rafmacalaba/datause-encoder-data huggingface dataset:
achieved. As the last rebel groups is demobilized and reintegrated, however, and the situation remains stable, the share of spending on military and policy are expected to decline.

Despite the limited fiscal space caused by the large security budget, the Burundi government has started to redirect its budget, including resources from interim debt relief, toward povertyreducing and social expenditures line with its PRSP goals. Total social expenditures (health, education and other social expenditures) rose from 5.1 percent of GDP in 2001 to a projected 8.8 percent in 2008 and are expected to increase further to about 10 percent in 2009, about 23 percent of total expenditure. Interim debt relief has been used as an integral part of the overall budget to increase social sector and poverty-reducing expenditures. More than half of HIPC resources are being allocated to key social sectors (education, public health and the fight against AIDS).

# 4.4. <u>Predictability of aid in Burundi</u>

As pointed out in section 4.1, low predictability of aid affects the composition and effectiveness of spending and can further exacerbate the bias towards government consumption and away from investment in production oriented sectors as well as human capital. In the case of Burundi, the low predictability of aid has likely aggravated the spending pattern of the government, which has not been conducive for promoting growth especially as the country already spends a very limited amount of its domestically financed budget on investment.

The average aid disbursement ratio, i.e. the disbursed amount divided by the committed amount, in Burundi was 68 percent between 2002 and 2007, but varied considerably from one year to the other (Figure 6). The average difference between commitments and disbursements was USD 133 million, peaking at USD 286 million in 2006 -when it only reached just over 50 percent of the committed amount has been disbursed.

**Figure 6:** Disbursement ratio and commitment-disbursement gap, 2001-2007

<!-- Start of picture text --> 120 300<br>Disbursement<br>ratio<br>100 250<br>80 200<br>60 150<br>40 100<br>Commitment-<br>disbursement<br>20 gap 50<br>0 0<br>2002 2003 2004 2005 2006 2007<br>Source : Authors’ calculation based on OECD – DAC data.<br>percent<br>USD million<br><!-- End of picture text --> In the next few years, the issue of low predictability as well as volatility of aid is especially important in the light of the financial crisis. Aid commitments and disbursements may decline as donors address domestic financial issues. The situation is particularly critical as Burundi is now entering the true post-conflict reconstruction phase, where according to Collier and Hoeffler 19
user firms), almost all have been privatized with only 1 to 4 firms per country remaining with significant (+50%) state ownership.

Regarding road freight providers, there are 165 enterprises, with Czech Republic and Hungary accounting for 50 and 52 enterprises, respectively, and Poland 63 enterprises. A few large firms are included for each country (2, 4 and 3 in the Czech, Hungarian and Polish samples respectively) though given the overwhelming number of small, owner-driver enterprises in the population, a larger number of smaller enterprises are represented, with micro enterprises accounting for 54% of the Czech sample, 50% of the Hungarian sample and 41% of the Polish sample. The median age of providers ranges between 7.5 and 9 years. There are proportionately fewer start-ups in the provider sample: 24% of the Czech sample but 17% of the Polish sample and 13% of the Hungarian sample. With respect to foreign ownership, 6% of both the Czech and Polish sample are 100% foreign-owned (3 and 4 firms, respectively), while no Hungarian road freight provider is fully foreign-owned. Finally, with respect to persistence of state ownership, Poland is the only country where there are still stateowned providers in the sample, corresponding to population statistics. Hungary, as previously mentioned, has no state-owned enterprises remaining in the road freight sector since 1998.

