rafmacalaba/datause-encoder-data huggingface dataset:
Uganda is another East African country whose structural change experience has been examined by Ahmed _et al_ . (2015). These authors document that Uganda’s economy underwent significant structural change in the 2000s, whereby the share of non-tradable services in aggregate employment rose by about 7 percentage points at the expense of the production of tradable goods. The process also involved a 12-percentage-point shift in employment away from small and medium enterprises and larger firms in manufacturing and commercial agriculture mainly to microenterprises in retail trade. In addition, the sectoral reallocation of labor on these two dimensions coincided with significant growth in aggregate labor productivity.

Nigeria’s structural change experience has been examined by Adeyinka _et al_ . (2013). This paper documents that structural change accounts for approximately one-fifth of the total change in labor productivity in Nigeria between 1996 and 2009. Labor moved out of the agricultural and wholesale and retail trade sectors into manufacturing, transportation and communications, business services, and general services. While structural change did occur in this period, significant gains to aggregate labor productivity are still available from further shifts of labor to higher-productivity sectors. The paper discusses the factors limiting structural change, which include poor agricultural productivity, insufficient infrastructure to support high productivity sectors, and a lack of appropriate skills in the labor force.

The World Bank 2018 Systematic Country Diagnostic (SCD) for Guinea comprises an analysis of labor productivity and structural change. According to the report, labor productivity growth was driven for the most part by structural change and, to a lesser extent, by within-sector productivity growth. More specifically, static structural change contributed approximately 12 percentage points of the 10 percent labor productivity growth during 1994-2015, and within-sector productivity changes accounted for -1.2 percentage points.

In Northern Africa, the experience of Tunisia has been examined by Marouani and Mouelhi (2015). These authors analyze the dynamics of sectoral productivity growth in Tunisia and assess the contribution of structural change to these dynamics. Using sectoral and firm data, the paper shows that productivity increased at a relatively sustained pace in Tunisia in the last three decades, but that the contribution of structural change remained limited. Trade and labor market reforms did 8
- Request bid and performance bonds to contain the ability of sponsors to walk out of the project.

The enforcement of these regulations will also limit the scope of collusion in the procurement of PPI projects.

# _4. Enhance regulatory oversight on PPI contracts_

A well designed PPI contract followed by a poor regulatory oversight leads inevitably to deficient sector performance. Although effective regulation is not a sufficient condition to fix all the problems arising from poor contract design, it is a powerful tool to ensure the sanctity of bids, and to hold the winning bidder and the government accountable. As shown by Guasch (2004), the existence of an autonomous regulatory agency has the largest marginal effect on the outcomes of PPI tenders. In Korea, regulatory oversight has been deficient in most infrastructure sectors, thus probably explaining the disappointing performance of some PPI projects as illustrated by the increasing claims of minimum revenue guarantees (Chart 5).

<!-- Start of picture text --> Chart. 5: Government Contingent Liabilities Due to Redeemed MRG<br>200<br>180<br>160 Mt.Woomyeon<br>Tunnel<br>140<br>Chunan-Nonsan<br>120<br>Highway<br>100<br>Gwangju 2nd<br>80 Beltway<br>60 Incheon Int'l<br>Airport Highway<br>40<br>20<br>0<br>2001 2002 2003 2004<br>Source: Dailami (2004)<br>billion KRW<br><!-- End of picture text --> 28
- 41 -

Francisco, Manuela, Nicola Pontara. 2007. Does corruption impact on firms’ ability to conduct business in Mauritania? Evidence from investment climate survey data. Policy Research Working Paper No. 4439, The World Bank.

- Glaeser, Edward, Rafael La Porta, Florencio Lopez-de-Silanes, Andrei Shleifer. 2004. Do institutions cause growth? _Journal of Economic Growth_ , Vol. 9, pp. 271-303.

- Grimm, Veronika, Riccardo Pacini, Giancarlo Spagnolo, Matteo Zanza. 2006. Division into lots and competition in procurement, in _Handbook of Procurement_ , eds. by Nicola Dimitri, Gustavo Piga, and Giancarlo Spagnolo, pp. 168-192. Cambridge University Press.

- Gupta, Srabana. 2002. Competition and collusion in a government procurement auction market. _Atlantic Economic Journal_ , Vol. 30, pp. 13-25.

- Iimi, Atsushi. 2006. Auction reforms for effective official development assistance. _Review of Industrial Organization_ , Vol. 28, pp. 109-128.

