rafmacalaba/datause-encoder-data huggingface dataset:
Motivated by the importance of energy efficiency for Turkey, the lack of credible studies in this area and the existence of a unique firm-level data set that covers all manufacturing industry over the 2005-2012 period, this study aims to understand how energy efficiency evolved in Turkish manufacturing industry by its components.

Implementing a decomposition analysis, specifically, this study examines the roles of:

- Change in energy intensity of firms (technical efficiency, firm intensity effect),

- Shift of production across firms in the same sub-sector (within sector reallocation - firm structural effect),

- Shift of production across sub-sectors (across sector reallocation – sector structural effect)

- Production change (activity effect)

in the change in energy consumption of Turkish manufacturing industry over 2005-2012.

The first and second components above can be calculated only by using firm level data. To our knowledge, there is only one study (Petrick, 2015) that used a firm level data set (for Germany) and investigated these two components in the literature. Moreover, to our knowledge, this is the first decomposition analysis for Turkish manufacturing industry in the literature.

Findings in this paper are expected to provide useful insights to design more effective and focused policies to increase energy efficiency in Turkish manufacturing. Therefore, the study aims to indirectly serve to policies for more favorable external balances and better climate results of the energy consumption in the country.

The organization of the paper is as follows: The second section discusses the literature. The third section describes the data set and provides several preliminary figures. The fourth section introduces the firm level decomposition methodology. The fifth section presents the decomposition findings. Finally, the sixth section discusses the findings.

# **II. LITERATURE**

Since their first application in late 1970s, index decomposition analyses have been increasingly used to study the impacts of structural change (i.e. changes in industry product mix) and sectoral energy intensity change in the energy economics literature. They are also widely used by national energy institutions around the world for policy-making purposes.

3
Table 3: International comparison of top incomes shares (2019)

||Top 0.1%|Top 1%|Top 10%|
|---|---|---|---|
|_Pivotal benchmark countries_||||
|China|7%|15%|42%|
|France|4%|11%|34%|
|India|8%|22%|57%|
|South Africa|6%|19%|65%|
|United States|9%|19%|46%|
|_High income share_||||
|Angola|11%|26%|58%|
|Bahrain|9%|24%|56%|
|Central African Rep|13%|31%|65%|
|Mozambique|13%|31%|65%|
|Yemen|8%|24%|58%|
|_LAC (good data quality)_||||
|Argentina|4%|14%|46%|
|Brazil|5%|20%|57%|
|Chile|8%|25%|61%|
|Colombia|4%|19%|59%|
|Costa Rica|4%|20%|55%|
|Dominican Rep|6%|21%|52%|
|Ecuador|3%|12%|42%|
|El Salvador|3%|14%|44%|
|_Honduras (WID data)_|5%|19%|52%|
|Mexico|6%|28%|65%|
|Peru|6%|21%|55%|
|Uruguay|3%|13%|43%|

_Note:_ This table presents the income share of the top 0.1%, top 1% and top 10% highest earners in selected countries in 2019. Data is from the World Inequality Database (WID). The figures for Honduras in this table are from the WID dataset, not the estimates in this paper.

28
-34-

The difference in expenses between individual and group annuities is remarkable. However, the difference in the level of profits is even more pronounced, suggesting that a different product may be involved. Another possibility is that competition is much tougher in the group annuity segment, maybe because buyers are more sophisticated.

The low level of expenses in group annuities, which averages 3.3% of premiums is confirmed by information on the period 1942-62: Table 10 _Group Annuity Expenses in 1942-1962_ (% of premiums)

