k∗_</sup> _|θ_ ]. Adaptivity increases the efficiency of learning for a given objective by over-sampling some arms based on what was learned, at the expense of other arms (and other objectives).

_Remark: Other Objectives._ Large literatures consider sampling for specific learning goals.

The classical multi-arm bandit problem (MAB) considers the objective to maximize average outcomes during the ongoing experiment, or equivalently to minimize in-sample regret, which introduces the wellknown exploration-exploitation trade-off (e.g Lai and Robbins, 1985; Bubeck and Cesa-Bianchi, 2012). The policy choice problem of choosing the arm with the highest average outcome can be seen as a special case 6
# **I. Introduction**

Analysis of welfare dynamics plays a crucial role in the design of development policies, particularly regarding poverty reduction. Without a clear understanding of the dynamic processes underlying poverty trends, policies can turn out to be inefficient, or even ineffective. For example, assume that two rounds of cross sectional household surveys indicate a slightly decreasing headcount poverty rate. This net fall in poverty certainly indicates progress in the right direction, but does not offer any insights on the nature of poverty mobility. It may be characterized by either a situation of extreme volatility in which nearly all the poor households in the first period escape poverty but are replaced by households that were non-poor in the first period, or a situation of near stagnation in which most households see no change in their welfare. The policies required to deal with these very different situations are also distinctly different. While strong social protection programs would most effectively address transitory poverty (as they help prevent the non-poor but vulnerable households from falling into poverty), chronic poverty may only be ameliorated with longer-term investments in human capital and infrastructure. Thus, analysis of welfare dynamics is an integral part of a well-informed development policy strategy.

We make use of different sources of survey data to advance our knowledge on welfare dynamics for the Middle East and North Africa (MENA) region during the transition period spanning the Arab Spring. Conceptually, our study combines both objective measures (i.e., moneymetric indicators of poverty) and subjective measures (i.e., life evaluation using “Cantril Ladder” scores) to investigate welfare dynamics. Subjective welfare measures are a good alternative to and can complement monetary welfare measures since the latter do not reflect many factors affecting well-being such as job satisfaction, the quality of public services (e.g., health, education, transportation, and control of corruption), the environment, and expectations about the future.

Relying exclusively on objective data to measure welfare dynamics accurately is difficult, if not 2
# **1. Introduction**

Household surveys serve a vital role in national statistical systems; inform official statistics on an extensive range of socioeconomic phenomena; and are required for tracking progress towards national and international development goals. Multi-topic household surveys are implemented frequently across the developing world to fill data and research gaps, and there is increasing international momentum to improve the scope of intra-household, self-reported, individualdisaggregated survey data collected on key dimensions of men’s and women’s economic wellbeing (FAO, World Bank and UN Habitat, 2019; Hasanbasri et al., 2021; ILO, 2018; UN, 2019).<sup>2</sup> Although household surveys have continued to grow in terms of topical coverage and complexity, particularly in low- and middle-income countries, there are persisting gaps in empirical evidence regarding various aspects of survey implementation, including respondent burden, survey costs and interviewer effects, which would be critical to assess for gauging data quality concerns – both during and after data collection – and for informing decisions regarding the design of future surveys.

A related promising development is that over the last decade, national statistical offices (NSOs) in low- and middle-income countries have accelerated their transition to computer-assisted personal interviewing (CAPI) for face-to-face surveys (Carletto et al., 2022) and have adopted computerassisted telephone interviewing (CATI) for phone surveys, particularly in response to the data needs brought on by the COVID-19 pandemic (Gourlay et al., 2021). As such, practitioners can address the aforementioned empirical knowledge gaps regarding survey implementation by leveraging _survey paradata_ , which are data that are generated as a by-product of computer-assisted data collection and that capture the entire process of creating a final survey dataset (Couper, 1998; Kreuter, 2013). To give an example, the Survey Solutions CAPI/CATI platform automatically provides an extensive paradata file for each survey. This ancillary dataset is a highly disaggregated account of the “life” of a survey and includes time stamped records of all “