rafmacalaba/datause-encoder-data huggingface dataset:
Macro-financial conditions have depressed financial intermediation in Brazil for decades, and finance for firms in the free market is characterized by high interest rates (Figure 1) and short maturities (Figure 2). The high interest rates are mainly driven by high inflation, perceived high default risk, and perhaps an unfortunate feedback mechanism from directed credit. This is because directed credit schemes aiming to mitigate high and volatile interest rates make extensively use regulated interest rates. This in turn limits the effectiveness of monetary policy, which may compensate for the low policy interest rate sensitivity in the earmarked market with greater policy rate amplitude and thus more volatile interest rates in the free market.

High banking spreads exacerbate the cost of credit and reflect a number of micro-economic and institutional factors. The burdens imposed by earmark requirements and the large part of credit provided at low rates may be important in explaining the high spreads for the free market credit. Deposit collection is associated with directed lending and high reserve requirements, leaving modest funding to be freely used. A financial intermediation tax, the Imposto Sobre Operações Financeiras (IOF), adds to the cost of finance, and, lastly, credit risk is high due in part to poor debt recovery by banks.<sup>3</sup> <!-- Start of picture text --> Figure 1: Interest rate on loans to firms Figure 2: Maturity of loans to firms<br>35 140<br>30 120<br>25 100<br>20 80<br>15 60<br>10 40<br>5 20<br>0 0<br>Free Earmarked Free Earmarked<br>Months<br>Percent per year<br>Mar‐11 Nov‐11 Jul‐12 Mar‐13 Nov‐13 Jul‐14 Mar‐15 Nov‐15 Jul‐16 Mar‐17 Nov‐17 Mar‐11 Nov‐11 Jul‐12 Mar‐13 Nov‐13 Jul‐14 Mar‐15 Nov‐15 Jul‐16 Mar‐17 Nov‐17<br><!-- End of picture text --> _Source: Banco Central do Brasil. Note: new credit operations._ The federal government intervenes in credit markets not only through direct ownership of commercial banks, but also in the form of direct quantity regulation through the earmarked credit system. Various schemes are used, which generally involve regulated and low interest rates with implicit embedded subsidies. Earmarked credit accounts for about half of total credit in the economy; it expanded rapidly after the global financial crisis (Figure 3), but started shrinking in 2016. Earmarked credit is mainly targeted to infrastructure and development projects, rural activities and housing. Total credit is roughly equally divided between firms and households, and within these categories, credit is roughly equally divided between earmarked and non-earmarked credit (Figure 4).

> 3 See, for example, World Bank (2012).

4
**Are Irrigation Rehabilitation Projects Good for Poor Farmers in Peru?** _A Spatial Regression Discontinuity Approach_

Gayatri Datar* Ximena V. Del Carpio<sup>*</sup> JEL Codes: D12, D24, O12, O18, O54, Q12 Keywords: Rural development, Infrastructure, Irrigation, Impact evaluation, Peru

* Independent Evaluation Group, World Bank.

We are thankful to Deither Beuermann, Brian Blankespoor, Gustavo Gutierrez, Irene Jillson, Kristian Lopez, Pamela Velez-Vega and Eduardo Zegarra for various contributions. We are most indebted to Norman Loayza and Maximo Torero for their excellent ideas and overall guidance in rewriting this paper. We are also thankful to Javier Baez, Kenneth Chomitz, Elisabeth Sadoulet, Mark Sundberg, Andrew Warner and participants at the “Evaluating the Impact of Agricultural Projects” conference at the Inter-American Development Bank and Impact Evaluation Conference in Cairo (NONIE) for excellent ideas on previous versions of this paper. We owe special recognition to the World Bank team that implemented this work and the project team at PSI in Peru for invaluable inputs throughout the process. Lastly, we would like to thank the Swiss Agency for Development and Cooperation (SDC) and the Independent Evaluation Group at the World Bank for financial support. All errors and omissions are our own. Correspondence author contact e-mail: <u>xdelcarpio@worldbank.org</u>
# **2.4.5 Other control variables**