- Iimi, Atsushi. 2007. Escaping from the resource curse: evidence from Botswana and the rest of the world. _IMF Staff Papers_ , Vol. 54, 663-699.

- Iimi, Atsushi. 2008. Endogenous participation in infrastructure project auctions with technical evaluation. Mimeograph.

- Kagel, John, Dan Levin. 1986. The winner’s curse and public information in common value auctions. _The American Economic Review_ , Vol. 76, pp. 894-920.

- Klemperer, Paul. 2002. How (not) to run auctions: the European 3G telecom auctions. _European Economic Review_ , Vol. 46, pp. 829-845.

- Li, Tong, Isabelle Perrigne. 2003. Timber sale auctions with random reserve prices. _The Review of Economics and Statistics_ , Vol. 85, pp. 189-200.

- NOA. 2007. _Improving the PFI Tendering Process_ . The National Audit Office.

- OECD. 2007. Development co-operation report 2007. _OECD Journal on Development_ , Vol. 9(1). Organisation for Economic Co-operation and Development.

- Ohashi, Hiroshi. 2008. Effects of transparency in procurement practices on government expenditure: a case study municipal public works. CIRJE Discussion Paper No. 548, University of Tokyo.

- Olken, Benjamin. 2005. Monitoring corruption: evidence from a field experiment in Indonesia. NBER Working Paper Series 11753.

- Paarsch, Harry. 1992. Deciding between the common and private value paradigms in empirical models of auctions. _Journal of Econometrics_ , Vol. 51, pp. 191-215.

- Porter, Robert, Douglas Zona. 1993. Detection of bid rigging in procurement auctions. _Journal of Political Economy_ , Vol. 101, pp. 518-538.

- Rezende, L. 2005. Auction econometrics by least squares. Mimeograph. University of Illinois, Urbana-Champlain, IL.
serves only as a broad overview: Its conclusions refer to the typical or average country afflicted by war and reflect mostly a descriptive and statistical examination. Future research should analyze the heterogeneity of post-conflict situations, their causal mechanisms, and the policies that make them successful.

The rest of the paper proceeds as follows. Section II describes the data, their sources, and methodology of analysis. Section III presents and discusses the results, first, on the comparison between the pre- and post-war periods and, second, on the trends of change after the war. Section IV offers some concluding remarks.

# **II.  Data and Methodology**

In exploring the patterns of behavior of various economic, social, and political variables in post-war countries, this study focuses on internal (or civil) conflicts. The information on conflicts comes from the Armed Conflict Dataset of International Peace Research Institute, Oslo (PRIO). We group _internal_ and _internationalized internal_ armed conflict as internal wars.<sup>1</sup> To focus on major conflicts, we limit our analysis to those with the highest intensity level in the PRIO dataset, i.e., more than 1000 battle-related deaths per year during the war.

In an attempt to provide a comprehensive set of stylized facts on post-war transitions, this paper examines the following dimensions: economic performance (GDP per capita growth rate, investment share, government expenditure, military expenditure), health and education (infant mortality, adult female mortality, adult male mortality, primary school enrollment, secondary school enrollment), political development (democracy and autocracy, civil liberties and political rights, law and order), demographic development (old dependency ratio, young dependency ratio, female-male ratio), and other forms of conflict (incidence of terrorist attacks). Detailed description of these variables, including definitions and sources, is provided in Appendix 2.

> 1 According to PRIO’s definitions, _internal armed conflict_ occurs between the government of a state and internal opposition groups without intervention from other states; _internationalized internal armed conflict_ occurs when such conflict involves intervention from other states.

5
# Aggregating Attainable Harvest

Maize <!-- Start of picture text --> Production Yield<br>8+.+<br>8g a<br>om rz<br>¥R 2s<br>g3<br>oo<br>ESS13 ESS15 ESS18 ESS13 ESS15 ESS18<br>[9 Maize production (I) Productivityloss | Area lo s [9 Maize yield =I) Productivityloss === Area loss<br>Sorghum<br>Production Yield<br>iJ<br>i=] = om<br>x gs 5G<br>8 3<br>So<br>o o<br>ESS13 ESS15 ESS18 ESS13 ESS15 ESS18<br>{) Sorghumproduction (Productivityloss | Area loss [29] Sorghum yield QQRNI) Productivityloss = Area los:<br><!-- End of picture text -->

Note: Excluding full losses. Yield is per hectare of the planted area.
# _The Effect of Bank Branching on Access to Finance_

Over ninety percent of the Kenya’s branches are in urban and rural districts (Figure 2). Branches in semi-arid districts account for 4.7 percent of the national total; those in arid districts account for 2.8 percent. Even if the definition of branch is expanded to include all types of access points - agencies, pay-points, mobile units, satellite branches, and sub-branches - this picture is essentially unchanged (93 percent in urban and rural areas, 7 percent in arid and semi-arid areas). The distribution also reflects a difference in geographical emphasis between private and government owned or influenced banks.

