rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>WPS3480</mark> Capital Accumulation and Resource Depletion: A Hartwick Rule Counterfactual

# Kirk Hamilton<sup>∗</sup> , Giovanni Ruta<sup>*</sup> and Liaila Tajibaeva<sup>+</sup>

**Abstract.** How rich would resource-abundant countries be if they had actually followed the Hartwick Rule (invest resource rents in other assets) over the last 30 years? We employ time series data on investment and rents on exhaustible resource extraction for 70 countries to answer this question. The results are striking: Venezuela, Trinidad and Tobago, and Gabon would all be as wealthy as South Korea, while Nigeria would be five times as well off as it is currently. We also derive a specific rule for sustainability – maintain positive constant genuine investment – and use this to obtain further empirical results.

# World Bank Policy Research Working Paper 3480, January 2005

_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, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ Environment Department, World Bank. The comments of Jean-Christophe Carret and Roberto Martin-Hurtado are gratefully acknowledged – the usual caveats apply. Corresponding author: Kirk Hamilton (khamilton@worldbank.org), fax 1-202-522-1735, World Bank, 1818 H St. NW, Washington DC 20433.

> + Department of Applied Economics, University of Minnesota. Funding by the Swedish International Development Agency is acknowledged with thanks.
where empl share _rj_ 2015 is industry _j_ share of CZ _r_ employment in 2015, and tariff exposure _jt_ is defined in the following sections. Pre-sample employment shares (2015) are used for exogeneity.

The CBP excludes farm employment, consisting of Crop Production (3-digit NAICS 111) and Animal Production and Aquaculture (3-digit NAICS 112), but includes Support Activities for Agriculture and Forestry (3-digit NAICS 115). Following Blanchard et al., we therefore allocate farm-related tariffs to employment in the associated support activities. Specifically, we calculate the average tariff across 3-digit NAICS sector 111 and allocate this to total employment in 4-digit NAICS sector 1151. We then calculate the average tariff across 3-digit NAICS sector 112 and allocate this to total employment in 4-digit NAICS sector 1152.<sup>11</sup>

# **3.4 Sectoral tariff exposure**

The following sections describe the construction of the sectoral tariff exposure measures used to generate local labor market exposure in equation 1: output exposure, input exposure, and export exposure. The three measures focus on tariff barriers specifically and do not take into account any changes in quotas, in the form of import quotas or voluntary export restraints, and hence could be seen as a lower bound estimate of the exposure to new trade barriers.

## **3.4.1 Exposure to reduced competition through import tariffs**

One channel through which imposing or increasing import tariffs could affect US businesses is by increasing the price of imports that compete with US products, hence rendering US firms more competitive in the domestic market. As in Blanchard et al. (2019), the sectoral exposure used in this paper creates an average dollar value of exposure to the tariff per worker in that sector, based on US imports in 2016:

> 11We do not remove sectors 111 and 112 from the BGT data for two reasons. First, the measure described in the text still accounts for the tariffs in these sectors, even if they are allocated to slightly different NAICS codes when
rather than the exporters themselves. There is evidence, based on the AGOA preference scheme, that the pass through of preference margins is indeed partial at best. Olarreaga and Özden (2005) find that the average export price increase for products benefiting from preferences under AGOA was about 6 percent, whereas the average MFN tariff for these products was some 20 percent. Thus, on average exporters received around one-third of the tariff rent. Moreover, poorer and smaller countries tended to obtain lower shares—with estimates ranging from a low of 13 percent in Malawi to a high of 53 percent in Mauritius.<sup>21</sup> In addition, based on our analysis in the previous section, we should expect administration costs related to these programs to also chew up some of the benefits. In the case of market power, the result is a simple redistribution of the benefits of preferences (rents) being transferred to importers. With administration costs, however, the share of the gains that is lost is not redistributed, but is really a deadweight loss. In both cases, the trade effects of preference programs will be less as well.

# **4.2 A numerical assessment**

We now turn to a numerical assessment of the likely magnitudes involved. While there has been a great deal of political weight attached to this issue, this debate has largely been taking place in a vacuum of real information on the costs and benefits involved. Moreover, the exceptions have focused almost exclusively on the effects of preference erosion on the exports of beneficiary countries.<sup>22</sup> Our assessment uses a global, multi-region general equilibrium model of trade. The model includes 34 regions/countries and 24 sectors (Table 5). The social accounting data come from the GTAP database (http://www.gtap.org) and are benchmarked to 2001. These data include national production and international trade flows.

