rafmacalaba/datause-encoder-data huggingface dataset:
human capital characteristics) relate to work by Borjas (2000), Duleep and Regets (1997) and Hu (2000).

# **_3.1. EMPIRICAL FRAMEWORK_**

The estimation employs OLS with country fixed effects to explain immigrants’ performance - proxied by occupational placement (OP) - as of Census year _t_ for migrant _i_ who came to the US in decade _d_ . Decades are attributed to cohorts in the sense used by Borjas (1985), where the implicit assumption is that migrants arriving within a decade are homogeneous with respect to ability _._ The estimations rely on data from the 5% PUMS samples of the US Censuses 1980, 1990 and 2000<sup>8</sup> and focus on employed male immigrants, not living in “group quarters”,<sup>9</sup> not enrolled in school as of 1980, 1990 or 2000, and whose reported years of immigration lie within the 19701989 interval. The sample in Census 1980 is random, each observation representing 20 individuals in the US population, while Censuses 1990 and 2000 are weighted so that each observation represents a different number of individuals in the US.

Three different metrics are used as the dependent variable, denoted as OP (Occupational Placement). The first indicates the prestige level and the other two quantify the educational content of each occupational category. The prestige index and ne educational content variables are available from Ruggles et al. (2009) whereas the third variable is based on our own calculations. The first metric, labeled as the Prestige Index, does not vary by census year, and measures the prestige associated with an occupation. The second, labeled Education Index 1, indicates the educational content of each occupation and is based on the share of individuals with undergraduate and graduate degrees who declare that as their occupation. Both the Prestige Index and Education Index take values from 1 to 100. The last variable, labeled Education Index 2, also indicates the educational content of an occupation, and is calculated as the weighted average of the number of education years of all individuals, male and female, native and foreign-born alike, who declare that category as their occupation. Education Index 2 has a much smaller variance than the other two measures of occupational placement, ranging from 9 to 17. Table 3 lists the three indices for selected occupations.

> 8 The data were downloaded from www.ipums.org where they were made available by Ruggles et al. (2009)

> 9 Group quarters are institutions and other group living arrangements, such as roaming houses and military barracks.

7
**-** **<u>Table 4: Encompassing model using the pre pandemic dataset</u>**

||Linear OLS|PLREG|Linear OLS|PLREG|
|---|---|---|---|---|
|Country fixed effects|No|No|Yes|Yes|
|Log GDP per capita|0.659||-0.987||
||(0.918)||(0.805)||
|Voice and Accountability index|0.694|0.490*|-0.191|-0.553|
||(0.807)|(0.250)|(0.404)|(0.354)|
|Government Effectiveness index|-0.655|-1.330***|-0.574|-0.221|
||(1.045)|(0.323)|(0.473)|(0.348)|
|Tax revenue over GDP|0.165**<br>(0.067)|0.183***<br>(0.022)|0.008<br>(0.036)|0.020<br>(0.020)|
|Percentage of population that uses the internet|0.037**<br>(0.017)|0.016**<br>(0.008)|0.045***<br>(0.012)|0.049***<br>(0.006)|
|Share of the population age 65+|0.689***<br>(0.084)|0.619***<br>(0.033)|0.006<br>(0.033)|-0.006<br>(0.044)|
|Constant|-7.132<br>(7.182)||21.836***<br>(7.050)||
|Test on the non-linear sub-function of log GDP p.c.||3.704||2.226|
|p-value||0.000||0.013|
|Observations<br>Note:Standard errors are shown in parenthesis below the coefficie|1519<br>nts, clustered at the count|1518<br>ry level. All specificat|1519<br>ions include a correction|1518<br>forselective reporting|

