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14
However, when relying only on indicators from traditional data, the estimated urbanization rate becomes significantly higher (table 8). It reaches 35.2 percent of the population when applying the random forests model and 32.1 percent when applying the Logit model. On the other hand, when only relying on indicators from modern data, the estimate can be significantly smaller. For example, the urbanization rate falls to 26.4 percent according to the Logit model.

**Table 8. The predicted urbanization rate of India in 2011**

||Urbaniza<br>Area|tion rate<br>Popu-<br>lti|With c<br>|ensus<br>|Jaccard<br> <br>|index<br>With other a<br>|pproach<br>|es<br>|
|---|---|---|---|---|---|---|---|---|
|Approach||aon|Area|Pop.|Area|St. dev.|Pop.|St. dev.|
|Logit (full)|3.4|30.2|0.56|0.84|0.83|(0.043)|0.94|(0.014)|
|LASSO (full)|3.3|29.7|0.53|0.83|0.83|(0.047)|0.94|(0.016)|
|Random forests (full)|3.2|29.9|0.54|0.85|0.83|(0.065)|0.94|(0.021)|
|Logit (traditional)|4.6|32.1|0.53|0.83|0.52|(0.054)|0.84|(0.017)|
|Random forests (traditional)|5.1|35.2|0.38|0.74|0.41|(0.018)|0.76|(0.012)|
|Logit (modern)|2.9|26.4|0.38|0.74|0.62|(0.049)|0.84|(0.016)|
|Random forests (modern)|5.7|30.3|0.33|0.73|0.41|(0.017)|0.79|(0.010)|

_Note_ : Other approaches include the Logit model (benchmark, quadratic and full), the LASSO method (quadratic and full) and the random forests method (benchmark, quadratic and full).

Estimations that come close at the national level may differ significantly at more disaggregated levels. Following de Bellefon et al. (2018), we use the Jaccard similarity index to measure the extent to which urban areas predicted by two different approaches overlap. A Jaccard index is the ratio between the size of the intersection of the urban areas predicted by two approaches and the size of the union of the two urban areas. When considering the full set of covariates, the average Jaccard index across approaches is 0.83 when size is measured in terms of surface, and 0.94 when it is measured in population terms. These results reveal a high coincidence in the delineation of the urban extent by our methodology even at disaggregated levels.

However, predictions are more volatile when relying exclusively on indicators from traditional or from modern data. For example, the average Jaccard index falls to 0.41 when measured by surface, and to 0.79 when measured by population, if the random forests prediction relies exclusively on modern data.

# _Urbanization by administrative category_

The similarity between our predicted urbanization rate for 2011 and the census estimate could hide important differences at subnational levels. To explore this possibility more systematically, we first reran the comparison for places falling under each of the seven administrative categories considered by the 2011 Census of India (figure 6).

26
**49**

**Countries for Review and Adjustment for Trade Taxes**

**69.** Table 12 below lists countries" with "implicit tariff rates" of at least **10** percent. In the context of the search for an appropriate general purpose conversion factor, the list represents the suggested countries for review, and adjustment if necessary, in the official exchange rate to take into account the cost-related restrictions in the and trade system. It should be emphasized that in the review process, Operations staff should be encouraged to provide data on subsidies if they form a significant part of the trade system.

Table 12: Selected Countries for Review and Adjustment of Official Rate for Trade Taxes

|Country|"ImplicitTariff<br>Rate"**(%)**|Country|"ImplicitTariff<br>Rate"**(%)**|
|---|---|---|---|
|Bangladesh|**17.9/1985**|*****Pakistan|**17.1/1986**|
|*Belize|**11.2****_/1985_**|*****Peru|**10.5/1986**|
|*Burkina Faso|**_10.5_/1987**|SierraLeone|**13.2/1987**|
|Eurundi|**21.6 /1986**|*****Somalia|**12.5/1978**|
|Comoros|**13.8/1987**|SriLanka|**10.7/1988**|
|***Egypt**|**11.5/1987**|*****Sudan|**17.5/1982**|
|Gambia|**13.6 /1987**|Togo|**11.0/1986**|
|Ghana|**13.5/1987**|Tunisia|**13.1/1984**|
|Guinea-Bissau|**24.1 /1987**|Uganda|**16.0/1986**|
|*India|**26.1/1987**|*****Vanuatu|**12.5/1986**|
|*Iran|**11.9 /1985**|Venezuela|**12.5/1986**|
|Myanmar|**14.6 /1985**|*****YemenAR|**13.2/1987**|

Source: Table **9**

> * Countries identified in the Fund's **Annual** Report on **Exchange Arrangements** and Exchange Restrictions with "cost-related import restrictions."