The import protection data are based on a thorough and careful effort to include
The third scenario considered is focused on the importance of South-South migration. It is a hypothetical extreme scenario of South-South migration where South-South migrant stocks are only 10 percent of the baseline LMI-LMI migrant stocks by 2030. This will imply not just a stagnation of new South-South migrant flows, but also a reversal of some South-South migration that has already taken place.

The total stock of LMI-to-LMI migrant workers reported in the GMig2 database amounts to 15.7 million workers in 2007 and this figure is projected to rise to 23.5 million workers by 2030 under baseline assumptions (Figure 1A). Regarding absolute size, the main origin regions in the benchmark year are low-income Sub-Saharan Africa (LI_SSA), India, Bangladesh, and other Latin America (OLAC). The main LMI destination regions for LI_SSA migrant labor are other LI_SSA and other middle-income Sub-Saharan Africa (OMI_SSA) countries (93 percent); the main LMI destination for Indian migrant labor is Pakistan (53 percent); the main LMI destination for Bangladeshi migrant workers is India (95 percent); and most intra-LMI migrant workers of OLAC origin go to other OLAC countries (90 percent). For some migrant sources like Bangladesh and low-income Sub-Saharan Africa, other developing countries are not just important destinations in terms of aggregate volumes of people, but also for large shares of their migrants (Figure 1B). For migrant-sending countries like Bangladesh and Indonesia that have large migrant populations in other LMI countries, remittances from other LMI countries are also substantial (Figure 2).

While part of the observed South-South labor migration may arguably not be motivated primarily by earning differentials between home and host country, the average wage earnings of a SouthSouth migrant still tend to be significantly higher than the average wage earnings of a native nonmigrant in the origin region.<sup>9</sup> Unskilled migrants from the poorest countries and regions like Bangladesh and Sub-Saharan Africa can gain substantially from migrating within the South. Unskilled migrants from Bangladesh, in particular, gain almost as much from going to other Southern
income, uising the ratios of mean expenditure (BBS, 1984, Table 15.25) to mean income (BBS, 1984, Table 15.28). Income data are from BBS (1984, Table 15.28). Welfare<sup>changes are estimated using the fact that,</sup> from equation (7), the monetary value the welfare effect (expressed as a % of income) of a I0O foodgrain price increase is given by

- Iincome share income share on wage rate elasticity income share

- 10 x from foodgrain - foodgrain + w.r.t. foodgrain x from wage production consumption price labour
realistic improvements in teachers’ subject-matter knowledge could lead to meaningful improvements in the quality of instruction in Indonesia and the learning levels of its students. It is estimated that a 1.0 (2.0) standard deviation increase in teacher’s subject knowledge can translate into a 0.25 (0.50) student-level standard deviation increase in the learning levels of students by the time they leave primary school.

The results of this paper suggest that policies which incentivize improvements in subjectmatter knowledge can work in Indonesia. This finding starkly contrasts results from experimental research about Indonesia’s recent, and very costly, teacher certification program (De Ree, Muralidharan, Pradhan, and Rogers (2015) and World Bank (2015)). Indonesia’s certification program was implemented in 2005/06. In a nutshell, the certification program required a university bachelor’s degree as minimum academic qualifications for primary teachers, and promised a generous _doubling of take-home pay_ after passing the certification program successfully. While the certification program led to hundreds of thousands of teachers starting course work to obtaining bachelor’s degrees, no important improvements in education quality was observed. The stability of the PISA scores is an example of this, but World Bank (2015) provides additional _micro level_ evidence.

The 2012 PISA results perhaps marked the starting points of redirecting policies more explicitly geared towards quality. Indonesia’s current minister of Education and Culture Anies Baswedan for example reflected on Indonesia’s most recent 2012 PISA results in _Republika_ , one of Indonesia’s mainstream daily newspapers, arguing that “Indonesia’s education is in a state of emergency” (Novia 2014). The research presented in this paper indicates that future policies to promote quality education could benefit from incorporating subject-matter tests in teacher evaluation systems.

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large firms; however, during 2013, “Government spending on energy subsidies rose almost 72% on the year to 60 billion pesos ($9.15 billion) between January and September, according to estimates by ASAP, a nongovernmental organization dedicated to improving public-sector finances.”<sup>56</sup>

# _New Supply Law (Ley 26.993)_

One overarching microeconomic policy consideration is the state’s ability to fix prices of consumer goods. With the new “supply law” of 2014, consumers were empowered to file complaints of price gauging by firms. In setting up the regulations of the law, in 2015, a supply monitoring system was established to be an early warning mechanism for supply shortages of basic goods. Large firms are required to report to the system. The new law opened wide scope for government intervention (price setting or goods confiscation) when judgments are reached against firms.

