rafmacalaba/datause-encoder-data huggingface dataset:
(at least, in a span of few years). A ”level of development” or ”cultural values of a government” could be such variables. Then, if we have measurements of corruption and decentralization for consecutive periods the fixed effect can be differenced out.

We do not have the same measurements of corruption and decentralization for two or more periods. The data on decentralization collected by us is for 2005 only, and WBES and GCB datasets, from which we draw our corruption measures, are cross-sectional (at least, those which are available to us). However, we construct a new decentralization index for 2000 from the data, which is available at the D. Treisman’s web site (and used in Fan et al. (2009)). We also use Corruption Perception Index from 2000 as an alternative proxy for corruption in 2000. Having different proxies for the same variables in two periods may also help to eliminate fixed effect from a regression. Indeed, suppose we have two equations: ′ where cort1, cort2<sup>aretwodifferentproxiesforcorruptionintwodifferentperiods,</sup> dect1, dec′t2<sup>- are two proxies for decentralization, c - is the fixed effect that we want</sup> ′ to eliminate, X - are some other controls, ǫ and ǫ are errors, which are assumed to be uncorrelated with each other.

We can express c from (13) and insert the expression in (12). We obtain: ′ Under our assumptions, β1 can be consistently estimated from (14), since ǫ and ǫ are uncorrelated. If we further assume that X’s do not change with time too, (14) reduces to: If we take cort1 to be, say, bribe burden, dect1 to be our main decentralization index, cort′2<sup>tobeCPIin2000,anddec</sup> ′t2<sup>tobedecentralizationindexin2000,thenwe</sup> expect β1 < 0, β2′′<sup>< 0,and−β</sup> 3′′<sup>> 0.</sup> The results of our estimation are presented in Table 10. We run 4 different specifications - 1 for each corruption variable except corruption perception. Further we use two decentralization indexes for 2005 - our main dec. index and auxiliary dec.index, defined in Table 4.

The results in Table 10 are inconclusive. All our expectations hold only in one specification - when informal gift is used as a dependent variable. In the first specification coefficients mostly have predicted sign, but are statistically insignificant. In specifications (2) and (4) decentralization of 2000 has the opposite sign to what we would expect. Even when significant, the coefficient near dec. index, 2005 is much smaller than the one reported in our main specification, suggesting that some part of the correlation between corruption and decentralization is driven by some omitted variable.

There may be several reasons for such twofold results. First, the data on decentralization - both for 2000 and 2005 - are very noisy. For most of the countries 33
How Equitable Is Access to Finance in Turkey? Evidence from the Latest Global FINDEX Joao Pedro Azevedo Osman Kaan Inan Judy S. Yang The World Bank<sup>1</sup> JEL Codes: D14, D63, E2, G21, I3 Keywords: Access to Finance, Equity, Human Opportunity Index, Tukey, Benchmarking, Poverty This paper has benefitted from comments by participants from presentations made in World Bank Washington, DC and Ankara offices, more specifically from the Poverty and Equity Global Practice’s Europe and Central Asia Team and the Turkey Country Office as well as inputs by our colleagues in the Finance and Markets Global Practice. The team thanks Martin Raiser for his guidance and support. We are also thankful for comments and assistance received from Alper Ahmet Oguz, Ilias Skamnelos, Jose Montes, and Minh C. Nguyen. The team is grateful to Leora Klapper and the Global FINDEX team for their assistance with the FINDEX dataset. The usual disclaimer applies. This paper is a product of the FY2015 Turkey Poverty Team in the World Bank’s Poverty and Equity Global Practice.

> 1Contacts: Joao Pedro Azevedo (Lead Economist, jazevedo@worldbank.org); Judy S. Yang (ET Consultant, jyang4@worldbank.org); Osman Kaan Inan (Junior Professional Associate, oinan@worldbank.org) This paper has benefitted from comments by participants from presentations made in World Bank Washington, DC and Ankara offices, more specifically from the Poverty and Equity Global Practice’s Europe and Central Asia Team and the Turkey Country Office as well as inputs by our colleagues in the Finance and Markets Global Practice. The team thanks Martin Raiser for his guidance and support. We are also thankful for comments and assistance received from Alper Ahmet Oguz, Ilias Skamnelos, Jose Montes, and Minh C. Nguyen. The team is grateful to Leora Klapper and the Global FINDEX team for their assistance with the FINDEX dataset. The usual disclaimer applies.  This paper is a product of the FY2015 Turkey Poverty Team in the World Bank’s Poverty and Equity Global Practice.
Table 3s Panel Estimation for the Level of the Parallel Premium

