rafmacalaba/datause-encoder-data huggingface dataset:
much further work remains to be done to improve the quality of expenditure data collection.

# 3.3 **Prices**

In order to use household expenditure as a measure of welfare, it is necessary to take into account the fact that prices are not the same in different parts of a country. It is thus necessary to deflate household expenditure with a regional cost of living index. This had been recognized by the designers of the CILSS and they added to the household survey a price questionnaire which called for the enumerators to obtain prices in markets for a limited number of items. For each item, three observations were to be made. The experience with this has been disappointing. First, as a practical matter, the collection of prices was insufficiently integrated with the rest of the survey so that delays in collection and data processing occurred and many price questionnaires were filled out incompletely. In addition, the three observations proved insufficient in light of the typically wide price variation in African markets, especially for perishable food items. Lastly, the commodity coverage was inadequate (seventeen food items and four non-food items). According to Glewwe (1987), the non-food price information could not be used at all and it was necessary for the construction of the regional price index to rely on one non-perishable food item, a can of tomato paste, as a proxy for all non-food items.

The limitations of such a price index are obvious and it was an important element of this research to construct a more comprehensive price index (see Grootaert and Kanbur, 1992a). The lesson from this effort is a positive one. The index was constructed on the basis of price data collected in CMte d'Ivoire in 1985 for the International Comparisons Project. In other words, the price data were not related to the CILSS. Nevertheless, it proved relatively easy (although time-consuming) to match the prices from the ICP with the expenditure categories in the CILSS. The resulting regional price index was based on prices for 260 product categories in the ICP which were matched with 27 food categories and 25 non-food categories from the CILSS.

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that female schooling outcomes were lower initially, and so they had less to lose after the genocide. In column 3, we estimate a regression including an interaction between the differencein-differences variable and the indicator for being non-poor. Somewhat surprisingly, it is among the children in non-poor households in which the negative shock is strongest. Young children in these non-poor households experience a decline of 1.223 years of schooling. It appears the genocide’s impact on schooling outcomes might have worked by leveling-off educational achievements to a low level for everyone, irrespective of gender or wealth.

Tables 4 to 6 follow the same logic as the Table 3 specifications, but for the dependent variables we use an indicator of whether the person completed grades 1 to 6, respectively.<sup>11</sup> The objective of these tables is to determine at which grade level the genocide’s negative impact is strongest. For all three tables, the household level controls and province and children’s age fixed effects are the same as in Table 3. Table 4 presents the baseline regressions as in column 1 of Table 3. Across the 6 grades, females have a lower probability of completing a given grade (not significant for grades 3 and 4) and individuals from non-poor households have a higher probability to complete the grade. The indicator for the 2000 DHS is always positive and significant at the 1 percent level (except for grade 6) showing that for the older cohorts, educational achievement increases over time. However, the difference-in-difference variable, an interaction of the 2000 DHS indicator with the young cohort aged 6 to 15, always carries a negative and significant coefficient. The magnitude of these coefficients is similar to the difference-in-difference tabulations in Table 2, Panels B to G, obtained without controls, and they peak for children in grades 3 and 4. The coefficient of -0.145 in the regression examining fourth grade completion suggests a decline of almost 40 percent compared to the baseline completion rate of 38.4 percent.

> 11 We estimate the regressions with a linear probability model, although results are consistent using logit or probit.

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sectoral activity estimates with emissions factors that vary by region; and (3) IFPRI estimates of rice production, which we combine with our CH4 anomaly observations to index relative methane intensities (emissions per unit of output) for different areas.

Regression analysis reveals a highly significant relationship between IFPRI irrigated field extents and local SP5 CH4 anomalies. We map expected values from the regression results, as well as regression residuals that identify areas where the CH4 anomalies identify methane concentrations that are higher and lower than expectations from field extents alone. For comparison, we also perform a regression analysis of the relationship between methane emissions estimated by EDGAR and irrigated field extents. As expected, we find a closer “fit” for EDGAR emissions because their “bottom-up” computation actually incorporates field extent. In a follow-on exercise, we regress local CH4 anomalies on field extent and EDGAR emissions and use the residuals to identify outlier areas with unexpectedly high or low emissions. These residuals represent the “value added” of the S5P data, because they identify local methane emissions variations that are not captured by IFPRI field extent or EDGAR estimates. The regression residuals identify a large number of positive and negative outlier clusters in all regions. We map these results and summarize them by identifying the states and provinces within countries that have the largest positive and negative outlier values.