**Performance of downstream enterprises and use of innovative logistics services.**

As reported in Table 2, increases in annual total operating costs (including labor, materials, transport and IT-related costs, among others) between 1997 and 1999 range between 10 and 20% for the median firm across the three countries. Hungary had the enterprise with the largest fall in costs (97%) but also the enterprise with the largest increase in costs (447%), resulting in the largest mean increase in costs (31.8%). Transport and logistics-related operating costs (including both own-account and third-party expenditures on transport, warehousing, inventory management, freight insurance, customs brokerage services and related expenditures) for the median enterprise ranged between 6 and 10% of total operating 11
Various indicators are used to track development, both across countries and over time. The World Bank‘s annual _<u>World Development Indicators</u>_ presents literally hundreds of development indicators (World Bank, 2009). The UN‘s _<u>Millennium Development Goals</u>_ are defined in terms of multiple indicators. Even in assessing specific development goals, such as poverty reduction, mainstream development thinking and practice is premised on a multidimensional view, calling for a range of separate indicators.

Faced with so many indicators—a ―large and eclectic dashboard‖ (Stiglitz et al., 2009, p.62)—there is an understandable desire to reduce the dimensionality, to form a single composite index. As Samuelson (1983, p. 144) put it (in the context of aggregating commodities): ―There is nothing intrinsically reprehensible in working with such aggregate concepts.‖ However, as Samuelson goes on to note in the same passage: ―... it is important to realize the limitations of these aggregates and to analyze the nature of their construction.‖ Two broad types of composite indices of development can be identified. In the first, the choices of the component series and the aggregation function are informed and constrained by a body of theory and practice from the literature. GDP, for example, is a composite of the market values of all the goods and services produced by an economy in some period. Similarly, aggregate consumption is a composite of expenditures on commodities. A standard poverty or inequality measure uses household consumption or income, which are aggregates across many components. In these cases, the composite index is additive and linear in the underlying quantities, with prices (including factor prices) as their weights. A body of economics helps us construct and interpret such indices. With a complete set of undistorted competitive markets, market prices are defensible weights on quantities in measuring national income, though even then we will need to discount this composite index for the extent of income inequality to derive an acceptable money metric of social welfare (under standard assumptions). And market prices will need to be replaced by appropriate shadow prices to reflect any market imperfections such as rationing. There is a continuing debate and reassessment related to these and other aspects of measurement, through which practice gets refined. Decisions about measurement are guided by an evolving body of theory and practice.

This is not the case for the second type of composite index. Here the analyst identifies a set of indicators that are assumed to reflect various dimensions of some unobserved (theoretical) 2
Table 4: Additional Heterogeneity Impacts of Trade

|Heterogeneity_Hc_:|Small|Capital|
|---|---|---|
||population|openness|
|Panel A: CIT rate|||
|Trade|-0.053***|-0.063***|
||(0.014)|(0.018)|
|Trade_∗Hc_|-0.034|-0.034|
||(0.054)|(0.079)|
|Coefcient on Trade in_Hc_|-0.088*|-0.094|
||(0.049)|(0.072)|
|Panel B:_ETRK_|||
|Trade|0.357**|0.617**|
||(0.177)|(0.274)|
|Trade_∗Hc_|-0.491|-0.483|
||(0.544)|(0.456)|
|Coefcient on Trade in_Hc_|-0.134|0.133|
||(0.456)|(0.224)|
|Panel C:_ETRL_|||
|Trade|0.169***|0.144|
||(0.061)|(0.158)|
|Trade_∗Hc_|0.145|0.159|
||(0.282)|(0.275)|
|Coefcient on Trade in_Hc_|0.314<br>(0.242)|0.304**<br>(0.139)|

_Notes:_ This table presents results from the heterogeneous IV analysis based on estimating equation (8). The top row denotes the source of heterogeneity _Hc_ , respectively across columns: a dummy for small population size (below 40 million); the Chinn-Ito index of capital account openness (Chinn and Ito, 2006), which is a continuous variable between 0 and 1; and, a dummy indicator for the post-1995 period. Across Panels, we estimate the effects of trade on the statutory corporate income tax rate (Panel A), the effective tax rate on capital (Panel B), and the effective tax rate of labor (Panel C). At the bottom of each panel, we report the coefficient on trade (and standard error) in the heterogeneity sub-sample as the linear combination of the coefficients on _Trade_ and _Trade ∗ Hc_ . Trade is measured as the sum of export and imports divided by net domestic product. We instrument for trade using the oil-price and the gravity-instruments from Egger, Nigai, and Strecker (2019). All estimates include country and year fixed effects and observations are weighted by net domestic product in constant 2019 USD at PPP. For more details, see Section 7. * p<0.10 ** p<0.05 *** p<0.01. Standard errors in parentheses are clustered at the country level.