# Dependent Variables LPRE,

|Regressor|Version 1|Version 2|Version 3|
|---|---|---|---|
|Constant|0.62<br>(5.87)|0.66<br>(6.07)|-0.50<br>(-1.78)|
|LRMOt-0|0.24<br>(2.61)|0.22<br>(2.47)|0.22<br>(2.61)|
|FSURt|-0.36<br>(-2.28)|-0.35<br>(-2.19)|-0.47<br>(-3.07)|
|DUMHYU|-3.24|-3.17|-3.19|
||(-7.77)|(-7.72)|(-6.86)|
|DUMMYO<br>||--|-0.50<br>(-1.53)|0.17<br>(0.48)|
|RESCUR|--|--|-0.06<br>(-0.15)|
|RESCAP||--|1.26<br>(2.89)|
||0.26|0.27|0.37|
|DW|1.40|1.43|1.52|

Notes: LRMO: _M2/E(1+6GNP)(1+6P*),_ PSUR: Fiscal Surplus/GNP DUMMYU: Dummy for periods of unified exchange rates, DUMMYO: Duymm<sup>for official</sup> dual exchange rate system.

RESCUR: Restrictions on payments on the current account,1RESCAPt Restrictions on payments on the capital account.

Annual data 1976-1988. List of countries in the panel is in Appendix Table 1. Source: IFS, World Currency Yearbook, Exchange Arrangements and Exchange Restrictions (various issues), RAL-2.
see that all the districts of Sikasso are in the upper right quadrant (high poverty-high vulnerability).

**Figure 9**

<!-- Start of picture text --> Poverty and Vulnerability<br>Provincial Level (Cercles)<br>Baroueli Kita Dioila KangabaSan KadioloBlaYanfolilaSikassoKoutiala Yorosso<br>Banamba<br>Yelimane BaDiema f ou l abe KayesNioroMacina D Bankassj enne TominianSegouKoulikoro Koro BougouniKolondieba<br>AbeibaraTin-EssakoTenenkouKidal YouwarouKatiTombouctouTessalitAnsongoBoueremNionoDireGaoMenakaNiafunkeMoptiKeniebaGourma-RharousGoundamBandiagara Nara Kolokani Douentza<br>Bamako<br>0 20 40 60 80 100<br>Poverty rate at the provincial level (%)<br>Source: ELIM 2010.<br>The red crosses are in Sikasso.<br>The dashed horizontal line represents the national aggregate for vulnerability incidence (38.6%),<br>and the dashed vertical line represents the national aggregate for the poverty rate (43.6%).<br>The solid line is a linear fit.<br>80<br>60<br>40<br>20<br>Expected vulnerability incidence (%)<br>0<br><!-- End of picture text --> In Table 11 we compare the poverty rate and the vulnerability incidence across regions. Overall, vulnerability among the poor is 3.5 times higher than among the non-poor. However, in Bamako, in other urban areas, and in the region of Sikasso vulnerability among the poor is between 5 and 6 times higher than among the non-poor.

21
**Table 3.** Sectoral factor intensity of illustrative SSA economy (percent)

|Sector|Labor|Prv Capital|Nat Res|Total|
|---|---|---|---|---|
|Agriculture|73.7|15.0|11.3|100.0|
|Forestry|36.0|0.3|63.7|100.0|
|Fishing|21.1|0.2|78.7|100.0|
|Petroleum and gas|27.3|38.1|34.6|100.0|
|Mining|27.3|51.5|21.2|100.0|
|Food|68.9|31.1|0.0|100.0|
|Beverages|23.0|77.0|0.0|100.0|
|Tobacco products|66.1|33.9|0.0|100.0|
|Textiles and leather|39.6|60.4|0.0|100.0|
|Wood|14.6|85.4|0.0|100.0|
|Paper|27.3|72.7|0.0|100.0|
|Refined petroleum products|9.3|90.7|0.0|100.0|
|Chemical products|19.2|80.8|0.0|100.0|
|Rubber and plastic|17.6|82.4|0.0|100.0|
|Non‐metalic mineral products|17.4|82.6|0.0|100.0|
|Metal products|17.9|82.1|0.0|100.0|
|Machinery and vehicles|15.1|84.9|0.0|100.0|
|Other manufactures|30.0|70.0|0.0|100.0|
|Electricity and gas|41.6|58.4|0.0|100.0|
|Construction|50.7|49.3|0.0|100.0|
|Trade, hotels and resturants|49.6|50.4|0.0|100.0|
|Transport|48.8|51.2|0.0|100.0|
|Communications|19.6|80.4|0.0|100.0|
|Government|100.0|0.0|0.0|100.0|
|Other services|52.9|47.1|0.0|100.0|
|Total|55.7|37.3|7.0|100.0|

_Source_ : Authors’ calculations based on illustrative SSA country SAM.

