rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6533

# **Abstract**

This paper builds on the analysis of job creation developed in World Bank (2013) to provide an empirical investigation of the industry and firm-specific determinants of the job creation process in eleven new European Union (EU11) economies. It relies on the Amadeus dataset of firms during 2002–2009. The main results indicate that during the years prior to the global financial crisis, traditional industries were crucial for the net creation of jobs in EU11. However, traditional industries were the ones most severely affected by the financial crisis. By contrast, services firms were less vulnerable to the economic downturn. At the firm level, small and young firms registered the highest employment growth rates. The empirical results also indicate that more productive firms tended to be less vulnerable to economic downturns. Moreover, the results demonstrate that the perceived quality of the business climate by the EU11 enterprises is correlated with not only the firms’ employment growth, but also their productivity. In the post-crisis period, poor business restrictions were negatively associated with the creation of jobs. All these findings hold for the group of high-growth firms that disproportionately accounted for the creation of new jobs in the EU11 economies.

This paper is a product of the Poverty Reduction and Economic Management Department, Europe and Central Asia Region. 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:// econ.worldbank.org. The authors may be contacted at gvincelette@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, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
18 unobservable—between countries that eventually adopted SHI and those that did not (a _selection_ effect) is the main task of our empirical work.<sup>21</sup>

# **_Labor market outcome indicators_**

Our labor models are estimated with gross wages, unemployment, employment and informality measures as dependent variables. We briefly describe their definitions and sources below, and their descriptive statistics are presented in Table 3.<sup>22</sup> Total annual gross wages and salaries in local currency units were drawn from the WBWDI ECA regional database and transformed into yearly constant PPP averages for the employed population aged 15-59 using purchasing power parity conversion factors and the US deflator, both available at the WB-WDI database as well. We use annual data on total unemployment and registered unemployment rates obtained from the _Key Indicators of the Labor Market_ (KILM) database, published by the International Labor Organization (ILO). The same database is used for gathering data on agricultural employment and self-employment as shares of total employment; these work categories serve as proxies for informal employment, as they have been argued to be closely associated with last resort, low-productivity jobs in the region, thus masking a situation of worsening underemployment and predominant subsistence farming (Svejnar 1999; Alam et al. 2005). As yet another employment measure, we use two employmentto-population ratios: the first is drawn from the ILO-KILM database, and employment-topopulation ratio for individuals aged 15-59 was obtained from the TransMONEE 2006 Database.

> 21 Moreover, the comparisons of descriptive statistics between SHI and non-SHI countries presented in Table 2 cannot be strictly interpreted as a preliminary assessment of SHI effects, because eventual SHI adoptions occurred on a staggered basis in our sample.

> 22 Table 10 in the Appendix contains the full definitions and sources for all our labor outcome variables.
groups for most assets are not significant. Moreover, non-beneficiary households appear better off (higher levels of assets), which would make positive effects of the treatment harder to find.<sup>18</sup> The level of attrition in our sample is relatively high. In the sample, 2,000 households were included, but complete interviews are only available for 1,592 households. Attrition is due to difficulties in finding households in the field three years after administration of the baseline (186 questionnaires), to the loss of survey questionnaires in three villages (108 questionnaires), and to the inability to match identifiers in some cases. Among the 1,592 completed questionnaires, 1,138 are in the common support sample. A formal test of baseline differences between attritors and non-attritors is presented in Table 2. In the common support sample (panel A), among all the variables collected in 2010, only ownership of cart (lower), sheep ownership (higher) and PMT score (higher) are significantly different at 5% for attritors. While some differences are observed, they are limited and as such appear unlikely to strongly affect results of the main identification strategy.<sup>19</sup> Table 3 presents descriptive statistics for all variables in the follow-up survey and the full sample, as well as separately for beneficiaries and non-beneficiaries. As expected based on the PMT formula, non-beneficiary households are smaller, have more physical assets, and have higher PMT scores on average. However, beneficiary households have a higher level of livestock, household enterprise activities, and _tontine_ usage at follow-up.

# **4. Results**

Table 4 contains Difference-in-Differences (DID) estimates of the impact of the cash transfers on beneficiaries 18 months after project termination for the limited number of outcome variables included in both the baseline and the follow-up surveys. Consistent with equation (7) above, the coefficient of the interaction between post-program and beneficiary dummies captures the cash transfer impacts. In Table 5, Simple Difference (SD) estimates are computed for a larger set of investment outcomes available in the follow-up survey. Column 1 presents the results for households in the common support sample without village fixed effects, and column 2 with

> 18 There are some additional significant differences between beneficiaries and non‐beneficiaries in the full sample, which is one of the reasons why the common support sample is preferred in the Simple Difference strategy. 19 There are additional significant differences between attrition and non‐attrition households in the full sample compared to the common support sample.