**Figure 2.  Number of Bank Branches by Location**<sup>**20**</sup>

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>Urban Rural Other Semi-Arid Arid<br>Top 10 Foreign Top 10 Private Domestic<br>Non-top 10 Foreign Non-top10 Private Domestic<br><!-- End of picture text --> Source: CBK The government and government-influenced banks represent about a fifth of total branches in urban districts, over half in rural districts, three-quarters in semi-arid districts, and almost ninety percent in arid districts. This suggests that government influence has a positive impact in promoting access to financial services but, in the absence of an analysis to assess the costs of government-influenced banks’ poor lending practices, it should not be concluded that

> 20 Foreign banks in the top 10 include Barclays, Standard chartered, and Stanbic. The domestic banks in the top ten are Equity, KREP, Baroda, and Commercial Bank of Africa. Cooperative, KCB, and NBK are the governmentowned banks that round out the top ten.

30
greatest incentive to formally register, for instance, in order to receive a Value Added Tax (VAT) number, which might be required for domestic and international sales.<sup>18</sup>

# **6.  Empirical Analysis**

In this section we examine various macroeconomic, financial, political, and regulatory indicators that might be related to business density and entry rates. Although we find significant relationships with these measures – i.e. more dynamic economies in countries with better business environments – we cannot postulate on the direction of causality. We plan to continue to collect this data over time and construct time-series of private sector entry and growth that will allow us to study the country characteristics that determine entrepreneurship and the effect of regulatory and institutional reforms.

## **_6.1 Importance of the Business Environment_**

Several results highlight the importance of the business environment for the growth and development of the private sector. For instance, the Doing Business Report, 2007 includes a ranking (from 1 to 175) of an “ease of doing business index,” which measures the relative strength of the regulatory environment as conducive to the operation of business. The index is constructed as the simple average of the countries percentile rankings on 10 topics: Starting a business, Dealing with licenses, Employing workers, Registering property, Getting credit, Protecting investors, Paying taxes, Trading across borders, Enforcing contracts, and Closing a business. We find a negative and significant relationship between the ease of starting a business with the entry and density rates per country (Figure 5).

> 18 We are unable to control for sectoral distribution in our empirical analysis, since the data are only available for a small sub-sample of countries and was not collected over time.  However, we expect changes in the distribution to be related to economic development and improvements in the business environments, which are measured by our explanatory variables.

18
The paper is organized as follows. Section 2 describes the data. Section 3 presents the empirical approach. Section 4 discussed the results. Section 5 concludes, expanding on the relevance of our findings for household and farm surveys that visit sampled households at least twice and in sync with a given agricultural season.

# **2 Data**

The Malawi Third Integrated Household Survey 2010/2011 (IHS3), and the Ethiopia Socioeconomic Survey Wave II 2013/2014 (ESS2), which were conducted respectively by the Malawi National Statistical Office (NSO) and the Central Statistics Agency (CSA) of Ethiopia inform our analysis. Both surveys were implemented under the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMSISA) program.

The IHS3 data were collected within a two-stage cluster sampling design, and are representative at the national, urban/rural, regional, and district levels, covering 12,271 households in 768 enumeration areas (EAs). ESS2 is part of a long-term project to collect panel data. It covered all regional states including the capital, Addis Ababa. Much of the sample is comprised of rural areas as it was carried over from ESS1. The survey is representative at the national, urban/rural and, 6 strata (4 regions plus Addis Ababa and the other regions) covering 5,262 households in 433 EAs.