52
production and investments to refugee inflows.

# **Notes**

> 1In a companion paper, Akg ̈und ̈uz et al. (2015), we found no effects of the refugee influx on formal employment. However, unlike that of Del Carpio and Wagner (2015) and Ceritoglu et al. (2017) our data are structured at the aggregate level. Also studying the Syrian refugee inflows in Turkey, Balkan and Tumen (2016) find negative effects on prices from hosting refugees. In a related study, Fakih and Ibrahim (2016) find no correlation between labor market outcomes and the arrival of Syrian refugees in Jordan.

2The literature on the impact of refugee crises on the native population has analyzed outcomes ranging from child health to prices in Tanzania and Darfur (Alix-Garcia and Saah, 2010; Baez, 2011; Alix-Garcia et al., 2012; Maystadt and Verwimp, 2014; Alix-Garcia and Bartlett, 2015).

> 3While the instrument is statistically significant in all estimations, the first-stage F-test seems be smaller value when 81 provinces are used compared to the case of 26 NUTS-2 regions. The instrument becomes even weaker if we use refugee to population ratios rather than the number of refugees, therefore we use the absolute number of refugees throughout.

> 4Including 2014 data in the DD estimations generally results in more statistically significant and larger coefficients but does not change the direction of the results.

> 5The weights are calculated using the Stata package _synth_ provided by the authors. We use the option _nested_ to calculate the weights through the nested optimization procedure described in Abadie et al. (2011).

> 6Tunceli’s 2011 export and import values are missing for 2011, therefore only 2009 and 2010 values could be used in calculating Tunceli’s average.

> 7The Chamber of Commerce also provides information on the number of firms that shut down. However, reporting exits is not mandatory and the indicator is therefore less reliable. We found no significant effects in both the IV and DD estimates on the number of firms that shut down.

> 8Since the number of new foreign firms is 0 in several observations, we add 1 to the value. As an alternative, we used the hyperbolic inverse sine transformation which does not have the same problem with 0s as log transformation and found similar results (Burbidge et al., 1988).

9Publicly available data from the Ministry of Science, Industry and Technology indicate that less than 10% of total revenue is from micro-establishments. Most of the net sales and gross profits reported stem from larger firms that should be included in the Chamber of Commerce data.

> 10All firms exceeding 200,000 Turkish Liras (ca. $85,000) in sales are obligated to report detailed balance sheets. Smaller firms may still report their balance sheets but would be doing so on a voluntary

25
-2 -

large majority of the Bank's social projects.

The methodology combines statistical analysis and case studies, with the latter based on a broad range of sectoral reports, Project Completion Reports (PCRs), Project Performance Audit Reports (PPARs), and sustainability analysis conducted by the Operation Evaluation Department (OED) of the World Bank. The statistical analysis draws on the OED project data as well as the country-level policy performance variables of the WDR'91 data set. Since the attempt at rigorously linking macro-determinants with project outcomes in the social sectors is a new area of research, our paper should be seen as an initial effort in this area. We expect to elicit comments and reactions leading to further research.

The next section briefly reviews the literature. To supplement the limited literature, Section Im examines some country- and project case studies which begin to suggest the relationship between economywide policies and project performance. Section IV presents a simple analytical framework of the possible linkages between the economic policy environment and project performance. Section V discusses the data and empirical method. Section VI presents the results from statistical analysis. Concluding remarks and implications are provided in the final section.