# **V. Macro-micro linkages and the political economy of future reforms**

Based on the historical survey above, there appears to be a macroeconomic dominance to the credibility of broader economic policy-making. Emerging from the hyperinflation of the 1980s, the top priority was stabilization. Once the decision was made to adopt the quasi-currency board arrangement, two problems emerged. First, to achieve macroeconomic consistency and credibility: fiscal institutions needed to be strengthened through a combination of fiscal rules, fiscal councils and reform of intergovernmental fiscal arrangements. Secondly, the only credible microeconomic approach that would assure growth would be to strive for flexible wages and prices via the liberalization of goods and factor markets. In other words, given the macro rules of the game, there was really only one credible path for microeconomic reform. Some authors cited above have argued that not enough was done on privatization, price liberalization and opening to international markets. Others have argued that internal devaluation through nominal and wage and price adjustment is almost impossible to achieve even in the most “liberalized” economic setting, and it is almost certainly _politically_ infeasible
Our classification aligns with 70% of the surveyed candidates (18 out of 26), while the remaining 30% are classified differently (see Table D.8). As a robustness test, we re-estimate our results using only the 26 presidential candidates included in the Parliamentary Elites survey. The findings remain consistent, reinforcing the robustness of our classification and main results (see Table D.9).

In sum, our results suggest that Colombian voters did not change their voting behaviors in response to migrant regularization programs.

# **VII DISENTANGLING POTENTIAL EXPLANATIONS**

There are two potential explanations for the null effects of migrant’s regularization programs in electoral outcomes: i) voter’s lack of information about the program, or ii) voter’s political indifference toward the program. In this section, we describe the theoretical framework that supports each of these potential explanations and test their validity with a survey experiment.

1. _Lack of Information:_ Voters may fail to respond to a policy if they are unaware of its existence or implications. Research on information and political accountability consistently finds that voters respond to policies when they receive clear, accessible information about them. For instance, evidence shows that providing information about politicians’ corruption records reduces support for corrupt candidates (Ferraz and Finan 2008), and increasing awareness about public policies strengthens political engagement (Besley and Burgess 2002; Bjorkman and Svensson 2009). More generally, research suggests that individuals update their beliefs and behaviors when exposed to credible information, influencing decisions in areas such as education (Campos 2024; Cohodes et al. 2022), social norms (Bursztyn et al. 2020), and technology adoption (Duflo et al. 2011; Beaman et al. 2021).

In the context of migration, studies show that factual information about immigration influences voters’ attitudes and policy preferences (Alesina et al. 2022; Barrera 29
1 were collected on a quarterly basis. This means that countries in East Asia that do not report quarterly employment and GDP data cannot be included in the analysis. Unfortunately, this includes all low and some middle income countries in the region. Some difficult decisions have to be made. For example, Indonesia reports semiannual employment data, however only from the late 2000s. Reducing the estimation to semiannual data for all countries from the mid-2000s would imply forfeiting too many data for a meaningful statistical analysis. China, on the other hand, does not report quarterly real GDP data – however, it does report quarterly real GDP growth data. From this, China’s quarterly GDP data were constructed allowing for China to be included in the analysis.

Overall, the sample includes the four most developed countries in East Asia (South Korea, Singapore, and the two Chinese autonomous territories Hong Kong SAR, China, and Taiwan, China) and four solidly middle income countries (China, Malaysia, the Philippines, Thailand). Data are available for all countries between the years 2000 and 2011 and for some countries (South Korea, the Philippines, and Taiwan, China) they reach back to 1996. All data were seasonally adjusted in order to remove transitory variation from the series that could obstruct the underlying relationship between economic and employment growth.

> 2 See World Bank (2010) for a similar finding for Indonesia.

3
Policy Research Working Paper 10804

# **Abstract**

The informal sector contributes significantly to the total output and employment of low-income countries. While women-owned businesses feature strongly in these informal environments, they are generally characterized by low productivity. This paper explores how household business performance may be influenced by owners’ personality traits and their attitudes toward gender roles. Using multi-topic household survey data collected in two regions of Ghana, the results show that among female business owners, being organized is an important determinant of business success, while among male business owners, power motivation and tenacity are important factors. However, increasing traditionalism tends to dampen the effects of these personality traits for both genders. Other factors that are positively correlated with women-owned business performance include business registration, separating expenses for home and business purposes, ownership of a business bank account, use of social media, as well as urban location of the business. For men-owned businesses, the results show that those that are located in traditional markets, have bank accounts, and use literate employees in their operations tend to perform better. The findings imply that policies that aim to boost women-owned business performance need to consider the main barriers, especially attitudes toward gender roles, that may determine how businesses operate in these settings. The results also suggest the importance of soft skills to boost business performance among men- and women-owned businesses.