A vector of control variables **zi** _,_ **t** includes several variables. First, we use the lagged domestic official sector holdings ratio _푑표푖,푡_ −1 as a control variable. Major central banks in advanced economies have been important players in the government bond markets, purchasing government bonds financed by the creation of central bank reserves through quantitative easing — which central banks have implemented to put downward pressure on interest rates when policy rates were at or near zero. The portfolio balance channel operates when the central bank’s bond purchases, which change the relative supply of assets held by the private sector, induce equilibrating changes in the relative yields.<sup>16</sup> In the United States, Gagnon et al. (2011) show a cumulative decline in the 10-year Treasury yields by about 91 basis points after announcements of quantitative easing. According to Joyce et al. (2011), the corresponding impacts in the United Kingdom is estimated to be 100 basis points.

Second, we add the lagged foreign official holdings ratio _푓표푖,푡_ −1 using data from Arslanalp and Tsuda (2014a) and Arslanalp and Tsuda (2014b). The U.S. long-term interest rates remained low in the mid-2000s despite increases in the federal funds rate, a phenomenon Alan Greenspan labeled as a “conundrum” (Greenspan (2005)). Bernanke (2005) hypothesized that a global saving glut – driven by net savings in Asia and oilexporting countries – lowered long-term interest rates through an accumulation of foreign exchange reserves. Warnock and Warnock (2009) show that a 12-month total of foreign flows of 1 percentage point of GDP is associated with a 40 basis point reduction. Beltran et al. (2013) find that a 10 percentage point increase in foreign official flows into and out of Treasuries lowers the five-year term premium by 135 basis points.

Third, following Gruber and Kamin (2012), we add two-year-ahead projections of real GDP growth rate E _푡 푦푖,푡_ +2 which can be obtained from Consensus Economics _Consensus Forecast_ , the IMF _World Economic Outlook_ , _Article IV Consultations_ , OECD _Economic Outlook_ , European Commission (EC) _European Economic Forecast_ and the EIU.

Finally, we control for expected exchange depreciation over a two-year horizon : the rate of change between the two-year-ahead projections of the expected exchange rate and the current year estimation of each currency’s

> for International Settlements _Debt Securities Statistics_ ), we make an approximate estimate of the foreign holdings share of general government debt securities denominated in local currency by removing the amount of debt securities denominated in foreign currency from the total. Finally, we divide the foreign holdings ratio into a foreign private holdings ratio _푓푝푖,푡_ −1 and a foreign official holdings ratio _푓표푖,푡_ −1 using data from Arslanalp and Tsuda (2014a) and Arslanalp and Tsuda (2014b). 16Christensen and Rudebusch (2012), D’Amico et al. (2012) and Joyce et al. (2017) provide further discussion.

12
- 13 -

# Table 1 Total Imports and Imports Exempt from Duty: Various Product Categorien - 1986

||||Duty P|aid|Nomi|nal Tariff|
|---|---|---|---|---|---|---|
|Tot<br>100|al<br>0 USS|XExempt<br>r4rom Duty|Xof Imports<br>Subject to Duty|Xof Totl<br>Imports|<br><br>Slop<br>Averago|Rate<br>(a)<br> <br>Weighted<br> <br>Average|
|Total|4724134|29|28.3|20.5|27.0|28.0|
|Section1|39964|<br>61|14.1|6.2|19.0|19.0|
|Section 2|245416|<br>42|12.6|7.3|19.2|18.3|
|Section1|7203|<br>14|27.5|23.7|21.8|22.0|
|Soction 4|67831|<br>30|17.5|12.1|26.2|26.9|
|Section 6|670617|<br>30|19.0|13.6|26.6|26.8|
|Section 6|1088182|<br>13|18.4|16.9|18.2|12.4|
|Section7|284992|<br>10|30.1|27.0|26.0|24.9|
|Section 8|2692|<br>24|81.7|24.0|26.3|24.0|
|Section 9|56102|<br>6|19.6|18.6|31.6|31.7|
|Section 10|123261|<br>15|26.4|22.6|23.4|27.0|
|Section11|110076|<br>22|26.4|19.8|36.0|U6.8|
|Section 12|1397|<br>55|33.6|18.9|87.0|U8.9|
|Section 13|47203|<br>s5|34.6|16.6|29.6|28.6|
|Section 14|4608|<br>0.0|21.6|21.6|24.0|22.5|
|Section 16|396409|<br>48|32.3|16.7|29.2|28.0|
|Section 16|1196651|<br>40|43.6|26.4|42.6|36.6|
|Section 17|282831|<br>23|46.6|36.0|28.0|28.6|
|Section 18|224409|<br>26|36.2|26.5|42.6|51.2|
|Section 19|676|<br>28|36.7|26.7|27.0|27.0|
|Section 20|24233|<br>27|40.6|29.6|30.6|27.2|
|Section 21|216|<br>100|0.0|0.0|0.0|0.0|

