rafmacalaba/datause-encoder-data huggingface dataset:
POLICY RESEARCH WORKING PAPER _2955_

# Abstract

Industrial countries maintain special tariff preferences, namely the Generalized System of Preferences (GSP), for imports from developing countries. Critics have highlighted the underachieving nature of such preferences, but developing countries continue to place the GSP at the heart of their agenda **in** multilateral negotiations. What effect do such preferences have on a recipient's own trade policies? Ozden and Reinhardt develop and test a simple theoretical model of a small country's trade policy choice, using a dataset of 154 developing countries from 1976 through 2000. They find that countries removed from the GSP adopt more liberal trade policies than those remaining eligible. The results, corrected for endogeneity and robust to numerous alternative measures of trade policy, suggest that developing countries may be best served by full integration into the reciprocity-based world trade regime rather than continued GSP-style special preferences.

This paper-a product of Trade, Development Research Group-is part of a larger effort in the group to study global trade regimes. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Paulina Flewitt, room MC3-333, telephone 202-473-2724, fax 202-522-1159, email address pflewitt@worldbank.org. Policy Research Working Papers are also posted on the Web at http:/Iecon.worldbank.org. The authors may be contacted at cozden@worldbank.org or erein@emory.edu. January 2003. (33 pages) _The Policy Research Workmig Paper Series disseminates the findings of wvork in progress to encourage the exchange of tdeas about development issues. An objective of the series is to get the findings out qutickly, 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._ Produced by the Research Advisory Staff
suggest that if anything, a commune with no exposure reports _fewer_ instances of cancer than a commune that experienced one herbicide spray application.

# _4.2.2. Measures of herbicide exposure_

Unlike in earlier studies, levels of dioxin actually absorbed by each surveyed individual are unknown. Instead, information about geographic location of herbicide spray applications is used to construct the exposure measures. The use of military records or self‐reported exposure assessments as measures of exposure in the case of war veterans has been subject to debate (Young, 2004; Young, Cecil and Guilmartin, 2004).<sup>10</sup> Furthermore, some studies based on military records draw conclusions of non‐occupational exposure (Michalek and Pavuk, 2008; Pavuk, Michalek and Ketchum, 2006; O’Brien, Decoufle and Boyle, 1991). In the context of the present analysis, past exposure at the current commune of residence is the adopted measure of herbicide exposure. Evidence of some positive correlation between measures constructed from military records and actual dioxin levels in adipose tissue in residents of South Vietnam has been documented by Verger et al. (1994). This could be evidence of direct wartime exposure, or exposure via dioxin‐contaminated food, soil or river sediments.

A first concern is that military record data might not reflect true exposure on the ground. Dioxin‐contaminated river sediments and fish might be located in places further downstream from herbicide application areas, while geographical conditions and soil movements have been found to affect the spatial distribution of dioxin contamination (Phuong et al., 1996). In addition, around 8 percent of recorded missions did not refer to a particular herbicide mixture although Agent Orange was most likely used (Stellman and Stellman, 2005). The lack of robustness of the results to the use of measures of exposure derived from dioxin‐contaminated herbicides might be attributed to such increased measurement error (appendix tables A1 and A2). Finally, the construction of the exposure measure itself is a source of measurement errors. The number of hits does not convey information on either the actual chemical composition of a spray application or the quantity sprayed. Moreover, the exposure opportunity measure is computed by aggregating over space, without taking into account spatial or time decay. However, as measurement errors are believed not to be correlated with cancer prevalence,

> 10 Michalek and Pavuk (2008), Pavuk, Michalek and Ketchum (2006), Pavuk et al. (2005), Bullman, Watanabe and Kang (1994), Mahan et al. (1997), Dalager et al.(1991, 1995), Kang et al. (1986, 1987) and Anderson (1990)  use military records to construct proxy measures of exposure opportunity; Giri et al. (2004) and Zafar and Terris (2001) rely instead on self‐assessed exposure.