<u><mark>Note: Standard errors are shown in parenthesis below the coefficients, clustered at the country level. All specifications include a correction for selective reporting</mark></u> <mark>using a cubic function of the propensity score (see Addendum table A3 for the probit regression results used to estimate the propensity scores).</mark> 40
and Type II errors, making it difficult to sign the measurement bias and rendering it imprecise as a measurement approach.<sup>10</sup> Our study relies on the measurement of parasites in the worker from thick film blood smears read in a dedicated laboratory. Although expensive to implement as it requires trained personnel and appropriate instruments, thick blood film microscopy is considered the diagnostic gold standard. In practice, our study team takes a blood sample from each consenting worker and conducts microscopy analysis in a lab that is two hours away (by car) from the plantation. The microscopy analysis counts the number of parasites, with workers above a specified threshold considered to be malaria positive.<sup>11</sup> Our adopted definition of malaria positivity is the presence of at least three parasites over the total examined fields in the blood smear.<sup>12</sup> This decision follows the clinical diagnostic standards in the study area (Government of Nigeria 2011). As there is no universally adopted standard for both symptomatic and asymptomatic cases in a population (Laishram et al., 2012), we rely on the clinical threshold in our study area as our objective measure following the recommendation in WHO (2010).<sup>13</sup> All workers diagnosed with malaria receive an adult dose of Artemisinin based Combination Therapy (ACT) along with clear instructions on use. ACT is the preferred first line treatment for malaria recommended by the World Health Organization, as there has been no resistance to ACT yet reported in Africa, and ACT has been proven to cure _falciparum malaria_ within 7 days with few to no side effects. ACT also provides protective effects between two and four weeks after treatment (White (2005), Sowunmi et al. (2007), and Woodring et al. (2010)).

> 10 Strauss and Thomas (2000) present evidence that self-reported health information could either be positively or negatively attenuated, and that the direction of the bias may be correlated with respondent characteristics. Self-reported health nevertheless remains a widely used approach in socio-economic and public health studies.

> 11 A professional laboratory technician read all the slides to record the number of parasites in five viewing fields. After recording the parasite count, the laboratory supervisor selected random subsamples of slides from each batch of 50 slides to verify. If discrepancies between the primary laboratory technician and the supervisor were found, the whole batch of slides was re-validated.

> 12 In the medical literature studies focusing on different settings use distinct parasite density thresholds to classify malaria infection as there is no unique or universal medically established standard for population based malaria testing which includes asymptomatic malaria cases (see dalla Martha et al. (2007), Toure et al. (2006), and Rottmann et al. (2006)).

> 13 Inspection of local clinic records showed that the malaria positivity rate observed in our population of workers is at a similar level to the clinical diagnostic rates found in the areas surrounding the study setting during the study months.

11
Figure 9 Dominican Republic and Haiti: Bilateral Real Exchange Rates, 1963-2007 **___** Dominican Republic _ _ _ Haiti <!-- Start of picture text -->
1.4 1.4<br>Bilateral Real Exchange Rate<br>1.2 1.2<br>RER_DR<br>1 1<br>0.8 0.8<br>0.6 0.6<br>RER_HT<br>0.4 0.4<br>0.2 0.2<br>0 0<br>-0.2 -0.2<br>-0.4 -0.4<br>1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text -->

Source: Author’s calculations based on IMF data (see Appendix B).

Given that the Dominican Republic and Haiti are both oil importers, accounting for changes in the price of oil, and studying its potential impact on both countries, is also important. In the absence of sufficiently long time-series on the domestic-currency retail price for each country (which would have the advantage of taking into account changes in subsidies and taxes), we use a common price index in foreign prices.<sup>19</sup> While the US output gap captures (as before) “trade” and “remittance” channels, the US interest rate captures a “financial” channel, that is, a measure of borrowing conditions on world capital markets. This is more appropriate for the Dominican Republic (given its ability to borrow on world capital markets in normal times) as opposed to Haiti, which cannot borrow on these markets.<sup>20</sup>

> 19See Appendix B for an exact definition of the index.

20Ideally, what would be more appropriate to measure financial shocks for the Dominican Republic would be a country-specific measure of borrowing costs on world capital markets, which would account for the country-specific spread above and over the risk-free US Treasury rate. Unfortunately, such a variable is not available for the whole period under study.

