rafmacalaba/data-use-mentions-extended huggingface dataset:
everyone knows that2'. But the implication of our positive model is that while "money matters" to educational outcomes, money also matters to those who receive it as income and that "mattering" will have implications for publicly determined outcomes, like the allocation of expenditures.

_Budgets and educational outcomes when objectives differ._ If non-experimental data are taken from jurisdictions (or time periods within the same jurisdiction) that differ in either the degree of educational output concern (1-a) or in the degree of professionalism (6) of the teachers then equal budgets could produce very unequal outcomes due to the allocation across inputs". For any given level of a, higher budgets lead to better outcomes (the sense in which money matters). But a higher a implies worse outcomes for any given budget. Any estimation of the relationship between expenditures and budgets from non-experimental data that differ in a which do not control for these differences can produce any relationship at all: positive, zero, or negative. For instance, Hanushek and Kim (1996) estimate the relationship between the internationally comparable test scores of achievement in math and science derived from the various IEA and IEAP studies and expenditures. They find that both public expenditure per student and the ratio of total educational expenditure to GDP are significantly 25 There has been a vociferous debate in the US around the question "does money matter?" In this political debate the rejoinder "the only ones who say resources do not matter have their children in schools with adequate resources" carries a good deal of common sense appeal.

26Differences in the relative weight of teachers also happen over time within a given jurisdiction as across jurisdictions. For instance, in Paraguay after the end of a long period of dictatorship teacher wages more than tripled in a six year period, with an almost complete lack of complementary reforms either in the school system or even in the structure of teacher pay. Given the total lack of objective comparable testing it is impossible to say for sure, but many doubt this increase led
Policy Research Working Paper 9524

# **Abstract**

How do macroeconomic expectations affect consumer decisions? This paper reports results from a natural field experiment with 2,872 credit card customers from a large commercial bank to answer this question. Participants of the experiment first completed as survey that measured consumer expectations about future inflation and the nominal exchange rate. This survey was combined with an information-provision experiment that generated exogenous variation in respondents’ macroeconomic expectations. The survey and experimental data were then merged with detailed administrative data on participants’ credit card transactions and balances. The experiment was designed to test three standard predictions from models of intertemporal consumption choice: inflation expectations should affect spending on durables; exchange rate expectations should affect spending on tradables; and, holding constant the nominal interest rate, inflation expectations should affect borrowing. The analysis finds that the information provided to participants strongly affects subjective expectations. However, no significant effects are found on actual consumer behavior (as measured in administrative data) or self-reported consumption plans (as measured in survey data). The preferred interpretation is that consumers are not sophisticated enough to factor inflation and exchange rate expectations into their consumption decisions. The absence of a link between consumer expectations and behavior has potentially important implications for macroeconomic policies such as forward guidance.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mkanz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations
# **Introduction**

For many observers, the capacity of African countries to attract foreign direct investment (FDI) is principally determined by their natural resources and the size of their local markets. Over the years, Nigeria and Angola have been two of the most successful countries because of their comparative location advantage in oil despite their unstable political and economic environments.

The apparent lack of interest of transnational corporations (TNCs) in African countries that have attempted to implement policy reforms has also contributed to support this argument. The balkanization of African countries is frequently used as an argument that this continent has been much less favoured than Asia and Latin America over the past decade. It has been argued that the reforms in many African countries have been incomplete and thus have not fully convinced foreign investors to develop activities that are not dependent on natural resources and aimed at regional and global markets. True, it takes time for a country to modify its image, especially when the State has a long tradition of policy intervention, and when the reforms have been mostly symbolic with the adoption of new texts that have not yet been translated into actions.

This note will identify which African countries have been able to attract FDI by improving their business climate. These countries show that pro-active policies and reform-oriented Governments can generate FDI interest. This conclusion does not differ from the one reached for countries such as Singapore or Ireland. It simply makes the point that African countries can also be successful in attracting FDI that is not based on natural resources or aimed at the local market, but rather at regional and global markets, by implementing policy reforms. An econometric analysis of 29 African countries and a detailed review of two successful ones - Mali and Mozambique - will illustrate which policy factors have played a significant role in the improvement of their business climate - at least in the views of foreign investors.