Compared to a standard CGE application, additional data is needed for the proximity parameter. As indicated in the preceding section, the values for this parameter should measure the extent to which a worker that belongs to activity _a’_ has the capabilities required from workers in a destination activity _a_ ; in other words, it should measure the efficiency of workers from _a’_ in _a_ compared to workers that belong to _a_ . Different approaches are possible and in the concluding section we highlight this as an important area for future research.

For the purposes of this paper, we found it convenient to draw on the PS approach, computing proximities on the basis of revealed comparative advantage 14
3 Hence, if the reasons for reducing NRW are so compelling and so much money can be saved and significant volumes of water can be used for more productive purposes, then why has it turned out to be so difficult to reduce NRW? The present paper looks into understanding the reasons why NRW is so difficult to reduce. The literature on what is driving NRW is limited in size. Most literature on NRW is focused on the practicalities of how to reduce NRW such as IWA (2006) and Liemberger et al. (2010). There is some literature on determining the optimal level of NRW (Wyatt, 2010), while the issue has been a focus of research in Spain (Saez-Fernandez et al., 2011; Gonzalez-Gomez et al., 2011). Reducing NRW has obviously many benefits, but may also require investments in some form or another to increase billing efficiency, introduce or expand water metering or implement the physical improvements to the water infrastructure in the form of among others replacement or rehabilitation of the distribution networks.

This paper is organized as follows. In Section 2 we undertake a literature review. In Section 3 we define what NRW is, how it is measured and we look at the trends that are occurring in NRW, using the database of the International Benchmarking Network for Water and Sanitation Utilities (IBNET)<sup>2</sup> . This database covers financial and operational performance data from utilities all over the world. In the fourth section, we define a panel data model with fixed effects to analyze what drives NRW. The estimation results of the model are presented in the fifth section, and we conclude in the last section.

# **2. Literature Review**

There are many articles published on NRW reduction, but they tend to be mostly practical guidelines as to how to reduce NRW (Farley and Trow, 2003; IWA Water Loss Task Force, 2003; and AWWA, 2006) focusing on the processes required to design and implement an NRW losses strategy. They are often focused on reducing NRW in developed countries. We also found success stories of reducing water losses in for instance Phnom Penh, Cambodia (Biswas, 2009)

> 2 IBNET is being developed by the World Bank with the objective to provide comparative international benchmark performance information that can inform utilities and policymakers on how to improve service delivery (for more information, see <u>www.ib-net.org)</u>

_._
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" employ logistic regression models to examine the relative importance of these variables after statistically controlling for a number of other factors.

We hypothesize that the likelihood that a household will have a connection depends on six variables: 1) the monthly household income proxy, 2) whether or not the household lived in a rural area (RURAL), 3) whether or not the household lived in a low-income country, with GNP per capita below US$760 (LOWINCY), 4) whether or not the household is among the poorest 30% of the population in its own country (POOR), 5) whether or not the household was a homeowner (HOMEOWNER), 5) the size of the household (HHSIZE), and 6) whether or not the household lived in an Eastem European or Central Asian country (EEUROPE). Table 4 presents results of logistic regressions for five different binary dependent variables: whether or not the household has electricity, in-house tap, house/yard tap, sewer connection, and telephone. All models are estimated with the pooled cross-country data set.<sup>26</sup> Among the six independent variables, three measure how income affects the likelihood that households will be connected to these services: the household income proxy, LOWINCY and POOR. The household income proxy measures household wealth across countries. The model results show that it has a significant and positive influence in all five models, and the magnitude of its effect is largest in the model for electricity, and smallest in the model for sewers. While the household income proxy measures the differences in wealth for households _across_ countries, the second income-related variable, POOR, measures such differences _within_ each country. The coefficients on POOR are very consistent for the five models. Being poor in one's own country thus dampens the chance of being connected to these services at all income levels.<sup>27</sup> The third income-related variable is LOWINCTY, which attempts to measure whether or not living in a low-income country would have an effect on the likelihood of having a connection. The results show that residing in a low-income country has a

> 27 The poor in some countries have incomes much lower than the poor in other countries.