14
**<u>Figure 9: Variation in Company Numbers compared to Herfindahl Index</u>**

<!-- Start of picture text --> Life Non Life<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40 Data Set Average<br>30 30<br>Data Set Average<br>20 20<br>10<br>10<br>0<br>0<br>0 2000 4000 6000 8000 10000<br>0 2000 4000 6000 8000 10000<br>Non Life Herfindahl<br>Life Herfindahl<br>No of Life Companies No of Non Life Companies<br><!-- End of picture text --> Source: Staff Analysis As would be expected, the larger the shares of the largest company the fewer competitors that it usually faces suggesting a more limited the space for new entrants and existing competitors (refer Figure 10). However, this can be viewed differently. As the market moves to a more natural level then it can be considered that the largest player has a role in the process, expecting its share to reduce or increase toward the average level. That is, where there is a progressive liberalization underway then the eventual market share for the largest player has a floor, and where consolidation is expected then the local largest player can be expected to play a role in acquisitions to increase market share to the more “normal” level. That is, as the number of companies fall the share of the largest company rises. It is clear that the largest participant does participate in industry consolidation in most circumstances.

**<u>Figure 10: Variation in Company Numbers compared to Top Company Share</u>**

<!-- Start of picture text --> Life Non Life<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30 Data Set Average<br>20 Data Set Average 20<br>10 10<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Life No 1 Market Share Non Life No 1 Market Share<br>No of Life Companies<br>No of Non Life Companies<br><!-- End of picture text --> Source: Staff Analysis Investigating variation in company numbers against the insurance penetration measure would give insight into whether or not the state of market development makes a difference to company numbers. Figure 11 is inconclusive but consistent with the view above that the penetration is not a good indicator of either the Herfindahl Index or the role of the largest firm.

19
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Air transport connectivity within the SADC has grown steadily since 2001, when the intraregional air transport market amounted to about 8 million seats. There was a steady increase in capacity to just over 10 million in 2004, and a further increase to 12 million in 2007 (figure 2.15).

In examining regional air transport markets and patterns within the SADC (figure 2.14), it is clear that Johannesburg is a major hub. Regional air traffic is heavily concentrated on routes from South Africa to Namibia, Zambia, and Zimbabwe. The connectivity matrix in table 2.9 shows how most countries in the region have at least two daily flights to South Africa (and as many as 70–80 in the cases of Mozambique and Zimbabwe). Even though bilateral connections to countries in the SADC region are largely absent, Johannesburg facilitates connectivity throughout the

**Figure 2.15 Seats for intra-REC travel within the SADC**

<!-- Start of picture text --> 16,000,000<br>12,000,000<br>8,000,000<br>4,000,000<br>0<br>2001 2004 2007<br>Source: Derived from Bofinger 2009.<br>Note: REC = regional economic community.<br><!-- End of picture text --> region. On the other hand, island nations such as Madagascar and Seychelles have a much lower level of air connectivity (though Mauritius is an exception to this pattern).

Flights in the SADC region operate between a varying number of city pairs. South Africa serves 149 city pairs, of which 69 are international city pairs within the SADC and 44 are intercontinental flights. The large number of city pairs served in the SADC is due to the dominance of South African Airlines. At least one international city pair is served in all countries. This reflects the strong hub-and-spoke structure of the SADC market, in which all SADC countries are connected to one another through Johannesburg (and possibly by other links). Angola, the Democratic Republic of Congo, Madagascar, Mozambique, South Africa, and Tanzania all have moderate domestic connectivity. In the other countries, the absence of domestic city pairs reflects small size, low traffic volumes, and limited purchasing power. These factors make it difficult for air transport to compete with surface transport alternatives, such as road transport services (table 2.10).