(a) The weights **are** th, number of STN positions corresponding to each chapter. To the abovo rates a 10X surcharge **&ust** be added In order to obtain the global nominal average tariff provailing In 1986. For a dofinition of sections **see** the following page.

Source: INDEC and Lucangell (1987).
<!-- Start of picture text --> 0.36<br>Shirak<br>><br>5<br>ZB 0.35 Yerevan ; ae?<br>2 Lori Aragatsotn .**°<br>w ee? m<br>=<br>2<br>vu o- Sjunik @ aery<br>B= ee?<br>3 Ararat ee” _—avus<br><!-- End of picture text --> <!-- Start of picture text --> ra ee**" — Kotayk<br>r) «Vayots Dzor Gegharkunik<br>> 0.32<br>=<br>uv<br>i=<br>2<br>=<br>0931 ‘- Armavir<br>0.3<br>20 25 30 35 40 45 50 55<br>Headcount (incidence) of multidimensional poverty<br><!-- End of picture text -->
consumption for the poor by unlocking the potential demand for basic goods that are not available (or available at a higher price) at the traditional local markets. When the z-score of online purchases over population increases by one, log household travel expenditure growth is associated with 0.695 increase for the poorest quartile, 0.443 for the middle two quartiles and 0.535 for the richest quartile. For communication and entertainment consumption expenditure, the coefficient for the e-commerce indicator is the highest for the middle two quartiles (0.138 and 0.386 respectively).

When online purchases over population increases by one standard deviation for the poorest quartile, log household consumption growth on cosmetics and beauty products increases by 0.568, log household consumption growth on travel increases by 0.695.<sup>31</sup> The large magnitudes of the coefficients suggest that online purchases over population has strong effects on the consumption growth of the goods and services of higher income elasticity / higher in style consumption nature (such as travel and cosmetics and beauty products), particularly for the poorer households, whose demand might have been disproportionately limited by their poor access to markets through traditional markets.

Consistent with the prediction of the model in Startz (2018), the results show that e- commerce development is associated with stronger growth in consumption of goods with rapidly evolving styles (such as cosmetics and beauty products, clothes, and entertainment), and goods with higher storage costs (such as durable goods that require more resources for stocking for local traders). We also see higher consumption growth of goods and services with higher income elasticities, such as travel and dining out, even though these are not online products or services. In the meantime, there is some indication that e-commerce development benefits consumers in areas with high travel costs (such as in the interior regions).

Across the different categories of consumption of goods and services, the association between e-commerce development and food consumption growth is particularly strong. From 2012 to 2016, China's e-grocery sales saw a compound annual growth rate of 52.9 percent, according to a recent report by Agriculture and Agri-Food Canada.<sup>32</sup> The extremely strong growth of online food consumption in China seems to defy the Engel curve. The puzzle is likely

> 31 Health and fitness consumption is different from medical treatment consumption. The former does not include medical necessities but only healthy supplements and fitness while the latter is associated with treatment in hospital or medicines bought from a pharmacy. The results are consistent with expectations as health and fitness expenditure might have a stronger income elasticity while medical expenditure is more need-based.