> **31** Based on the Fund's **1989** Annual Report on Exchange Arrangements and Exchange Restrictions, and

> supplementary information from the Fund's Government Finance Statistics file.
37

# **II. Survey of Public Pension Plan Participants in 1989**

|Pe|nsion Feature|
|---|---|
|Mean Retirement Benefit'|$9,31°|
|Median Retirement Benefit'|$7,200|
|Median Public Pension as % of Pre-retirement Earnings (total)<sup>2</sup>|42%|
|Median Public Pension as % of Pre-retirement||
|Earnings for those not receiving Social Security<sup>3</sup>|50%|
|Fraction Receiving Any Post Retirement Benefit Increases'|34%|
|Fraction of Participants Covered by Defined Benefit Plan<sup>5</sup>|70%|

Sources:

|'Phillips (1992), Table 14.9, p. 367.|
|---|
|'Phillip' (1992), Table :4.13, p. 371.|
|'Phillips (1992), Table 14.7, p. 375.|
|4Phillips (1992), Table 14.20, p. 379.|

'Phillips (1992), Table 14.25, p. 384. Fraction excludes more than 10% of respondents unable to identify their plan type.

Olivia S. Mitchell - Public Pension Governance and Performance
the project local governments on the APA to undertake the same process of data gathering and scoring for non-project LGAs. However, due to the intensity of data gathering required for the APAs under the project, the same auditing firm could not administer APAs in comparison LGAs under the same timeline as project LGAs. Given both this and the additional cost, the evaluation draws on the survey data for the difference-in-differences analysis. We also report single differences results using the APAs in project LGAs (see Table B1).

Through extensive field testing and in collaboration with the government to ensure we measured indicators of state capacity that were locally valued, we designed the surveys to gather the following types of data grouped into modules under the different categories covered by the APAs. In each module, we aimed to include at least some questions in accordance with the project guidelines on how to undertake the APAs, so that the measures are as close as possible to the outcomes incentivized by the project. In the list below, we discuss the consistency between the APA questions/scoring guidelines and the questions in our survey.

# **1. On urban planning systems:**

Government officials were asked the following questions that are likely to be informative about institutional capacity for urban planning.

- Whether a General Plan had been approved since 2015, and conditional upon an affirmative answer, whether the official in charge of planning could show interviewers the plan. This set of questions follows the guidelines issued by the project to the firms that were contracted to undertake the APAs. Specifically, the APA scoring guide indicates to assign points if LGAs are compliant with steps including plan preparation process, data analysis and plan adoption and approval

- The respondents’ estimate of the percentage of unplanned settlements in the LGA

- The respondents’ view of whether the LGA experiences delays in receiving guidelines for preparing their budget. This question and the following one directly measure one of the key outcomes targeted by the project—to reduce delays in communication between the President’s Office for Regional and Local Governments (PO-RALG) and the local governments.

- The respondents’ view of whether disbursements from central government are timely

- The respondents’ view of whether the budget was executed in accordance with expected results. APAs review planning and utilization of annual plans for development budget.

To illustrate how such data can be useful in evaluating the impact of the project on institutional capacity, we can examine whether project local governments are more likely to have a General Plan and are able to produce it when compared to local governments not targeted by the program; whether government officials in project local governments estimate a smaller share 9
# **Figure 7. Length of GVCs by industry, 2008**

<!-- Start of picture text --> Domestic International<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00 ... ... ...<br>TV and communication equipment Motor vehicles Basic metals Electrical machinery Textiles, leather and footwear Other transport equipment Rubber and plastics products Machinery and equipment n.e.c Chemicals and chemical products Precision and optical instruments Fabricated metal products Food products and beverages Office and computing machinery Wood and products of wood Manufacturing n.e.c; recycling Other non-metallic mineral Construction Paper, printing and publishing Petroleum products and nuclear Electricity, gas and water supply Research and development Transport and storage Agriculture Hotels and restaurants Post and telecommunications Computer and related activities Other Business Activities Wholesale and retail trade Other social and personal services Public administration Renting of machinery and Health and social work Finance and insurance Mining and quarrying Education Real estate activities<br><!-- End of picture text --> Note: The minimum value of the index is 1 when no intermediate inputs are used to produce a final good or service.