32
# **Box 4: Unit Record Approach: The Envelope Model Gainers and Losers in Trade Reform in Morocco**

In a background paper for the poverty assessment for the Kingdom of Morocco, Ravallion and Lokshin (2004) use the envelope approach to identify the gainers and losers in agricultural trade reforms. The government imposed high tariffs (100 percent) on cereal imports to create incentives for domestic cereal production. The elimination of the high tariffs would reduce domestic cereal prices and therefore hurt net cereal producers at least in the short run. On the other hand, consumers would benefit from lower cereal prices.

The identification of winners and losers based on a representative sample of 5,000 households focuses along two dimensions: (1) the position on the distribution of consumption and (2) non-income characteristics (for example, geographic residence, sector of employment). Based on these dimensions, the authors decompose changes in inequality into a vertical component and a horizontal component. The impact of the reforms is considered “vertical” (that is, between income levels) if the initial income level predicts perfectly the gainers and losers in the reforms. In the case of no systematic link between reform impacts and income, there is a “horizontal” element reflecting the fact that impacts vary among people at the same level of initial income. This variation is likely due to non-income characteristics (observable and nonobservable).

Four policy options are considered: (1) a 10 percent cut in the tariff rate, (2) a 30 percent cut, (3) a 50 percent cut, and (4) a complete elimination of tariffs. (Summary results are presented below.) Ravallion and Lokshin (2004) find that the overall impact of the complete liberalization of the cereal trade on household mean consumption and inequality is very small. However, the impacts vary greatly by household type and region with different income sources and patterns of consumptions. A decomposition of the overall change in inequality as measured by the mean logarithmic deviation (identified as the “impact on inequality” in the table) shows that all the impact on inequality is horizontal rather than vertical.

# **Gainers and Losers in Four Trade Reform Scenarios**

||Baseline|Policy1(10%) Policy2(30%)|Policy 3 (50%)Policy4(100%)|
|---|---|---|---|
|**National**||||
|Poverty rate (%)|19.61|20.01<br>20.33|21.04<br>22.13|
|Mean log deviation|0.2850|0.2892<br>0.290|0.2914<br>0.2917|
|Gini index|0.385|0.387<br>0.389|0.391<br>0.395|
|Per capita gain|0|6.519<br>−23.967|−54.816<br>−133.81|
|Production gain|0|−32.078<br>−69.012|−106.308<br>−201.017|
|Consumptiongain|0|38.598<br>45.046|51.492<br>67.207|
|||**Policy 2:**|**Policy 4:**|
|||**Partial de-protection**(30%)|**Full de-protection**(100%)|
|Impact on inequality:||||
|Baseline (0.2850)||0.289|0.292|
|Vertical component||58%|−20%|
|Horizontalcomponen|t|42%|120%|
|_Source_:Ravallio|nandLok|shin 2004.||

Policy reforms will generally have implications for the domestic structure of prices and wages and thus for household welfare. In order to analyze the _first-order_ 18
Table A.23: Relationship between FAT survey variables and log of wages from administrative data for Brazil

|Variable|(1)<br>log(sales per worker)|(2)<br>GBF|(3)<br>SSBF|
|---|---|---|---|
|ln(Wage) RAIS|0.882***|0.400***|0.299***|
||(0.157)|(0.111)|(0.101)|
|Observations|592|675|674|
|R-squared|0.346|0.364|0.800|
|Controls:||||
|Sector FE|Y|Y|Y|
|Region FE|Y|Y|Y|
|Size-group FE|Y|Y|Y|
|Age|Y|Y|Y|
|Exporter|Y|Y|Y|
|Foreign owned|Y|Y|Y|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Average wage information for each establishment is obtained from the 2017 _Relação Anual de Informações Sociais_ (RAIS) merged with the Firm-level Adoption of Technology (FAT) data used in this exercise, including sales per worker, the technology adoption index ( _MOSTj_ ) for GBF and SSBF, and firm characteristics used as controls. Regressions estimated using establishment-level sampling weights. Robust standard errors in parenthesis.

firms in RAIS that are part of our universe for the State of Ceará, in Brazil<sup>41</sup> . We then perform a t-test to compare the differences. Table A.24 shows that the differences are not statistically significant.