2
negative association between trade and protectionism in government procurement concerns public and private purchases in qualitatively similar ways. This finding corroborates our initial hypothesis that protectionism in government procurement can extend beyond the conduct of procurement auctions and affect the sourcing decisions of private firms as well (notably, the domestic ones who are awarded procurement contracts).

The rest of the paper is organized as follows. Section II scopes out the existing literature on trade policy and government procurement. Section III describes the construction of the indicators of protectionism based on national laws on government procurement, and the trade data. We then present in Section IV the results from the cross-country regressions and from the estimations of the gravity model, both investigating the relationship between protectionism in the law and trade openness. Section V concludes by summarizing the evidence and outlining limitations and policy implications.

# II. Literature review

This paper examines the importance of protectionist rules in government procurement across countries and assesses their impact on cross-border flows, drawing on insights from different 3
**Data for each module come from a variety of sources.** The program pools together existing information in the United States’ labor market information system<sup>4</sup> and collects additional data through 7 specific questionnaires: education and training (6 items), knowledge (32 items), work activities (41 items), work context (57 items), work styles (14 items), abilities (52 items), and skills (35 items). The questionnaires use a set of scales to measure the requirements. Many of them use an importance scale, which goes from 1 ( _not important_ ) to 5 ( _extremely important_ ), and a level scale, which goes from 1 ( _basic_ ) to 7 ( _sophisticated_ ). To avoid burdening respondents, O*NET mostly uses occupational analysts for questions containing more abstract concepts (i.e., abilities and skills) and occupational experts for occupations for which workers are difficult to find (Fleisher and Tsacoumis 2012; Rivkin, Lewis, and Cox 2001). However, a study comparing skills ratings across a large sample of occupations found minimal differences between ratings provided by job incumbents and those provided by occupational analysts.<sup>5</sup>

**The O*NET content model is attractive for replication for several reasons.** First, the O*NET content model describes occupations at a level of detail not found elsewhere, with the information included ranging from tasks and activities performed to tools and technologies to skills and abilities required to perform a job well. Second, the model has a modular format that allows for replication of individual modules of interest (e.g. on tasks or skills) with the potential to build sophistication as capacity develops. Third, O*NET is a globally recognized database widely used by academics, policy makers, and end users. Finally, O*NET questionnaires are publicly available.

# _PIAAC and STEP_

**The SDS also draws on two other
reach 70–80% of the genetic yield potential ceiling. Based on these observations from the agronomic literature, we specify the following logistic function determining the evolution of the crop productivity index over time: where _✓_ 0<sup>_c_is the value of the crop productivity index in period 0, which we calibrate to match</sup> observed weighted yields in key staple crops (corn, rice, soybeans, and wheat), _✓T_<sup>_c_is the crop</sup> yield potential in the end of the current century, that is, “the yield an adapted crop cultivar can achieve when crop management alleviates all abiotic and biotic stresses through optimal crop and soil management” (Evans and Fischer, 1999), and _c_ is the logistic convergence rate to achieving potential crop yields.

Though the initial value of the crop productivity index is known with certainty, potential crop yields are highly uncertain. We assume that potential crop yields are a↵ected by a two-dimensional stochastic process of climate and technological shocks, _J_ 1 _,t_ , and _J_ 2 _,t_ , respectively. For the technological shock, _J_ 2 _,t_ , we assume that there are three states of technology: “bad” (indexed by _J_ 2 _,t_ = 1), “medium” (indexed by _J_ 2 _,t_ = 2), and “good” (indexed by _J_ 2 _,t_ = 3). In the optimistic (i.e., “good”) state of advances in crop technology, we assume that yields continue to grow linearly throughout the coming century, eliminating the yield gap by 2100. In the “medium” state of technology, rather than closing the yield gap by 2100, average yields in 2100 are just three-quarters of yield potential at that point in time. In the “bad” state of technology, there is no technological progress, and the crop yields stay the same as at the beginning of
designed to be nationally representative, and in the latest round covered 34 countries, with a target number of interviews of 1,500 per country.<sup>8</sup> The European Social Survey (ESS) collects information on social conditions, attitudes, and values and behavior. It is a cross-national survey of 36 European countries and is conducted by the European Research Infrastructure Consortium (ERIC) which is an independent legal entity that represents national governments. The survey is regularly undertaken every two years and Russia participated in survey rounds 2006, 2008, 2010, 2012 and 2016. The sample is designed to be nationally representative of all people aged 15 and over, and uses random probability sampling and a minimum target sample size of 1,500. This data were previously used to study informality in the EU in (Packard, 2012).<sup>9</sup> Next, we identify the questions and the various definitions of informal employment that can be derived from each survey. The main questionnaire of the RLMS allows for the identification of two groups as informally employed: i) workers who are employed at an enterprise without a contract; and ii) workers employed at an entity with no more than one employee. For reasons that will become clearer later, we call these two groups type-1 and type-2 informals (or more colloquially salaried and self-employed, respectively). Workers with a secondary job and individuals that engage in irregular economic activities are not included in this study.<sup>10</sup> Similar measures have been used by Russian and international researchers such as (Lukiyanova, 2015); (Slonimczyk & Gimpelson, 2013); (Lehmann, Razzolini, & Zaiceva, 2009). In 2009, a special supplemental module was conducted within RLMS which asked additional questions on oral contractual agreements, employer’s social security contribution, whether business is registered, and whether labor laws and work agreements are respected. While very useful, the supplementary data are outdated at this point and
tax evasion, so it likely represents a lower bound.