|Year|Commissions|Other Expenses<br>Total Expenses<br>|
|---|---|---|
|||(excluding profits and taxes)|
|1942<br>|0.53%<br>|1.36%<br>1.89%<br> <br>|
|1943<br>|_0.50%_<br>|1.33%<br>1.83%<br> <br>|
|1944<br>|0.49%<br>|1.13%<br>1.62%<br> <br>|
|1945<br>|0.48%<br>|1.34%<br>1.82%<br> <br>|
|1946<br>|0.42%<br>|1."%<br>1.86%<br> <br>|
|1947<br>|0.43%<br>|1.46%<br>1.89%<br> <br>|
|1948<br>|0.38%<br>|1.43%<br>1.81%<br> <br>|
|1949<br>|0.40%<br>|1.45%<br>_1.85%_<br> <br>|
|1950<br>|0.32%<br>|1.24%<br>1.56%<br> <br>|
|1951<br>|0.33%<br>|1.33%<br>1.66%<br> <br>|
|1952<br>|0.32%<br>|1.27%<br>1.59%<br> <br>|
|1953<br>|0.31%<br>|_1.51%_<br>1.82%<br> <br>|
|1954<br>|0.29%<br>|1.53%<br>1.82%<br> <br>|
|1955<br>|0.28%<br>|1.62%<br>1.90%<br> <br>|
|1956<br>|0.29%<br>|1.79%<br>2.08%<br> <br>|
|1957<br>|0.29%<br>|1.94%<br>2.23%<br> <br>|
|1958<br>|0.28%<br>|2.13%<br>2.41%<br> <br>|
|1959<br>|0.25%<br>|2.10%<br>2.35%<br> <br>|
|1960<br>|0.30%<br>|2.67%<br>2.97%<br> <br>|
|1961<br>|0.30%<br>|2.90%<br>3.20%<br> <br>|
|1962|0.30%|2.92%<br>3.22%|

Source: McGill. _D., Fundamentalk of Private Pensions._ First (1955) and Second editions, tables 14 and 7 respectively. These tables report the wheighted average cost for seven leading companies, so smaller companies are excluded.

Care must be exercised in interpreting these figures, because they do not include profits. In addition, a low expense to premium ratio may be<sup>due</sup> to large premiums, not to low costs. A cartel in the annuity industry may charge large premiums in comparison to the expected benefits, implying a low rate of return. That would show up as a large profit and a small
The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa. Kuwait, Qatar, Bahrain, Saudi Arabia, UAE, and Oman are aggregated into a GCC composite group. In addition, the 57 sectors in the GTAP 8 database are aggregated into 22 sectors based on their importance for the countries in the MENA region (Table 1). The resulting MENA-specific database contains 26 countries, among which are the six Levant economies of interest in this paper (Turkey, Lebanon, Syria, Iraq, Jordan, and Egypt) and the rest of the developing MENA countries (Table 1).

The procedure used to construct the individual country information employs data from several sources. The UN Statistics Division data for 2007 is the source for the six components of GDP – agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); construction (ISIC-F); transport, storage, and communication (ISIC I); wholesale, retail trade, restaurants and hotels (ISIC G-H); and other activities (ISIC J-P).