In terms of questionnaire instruments, the IHS3 and the ESS2 both had Household, Agriculture, and Community Questionnaires. In each setting, the sample households were administered a multi-topic Household Questionnaire that collected individual-disaggregated information on demographics, education, health, wage employment, nonfarm enterprises, anthropometrics, and control of income from non-farm income sources, as well as data on housing, food consumption, food and non-food expenditures, food security, and durable and agricultural asset ownership, among other topics. In addition, agricultural 6
6

**There** are **a** number of notable antecelents to this<sup>work. Goldstein and</sup> Montiel (1986) stress the importance<sup>of vntrolling for differences in</sup> environment and present an analytical<sup>blueprint for doing so. World Bank</sup> (1988) piovides preliminary empirical<sup>evidence on the success of AL p:ograms,</sup> but does not provide statistical<sup>tests. Among contemporary</sup> papers, World Bank (1990) (as revised in Corbo<sup>and Rojas (1990)] applies the Goldstein/Montiel</sup> blueprint to evaluating AL programs.<sup>The latter paper differs from this one</sup> in a number of methodological and statistical ways; its results when comparable are similar to those<sup>of this paper.</sup>

# II. Evaluating a Country's Economic<sup>Performance.</sup>

Measures of economic performance will respond systematically to four sets of determinants: external<sup>incentives, secular economic development</sup> trends, economic policy choices and<sup>country-specific</sup> structural factors. The observed historical performance<sup>will be attributable to all of these factors,</sup> and thus observed success could<sup>be due either to the refo:ms .ssociated with</sup> adjustment lending or to the serendipity<sup>of</sup><sup>**a**favorable externa. environment.</sup> From the country's point of view,<sup>of course, either is welcome. However, for</sup> purposes of measuring the<sup>success of adjustment lending it is important to</sup> decompose performance into that<sup>part attributable</sup> to the policies and that part due to external incentives<sup>or secular trends.</sup> The 'fixed-effect" statistical methodology provides this decomposition. The decomposition is derived rigorously<sup>in the appendix, but the gist of the</sup> method **can be presented** in **Figure**<sup>**1. Suppose that the economic**perforriance</sup> measure of interest is the current<sup>account/GNP ratio (C), and that Count.ies A</sup> and B have observed (negative) ratios<sup>of CA and CB, respectively,</sup> in this
levels of education (equal to eight or more years of schooling) by 9.5 to 10.8 more women, and the estimated increases are signicant at the 1 percent level of condence. Although this appears to be a small eect, it represents a substantial change in the stock of educated women. With eight women in the median village (without a GSS) in 1998 reporting higher levels of education, a GSS more than doubles this number. Column (5) utilizes a similar IV strategy and, as before, shows that while the IV estimate is signicant, it is substantially larger than the OLS estimate. This is due to the relatively small rst stage coecient (see Table II). Column (6) makes this clear by presenting the reduced form estimate. While the large magnitude of the IV estimate is dicult to take literally and we believe the OLS/rst dierence estimates are more realistic, the point is that GSS existence substantially increases the number of educated women in the village even when potential selection concerns are taken into account.<sup>20</sup> Columns (7) and (8) then examine the importance of secondary school educated women for the existence of a private school. In both the OLS and rst-dierence specications, the impact of women with eight or more years of schooling is large and very signicant, while the percentage of similarly educated males has no impact on the existence of a private school. In fact, the point estimate is one-tenth that of the female eect (and of the wrong sign in the rst-dierence specication).

Another potential approach to isolating the supply-side is to use variation in the timing of the public school construction since supply-side channels suggest that private schools will emerge ve to eight years after the construction of a GSS (or three years if there was a preexisting primary school). Unfortunately, the data are too limited to exploit this variation but there is suggestive evidence that this is indeed the case.<sup>21</sup>

> 20We should note that the OLS/rst-dierence are large enough to generate (the few) teachers one would need for the supply channel, but not enough to produce sucient educated mothers that one would expect if the demand channel were the primary driver. While the IV estimates could generate such a demand channel, they are implausibly large: The median village in our sample has only 9 women with higher education in 1998, with a mean of 26 and, with a typical GSS only graduating around 5 or so girls per year. Even by 2005, an increase of 220 women is therefore quite implausible.

> 21We require villages with both private schools and a GSS. Since only 232 villages received a GSS, and of these, 26 percent had a private school, we are unable to identify any discontinuities using the 60 or so villages

24
# **Introduction**

Since the advent of human capital theory in economic thought, estimating the returns to investment in education has been a very popular subject among researchers (see Psacharopoulos and Patrinos 2018 for a recent review on the subject).

Yet, most of the recent estimates in this proliferating literature have several limitations:

- Conceptually, the authors rarely make a distinction between private and social returns, typically estimating only private returns – often not even using the word _private_ in their papers.