These findings provide policy makers with a much clearer picture of just how widespread tax evasion is by registered firms in Indonesia. To date, there have not been precise estimates of the underlying level of tax evasion as only limited audit data exists, and surveys asking directly about tax compliance and/or tax morale can have substantial issues. Further, while analyses based on tax administrative data can provide valuable insights about the tax-paying behavior of firms, they do not necessarily shed light on intentional tax evasion in the same way that a double list experiment can. Arguably, the findings we present, which are based on nationally representative data and are internally consistent, provide among the best estimates of the levels of tax evasion by firms in Indonesia. Existing studies using single-list experiments to estimate tax evasion have typically focused on individuals as opposed to firms, and in these instances, reported tax evasion has been lower than what we observe (e.g., see Genest-Gr ́egoire et al., 2022 and Iraman et al., 2022). At a minimum, our findings suggest substantial increases in tax revenue are feasible if the government could increase the compliance of registered firms.

The double list experiment also provides unique insights for policymakers into the types of firms that are more likely to be non-compliant,<sup>11</sup> by extensively exploring heterogeneity and identifying three key dimensions. Firstly, there are higher rates of tax evasion among firms that do not export. A potential explanation for this heterogeneity is that exporting firms are more likely to have a paper trail of their economic activity (e.g., from custom licenses), which means that the volumes of sales of these firms are easier to monitor. In contrast, firms that do not export may perceive that the revenue authority has less third-party information about their business activities. Consequently, they are more willing to evade taxes. Secondly, firms that find tax administration to be a major obstacle to
<!-- Start of picture text --> Figure 4a: Concentration of Direct Taxes by Figure 4b: Concentration of Direct Taxes by<br>Income Groups, Men Income Groups, Women<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Pers. Income tax Market income Pers. Income tax<br>Firm income tax Agri. Income tax Firm income tax Agri. Income tax<br>Housing tax Other income tax Housing tax Other income tax<br>Direct tax Population share Direct tax Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of direct taxes Cumulative proportion of direct taxes<br><!-- End of picture text -->

# _Direct Transfers_

Direct transfers can be classified as PSNP-based and non–PSNP. Both types of transfers are progressive. While both PSNP and non-PSNP transfers are pro-poor (progressive in absolute terms) for women (Figure 5b), non-PSNP transfers appear to be regressive for men (Figure 5a).

The poorest women, those in the bottom income decile, capture about 1.9 percent of market income but 16.2 percent of the PSNP transfer; men in the bottom decile receive about 1.8 percent of market income but 14.8 percent of the PSNP transfer. Similarly, for women the bottom 40 percent account for 14.6 percent of market income and 57.5 percent of the PSNP benefit. For men, about 52.4 percent of the PSNP benefit goes to the bottom 40 percent, who also capture 14.3 percent of market income. Women
where ln ~~�~~ G <u>c</u> c � is the natural logarithm of emissions per GDP for industry s and country c, c is the share of informal employment in that industry and country and EEEE c are the control variables. Given the ss<sup>EE</sup> potentially large differences across industries, Equation (8) is also estimated separately as a cross-section xx across countries for a given industry. As mentioned in the model section, implies that emissions per worker are larger in the informal sector than in the formal sector and implies the opposite. If cannot be rejected, this implies αα> 0 that the emissions per worker are the same in both sectors. As the formal sector might produce very αα< 0 αα= 0 different goods from the informal sector, it cannot be determined whether the production of a specific good generates more emissions in either sector. Also, only if it is found that , there is a direct implication for the informality based on GDP. αα> 0