# II. Brief Review of the Literature

Literature on the impact of economy-wide policies on project performance in the social sectors is quite limited. Very few cross country studies exist, although some sectoral reports and PCRs and PPARs have touched upon this issue.' The relevant studies we found include one sustainability study conducted by OED, a series papers on the implementation of educational policies, and a few World Bank Policy Studies. We will review this literature briefly in this section.

I This part is based on a survey of literature covering the summaries of 158 PPARs and/or PCRs, about 20 sectoral reports and about 20 Working papers, discussion papers, and policy studies.
and enrollment (using the household survey). We motivate this analysis by graphing outcomes as a function of the within-school ranking of applicants by the dropout-risk score, relative to the cut-off.<sup>11</sup> These results are presented in Figure 2, which gives the mean at each value of attendance (or enrollment) of the within-school rank; and the predicted value from a regression of attendance (or enrollment) on a quartic in the rank and an indicator variable for CSP recipients. Distinct “jumps” at the cut-off would suggest that the program affected outcomes.

Figure 2 suggests that the CSP scholarships had a substantial effect on school enrollment and attendance, about 20 to 25 percentage points. The results from the school-based data are less noisy, which is not surprising given the much larger sample sizes.

Figure 3 presents similar results for hours of schooling attended in the last week (left-hand panel), annual expenditures on schooling (middle panel), and the years of schooling attained (right-hand panel); all of these are based on the household survey. These figures suggest a CSP impact of about 7 hours on school attendance in the last week, of about US $10 on school expenditures, and of about 0.2 years on school attainment.12 Estimates of CSP program effects on school attendance, enrollment, time in school, expenditures and school attainment are presented in Table 2. The raw differences in the first column of the table suggest that CSP recipients are 16 percentage points more likely to be enrolled in school, 17 percentage points more likely to be attending on the day of the unannounced visit, spend 5.4 more hours in school per week, had US $9.0 higher school expenditures, and have attained about 0.21 more years of schooling than non-recipients. However, these raw differences under-estimate the CSP program effects because they do

> 11 Because the cut-off falls at different values of the underlying score in different schools, depending on the number of applications, the mean characteristics of applicants, and whether a school was defined as “large” or “small”, it is not informative to graph outcomes as a function of the score. Rather, for these figures we redefine an applicant’s score in terms of the distance to the school-specific cut-off, so that (for example), a value of -1 represents the “nextto last” applicant to be offered a scholarship within a school, 0 the “last” and a value of +1 represents the “first” applicant within a school who was turned down. The figures then graph outcomes as a function of this relative rank. Parametric regressions corresponding to these figures include a quartic in the relative rank, but not the vector of school fixed effects.

> 12 School expenditures include various fees (registration fees, examination fees, allowances and fees for school events and ceremonies), school material, textbooks, uniforms, and daily expenditures (snacks, extra classes, bicycle parking, lesson copies and other daily expenses).

13
**Figure 5: Number of guarantees issued per year**

_(per million people, 2009)_ <!-- Start of picture text --> 9000<br>8000<br>8000<br>7000<br>6000<br>5000<br>5000<br>4440<br>4000<br>3100<br>3000<br>1800<br>2000<br>1250<br>1000<br>18 33 41 45 52 100 128 130 200 285 292 300 400<br>0<br><!-- End of picture text --> _Source: Respective PCG schemes_

**Figure 6: Average size of guarantees**

_(scaled by GDP per capita, 2009)_ <!-- Start of picture text --> 70<br>60<br>60<br>50<br>40<br>33.5<br>30<br>22<br>20<br>14.6<br>13<br>11.4<br>9.4 10 10 10<br>10 7<br>5 5.5<br>1 1.4 2.4 2.5 3 3.2<br>0<br><!-- End of picture text -->