**26** A country is only left out of these models if information on the dependent variable is not available for that country.

12
and high-skilled engage in and informal labor. High-skilled workers are simply not employed in industries with a lot of informality. Similarly for women, there is practically no formal female employment in agriculture or construction, two industries in which anecdotally there is a lot of employment of refugees (see Table 3). Hence, Turkish women are in a much worse position to take advantage of the opportunities afforded by the inflow of low-cost informal labor.

Finally, note that the arrival of refugees has two countervailing effects on prices. First, lower labor costs should decrease producer prices. Second, increased demand for goods and services, as refugees are consumers as well as providers of labor, should increase consumer prices. In practice, we find that the inflow of refugees increases consumer prices, as measured by the Turkish Consumer Price Index (CPI) for NUTS 2 subregions. Specifically, we regress the log of the CPI on the refugee to population ratio, using the specification given by equation (1). We obtain a point estimate of 0.16, significant at the 5 percent significance level (the standard error is 0.081). At the average refugee to population of ratio of 2 percent the impact is a modest 0.3 percent increase in average consumer prices in a subregion.

# **4.3 Impact on Native Wages**

Identifying the impact of refugees on Turkish wages is difficult. The reason is that, as described above, there are very large changes in the composition of Turkish employment due to the arrival of Syrian refugees. In particular, there are decreases in informal, female and part-time employment, but increases in formal and male employment. Formal jobs on average pay double as much as informal jobs and men earn more than women, by around 15 percent (see Table 2). Moreover, the large observed changes in the composition of the Turkish workforce are likely accompanied by large changes in unobserved characteristics as well. For both reasons, the estimated causal impact of refugees on average wages in a Turkish subregion will not simply reflect changes in the marginal product of existing workers but also changes in the composition of the workforce.

In Column 1 of Table 7 we report the IV estimates of the impact of refugees on average wages. In Column 2 we report the change in average wages attributable to the causal impact of Syrian refugees on the observed composition of Turkish employment in a subregion (at 2011 wages). The decomposition is described in Section 3.2, above. Column 3 shows the residual change in wages (once accounting for changes in observed characteristic). These reflect actual changes in marginal product, which is what we typically mean when thinking about wage changes. They also reflect changes in unobserved characteristics of those 22
In the first panel of Table 7 we show the coefficient capturing the (pooled) impact of ENTRE on different self-reported indicators (each coefficient corresponds to a different regression). The second panel reports the number of observations (schools) corresponding to each regression. The outcome variables are expressed as changes between the baseline and follow-up surveys. We measure the impact at two different moments of follow-up: short-term (about 2 months after the training was implemented and about a month before the enrollment for the next academic year begins) and mid-term (in the middle of the next academic year, about 8 months after the training was conducted).

Table 7. Impact of the program on different self-reported indicators at school level

|_ΔY j: Change between baseline and follow-up in:_|(1)Coe|fficient|(2)Obse|rvations|
|---|---|---|---|---|
||Short-term|Medium-term|Short-term|Medium-term|
|_Dropout expectations (standardized)_|-0.22***<br>(0.08)|0.03<br>(0.06)|1,005|2,385|
|_Expected Returns to education (standardized)_|0.25**<br>(0.10)|0.02<br>(0.07)|1,005|2,255|
|_Prioritized tasks (dropout)_|0.45***<br>(0.13)|0.27***<br>(0.08)|1,005|2,385|
|_Prioritized tasks (Authorities' requests)_|-0.22**<br>(0.11)|-0.16**<br>(0.07)|1,005|2,373|
|_Beliefs about principal's influence on the dropout decision (standardized index)_|0.03<br>(0.08)|0.07<br>(0.06)|1,005|2,383|
|_Beliefs about main influence on the dropout decision (Principal)_|-0.01<br>(0.01)|0.02*<br>(0.01)|1,005|2,381|
|_Belief that all students should complete secondary education (standardized index)_|0.01<br>(0.10)|-0.00<br>(0.06)|1,005|2,400|
|_Growth mindset proxy (principal's perception)_|0.03<br>(0.02)|0.03*<br>(0.02)|1,005|2,352|
|_Knowledge of students' families (standardized index)_|-0.09<br>(0.08)|0.01<br>(0.05)|1,005|2,394|
|_Frequency of meetings (standardized index)_|0.10<br>(0.09)|0.04<br>(0.06)|1,005|2,396|
|_Targeted support (standardized)_|0.61***<br>(0.08)|0.12**<br>(0.05)|970|2,387|