We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1. As part of this procedure, all entries in the two composite regions (rest of Western Asia and rest of Northern Africa) were split and assigned the split values to the newly created economies, while all entries for the two composite regions from the GTAP database were removed from the database. Each entry was split using the most thematically relevant external source. Sectoral GDP shares were used to split consumption and production values, trade data were used to split export and import values, and tariff information was used to assign tariff values. Export shares were used to split further production and consumption information into the final set of industries presented in Table 1. For internal consistency purposes, the required accounting relationships were imposed on the split database 8
|P071340|2007|2011|3/22/2011|9 Closed|Nigeria|
|---|---|---|---|---|---|
|P071340|2007|2012|12/13/2011|10 Closed|Nigeria|
|P071340|2007|2013|7/11/2012|11 Closed|Nigeria|
|P071340|2007|2013|1/14/2013|12 Closed|Nigeria|
|P071340|2007|2014|10/11/2013|13 Closed|Nigeria|
|P071347|2002|2002|6/19/2002|2 Closed|Bosnia and Herzegovin|
|P071347|2002|2003|6/4/2003|4 Closed|Bosnia and Herzegovin|
|P071347|2002|2004|6/8/2004|6 Closed|Bosnia and Herzegovin|
|P071347|2002|2005|5/31/2005|8 Closed|Bosnia and Herzegovin|
|P071347|2002|2006|11/23/2005|9 Closed|Bosnia and Herzegovin|
|P071347|2002|2006|5/24/2006|10 Closed|Bosnia and Herzegovin|
|P071347|2002|2007|12/22/2006|11 Closed|Bosnia and Herzegovin|
|P071347|2002|2007|4/2/2007|12 Closed|Bosnia and Herzegovin|
|P071347|2002|2007|6/29/2007|13 Closed|Bosnia and Herzegovin|
|P071371|2002|2002||0 Closed|Burundi|
|P071371|2002|2003|6/17/2003|3 Closed|Burundi|
|P071371|2002|2004|3/26/2004|5 Closed|Burundi|
|P071371|2002|2005|5/24/2005|7 Closed|Burundi|
|P071371|2002|2006|12/20/2005|8 Closed|Burundi|
|P071371|2002|2006|3/30/2006|9 Closed|Burundi|
|P071371|2002|2007|8/25/2006|10 Closed|Burundi|
|P071371|2002|2007|6/20/2007|11 Closed|Burundi|
|P071371|2002|2008|12/18/2007|12 Closed|Burundi|
|P071371|2002|2008|6/20/2008|13 Closed|Burundi|
|P071371|2002|2009|6/20/2008|13 Closed|Burundi|
|P071371|2002|2019|5/7/2019|14 Closed|Burundi|
|P071374|2003|2003|6/11/2003|1 Closed|Rwanda|
|P071374|2003|2004|5/6/2004|5 Closed|Rwanda|
|P071374|2003|2005|3/22/2005|8 Closed|Rwanda|
|P071374|2003|2006|7/12/2005|9 Closed|Rwanda|
|P071374|2003|2006|9/22/2005|10 Closed|Rwanda|
|P071374|2003|2006|3/10/2006|11 Closed|Rwanda|
|P071374|2003|2007|9/25/2006|12 Closed|Rwanda|
|P071374|2003|2007|1/24/2007|13 Closed|Rwanda|
|P071374|2003|2008|10/10/2007|14 Closed|Rwanda|
|P071374|2003|2008|4/18/2008|15 Closed|Rwanda|
|P071374|2003|2009|8/31/2008|16 Closed|Rwanda|
|P071374|2003|2009|12/23/2008|17 Closed|Rwanda|
|P071375|2002|2002||0 Closed|Cote d'Ivoire|
|P071375|2002|2003|4/24/2003|3 Closed|Cote d'Ivoire|
|P071375|2002|2004|10/24/2003|4 Closed|Cote d'Ivoire|
|P071391|2006|2006|12/6/2005|1 Closed|Nigeria|
|P071391|2006|2006|3/7/2006|2 Closed|Nigeria|
|P071391|2006|2007|9/8/2006|3 Closed|Nigeria|
|P071391|2006|2007|10/11/2006|4 Closed|Nigeria|
|P071391|2006|2007|4/19/2007|5 Closed|Nigeria|
|P071391|2006|2008|11/27/2007|6 Closed|Nigeria|
**Table 5**