> 32 Source: http://www.agr.gc.ca/eng/industry-markets-and-trade/international-agri-food-marketintelligence/asia/market-intelligence/e-grocery-market-in-china/?id=1504037238257.

·· 20
Policy Research Working Paper 4969

# **Abstract**

Ethiopia has experienced a historically unprecedented increase in inflation, mainly driven by cereal price inflation, which is among the highest in Sub-Saharan Africa. Using monthly data from the past decade, the authors estimate error correction models to identify the relative importance of several factors contributing to overall inflation and its three major components, cereal prices, food prices, and non-food prices. The main finding is that, in a longer perspective, over three to four years, the main factors that determine domestic food and non-food prices are the exchange rate and international food and goods prices. In the short run, agricultural supply shocks and inflation inertia strongly affect domestic inflation, causing large deviations from long-run price trends. Money supply growth does affect food price inflation in the short run, although the money stock itself does not seem to drive inflation. The results suggest the need for a multi-pronged approach to fight inflation. Forecast scenarios suggest monetary and exchange rate policies need to take into account cereal production, which is among the key determinants of inflation, assuming a decline in global commodity prices. Implementation of successful policies will be contingent on the availability of foreign exchange and the performance of agriculture.

This paper—a product of the Agricultural and Rural Development Unit, Africa Region—is part of a larger effort to understand the sources and implications of commodity price shocks in Sub-Saharan Africa. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dick.durevall@economics.gu.se, jloening@worldbank.org, or y.a.birru@sussex.ac.uk.

_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
of Mexico where municipalities that had an Azteca branch in the fourth quarter of 2002 are marked in dark blue, and municipalities that had a different bank branch, but no Azteca branch, are marked in light blue. Both types of municipalities are distributed throughout Mexico.

Since our analysis applies to potential Banco Azteca clients who could be economically active, we keep only individuals of working age (between the ages of 20 and 65) in our sample. We then construct three of our main outcome variables by creating dummy variables for each person in the sample, indicating whether the person a) owns an informal business, b) owns a formal business, and c) is a wage earner<sup>14</sup> . These are the three possible occupations for somebody who is employed. People who do not fall into either of these categories are not employed (they are either unemployed or not in the labor force).

All dummies are defined for the whole sample, meaning that they denote the percentage of all individuals in the sample who fall into each category. For example, a value of 0.08 of the formal business owner dummy means that 8 percent of all people between the ages of 20 and 65 own a formal business.

Finally, we also use monthly income as an outcome variable. While the dummy variables are defined for everybody in our sample, income is available only for the gainfully employed and is missing for the unemployed, for individuals who are out of the labor force, and for unpaid workers. Unfortunately, the income data from the ENE is probably not as reliable as the occupation data since it is not the focus of the survey. The survey only includes one question on income, asking for the earnings from the individual’s main occupation. 4.2 percent of employed individuals do not answer this question. These individuals are then asked to report their income in terms of bins of the minimum wage. About 60 percent of the people who did not report their income in the first question report it in terms of the minimum wage (2.4 percent of the total sample). Taking these responses into account, we create a dummy variable indicating whether an individual earns above minimum wage<sup>15</sup> . We use this as an additional outcome variable.

> had a bank branch in Mexico in the fourth quarter of 2002, 120 of which are not covered in our data. However, only 8 municipalities that had an Azteca branch are not in our data.

> 14 A detailed description of how these variables were constructed is available in Bruhn (2008).

> 15 We convert the incomes of the 95.8 percent individuals who reported them as amounts to multiples of the minimum wage using information on the minimum wage from the National Commission for Minimum Wages (Comision Nacional de Salarios Minimos, CONASAMI, http://www.conasami.gob.mx).