Overall, these ex post checks appear to validate the quality of the data collected.

> 41The variables are number of workers, average wages, share of workers with college degree, share of low skilled, and share of high-skilled workers, where high- and low-skilled workers are defined as in Autor and Dorn (2013).

111
<!-- Start of picture text --> Sources of new formal and informal loans<br>3.1 FIGURE<br><!-- End of picture text --> Adults borrowing from source in the past year (%) <!-- Start of picture text --> 40 INFORMAL LENDER<br>BANK, CREDIT UNION, OR MICROFINANCE INSTITUTION<br>RETAIL STORE (STORE CREDIT)<br>30<br>FRIENDS OR FAMILY<br>20<br>10<br>0<br>HIGH-INCOME LATIN AMERICA SOUTH EAST ASIA EUROPE & MIDDLE EAST SUB-SAHARAN<br>ECONOMIES & CARIBBEAN ASIA & PACIFIC CENTRAL ASIA & NORTH AFRICA AFRICA<br><!-- End of picture text --> _Note:_ Respondents could report borrowing from more than one source. _Source:_ Demirguc-Kunt and Klapper 2012.

A few economies stand out for the reported use of formal loans: Bangladesh, where 23 percent of adults report having borrowed from a financial institution in the past 12 months, and Bolivia, Sri Lanka, and Thailand, where more than 15 percent report having done so. This may reflect the broad coverage in these economies of community-based models (such as cooperatives, village banking, credit unions, and self-help groups) that make small formal loans to the poor.

In all regions only about 5 percent or fewer adults report having borrowed money from a private informal lender in the past 12 months. But a few economies are exceptions to regional patterns: more than 10 percent of adults in Cambodia, the Dominican Republic, Liberia, and the Syrian Arab Republic report having taken out a loan from a private informal lender in the past 12 months. Social norms may have a large effect on the degree to which this type of borrowing is reported.

Another group of adults report having borrowed in the past 12 months both from a formal financial institution and from another source. For these adults the formal financial sector appears to be meeting some but not all of their credit needs. These borrowers make up more than 13 percent of all adults who report having borrowed from at least one source in the past year worldwide and more than 20 percent in such economies as Belarus, Croatia, and Mongolia.

In both high-income and developing economies a significantly larger share of men than women report having originated a new loan from a formal financial institution in the past 12 months. And in both income groups new formal loans are most common among those ages 25–64. Disparities among income quintiles are much larger on average in high-income than in developing economies. While there is almost no increase in the origination of new formal loans between the poorest and richest income quintiles in developing economies on average, in high-income economies those in the richest income quintile are almost twice as likely on average as those in the poorest to report having originated a new formal

**38**

MEASURING FINANCIAL INCLUSION
# **Appendix 4. Additional information on the Policy Priority Inference Model Setup**

Besides the data on the network structure, PPI also requires data on 1) government expenditure over time (during the retrospective analysis), 2) data on the quality of law and monitoring, and 3) data on the initial estimates for the growth probabilities c which in the initialization are assumed to be fixed values within the data (they do not change over the calibration period). ee (tt)

**Government expenditure data:** For the retrospective analysis, we approximate the government expenditure using annual data from the World Development Indicators on general government final expenditure as a percentage of GDP. We transform this data to be in per capita constant 2021 USD BB(tt) (in PPP) simply by multiplying with World Bank’s GDP per capita estimates. PPI also requires the government expenditure data to be de-trended we do this by estimating a linear trend regression model

0 1 BB(tt) = aa + aa tt+ uu(tt), and removing the trend component from the model. That is, for each year we calculate an estimate for the government expenditure and use it as an input in PPI as 0 . Also, PPI typically runs its calibration process with time increments that are much smaller than a year (in order BB<sup>�</sup> (tt) BB<sup>�</sup> (tt) = aa + uu(tt) to converge PPI requires more than 50 simulation time steps). Here we assume that there are 6 simulation steps in a year (meaning that we calibrate PPI with a total of 54 steps). Due to this, the yearly data needs to be transformed such that the total budget of the country within the six steps that correspond to a one year matches the government expenditure. We do this by simply assuming that the government distributes its budget equally within each simulation step in a year ( /6). BB<sup>�</sup> (tt) In the prospective analysis we assume that the yearly value of the government budget is the same always and it is equal to the average of the estimated values for the government expenditure between 2011 and 2019.