Finally, we compute a spatial measure of methane intensity that indexes CH4 emissions per unit of output. This information is particularly interesting from a development perspective, since it incorporates the food value of rice production as well as the associated methane emissions. We find particularly high and low methane intensities among Indian states and Indonesian provinces, respectively, along with smaller clusters of positive and negative outlier states/provinces in Asia and other global regions.

In summary, this first application of the new World Bank database suggests that CH4 atmospheric concentration measures from the ESA’s Sentinel-5P system can provide useful new information for identifying and assessing local variations in methane emissions from irrigated rice production. While more ground-truthing would undoubtedly be useful, we believe that recent research has already established the accuracy of space-based CH4 measures. For irrigated rice production, future survey research could shed more light on the local sources of variation in measured emissions. This could in turn support incentive-based programs that use the World Bank database to track, verify and reward adoption of methane-reducing techniques. If this approach proves valuable for irrigated rice production, we believe that it can work for other methane sources as well.

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_Africa’s Macroeconomic Story_ in those situations. Policy makers need to design and implement a long-term fiscal plan to ensure that resource wealth is shared equitably across current and future generations. Sovereign wealth funds (set up in Angola and Nigeria) can help achieve this objective by developing such expertise.

# **Figure 25: Value of Net Oil Exports, 2000 and 2018**

_Percent of GDP_ <!-- Start of picture text --> 80<br>2012 2018<br>60<br>40<br>20<br>0<br>Equat. Rep. of Angola Gabon Nigeria Chad<br>Guinea Congo<br><!-- End of picture text --> _Sources:_ IMF _World Economic Outlook_ April 2013. _Note:_ projection for 2018.

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documentation to open these accounts, are less likely to accept loan applications through nontraditional channels, impose higher minimum balances on consumer loans, and are slower at processing loan applications. Banks in economies where entry into the corporate sector is more costly are less likely to allow customers to open accounts outside headquarters, charge higher fees on checking accounts, require a greater number of documents to open accounts, charge higher fees for SME loans and for using ATM cards, and take longer for processing consumer loans.

More transparent societies might allow for lower barriers to banking, as banks in economies where clients have more access to information might have less leeway to impose high barriers to banking. More transparency might also imply a higher degree of competition, as customers can more easily compare products across banks. To gauge the relationship between transparency and bank barriers, we use a bank disclosure index (World Bank, 2006b) that captures how informative banks’ balance sheet and income statements are. While this indicator was constructed to assess to what extent banks include information relating to their risk-taking and thus stability, it might indicate the general transparency of banking. We also utilize an indicator of media freedom, which measures the share of press outlets that are owned by the government. This indicator comes from Djankov et al. (2003), who show a negative association between this and other measures of media freedom with economic and political freedom.

Our results suggest that banks in countries with higher disclosure standards require lower minimum balances to open a checking account and charge lower fees on these accounts, require fewer documents to open such an account, are more likely to accept loan applications through non-traditional channels such as phone or Internet, need fewer days to process loan applications, and charge lower fees for using ATMs. Banks in countries with lower degrees of media freedom (i.e., where a greater share is controlled by the government) restrict the locations where accounts 26
**Statistical Performance Indicators and Index: A New Tool to Measure Country Statistical Capacity**

Hai-Anh H. Dang, John Pullinger, Umar Serajuddin, and Brian Stacy<sup>*</sup>

**Key words** : statistical capacity, statistical performance, statistical indicators, statistical capacity index, national statistical system