Source: OECD, 2012b

# **(3)** **<u>The distance to final demand</u>**

Once the length of the GVC is assessed, it is important to determine what is the position of the country in the value chain. It can be upstream (production of inputs at the beginning of the value chain) or downstream (production of goods and services towards the end of the value chain) depending on its specialization. Countries specialized in upstream activities produce the raw material or the intangibles involved at the beginning of the production process (e.g., research and design). Countries downstream do the assembly of the final products or specialize in customer services. Finally, countries involved in activities at the center of the value chain focus on the standardized labor-intensive manufacturing jobs. Turkey falls into this category of countries. Thus, some indicators were introduced to measure the “upstreamness” of a country’s specialization, i.e., its distance to final demand (Antràs, Chor, Fally and Hillberry, 2012). Evidence suggests that only a few countries have managed to move downstream (such as Poland and Romania in Figure 8). Most countries have increased their upstreamness because the overall length of value chains has increased with the fragmentation of production. Moreover, the offshoring-outsourcing process, that lengthens GVCs, tends to primarily affect early stages of production, although a new wave of services offshoring-outsourcing has been taking place in recent years.

12
# **Table 4: EPAs and exports of homogeneous goods: controlling for unobserved heterogeneity**

|Conservative def.|Liberal defnition|
|---|---|
|Interaction EPAs Budget and Homogeneous good dummy<sup>_a_</sup><br>-0.047|-0.046|
|(ln _Bud/pop_*Homo)<sup>_b_</sup><br>( 0.003 )|( 0.003 )|
|[ 0.010 ]|[ 0.010 ]|
|_{_ 0.006 _}_|_{_ 0.006 _}_|
|SITC 4 digit dummies<br>Yes|Yes|
|Country dummies<br>Yes|Yes|
|_P −value_ of F -test.<br>0.000|0.000|
|Number of observations<br>60923|60923|
|R-squared<br>0.619|0.620|

> _a_ This is Rauch dummy for homogeneous goods (see Rauch, 1999). The first column uses Rauch conservative definition of homogeneous goods and the second column the more liberal definition.

> _b_ White robust standard errors are in parenthesis ( ); Standard errors clustered by country are in squared brackets [ ], and standard errors clustered by SITC 4 digit product are in curly brackets or braces _{ }_ .
<!-- Start of picture text --> Figure 12. Cost-recovery capabilities of residential effective tariffs at 100 kWh/month consumption<br>a. Total cost recovery b. Operational cost recovery<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>level of total cost recovery level of operational cost recovery<br>percentage of countries percentage of countries<br>cost cost<br>0%- 30% 30%-50% 50%-80% 80%-99% recovery 0%- 30% 30%-50% 50%-80% 80%-99% recovery<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database <!-- Start of picture text --> Figure 13. Household billing as a share of total power billing<br>a. Composition of annual billing per type of customer b. Residential share of billing and consumption<br>80<br>100%<br>90%<br>80%<br>70%<br>60% 60<br>50%<br>40%<br>30%<br>20% 40<br>10%<br>0%<br>20<br>0<br>0 20 40 60 80<br>High voltage industrial<br>Medium voltage commercial Share of residential bill<br>Low voltage residential<br>% of amount billed<br>Chad Mali Ghana Senegal Niger Tanzania Nigeria Kenya Ethiopia Rwanda<br>Burkina Faso Cameroon Cape Verde Congo, Rep. Congo DRC Cote d'Ivoire Mozambique Zimbabwe South Africa Share of residential supply<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ See annex 9 for details.