**Quality of law and monitoring:** We approximate the quality of law and monitoring using data from World Bank’s World Governance Indicators. The data on the quality of law comes from the rule of law variable and the quality of monitoring from the control of corruption variable. In the retrospective analysis we set their value to the ones observed in 2011, whereas in the prospective analysis we set

40
women also reported reduced income due to job or business losses than women while more men did so due to reduced working hours than women.<sup>52</sup>

**Age Group** : Our method also projects, as in the case of Jordan, that the rate of job loss will be lower for prime age workers (aged 25-45) than for youth and older workers (and the survey-measured job loss projections were more even by age group as in the case of Jordan). Similarly, the GHFPS shows a 27.2 percent rate of private sector job losses for prime age workers relative to 44.1 percent for young workers aged below 25 (Table 5-20). Young workers also experienced greater rates of reduced income due to job or business losses.

_Table 5-19 Georgia: High-Frequency Phone surveys. Labor Market Impacts of COVID-19 by Gender, Percent of Private Sector Employees in March 2020_

||**Lost jobs**<br>**because of job**<br>**or business**<br>**losses due to**|**Stopped**<br>**working to**<br>**avoid exposure**<br>**to the virus**|**Reduced income**<br>**because of job**<br>**or business**<br>**losses due to**|**Reduced income**<br>**due to reduced**<br>**working hours**|
|---|---|---|---|---|
||**COVID-19**||**COVID-19**||
|**Male**|22.9|1.5|56.7|22.1|
|**Female**|40.8|4.3|77.1|15.6|
|Source: GHFPS.|||||

**Nationality:** Our method projects divergent effects by nationality in Georgia as well. In particular, it estimates a greater rate of job loss for Azeri and Armenian than for Georgian workers, indicating that pandemic conditions would exacerbate nationality-based inequality.<sup>53</sup> We cannot report observed job losses by nationality, as the GHFPS does not capture data on or stratify by nationality.

_Table 5-20 Georgia: Observed Labor Market Impacts of COVID-19 by Age Group, Percent of Private Sector Employees as of March 2020_

||**Lost jobs**<br>**because of job or**<br>**business losses**<br>**due to COVID-19**|**Stopped working**<br>**to avoid**<br>**exposure to the**<br>**virus**|**Reduced income**<br>**because of job or**<br>**business losses**<br>**due to COVID-19**|**Reduced income**<br>**due to reduced**<br>**working hours**|
|---|---|---|---|---|
|**Age Group**|||||
|**below 25**|44.1|5.7|77.4|14.7|
|**25 to 45**|27.2|2.6|62.4|19.0|
|**above 45**|23.8|0.6|58.5|23.6|

Source: GHFPS.

52 In both Jordan and Georgia, further research is required to fully understand gender disparities in the effects of the pandemic on labor markets. First, females are affected by intrahousehold resource allocation, meaning that they may suffer greater food insecurity, or non-monetary poverty even if their employment income is not affected. Second, the analysis would capture relatively short-term effects of the pandemic as we rely on the data collected during 2020-21. Finally, the analysis does not reveal situations of under-employment and low-paying jobs among female workers. 53 Available data from the 2018 Household Incomes and Expenditure Survey (HIES) show that prior to the pandemic the Azeri population was more likely to be poor than other nationalities in Georgia (World Bank, 2021) due to the types of firms, sectors, and occupations they were employed in.

39