# **Long-Run Estimates from Panel Autoregressive Distributed Lag Model, Terms of Trade (ToT)**

||**‐‐‐‐‐‐‐‐‐‐ Me**|**an‐Group (M**|**G) Estimato**|**r ‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐ Poole**|**d‐Mean Gro**|**up (PMG) E**|**stimator ‐‐‐‐‐**|
|---|---|---|---|---|---|---|---|---|
||**‐‐‐‐‐‐ Lag 1**|**‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐ L**|**ag 2 ‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐ La**|**g 1 ‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐**|**Lag 2 ‐‐‐‐‐‐‐‐‐**|
||�|�|�|�|�|�|�|�|
|**GDP measured at M**|**arket Prices, c**|**onstant US$2**|**000, total**||||||
|World|‐0.41***|‐0.47***|‐0.45***|‐0.51***|‐0.46***|‐0.47***|‐0.47***|‐0.50***|
|High income|‐0.43***|‐0.48***|‐0.49***|‐0.53***|‐0.51***|‐0.49***|‐0.53***|‐0.53***|
|Middle income|‐0.35***|‐0.44***|‐0.35***|‐0.46***|‐0.37***|‐0.43***|‐0.37***|‐0.46***|
|Low income|‐0.49***|‐0.42***|‐0.40***|‐0.43***|‐0.52***|‐0.43***|‐0.51***|‐0.45***|
|**GDP measured at Pu**|**rchasing Pow**|**er Parity, tota**|**l**||||||
|World|‐0.41***|‐0.46***|‐0.44***|‐0.49***|‐0.45***|‐0.46***|‐0.46***|‐0.49***|
|High income|‐0.43***|‐0.48***|‐0.50***|‐0.53***|‐0.52***|‐0.49***|‐0.54***|‐0.53***|
|Middle income|‐0.34***|‐0.43***|‐0.34***|‐0.45***|‐0.36***|‐0.43***|‐0.35***|‐0.46***|
|Low income|‐0.47***|‐0.42***|‐0.39***|‐0.43***|‐0.50***|‐0.43***|‐0.49***|‐0.45***|
|**GDP measured at M**|**arket Prices, c**|**onstant US$2**|**000, per capit**|**a**|||||
|World|‐0.84***|‐0.45***|‐0.97***|‐0.48***|‐1.01***|‐0.45***|‐1.05***|‐0.48***|
|High income|‐0.60***|‐0.49***|‐0.69***|‐0.54***|‐0.73***|‐0.50***|‐0.75***|‐0.54***|
|Middle income|‐0.56***|‐0.39***|‐0.51***|‐0.40***|‐0.61***|‐0.39***|‐0.60***|‐0.41***|
|Low income|‐0.35|‐0.32***|0.09|‐0.34***|‐0.55|‐0.33***|‐0.32|‐0.34***|
|**GDP measured at Pu**|**rchasing Pow**|**er Parity, per**|**capita**||||||
|World|‐0.79***|‐0.43***|‐0.87***|‐0.45***|‐0.91***|‐0.43***|‐0.94***|‐0.46***|
|High income|‐0.60***|‐0.49***|‐0.70***|‐0.53***|‐0.74***|‐0.49***|‐0.76***|‐0.54***|
|Middle income|‐0.55***|‐0.39***|‐0.47***|‐0.39***|‐0.58***|‐0.39***|‐0.56***|‐0.40***|
|Low income|‐0.37|‐0.33***|0.10|‐0.34***|‐0.61*|‐0.33***|‐0.43|‐0.34***|

**Notes** : θ denotes the long‐run impact of income on ToT, λ is the speed of adjustment to long‐run equilibrium. One (*), two (**), and three (***) asterisks indicate significance at 10%, 5%, and 1% levels, respectively.

_20_
increasing use of digital communication technologies in the 1990s and substitution away from mail, however, forced post operators to rethink their overall business strategy and to diversify product offerings. As a result, some postal operators decided to leverage their existing post office network and expand the number of financial services provided. While posts in some countries such as Brazil chose to partner with existing financial institutions, other posts decided to convert their postal savings banks into fully-fledged postal banks (Berthaud and Davico, 2013).

The rest of the paper is organized as follows. Section 2 presents the data both at the individual and country-levels. Section 3 introduces the empirical methodology. Section 4 shows the econometric estimation results according to individual (demand-side) and country (supplyside) characteristics. Section 5 concludes.

# **2. Data**

# **2.1 Individual-Level Data**

Our data on account ownership come from the first round of the Global Findex database.<sup>2</sup> The Global Findex data were collected over the calendar year 2011 by adding a questionnaire on financial inclusion to the annual Gallup World Poll. The 2011 World Poll surveyed at least 1,000 individuals in 148 economies using randomly selected, nationally representative samples.<sup>3</sup> The target population is the entire civilian, non-institutionalized, adult population (age 15 and over) in each country. The questionnaire includes a question on ownership of an individual or joint account at a formal financial institution. For 62 countries in which post office savings accounts are offered, the questionnaire also includes a question on ownership of an individual or joint

> 2 See Demirguc-Kunt and Klapper (Forthcoming) for a description of the database.

> 3 Detailed country-level information about the data collection dates, sample sizes, excluded populations and margins of error can be found at: <u>http://www.worldbank.org/globalfindex. In BRIC countries, the sample size ranges from</u> 2000-4000 individuals.