23
Table 11: Spillover through Innovation Network

||(1)|(2)|(3)<br>**Patent A**|(4)<br>**pplication**|(5)|(6)|
|---|---|---|---|---|---|---|
||Total|Intl.|High-Tech|Total|Intl.|High-Tech|
|Downstream|-0.547**|-0.171|0.034||||
||(0.278)|(0.190)|(0.220)||||
|Upstream|0.531**<br>(0.248)|0.237<br>(0.168)|0.003<br>(0.192)||||
|Dummy downstream||||-0.034***<br>(0.008)|-0.004<br>(0.005)|-0.023***<br>(0.006)|
|No. Observation|192,262|192,262|192,262|192,262|192,262|192,262|
|R-squared|0.411|0.426|0.484|0.411|0.426|0.484|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

Table 12: Pre-Sanction Chinese Collaboration and Post-Sanction Innovation Performance

||(1)<br>Total|(2)<br>Intl.|(3)<br>**Patent **<br>High-Tech|(4)<br> **Application**<br>Total|(5)<br><br>Intl|(6)<br>High-Tech|
|---|---|---|---|---|---|---|
|Treatment_×_Post|-0.003|-0.016|-0.006|0.209***|0.218***|0.177***|
||(0.012)|(0.017)|(0.010)|(0.018)|(0.018)|(0.024)|
|Treatment_×_Post_×_Pre-CN||||-0.266***|-0.293***|-0.229***|
|||||(0.014)|(0.011)|(0.022)|
|No. Obs.|219,728|219,728|219,728|219,728|219,728|219,728|
|R-squared|0.224|0.232|0.362|0.226|0.236|0.365|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

42
WPS 3251

# **_Aid, Policies, and Growth: Revisiting the Evidence_**<sup>**_*_**</sup>

## **_By Craig Burnside_**<sup>**_†_**</sup> **_and David Dollar_**<sup>**_‡_**</sup>

**_Abstract:_** We revisit the relationship between aid and growth using a new data-set focusing on the 1990s.  The evidence supports the view that the impact of aid depends on the quality of state institutions and policies.  We use an overall measure of institutions and policies popular in the empirical growth literature.  The interaction of aid and institutional quality has a robust positive relationship with growth that is strongest in instrumental variable regressions.  There is no support for the competing hypothesis that aid has the same positive effect everywhere.  We also show that in the 1990s the allocation of aid to low-income countries favored ones with better institutional quality. This “selectivity” is sensible if aid in fact is more productive in sound institutional and policy environments.  The cross-country evidence on aid effectiveness is supported by other types of information as well: case studies, project-level evidence, and opinion polls support the view that corrupt institutions and weak policies limit the impact of financial assistance for development.

## World Bank Policy Research Working Paper 3251, March 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * We would like to thank Aart Kraay for helpful comments on an earlier draft and Victoria Levin for excellent research assistance.

> † Department of Economics, University of Virginia; burnside@virginia.edu

> ‡ DECVP, World Bank; ddollar@worldbank.org
Altogether there are 145 countries<sup>3</sup> with at least one observation of public employment in any of the three aggregates, with 60 of the countries classified as high income. On average, countries have 13 years of data when considering the general government aggregate. The sample period starts in 1980, although the number of countries with data changes over time, making it an unbalanced panel. It is important to have in mind the level of agregation (public sector, general government, and central government) when comparing with wage bill data (IMF, 2016).

Total government expenditure as a share of GDP and government expense in compensation to employees as share of GDP are obtained from the IMF’s World Economic Outlook and Government Finance Statistics (October 2018).

# **ii. Expenditure-based measures and public employment: The wage bill**

Most of the literature that explores the size of government uses expenditure-based measures, either of the wage bill or other aggregates. The relationship between the size of the government, measured as expenditure as percentage of GDP, and public employment as percentage of the labor force can differ considerably (Figure _1_ ). On the other hand, the wage bill, although highly correlated with total government expenditure (Figure 2), shows significant heterogeneity across countries, with some having a wage bill that is twice the size of others that have similar government expense levels.

Figure 1: Public employment (% labor force) and general government expenditure (% GDP) (each dot is a country-year obs.)

> government; i.e. all government units, social security funds and non-market nonprofit institutions (NPIs) that are controlled and mainly financed by public authority (Hammouya, 1999).  Finally, the central government aggregate is composed of departments or ministries, of autonomous agencies carrying out special functions, and of all NPIs which are controlled and mainly financed by public authority.  Their fiscal, legislative and executive authority extends over the entire territory of the country.

> 3 The number is reduced to 122 when we consider employment as percentage of the labor force.

4