**JEL** : C8, H00, I00, O1

* Dang (hdang@worldbank.org; corresponding author) is senior economist with the Data Production and Methods Unit, Development Data Group, World Bank; Pullinger (j.pullinger@imperial.ac.uk) is former head of the UK National Statistical Service; Serajuddin (userajuddin@worldbank.org) is manager of the Indicators and Data Services Unit, Development Data Group, World Bank; Stacy (bstacy@worldbank.org) is economist with the Indicators and Data Services Unit, Development Data Group, World Bank. We would like to thank Lisa Bersales, Joao Pedro Wagner De Azevedo, Mustafa Dinc, Haishan Fu, Pietro Gennari, Johannes Hoogeveen, Dean Jolliffe, Christoph Lakner, Hiroko Maeda, Daniel Mahler, Jimmy McHugh, Yongyi Min, Yusuf Murangwa, Juan Oviedo, Viveka Palm, Carolina Sanchez Paramo, Francesca Perucci, Martin Rama, Walter Radermacher, and Tara Vishwanath for useful discussion on earlier versions of this paper. We would like to thank Juderica Dias and Dereje Ketema Wolde for helpful support with the data. We are grateful to the UK Foreign Commonwealth and Development Office (FCDO), formerly the Department of International Development, for funding assistance through various Knowledge for Change (KCP) grants, including one for the World Development Report 2021 “Data for Better Lives”.
firms to formalize may be more cost-effective than the interventions here which instead provided services and support to firms.

# **6.6 Why don’t more firms formalize?**

In the third treatment group, which combined all packages of incentives and in which the impact was the greatest, the formalization rate was 18.6 percent (16.3 percentage points more than in the control group). This impact is greater than for similar programs in other contexts (Bruhn and McKenzie, 2014) in which formalization is also linked to taxes. But it means that even though this type of program had a significant impact, the majority of the informal firms still remain informal. This is true even if we consider specific sub-population like businesses operating outside the market or with an owner who went to secondary school, for which impact on formalization remained below 23 percentage points.

Why do most firms remain informal? A first potential explanation is the presence of other legal barriers to formalizing. Data from our midline survey reveals that only 54 percent of informal business owners have legal identification needed to formalization (either a passport or a Beninese ID card). In contrast, 85 percent have a birth certificate and 75 percent an electoral card, so amending the process to allow these alternative forms of identification to be used would alleviate this constraint for many firms. However, lack of identification does not seem to be the binding constraint to formalizing for most informal firms: only 0.6 percent of the control group said this was one of the two main reasons for not formalizing (table A8).

Our endline survey asked informal business owners the two main reasons why they were still informal (table A8). The most common responses in the control group were that firms did not see any benefits from doing so (32 percent), or that they do not want to have to pay more taxes (26 percent). The other main reason was that they viewed the process as too costly, complicated, or time-consuming (31 percent). These responses are similar among those who remain informal in the treatment groups, despite the visits by CGA advisors to explain the new simpler process of registration and the potential benefits of registering. It is consistent with the idea that many of these informal firms are so far from the formalization margin that they consider this information irrelevant – and indeed, as noted before, two years after program launch, only 2025 percent of businesses in the treatment groups could even remember what the _entreprenant_ program is.

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(4) through (7), we find that whether size is measured as employment or revenue, increased foreign ownership in firm _i_ is associated with greater investment for low revenue and small firms. As before, we only see a significant impact of horizontal spillovers for the small firms, and then only in exits. Similarly, we find that backward exits affect small and low revenue firms only. Unlike the previous results, however, we do find an impact of backward entries on high revenue firms. Testing the coefficient estimates we find a significant and persistent effect of backward spillovers.<sup>35</sup> Also different from the results of Table 6, we find some slight significance of the forward spillover results, although this is only for the large employers. In any case, these firm size results lend further support to asymmetric effects and transitory spillovers, particularly when FDI exits those sectors supplied by small firms. Finally, in the age results of columns (8) and (9), we find little significance of the spillovers, although as before we find a significantly positive effect from backward exits on young firms suggestive of transitory spillovers.

Thus, while there are some differences between the employment- and capital-based results, three lessons stand true. First, we provide evidence that using the net change in spillovers conceals important asymmetries between FDI entry and exit. Second, we find significant evidence of transitory leaning effects since, if spillovers were to originate from permanent learning effects, exits should have no impact on the behaviour of local firms. Third, we show that changes in the backward spillovers, and particularly when driven by exits, have the most robust impacts. Fourth, we find that the asymmetric transitory spillovers from FDI are particularly strong for service sector and smaller firms. This implies that the existing literature focusing exclusively on net changes in FDI is missing important aspects of the spillover process.