32
partly due to data availability, this timeframe has two interesting features that make it crucial for China’s rise. First, it began shortly after the Southern Tour of Mr. Deng Xiaoping, which signaled China's determination to turn to a market economy after the end of the Cold War. Second, the period covers the large wave of privatization and decentralization during the second half of the 1990s, the country's preparation and subsequent entry to the World Trade Organization in 2001, as well as the passage of the Property Rights Law in 2015 (Jefferson and Su 2006, Xu et al. 2005, and Huang et al. 2017). All are landmark events of China’s economic reform toward a market economy.

As the largest emerging economy in the world, China offers us a unique opportunity to explore the joint dynamics between the BE and entrepreneurship. In 2012, China accounted for 43% of the total GDP of other developing countries included in the WBES, and 32% of the total population in other WBES developing countries.<sup>5</sup> On one hand, like many transition countries, China began its economic reforms in the absence of well-functioning marketsupporting institutions such as courts and financial institutions. Central and local governments played active roles in allocating economic resources and facilitating regional development (Xu, 2011a; Cull et al. 2017). To conduct business in such an environment, Chinese entrepreneurs relied heavily on informal institutions such as family networks and political connections (Allen et al., 2005; Li et al, 2006; Cai et al. 2011). On the other hand, China has also witnessed drastic transformations in its domestic BE, partly due to substantial efforts made by the central and local governments. At the macro level, China has experienced rapid economic growth over the past decades, with its GDP per capita increasing by around six-fold over our sample period (China Statistical Yearbook, 2014). At the micro level, both private firms and private entrepreneurs have emerged in multitudes during the reform era. The growth rate of the private sector has far outpaced that of the public sector, making it a defining feature of China's growth patterns (Song et al., 2011).

In our study, we shed light on the dynamics of the BE and entrepreneurship in China’s private sector by providing the most ambitious and comprehensive descriptive analysis over

> 5 Data sources: Penn World Table version 9.0. The share of GDP is based on output-side real GDP at chained PPPs (in mil. 2011US$).

3
<!-- Start of picture text --> 2-<br>WwBi-<br>' |<br>j<br>“a FID PE app grown itary MES<br>Coefficient<br><!-- End of picture text -->

<!-- Start of picture text --> eet ee Q<br>ot<br>ee<br>°.<br>0.0- = —<br>0<br>°<br>-0.1-<br>C °<br>02:, 2000 2005 2010 2015 202<br><!-- End of picture text -->
# **Figure 3.2: Incidence of Training Among HE Operators, by Education Level, 2006 (percent)**

<!-- Start of picture text --> Incidence of training by education level, HEs<br>0 .2 .4 .6 .8<br>No education<br>Incomplete primary<br>Completed primary<br>Incomplete ord. secondary<br>Completed ord. secondary<br>Advanced secondary / univ<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data_ 25. **Migration profile.** HEs provide recent migrants with economic opportunities in the urban areas. Although many migrants find private sector wage and salary jobs (they have a higher proportion in these jobs because they are, on average, more educated that non-migrants), in 2006, about 40 percent of HE operators in Dar es Salaam and in other urban areas were migrants (Table 3.1).

**Table ‎3.1:  Distribution of Recent Migrant Population in Urban Areas, by Job Type, 2006 (percent)**

||||Dar<br>||Oth<br>|er urban<br>|
|---|---|---|---|---|---|---|
||Not migrant|Migrant|Total|Not migrant|Migrant|Total|
|Public wage non-agri|6.8|4.3|6.4|5.5|9.9|6.1|
|Private wage non-agri|25.0|36.0|27.0|13.7|27.2|15.3|
|HE non-agri|47.6|39.6|46.2|34.4|40.9|35.2|
|wage agri|0.7|3.0|1.1|0.7|1.9|0.8|
|Family farming|19.9|17.2|19.4|45.8|20.2|42.7|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

_Note: Job type refers to primary employment only .Migrant indicates a migration in the last 5 years only. Source: Calculations based on the ILFS 2000/01 and 2006 data_ 26. **Sectoral and Occupational Distribution of HEs:** Although HEs operate in different sectors, there is a high concentration in certain sectors or activities. In 2006, over 70 percent of HEs in the urban areas and more than 60 percent in the rural areas were in small businesses related to trade, hotels, and restaurants. These activities have no or low barriers to entry, and are highly competitive. However, HEs are increasingly diversifying out of the trade sector. Between 2001 and 2006, the share of HEs engaged in trade declined (Table 3.2). Following a transformation in the economy, more changes in the sectoral distribution of HEs can be expected as HEs develop new expertise and operate in other areas.