Notes: (1) Each coefficient in panel (1) corresponds to a different regression of treatment assignment on the change between baseline and follow-up in the outcome variable mentioned in the name of each raw. (2) Short-term refers to the change between the baseline and the first follow-up and Medium-term corresponds to the change between the baseline and the third follow-up. (3) Observations in panel (2) corresponds to the number of schools without missing data who were therefore used in the regressions. (4) All regressions control for the average characteristics of the students in the school (proportion of men, average risk of dropout and proportion of students of age 11, 12, 13, 14, 15, 16, 17, 18, and 19 & more). (5) Standard errors in parenthesis. (6) *** p<0.01, ** p<0.05, * p<0.1. (7) Standardized variables were transformed to have a mean of 0 and a standard deviation of 1.

The analysis shows some interesting findings. First, being assigned to ENTRE reduced principals’ dropout expectations in the short-term by 0.22 standard deviations and increased the perceived returns to education (one of the topics most highlighted in the ENTRE training) by 0.25 standard 28
We use measures of R&D inputs, rather than outputs. Within a country the difficulty of measuring R&D inputs is well known. The concept itself is bard to define; according to the OECD definition, the main criterion to distinguish R&D inputs from related scientific activities is "the presence in R&D of an appreciable element of _novelty"_ (Frascati Manual (1981]). There are no adequate measures of R&D output.' Much of R&D output is in the form of newly improved conmnodities whose quality-adjusted price indices are rarelv available. A significant portion of R&D output is produced in industiies such as health, deferse, and space, where extemalities are large and are difficult to measure and capture statistically.

To measure R&D inputs, we construct a stock variable and a flow variable: (i) Personnel: As a r%roxy for the accumulated stock of R&D capital, we use the number of scientists and engineers employed in R&D activities.

(ii) Expenditures: As a proxy for the change in R&D stock, we use gross domestic expenditure on R&D. Following commnop --actice, we ignore the depreciation and lagged effects of past R&D investments.6 5 There is no internationally agreed system of output measurement. TWO indicators, the number of patent ap;lications and the technical balance of payments, are available for OECD member countries for a limited samp!e peiiod.

6In the UNES ) yearbooks, these measures are broken down by specialization (natural science, social science, etc.), sector of performance (private industries, universities, the governments), source of funds (government funds, productiv **e** rnterprise and special funds, etc.), and type of R&D activity (fundamental research, applied research, etc.). 1The more specific the measure, the larger are the differences in definitions across countries and tle shorter are available sample periods.

3
<!-- Start of picture text --> Amazon<br>Forest cover change 1996-27002<br>ee<br>F ‘<br>id .<br>e 72.00% STRSTaESELS eararea<br>Fys 72,009 f---- 22-22-222-222 Tbe pete]<br>B -1,0096 }----"--------+—-—__»——* |<br>Ww 72-22 TTS. -----------)<br>5<br>uz 0.00%<br>1996-97 1998-99 2000-01 2002<br>—e—high (> 20%) @—low (= 9096)<br><!-- End of picture text -->

<!-- Start of picture text --> Transition<br>Forest cover change 1996-2002<br>O_o<br>at<br>o<br>£i<br>Ey +o<br>Boy oom suesyune yume sued ce eeE ee Me wus<br>paa ;<br>GC o.0o%<br>1996-97 1998-99 2000-01 2002<br>—w—high (> 50%) low f= 50%)<br><!-- End of picture text --> Cerrado Forest cover change 1996-2002 <!-- Start of picture text --> spo 40096<br>a |<br>Os O09 f----- 2c ence ence ence ence e reece ee<br>ct)<br>uw1.a2,00% +------7e,.------------------------4~ tg ,<br>=<br>: -1,00% cae cee oe i, - = hy ao —— oe<br>2 ro hg<br>2 o,00%<br>1996-97 1999-99 2000-01 2002<br>—#—high (> 3553) S—low (* 35%)<br><!-- End of picture text -->