4
**Table 14: Participation in the Nonfarm Enterprise Sector: Probit Results (marginal effects)**

||**Particip**|**ation**|**Ent**|**ry**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|**_Characteristics of the Household Head_**|||||
|Age|-0.003**|-0.001|-0.001**|-0.001**|
||(0.001)|(0.001)|(0.001)|(0.000)|
|Years of schooling|0.007**|-0.009**|0.004*|0.001|
||(0.004)|(0.004)|(0.002)|(0.001)|
|Male|-0.213***|-0.114**|-0.024|0.000|
||(0.052)|(0.051)|(0.030)|(0.019)|
|Married|0.216***|0.208***|0.043|0.031|
||(0.068)|(0.065)|(0.032)|(0.024)|
|Divorced or separated|0.380***|0.422***|0.089|0.068|
||(0.098)|(0.099)|(0.080)|(0.065)|
|Widowed|0.148|0.168|0.061|0.042|
||(0.111)|(0.115)|(0.073)|(0.056)|
|**_Household demographics_**|||||
|Number of adult men|-0.002|-0.010|-0.006|-0.007|
||(0.012)|(0.012)|(0.007)|(0.006)|
|Number of adult women|0.069***|0.068***|0.012*|0.011*|
||(0.016)|(0.016)|(0.007)|(0.006)|
|Number of children aged 5 or less|-0.038**|-0.014|0.004|0.005|
||(0.018)|(0.018)|(0.012)|(0.009)|
|Number of elderly|0.035|-0.004|0.030|0.022|
||(0.048)|(0.047)|(0.025)|(0.018)|
|**_Local demand_**|||||
|meanwrsi2006|0.006|0.011**|0.003|0.006***|
||(0.007)|(0.006)|(0.002)|(0.002)|
|**_Geography_**|||||
|Remote location||-0.058||-0.015|
|||(0.044)||(0.020)|
|Location in rural town||0.332***||0.052|
|||(0.066)||(0.038)|
|Distance to nearest financial institution||-0.003||0.016|
|||(0.022)||(0.010)|
|Distance to nearest all weather road||0.014||-0.015*|
|||(0.016)||(0.007)|
|Distance to nearest market||-0.033||-0.001|
|||(0.035)||(0.014)|
|**_Opportunity Cost of Labor_**|||||
|Daily Wage casual worker in agriculture||0.026||0.033*|
|(log)<br>Proportion of firms in community saying<br>_water_||(0.045)<br>-0.020||(0.019)<br>-0.024|
|**_Investment climate services_**<br> <br>_water supply_is a major problem||(0.088)||(0.036)|
|Proportion of firms in community saying<br>Proportion of firms in community saying||-0.019<br>-0.086||-0.056<br>-0.016|
|_electricity_ is a major problem<br>_technology_is a major problem||(0.083)<br>(0.078)||(0.039)<br>(0.025)|

-33-
adjustment costs include a fixed cost, then firms may react to a demand shock only if it is sufficiently large. In that case we expect the parameter β to be zero for small demand shocks, and to be positive for larger shocks. We have therefore also run the above regressions including quadratic terms for the demand shock variables (results not reported). We do not find them to be significant, however, suggesting that fixed costs do not drive the adjustment pattern in labor inputs.

Then we also check whether the above results suffer from an endogeneity bias because firms that reduce labor inputs will also tend to reduce sales because of a reduction in output. However it should be noted that we are primarily interested in the _relative_ impact of demand shocks – comparing temporary versus permanent shocks or demand shocks in credit constrained firms versus in firms that are not credit constrained. Although endogeneity bias will lead to an overestimate of the absolute impact of demand shocks, there is no a priori expectation whether and in which direction there would be a bias in the relative size of the impact across different shocks and firms due to endogeneity. Moreover, because we analyze the impact of (first) differences in sales on differences in inputs, all time-invariant firm-level (and labor type-level) factors affecting the level of sales and inputs are controlled for. Most interestingly, however, we expect endogeneity bias to be of even a less concern in case the firm applies input smoothing after a sales shock. In this case, if inputs are unexpectedly low for an unobserved reason, this will not affect total sales because the production constraint is non-binding (λ2 = 0 in the model of section 3). We therefore expect that endogeneity bias, if present, will be more of a concern for credit-constrained firms than for firms that are not credit constrained.