14
one to bind the lagged variables either to the actual historical data (in the case of a _static forecast_ ), or to the values solved for in the previous periods (in the case of a dynamic forecast). In the case of exogenous variables, aliasing is applied when using model scenarios. A _scenario_ represents a set of assumptions concerning variables that are determined outside the model (i.e. exogenous variables). In a scenario, one can change the path of an exogenous variable by _overriding the variable_ . _When a variable is overridden, the values for that variable will be fetched from a workfile series specific to that scenario_ . The name of the series is formed by adding a suffix associated with the scenario to the variable name. The same suffix will be used when storing the solutions of the model for the scenario.

The outline of the paper is as follows. Section 2 is an introduction to simple general equilibrium modeling using the generalized Salter-Swan model (also known as the 1-2-3 model<sup>4</sup> ) of an open economy. We consider two variants of this framework. The first is a stripped down version with three representative agents: a producer, a consumer and the rest of the world. Following a description of the basic structure of this variant, we demonstrate its numerical implementation using a simple example from de Melo and Robinson (1989). In particular, we show how to analyze welfare and structural implications of external shocks such as foreign capital inflow and the deterioration of the terms of trade. The second variant, presented in Section 3 of the paper, introduces government, savings and investment in the basic framework. The empirical implementation is based on a macroeconomic social accounting matrix (SAM) for Indonesia in 2002. Concluding remarks are made in Section 4.

# **_2.  A Basic Model of a Small Open Economy_**

In this section we consider the simplest model of a small open economy. We rely on the Salter-Swan framework, which provides a foundation for the study of the impact of macroeconomic imbalances and adjustment policies on the real sector of a small open economy. We then present an analytical expression of the model before proceeding to numerical implementation.

> 4 Because it refers to one country with two sectors of production and three goods (Devarajan, Lewis and Robinson 1990:627).

4
# MISUNDERESTIMATING CORRUPTION

Aart Kraay (The World Bank) Peter Murrell (University of Maryland)

## **JEL Classification Codes:** C83, O17, O43

**Keywords:** Corruption, reticence, random response questions

_____________________________ 1818 H Street NW, Washington DC 20433, akraay@worldbank.org; Department of Economics, University of Maryland, College Park, MD 20742, murrell@econ.umd.edu. We would like to thank Nona Karalashvili and Tatjana Kleineberg for invaluable research assistance, and David McKenzie, Gale Muller, Luis Serven, Carlos Silva-Jauregui and Rajesh Srinivasan for helpful comments. We are grateful to the World Bank's Enterprise Survey team (and especially Jorge Meza, Federica Saliola, and David Francis) for fielding the random response questions discussed in the paper. We are indebted to Gallup for their collaboration in fielding these questions in the Gallup World Poll and making the data available to us, and especially to Nicole Naurath and Rajesh Srinivasan for their support of this project. Financial support from the Knowledge for Change Program (KCP) of the World Bank is also gratefully acknowledged. The views expressed here are the authors', and do not reflect those of the Gallup Organization, the World Bank, its Executive Directors, or the countries they represent.
**Table 1: Example of multiple caps used in South Africa**

|**Credit type**|**Maximum prescribed interest rate**|
|---|---|
|Mortgage agreements|Repo Rate + 12% per year|
|Credit facilities|Repo Rate + 14% per year|
|Unsecured credit transactions|Repo Rate + 21% per year|
|Development credit agreements*|Repo Rate + 27% per year|
|Short‐term transactions|5%/ 3% per month**|
|Other credit agreements|Repo Rate + 17% per year|
|Incidental credit agreements|2% per month|
|SARB Repo Rate stands at 6.75% as of Novem<br>* SMEs and low income housing; ** 5% per m<br>loans within a calendar year.|ber 2017.<br>onth on first loan and 3% per month on subsequent|
|_Source:_www.saica.co.za/Portals/0/Technical|/LegalAndGovernance/39379_gon1080_Fees.pdf|