This paper is a product of the Development Data 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 aamankwah@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<
is not significant (t(157.338) = 0.535; p = 0.296). Shippers who master their logistics seem the most efficient. The information appears to be the key to improve performance (Figure 19). These findings hold for Kenya, South Africa, and, to a lesser extent, Uganda (Figure 20).

**Figure 19: Cargo Dwell Time, by Level of Information about the Clearance Process Provided by C&F Agents, 2011**

<!-- Start of picture text --> 12<br>9.75 9.82<br>10 8.86<br>8.03<br>8<br>6<br>4<br>2<br>0<br>no info until cargo cleared & some info but no reliable clearance delays well accurate forecast & real time<br>delivered clearance delay documented in advance but info<br>unxpected events might<br>hamper progress<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

**Figure 20: Cargo Dwell Time in Select African Countries, by the Level of Information about the Clearance Process Provided by C&F Agents, 2011**

<!-- Start of picture text --> no info until cargo cleared & delivered<br>some info but no reliable clearance delay<br>clearance delays well documented in advance but unxpected events might hamper progress<br>accurate forecast & real time info<br>15<br>10<br>5<br>0<br>Kenya Nigeria South Africa Uganda Zambia<br>Source: Authors based on firm surveys.<br><!-- End of picture text -->

# **Main Determinants in the Selection of C&F Agents**

When shippers select C&F agents based on their professionalism, cargo dwell time is likely to
_Figure 1: Vaccination rates by income group_ _Figure 2: Vaccine rollout rates by income group_ <!-- Start of picture text --> Share of people vaccinated against COVID-19, Mar 22, 2022 How many vaccine doses were administered in the previous 12 months?<br>Per 100 people in the population. The value shown for each date is the total number of vaccine doses administered<br>in the 12 months preceding that date. All doses, including boosters, are counted individually.<br>Share of people with a complete initial protocol Share of people only partly vaccinated<br>Upper middle income<br>Upper middle income 76% 4.8% 81% 100 High incomeWorldLower middle income<br>Low income<br>High income 74% 5.2% 79% 10<br>World 57% 6.9% 64%<br>1<br>Lower middle income 49% 10% 59%<br>.01<br>Low income 11% 14%<br>.001<br>0% 10% 20% 30% 40% 50% 60% 70% 80% Dec 15, 2020 Jun 4, 2021 Sep 12, 2021 Mar 22, 2022<br>Source: Official data collated by Our World in Data CC BY Source: Official data collated by Our World in Data CC BY<br>Note: Alternative definitions of a full vaccination, e.g. having been infected with SARS-CoV-2 and having 1 dose of a 2-dose protocol, are<br>ignored to maximize comparability between countries.<br><!-- End of picture text --> Data source: Ritchie et al. 2022 as of March 22, 2022.

Vaccine hesitancy refers to delay in acceptance or refusal of vaccination despite availability of vaccination services (MacDonald et al. 2015). It is not a new phenomenon, and some level of hesitancy exists for most vaccines, usually revolving around concerns about
perform less well when predicting income or consumption-based poverty measures. For example, the RWI only explains 32 percent of the variation in average per adult equivalent consumption across Cantons in Togo (Aiken et al, 2021). Furthermore, for a microcensus conducted in rural Kenya, the RWI explains 70 percent of the variation in wealth but only 17 percent of the variation in the predicted probability of being poor, defined using household consumption (Chi et al, 2022). Thus, the performance of the RWI varies greatly depending on the context, and particularly depends on whether it is evaluated against a wealth or consumptionbased measure of welfare. This study therefore contributes additional information on the performance of the RWI in distinguishing among very poor villages by comparing it to the benchmark measure of village welfare in Malawi.

# IHS plus geospatial indicators.

The third alternative method for targeting consists of using the IHS survey data to train a welfare model against satellite indicators. This model can then be used to generate out-of-sample predictions into villages for which matched census data is available, to compare against the benchmark. This method has the advantage of being free, but may suffer from limited training data. We estimate the model only for the poorest 50% of households in each village to resemble the structure of the UBR data set and train it using extreme gradient boosting techniques.

Partial registry As a final alternative method to target the poor, we consider a hypothetical collection of a partial registry data set from a sample of villages. This exercise would consist of collecting the subset of household welfare proxies used in the census model from all households in a random sample of villages, similar to an expanded sample listing procedure of the type typically carried out for household sample surveys. In practice, for this analysis we simulate a partial registry by drawing a random sample of villages from the census extract. This sample, consisting of all households in the sampled villages, is used as the hypothetical partial registry in the two-step procedure. The first step involves utilizing the parameters from the