We therefore also estimate the model with instrumental variables in order to control for possible endogeneity bias. As instruments for the demand shock, we use the information on exporting behavior from the pre-crisis ICS 2007 survey as (pre-crisis) international exposure will most likely have affected the extent to which a firm has been affected by the global financial crisis. We include three proxies for pre-crisis international exposure, namely the percentage of sales that the firm exported in 2007, the destination of these exports in 2007 (USA, EU or other international destination) and the first year of exporting (before 2000, between 2000 and 2003, and after 2003). Because we use these instruments for both the sales shock and its interaction with the dummy variable for temporary demand shock, these instruments are also interacted with this dummy variable.

23
> UNESCO, The World Bank, OECD, the Ibero American Science and Technology

> Indicators Network (RICYT)<sup>5</sup> and the Taiwan Statistical Data Book. The definition of R&D in all these surveys is the same and "includes fundamental and applied research, as well as experimental development." 6 The data thus include not only the basic science expected in the more advanced countries, but also investments in the adoption and

> adaptation of existing technologies often thought more germane to developing countries.

Though it would be desirable to study the evolution, rate of return to, and determinants of private R&D, we work with aggregate R&D for several reasons. First, the data sources divide R&D not into private and public R&D, rather they distinguish between productive and non-productive sectors, the latter accounting for roughly 20% of the total.<sup>7</sup> The definition of "productive sector" includes both public and private for profit and not-for profit firms while "non-productive sector" includes R&D financed or undertaken by the executive branch of government. Since the productive sector may well include mining, public utilities or other state owned enterprises, the exercise of analyzing how its R&D evolves and its rate of return relative to that of non-productive sector is less interesting than the public/private sector split.

Second, this division seems to occasionally lead to some critical issues in categorization. For instance, if a public company finances its R&D from retained earnings, this will count as productive sector R&D. If instead that R&D is financed by a transfer from the treasury to the firm, it counts as "non-productive" R&D. For several countries in our sample, there were striking shifts in composition from one year to the next suggesting such sensitivity to accounting practices. In contrast, the total R&D series were reasonably stable. The final consideration is more prosaic: many developing countries tabulate only the aggregate values and as they are the focus of this paper, we want to include as many as possible.

> **_5_** Red Iberoamericana de Indicadores de Ciencia y Tecnologfa

> **6** UNESCO Statistical Yearbook (1980) pg 742. Definitions are common to the OECD, RICYT, World Bank and all are based on the Frascatti manual definition.

> **7** The median for countries with both series is 21%.

_5_
**<u>Table 2. The Cost of CONAFE Students, 1994-2002</u>**

||**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|---|---|---|
|Real Cost|$17|$25|$41|$40|$45|$48|$51|$48|$51|
|Annual||0.49|0.66|-0.02|0.11|0.07|0.06|-0.04|0.06|
|Growth(%)||||||||||

Note: Costs are per Mexican student, in 2002 U.S. Dollars, using an exchange rate of 0.103MXP = US$1. Source: CONAFE data. Costs deflated by Banco de México’s Consumer Price Index.

The World Bank and the Inter-American Development Bank (IDB) support CONAFE based on the argument that CONAFE improves the educational outcomes of Mexico’s most disadvantaged students. A variety of previous evaluations have examined the effectiveness of CONAFE with some or no control group construction, and they have generally found that CONAFE improves short-term educational outcomes. A World Bank (2002) evaluation compared CONAFE-supported schools between the years 1992 and 1995 with comparable schools in the Mexican state of Michoacan that at the time received no CONAFE support. That evaluation found that CONAFE significantly increased test scores of indigenous and other rural students. CONAFE appeared to increase the test scores of indigenous students by 25 percent over the four-year period, and CONAFE appeared to increase rural non-indigenous test scores by 12 percent over the period. The evaluation concluded that full implementation of CONAFE could cause indigenous student performance increases of 45 percent to 90 percent, and rural school performance increases of 19 percent to 38 percent.

A separate World Bank (2002) evaluation summarized in the same document found that CONAFE’s support significantly benefited the one million indigenous primary school students in CONAFE schools. The report found that indigenous students supported by CONAFE were catching up to their non-indigenous peers in test scores by about 10 percent per year. The evaluation did not control for relevant differences in background between indigenous and non7