# How are they calculated (type, methodology and benchmark)?

Interest rate restrictions do not only differ in scope but also in how they are calculated. One important distinction is how the maximum level of interest that banks or other MFIs can charge is determined. There are two main ways of doing that. The first way is setting an _absolute cap_ , i.e. a fixed nominal rate that may not be exceeded. We find evidence that at least 26 countries rely on absolute interest rate caps. Two‐thirds of the countries using absolute caps are either low or lower‐middle income countries, with only few high‐income countries using them. The level set by absolute caps varies substantially. The Republic of Korea, for example, applies an absolute cap of 24% per annum, Thailand of 28% per annum, and Jamaica uses a cap of 40% per annum. As mentioned above, most countries in our sample do not use a single absolute limit, but have multiple, different caps based on the size, or type of loans, the socio‐economic characteristics of the borrower, or the industry.

The majority of countries in our sample, 54% of the countries for which we have this information, uses a _relative cap_ . Relative caps tend to be used more frequently in more developed countries. Out of the 30 countries using relative caps, 16 are high or upper‐middle income countries. A relative interest rate cap means that the maximum level of the allowed interest rate depends on the level of a benchmark rate. The cap is then usually either defined as a certain spread over the benchmark (cap= benchmark + xx percentage points), or a multiple of the benchmark rate (cap = xx*benchmark rate). In contrast to absolute caps, relative caps vary over time based on the movement of the benchmark rate. In most cases the 15
# **Figure 7: Brazil’s forest reference emissions levels (FRELs)**

This figure depicts Brazil’s submitted FRELs for the Amazonia biome (Ministry of the Environment of Brazil, 2018; Ministry of the Environment of Brazil et al., 2014), and reflects how the benchmarks are computed as historical averages over an expanding window since 1996, which remain constant within 5-year periods.

<!-- Start of picture text --> FREL A, reference period: 1996 2005<br>1,400 FREL B, reference period: 1996 2010<br>FREL C, reference period: 1996 2015<br>1,200 FREL A, reference level: 2006 2010<br>FREL A FREL B, reference level: 2011 2015<br>FREL C, reference level: 2016 2020<br>1,000<br>FREL B<br>800 FREL C<br>600<br>400<br>200<br>0<br>1996 2000 2004 2008 2012 2016 2020<br><!-- End of picture text --> Source: Ministry of the Environment; Ministry of Science, Technology and Innovation of Brazil.

et al., 2016; Huettner et al., 2009; Mertz et al., 2018), see Arts et al. (2019) for an overview.

From a principal-agent perspective, a constant benchmark that does not satisfy the _controllability principle_ (see Section 3.4.1) will weaken the performance incentives for the issuer. If the benchmark does not reflect external factors, then the issuer’s rewards or penalties will be decoupled from its performance. Moreover, funding providers may end up rewarding results which may have taken place regardless of the issuer’s efforts.

For instance, the spike in deforestation in Brazil’s Legal Amazon until 2004 implies a high baseline deforestation rate for all FRELs used in projects that started in subsequent years, as shown in Figure 7. As a result, West et al. (2020), West et al. (2023) argue that a significant part of the carbon credits claimed through such projects do not reflect additional emissions reductions. This highlights the importance of appropriate and robust benchmark choices, also for the credibility of carbon offsets. The authors also show that during the implementation phase of such projects, deforestation decreased in most areas of the Brazilian Amazon (in part) due to macroeconomic conditions.

# **4.5 Discussion**

If we compare the FREL benchmarks described Figure 7 with the benchmarked KPI in Figure 6, we can make several observations with respect to the REACH framework. Both approaches satisfy the requirement of a “transparent benchmark model”. In the case of FRELs, the benchmark is a historical average over a reference period, updated every five years. For our proposed benchmarked KPIs, the econometric benchmark model is described in Section 4.2. However, different from the benchmarked KPI approach, FRELs do not provide any probability distributions for their benchmarks. This makes it difficult to assess if the outcomes were due to chance or actual efforts.

17