- Methodologically, the estimates are done by what we describe as the easy-way; that is, by using the convenient Mincerian earnings function rather than the full-discounting method.

- Policy-wise, invalid recommendations are made based on private returns.

Anne Krueger (1972) estimated a very high return to education including higher education using data for Turkey from 1968. She used those estimates to explain the high demand for higher education at the time. The purpose of this paper is to estimate private and social returns to investment in education based on different methodologies using data from the Turkish 2017 Household Labor Force Survey (HLFS).

Turkey provides a useful ground for cost-benefit analysis of its education system. The education system in Turkey has shown remarkable improvement in the last 15 years in terms of better student performance and reduced inequality with a concurrent and sustained increase in enrollment. The expansion of primary and secondary schooling was accompanied by the development of a new curriculum in primary and secondary education. A recent independent assessment of the revised secondary school curriculum regarded it as an improvement over the curriculum used in the past. Curriculum reform was accompanied by initiatives to improve the teaching profession policies (World Bank 2013).

The paper is organized as follows: we start with a review of the different methodologies used to estimate returns to education, followed by a compilation of previous estimates for Turkey. Then we move on to estimate private returns based on the Mincerian earnings function for comparison with other studies that have used this methodology. Finally, we estimate private and social returns based on the discounting method that we consider to be the most valid method and discuss policy implications.

2
|**Table 5: Interaction te**<br>Dependent variable:_Female Workers_|**rm results for**<br>**(1)**|**gender-based**<br>**(2)**|**laws**<br>**(3)**|**(4)**|
|---|---|---|---|---|
|Exports (%, cell average)*Women can work<br>the same night hours as men Y:1 N:0|0.318***<br>(0.091)|0.305***<br>(0.095)|||
|Exports (%, cell average)*Women can work<br>in the same industries as men Y:1 N:0|||0.272***<br>(0.100)|0.280***<br>(0.098)|
|Exports (%, cell average)|-0.128|-0.626|0.03|-0.723|
||(0.083)|(0.535)|(0.055)|(0.507)|
|Women can work the same night hours as|0.503|-0.971|||
|<br>men Y:1 N:0|(2.945)|(3.954)|||
|Women can work in the same industries as|||-4.652*|-3.746|
|men Y:1 N:0|||(2.459)|(3.127)|
|Exports (%, cell average)*GDP per capita||0.065||0.096|
|(logs)||(0.078)||(0.072)|
|Exports (%, cell average)*WBL Index||-0.112||-0.165|
|||(0.392)||(0.370)|
|No. of Workers (logs)*Women can work the<br>same night hours as men Y:1 N:0||0.445<br>(0.726)|||
|No. of Workers (logs)*Women can work in<br>the same industries as men Y:1 N:0||||-0.202<br>(0.529)|
|No. of Workers (logs)*WBL Index||-3.368*<br>(1.732)||-2.942*<br>(1.712)|
|No. of Workers (logs)|0.902***<br>(0.291)|2.887**<br>(1.271)|0.916***<br>(0.290)|3.066**<br>(1.225)|
|GDP per capita (logs)|5.876|4.791|7.974|6.291|
||(5.710)|(5.74)|(5.494)|(5.524)|
|Herfindahl-Hirschman Index|-5.197|-5.022|-7.021**|-6.775*|
||(3.662)|(3.709)|(3.446)|(3.482)|
|Age of Firm (logs)|-0.833**|-0.831**|-0.834**|-0.834**|
||(0.358)|(0.359)|(0.355)|(0.358)|
|Foreign Ownership (proportion)|0.103<br>(0.961)|0.130<br>(0.962)|0.183<br>(0.961)|0.218<br>(0.961)|
|Female Ownership Y:1 N:0|5.975***|5.987***|5.905***|5.890***|
||(0.652)|(0.654)|(0.652)|(0.653)|
|Skills Obstacle (0-4)|0.636**|0.636**|0.625**|0.622**|
||(0.269)|(0.269)|(0.270)|(0.269)|
|How Much Of An Obstacle: Transport?|<br>-0011|<br>-0016|<br>-001|<br>-0012|
||.<br>(0.209)|.<br>(0.209)|.<br>(0.209)|.<br>(0.209)|
|Firm Purchased Fixed Assets Y:1 N:0|-1.031*|-0.994*|-1.042*|-0.999*|
||(0.556)|(0.558)|(0.557)|(0.558)|

50