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Figure 1: Gender gaps in Albania, Bosnia-Herzegovina, Croatia and Kosovo <!-- Start of picture text --> 60%<br>60%<br>Albania Bosnia and Herzegovina<br>50% 50%<br>40% 40%<br>LFP<br>LFP<br>30% Self- 30%<br>Self-employed<br>employed<br>Employers<br>Employers<br>20% 20%<br>10%<br>10%<br>0%<br>0%<br>2013<br>2008 2012<br><!-- End of picture text --> <!-- Start of picture text --> 60% Croatia 90% Kosovo<br>80%<br>40%<br>70%<br>LFP<br>60%<br>20%<br>Self-<br>employed 50% LFP<br>0% Employers 40% Self-employed<br>2004 2009 Employers<br>30%<br>-20%<br>20%<br>-40% 10%<br>0%<br>2012<br>-60%<br><!-- End of picture text --> 5
**Figure 2: Non‐consent to the conflict module.**

**Figure 3: Density plot of consent by area.**

<!-- Start of picture text --> 100% 0.04<br>90%<br>80% 0.03<br>70%<br>60%<br>0.02<br>50%<br>40%<br>0.01<br>30%<br>20% 41% 38%<br>26% 0<br>10%<br>13% 14% 0 20 40 60 80 100<br>0%<br>Cluster-wise percentage of consent<br>Juba Rumbek Torit Yei Total<br>No consent Juba Rumbek Torit Yei<br>Density<br>Number of respondets<br><!-- End of picture text -->

**Table 2: Characteristics of consenting and non‐consenting respondents.**

|Characteristics<br>(mean)|Age|HH<br>size|Years at<br>residence|Years of<br>education|Number<br>of IGAs|
|---|---|---|---|---|---|
|Consenting|22.1|10.5|5.6|7.7|0.9|
|Non‐consenting|21.7|9.8|5.5|7.6|1|

About 40 percent of all consenting individuals experienced at least one conflict event (Figure 4). Additionally, about 30 percent of consenting individuals stated that a member of the household was harmed or died due to the conflict (Figure 5). The highest incidence is found in Rumbek, where about 67 percent of the consenting individuals experienced one or more conflict events, compared to less than 40 percent in the other three areas. Accordingly, Rumbek’s residents also report the majority for most conflict events such as a member dying or being harmed, while both Rumbek and Juba’s residents more often reported that members were displaced. As Rumbek also has the highest non‐ response rate, it is likely that the overall extent of conflict exposure is underestimated.

11
Cargo reservation schemes still exist under the UN Liner Code, but regulation has reduced the extent of anticompetitive practices such as price-fixing agreements and maritime conferences.<sup>1</sup> Other forms of protection, such as barriers to investment in maritime transport service, still exist (one example is the limitations foreign investors face when establishing local offices). Bertho, Bochert, and Mattoo (2014) find that policy barriers reduce trade by 28–46 percent through higher transport costs.

Port efficiency has received attention in the literature as an important determinant of the level of and changes in transport costs. The evidence shows a strong negative link between port efficiency and shipping costs (Micco and Pérez 2002; Sanchez and others 2003; Clark, Dollar, and Micco 2004; Wilmsmeier, Hoffmann, and Sanchez 2006). However, there is no agreement on the measure of port efficiency. Consequently, the coefficient on efficiency in maritime transport cost estimations varies significantly across papers.

Clark, Dollar, and Micco (2004) incorporate ad hoc measures of port efficiency derived from the World Bank’s _Global Competitiveness Report_ . They construct an index ranking of port efficiency from survey responses of cargo handling firms to the statement “Port facilities and inland waterways are extensive and efficient” (1 = strongly disagree, 7 = strongly agree). They find that for the average country, an increase in port efficiency from the 25th percentile to the 75th percentile reduces costs by about 10–15 percent, everything else equal. Wilmsmeier, Hoffmann, and Sanchez (2006) estimate the effects of port efficiency on transport costs for 16 Latin American countries in 2002 using data from the International Trade Database, which is maintained by the United Nations. They note that port efficiency need not lead to a reduction in freight rates (ports might charge higher rates if they provide faster or more reliable services). Based on port efficiency data from the 2004 World Economic Forum, however, they show that a 1 percent increase in port efficiency leads to a 0.38 percent reduction in trade costs. If a country with the lowest efficiency improved efficiency to the levels of the country with the highest efficiency, freight charges would decrease by 26 percent.

Sanchez and others (2003) uses a similar methodology to study the effects of using a different measure of port efficiency on transport costs. They derive their port efficiency measure from a 1999 survey of 41 port terminals. Based on the survey responses, they compile weights, using principal components analysis, to construct efficiency scores. Only their productivity factor— which included container handling capacity at port, the average number of containers per vessel, and the hourly container load rate—had a statistically significant impact on transport costs: A 1

> 1 Competition laws such as the United States’ Ocean Shipping Reform Act of 1999, European regulation that abolished block exemptions from shipping conferences, and the emergence of large shipowners in the 1980s and 1990s weakened conferences (WTO 2010).