10
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<!-- Start of picture text --> Life tables survival estimate<br>wo<br>S<br>© \<br>+enN<br>°<br>T T T T T T<br>0 10 20 30 40 50<br>analysis time<br><!-- End of picture text -->
# _4.1 Fuelwood Savings of the Mirt Stove Compared to the Three-Stone Tripod_

As shown in Table 2, we find significant average fuelwood savings per week using the Mirt stove. Across all rounds we estimate that the Mirt stove saved about 10.55 kilograms of fuelwood per week since households received their stoves. Average savings increased steadily across rounds, reaching 12.40 kilograms per week saved by round 4. By round 2 of the study, user experience and frequency of use had increased substantially and as shown in Table 1 by fall 2013 seventy-five percent of users had positive measured fuelwood savings. By round 4 in summer 2014, virtually all users had positive measured fuelwood savings using the Mirt stove.

Table 2 Fuelwood Savings Overall and by Round (in Kilograms per Week per Mirt Stove)

||Overall<br>Sample|Round 1<br>(June-August<br>2013)|Round 2<br>(August -<br>October 2013)|Round 3<br>(March-May<br>2014)|Round 4<br>(May – July<br>2014)|
|---|---|---|---|---|---|
|Mean|10.55|5.22|12.12|12.18|12.40|
|Minimum|-29.24|-29.24|-26.62|-25.07|-12.09|
|Maximum|68.59|58.94|63.06|68.59|67.86|
|10<sup>th</sup>percentile|-2.52|-7.95|-1.70|-1.07|-0.01|
|25<sup>th</sup>percentile|0|-1.31|0.155|0.012|0.01|
|50<sup>th</sup>percentile|3.98|1.48|5.55|4.96|4.55|
|75<sup>th</sup>percentile|16.60|11.05|19.00|21.26|21.01|
|90<sup>th</sup>percentile|38.51|22.92|39.93|41.83|39.58|
|Observations|1090|262|276|280|272|

The review period preceding our cooking round 1 evaluation appears to have been one of startup both in terms of having the lowest average per-minute fuelwood savings according to the CCTs and lowest Mirt stove use as measured by the SUM. We therefore view average fuelwood savings from later rounds as more typical. For example, 12.20 kilograms per stove per week is a reasonable overall estimate of the fuelwood Mirt stoves save on average every week in the long-run compared with traditional stoves. As our sample covers a full year and respondents travel infrequently, the Mirt stove is estimated to save approximately 634 kilograms per year.<sup>7</sup> ~~.~~

> 7 Leakage would scale this estimate down.  For example, if with rebound effects net fuelwood savings is only 80% of estimated, the average savings per stove per year would be 507.20 kilograms per year.  We also remind, however, that

**20 |** P a g e
directly by its importer ( **DAVAX** _sr_ ): Therefore, the GVC share in bilateral exports is which can be computed for the exporting country _s_ as a whole: or even at world level: The GVC-related trade indicator proposed above is not the first measure based on ICIO tables that has been developed to gauge the relevance of GVCs in international shipments. Following the seminal article of Hummels et al. (2001), various measures of a country or a region’s integrations in international production networks have been proposed (Johnson and Noguera, 2012; Rahman and Zhao, 2013; Los et al., 2018). The ‘vertical specialization’ index ( **VS** ) of Hummels et al. (2001) is probably one of the first and most popular of these measures. However, as pointed out by the authors themselves, this is a partial measure of participation in global value chains, since it only considers the backward linkages (i.e. it measures the import content of a country’s exports, see equation (1)). In order to take forward linkages into account, Hummels et al. (2001) also suggest considering the exports of intermediate products that later are further processed and re-exported (they label it **VS1** ). However, they do not propose a precise formulation of this measure, since it can be implemented only in a fully-fledged ICIO framework that was not available at the time of writing.

By exploiting the bilateral source-based decomposition presented here above, we can provide a precise measure of the share of exports related to ‘forward’ supply linkages ( **GVC** _forward_ or **VS1** in Hummels et al., 2001 nomenclature), which can be computed as the difference between **DVA** _source_ and **DAVAX** . Then, the overall **GVC** indicator of equation (22) can be decomposed into a ‘backward’ component, corresponding to the **VS** Index (see proof in Appendix C) and ‘forward’ component, 19