# **4. Data and Descriptive Statistics**

The study uses data from two different sources: the International Income Distribution Data Set (I2D2) for informal employment shares and the Global Trade Analysis Project (GTAP) for other sector indicators: value added, capital per worker, share of value added in total GDP, and the share of imported intermediate goods in the total intermediate goods (Aguiar et al., 2019). The I2D2 data set compiled by the World Bank is a global harmonized labor force survey database that provides comparable labor market indicators such as employment status, wages, and hours worked at both household and individual levels across countries and time. It contains 600 surveys for 120 countries collected at different points of time. For many countries, especially low-income and fragile or conflict-affected states, it has data only for a single year. The GTAP 10 database combines detailed data on bilateral trade, transport, and protection characterizing economic linkages among regions,
in both dwelling types on their Facebook feeds, with an average frequency of 4.9 ads per person.<sup>5</sup> Two weeks after the end of this remarketing campaign, we sent them a follow-up survey (N=1,542) asking about bed net use and treatment seeking in case of symptoms. We find that the campaign was equally effective for users residing in both types of dwellings, reporting an 8-pp-increase in the probability of sleeping under a bed net the previous night, and a 2.5-pp-increase in timely treatment seeking in the event of a fever.

We conclude that the ad content itself was effective at informing people and persuading them to take preventative and response actions to fight malaria. The campaign was also effective at large scale for those it reached, but it did not sufficiently reach those most at risk, whose engagement on social media is lower and more expensive. This emphasizes the importance of accounting for algorithmic targeting in social marketing. Cost effectiveness analysis based on administrative data indicates that the costs per avoided (officially reported) malaria case range between $3.4 in urban areas and $6.5 overall. Given that the estimated number of cases is roughly twenty-two times greater than the amount of reported cases (WHO, 2021), it is plausible that the cost per averted estimated case could fall below $0.20. Altogether, our results suggest that social media campaigns with public health objectives are cost effective tools, yet they should employ more advanced targeting strategies to ensure ads reach not only those who are the cheapest to advertise to or the most likely to engage with the ad, but also those who are most in need of the campaign.

The rest of this paper is organized as follows: Section 2 describes the contribution to the literature and Section 3 discusses the intervention and its theoretical framework. Section 4 outlines the study design. Section 5 presents the study findings, while Section 6 disentangles the mechanisms of content effectiveness and targeting. Section 7 presents
A second route to evaluation is offered through prioritization. In cases in which the number of eligible applicants in a municipality exceeds the number of available slots in a given year, applicants are ordered by a number of vulnerability criteria, each of which adds or subtracts a given amount to a numeric score. Applicants with the highest such score are then enrolled first, giving rise to a municipalityspecific threshold value below which enrollment in the program is postponed. The score ranges from a minimum of 7 to a theoretical maximum of 22. The only component that adds negative values is age for individuals 64 years old or younger. In theory, this mechanism allows identification to be based on a comparison between closely similar households living in different municipalities with different thresholds. Closer examination, however, reveals that the prioritization score is only binding in a few cases and almost only for the relatively young (that is, applicants in their 50s or early 60s), given that age is the single most important element in the score. Table 6 shows that, of the 1,105 municipalities on which we have 2013 prioritization data, only 78 did not apply prioritization at all. However, almost − − all the other municipalities had low threshold values; the lion’s share was between 4 and 7 (a score that can only correspond to a person in his 50s), and only 18 municipalities had a positive threshold value. The bottom line is that the prioritization threshold does not yield sufficient variance to act as a feasible identification strategy.

**Table 6: Distribution of the Prioritization Threshold across Municipalities**

|_Threshold value_|_Municipalities, number_|
|---|---|
|Did not apply prioritization|78|
|−7|258|
|−6|267|
|−5|186|
|−4|133|
|−3|86|
|−2|42|
|−1|27|
|0|9|
|1|13|
|2|1