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# **<u>Table 5: Determinants of land rental market participation</u>**

||**Without ability**|**Specification**<br>**With ability**|**With ability**<sup>**22**1</sup>|
|---|---|---|---|
|Agricultural ability||0.402***|0.310***|
|||(8.89)|(6.88)|
|Household land endowment (log)|-0.199***|-0.311***|-0.309***|
||(24.24)|(27.44)|(27.32)|
|Number of members aged 15-60 (log)|0.071***|0.062***|0.154***|
||(8.23)|(6.87)|(5.21)|
|Value of total assets (log)|-0.056***|-0.035**|-0.034**|
||(4.27)|(2.58)|(2.40)|
|Head’s age (log)|1.777**|0.934|1.715|
||(2.31)|(1.60)|(1.36)|
|Head’s age squared|-0.262**|-0.152*|-0.250|
||(2.56)|(1.95)|(1.49)|
|Head completed secondary education|-0.010|0.001|0.000|
||(0.42)|(0.05)|(0.02)|
|Head completed higher education|-0.050*|-0.043|-0.055*|
||(1.71)|(1.40)|(1.80)|
|Village per capita income|-0.144***|-0.139***|-0.088***|
||(5.50)|(5.20)|(3.22)|
|**Lower bound equation (lease out to autarky)**||||
|Share of village workers migrating out of province|0.367***|0.407***|0.619***|
||(8.52)|(9.03)|(5.12)|
|Own land certificate|0.070**|0.079**|0.082**|
||(1.98)|(2.11)|(2.18)|
|Rule: Renting to outsiders not allowed|-0.076**|-0.083**|-0.080**|
||(1.96)|(2.06)|(1.96)|
|Rule: Village takes back non-cultivated land|0.261***|0.253***|0.247***|
||(6.91)|(6.39)|(6.22)|
|Village per capita land|-0.044**|-0.049**|-0.069***|
||(2.15)|(2.29)|(3.21)|
|**Upper bound equation (autarky to lease-in)**||||
|Share of village workers migrating out of province|-0.420***|-0.385***|-1.185***|
||(10.49)|(9.34)|(10.86)|
|Own land certificate|-0.033|-0.011|0.030|
||(1.06)|(0.35)|(0.92)|
|Rule: Renting to outsiders not allowed|-0.014|-0.030|-0.011|
||(1.96)|(0.80)|(0.29)|
|Rule: Village takes back non-cultivated land|<br>-0.131***|<br>-0.137***|<br>-0.129***|
||(3.58)|(3.63)|(3.39)|
|Village per capita land|<br>-0.095***|<br>-0.117***|<br>-0.130***|
||(5.27)|(6.29)|(6.93)|
|Log-likelihood|-13032.05|-12070.83|-11754.68|
|No. of observations|19,570|18,390|17,969|

_Note:_ Dummies for time and provinces included but not reported. Adjusted for clustering at village level. Absolute value of z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

> 22 Using only ability estimate derived from household-year combinations that do not involve any rental to avoid bias.

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Page 62 152. One problem with measuring the impact of adjustment in a given country is the lack of time series data -- especially gender-disaggregated date -- at the household level. The dearth of such data limits analysis of women in development generally, but the data needed to assess the impact of adjustment are particularly scarce. It may be useful to develop gender-specific indicators to track the status of the most vulnerable groups. Sample surveys at the household level, including those now carried out or planned by the Bank, are a large step in the right direction. But most could give greater attention to nutritional status by gender, pregnancy history, sources of income by gender, types of assets under female or male control, female head-of-household status, access to and consumption of particular kinds of social services, and other defining characteristics of groups likely to be particularly vulnerable. Such data collection is not costless, but it would allow more solid research on how particular policy and program decisions affect specific groups and how greater economic mobility and efficiency can be achieved.
Policy Research Working Paper 6988

# **Abstract**

This paper seeks to find empirical evidence of a link between tax simplification and corruption in tax administration. It attempts to do this by first defining “tax simplicity” as a measurable variable and exploring empirical relationships between simpler tax regimes and corruption in tax administration. Corruption in tax administration is calculated with data series from the World Bank’s Enterprise Survey Database. The focus is on business taxes. The study includes 104 countries from different income groups and regions of the world. The time period is 2002–12. The empirical findings support the existence of a significant link between the measure of tax corruption and tax simplicity, so a less complex tax system is shown to be associated with lower corruption in tax administration. It is predicted that the combined effect of a 10 percent reduction in both the number of payments and the time to comply with tax requirements can lower tax corruption by 9.64 percent. Some interesting regional differences are observed in the results. Similarly, the income level of countries plays an important role in determining the impact of tax simplification on tax corruption; specifically, the link is stronger for lower-income level countries. The positive link between tax simplicity and lower tax corruption has useful policy implications.