# **5 Conclusion**

One of the prime reasons governments aim to attract foreign investment is the belief that, by bringing in new technologies, ways of doing business, and global connections, multinationals will increase the productivity, employment, and investment of local firms. Although there is a large body of evidence supporting these effects, heretofore no one has investigated whether these effects are persistent learning spillovers, remaining after the foreign firm disinvests, or transitory leaning effects, meaning that in order to maintain the spillover it is necessary to prevent exit. We fill this gap using Jordanian firm level data during a period of both entry and exit by foreign firms. On the whole, we find notable asymmetries between entry and exit of foreign firms, with the estimates indicating a significantly transitory nature of FDI

> 35F-stat(1,2175)=6.21, P-value=0.01.

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<!-- Start of picture text --> 100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>0.65 0.7 0.75 0.8 0.85 0.9 0.65 0.7 0.75 0.8 0.85 0.9<br>Strength of On Board Security Strength of On Board Security<br>(a) Large Ships (b) Small Ships<br>50 25<br>150<br>75<br>75<br>125<br>50<br>100<br>75<br>25<br>100 50<br>100 125<br>755025<br>25<br>125<br>100<br>25<br>75 50 25<br>125<br>125 100<br>Number of Navy Patrols Number of Navy Patrols<br><!-- End of picture text --> Figure 14: iso-crime curves for small and large vessels Note: the figures plot the combination of navy patrols and the strength of private maritime security in year _t_ that achieves a certain number of attacks on large and small vessels in year _t_ + 1. The black line in each graph indicates the optimal combination of navy patrols and private maritime security contractors that minimizes the total costs for each level of attacks. All the other parameters are set to their benchmark value.

In the following years, the OBP reports provided detailed breakdown of the costs of patrols. The 2012 report estimated the total costs of operation to be around US$960 million for 63 patrols, and for the next year, US$912 million for 61 patrols. We take the difference of these two years, and estimate the cost per patrol to be around US$23.7 million. This cost estimate is closer to the lower bound of the true costs, as the difference-estimator takes away any fixed costs of maintaining navy bases in the region. In the end, we use the average of the two estimates, US$27.0 million per patrol for all the years. With the costs of both measures estimated, we then proceed to compute the cost-minimizing combination of policy measures for every level of attack-reduction in each year. We plot the optimal policy rules as the black solid lines in Figure 13.

Private armed protection appears relatively cost-effective in curbing the number of attacks at the early stage. In early years, private protection alone is able to eradicate the piracy problem. In later years, it is also more cost-effective to first implement private protection of largest vessels to reduce the number of attacks from around 300 attacks per year to fewer than 100 attacks. However, private protection of largest vessels alone cannot entirely solve the piracy problem due to crime displacement. Displacement effects 42
<!-- Start of picture text -->
----- 10thpe —— average<br>median --- 90thpc<br>2<br>° ! .<br>1 ; .<br>F 4 e .<br>3 :<br>oOg Sy... Te - - ~~t<br>2018 2019 2020 2021 2022 2022<br><!-- End of picture text -->

<!-- Start of picture text --> “> 10th pc ——~ average<br>median --- 90th pc<br>67.<br>a4 ae +t<br>8<br>an<br>SB fo __<br>cyo | ,<br>2018 2019 2020 2021 2022 2023<br><!-- End of picture text -->
Col. 4 in Table 3.6 shows the SMI averages reported to the World Bank by the SPO (the averages reported to the OECD appear to be somewhat different). These are allegedly based on the surveys and present averages per person engaged for the years 1960 onwards with some gaps in the 1960s and for 1971. How this is possible for the years before 1970 is not clear. Col.5 shows the SII averages for insured persons.

Both censuses and surveys lead to averages of annual remunerations. To obtain average daily remuneration, I assumed 300 workdays per year. How annual remuneration has been converted into daily remuneration by the SMI and SII series is unknown to me.

Comparing census and survey averages we find the former systematically lower than the latter. This is to be expected, recalling that small establishments have lower wages per employee than larger ones and that the survey averages are calculated on a larger number of persons than the census averages. That the SMI averages are lower than the census averages is perplexing and indicates that adjustments must have been made. The SII averages are systematically lower than all other averages, possibly a consequence of underreporting from the side of the establishments.

The lower panel of Table 3.6 permits easy comparison of growth rates of nominal wages. Census and survey averages show

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