# _5.2 Additionality_

_Measurement and International Experience_

**Additionality is one the primary objectives of guarantee schemes.** Additionality refers to the capacity of a guarantee scheme to provide access to finance to SMEs which are

28
Policy Research Working Paper 8474

# **Abstract**

Automation will be a boon or a catastrophe depending on whom you listen to. This paper proposes an overlapping-generations model with endogenous school choice in which the quality of a country’s education system determines how well skill supply can respond to increased demand from automation and subsequently whether automation will be beneficial or detrimental. In this sense, education quality in the model offers a bridge between the optimistic and pessimistic perspectives on automation. In testing the model’s assumptions, the paper finds evidence that educational attainment, cognitive skills, and select noncognitive skills are associated with avoiding automation-prone occupations. Consistent with the model’s predictions, census data indicate that countries have historically relied most on these types of occupations at middle-income status. The model and empirical findings suggest that it is middle-income countries that are most vulnerable to automation if their education systems are unable to affect cognitive and noncognitive skills sufficiently. As a result, automation may herald a much different growth model for developing countries: one in which developing these skills is central.

This paper is a product of the Education Global Practice. 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/research. The authors may be contacted at hpatrinos@ 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
**Appendix Table A10: List of Business Practices Measured in Surveys**

|**Label**|**Name of the Practice**|**Survey Question**|
|---|---|---|
|**Recor**|**d Keeping:**||
|_R1_|Keeps written business records|Do you keep written business records?|
|_R2_|Records every purchase and sale|Do you record each purchase and sale of your business?|
|_R3_|Can use records to check cash on hand|Are you able to use your records to see how much cash your business<br>has available at any point in time?|
|_R4_|Uses records to check sales of product|Do you regularly use your records to know whether sales of a particular<br>product are increasing or decreasing?|
|_R5_|Works out costs of main products|Do you work out the costs to the business of each main product you<br>sell?|
|_R6_|Knows products with most profit per item|Do you know your three products with the highest profit per item<br>selling in your business?|
|||Do you have a written budget which states how much you have to pay|
|_R7_|Has written monthly expenses budget|each month for rent, electricity, equipment maintenance, transport,<br>advertising, and other indirect costs to business?|
|||Imagine you wanted to take out a bank loan for your business and you<br>|
|_R8_|Can use records to service potential loan|needed to check if you can pay the monthly rates. Do you have records<br>showing whether you have enough money each month after paying all<br>|
|||business expenses?|
|_R9_|Keeps records in ledger book|What kind of business records do you keep?<br>a) Electronic records (phone, computer, etc.)<br>b) Ledger book or equivalently organized notes<br>c) Disorganized notes<br>d) Orderly filed receipts<br>e) Loose receipts<br>|
|||f) Other<br>|
|||g) None|
|_R10_|Calculates profits considering all costs|<br>How do you calculate profits?<br>a) Only from the sales (no calculation)<br>b) Sales minus product purchases|
|||c) Sales minus product purchases minussome other business expenses|
|||d) Sales minus all business expenses (salaries, rent, electricity,<br>interest, etc.)|
|**Plann**|**ing:**||
|_F1_|Reviews and analyses financial performance|Do you review your financial performance and analyze where there are<br>areas for improvement at least on a monthly basis?|
|_F2_|Has sales target set for next year|Do you have a target set for sales over the next year?|
|_F3_|Compares target with sales at least monthly|Do you compare your sales achieved to your target at least monthly?|
|_F4_|Has cost budget for next year|Do you have a budget for the anticipated costs your business will have<br>to pay over the next year?|
|_F5_|Issues annual profit/loss statement|<br>Do you have an annual profit and loss statement?|
|_F6_|Issues annual cash-flow statement|Do you have an annual statement of cash flow?|
|_F7_|Issues annual balance sheet|Do you have an annual balance sheet?|
|_F8_|Issues annual income and expenditure sheet|Do you have an annual income/expenditure statement?|