This paper is a product of the Governance Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at rawasthi@ 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, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Systematically collecting and disseminating such data should follow best practices. Balancing reporting costs against obvious benefits, surveys could be updated to incorporate critical information missing in existing questionnaires. Closer collaboration between transport ministries or departments and statistical agencies would leverage existing knowledge base, inform data needs and capabilities, and help avoid replication of efforts. As alluded to in Table 1, governmental agencies can increase the use and benefit of their existing products by providing transparent procedures to facilitate access to micro-data and sharing aggregated statistics in their websites—and making these available in English for global reach. Moreover, low-cost improvements to existing efforts could make them more informative for measuring overland transportation costs. For example, to aide estimation from price gaps, data that is regularly collected for compiling the consumer price index (CPI) could cover additional locations so as to generate sufficient geographic and spatial variation for this methodology to be more reliable. Another consideration described above is the comparability of products for which prices are collected. To the extent possible, targeting the same brands for any given product would be a good practice for not just precisely measuring transport costs but also the CPI. Other potentially helpful data products are consistent and frequent measures of road quality and traffic volumes. Once again, such data are often collected in order to monitor maintenance needs and congestion. There are wellestablished methods to define and quantify road quality such as the International Road Roughness Index. Appropriately designing the spatial features of these collection efforts would come at minimal additional cost but increase their usefulness for the purpose of measuring and lowering overland transportation costs.

# **References**

Alder, S., Song, Z. and Zhu, Z. (2021). Unequal returns to China’s intercity road network. Working Paper.

Allen, T., and Arkolakis, C. (2014). Trade and the Topography of the Spatial Economy. The Quarterly Journal of Economics, 129(3), 1085-1140.

Allen, T., Atkin, D., Cantillo, S. and Hernandez, C. E. (2021) Trucks. Working Paper Slides.

Anderson, J. E., and Van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. American Economic Review, 93(1), 170-192.

Anderson, J. E., and Van Wincoop, E. (2004). Trade costs. Journal of Economic literature, 42(3), 691-75.

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# **2. The cashew sector in Guinea-Bissau**

Guinea-Bissau (GNB) is a tropical country located in West Africa, bordering Senegal (north) and Guinea (east and south). It covers 36,125 sq km with a population of over 1.5 million inhabitants (42 people/sq km). The country is one of the largest world producers of raw cashew, together with India, Vietnam, Brazil and Côte d’Ivoire. Raw cashew products represent about 90 percent of total exports, and are destined to India, Vietnam and Brazil. About 80 percent of the population is linked to this sector.

According to Comissão Nacional de Caju - CNC (2012), annual production capacity is estimated at 200,000 tons of cashew nuts and 1.5 million tons of cashew apples. Production is scattered throughout the country (small producers represent 85 percent of total production). On the other hand, raw cashew nuts can be processed to get cashew almonds, with an approximate yield of 2028 percent for kernels and 72-80 percent for shells. But the activity is underdeveloped in GNB. In 2011, the processing activity restarted operations after a shut-down period but still could not take off. Only 2,000 tons of raw cashew nuts were processed in 2013.

Table 1 summarizes the situation in the cashew sector as of 2013. GNB exported 138,000 tons of cashews, which together with the reserved stock for processing (2,000 tons) represented about 70 percent of total estimated production of 200,000 tons. The remaining 60,000 tons were traded in the informal market (smuggling) or correspond to missed market opportunities.

**Table 1. Summary of cashew sector, year 2013 (in ‘000 tons)**

|Estimatedproduction|200.0|
|---|---|
|Exports|138.0|
|Stock for localprocessing|2.0|
|Exports + localprocessing|140.0|
|Estimated illegal trade – missed opportunities|60.0|

Source: ANCA (2014) and World Bank.

There are five main actors in the cashew supply chain: producers, sub-brokers, traders, exporters and cashew processors. Next, we provide a brief description of each of them.

**<u>Producers:</u>** Production of raw cashews is atomized, as 85 percent of producers are small units. In 2013, they received approximately 165 XOF/kg of cashews (about 351 USD/ton, using an exchange rate of 470 XOF/USD). Total revenue linked to formal activities reached USD 49.1 millions (USD 48.4 millions from exportable cashews and USD 0.7 millions from cashews sold for processing).

In 2013, the government set a reference price of 210 XOF/kg, which was not binding (unlike the case in 2010). In fact, farm-gate prices went down with international prices in 2012 and 2013, from record levels achieved in 2011 (430-500 XOF/kg).

3