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_Figure 1. Total factor productivity (index, LHS) and year net change in Legal Amazon forest cover ( ‘000km2, RHS)_ <!-- Start of picture text --> 120.0 0<br>115.0 -5<br>110.0 -10<br>105.0 -15<br>100.0 -20<br>95.0 -25<br>90.0 -30<br>Total factor productivity Net change in Legal Amazon forest cover (RHS)<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> Source: FGV, INPE PRODES and authors’ representation Agriculture only accounts for about 5 percent of GDP in Brazil,<sup>4</sup> therefore what matters in the rest of the economy also matters for deforestation. The low productivity of manufacturing provides an artificial boost to agriculture: if Brazil were to advance in its structural transformation toward more urban, traded sectors (notably manufacturing), this would reduce the external competitiveness of agriculture and thus attenuate the structural drivers of deforestation, or the “Brazilian disease”. This is further aided by a second channel. As machinery and inputs are produced by the manufacturing sector, productivity gains in manufacturing would reduce the cost of capital and inputs for agriculture, relative to the price of land—or, alternatively, higher Brazilian purchasing power associated with productivity gains would lower the cost of importing machines or inputs. Irrespective, higher productivity in urban sectors would then reduce the demand for land and thus deforestation.

In addition, this paper also suggests that there are differential effects across Brazilian regions. Agriculture is highly developed in Brazil’s south, southeast and center-west, with much lower productivity in the Legal Amazon (Souza-Rodrigues, 2019). States outside the Legal Amazon have already gone through a process of deforestation. This also suggests that the land supply there is already relatively low since natural forest stocks are used up in those areas and land markets are more developed (Helfand and Taylor 2018). Therefore, agricultural productivity gains in these parts of Brazil would reduce deforestation pressures by shifting more production to those parts of the country and away from the forests of the Legal Amazon.

Finally, this paper draws attention to the fact that deforestation is not merely a problem of the Legal Amazon states. While it is well known that global agricultural demand is a driver of deforestation in Brazil (Assunção et al. 2015) and globally (WRI, 2019), the role of economic development and deforestation within the Brazilian federation is less well researched. Cattaneo (2005, 2008) analyzed the differential effects of productivity increases in agriculture, when

> 4 According to Brazil’s 2019 National Accounts.

5
The Direct and Indirect Costs of Tax Treaty Policy: Evidence from Ukraine Oleksii Balabushko<sup>a</sup> , Sebastian Beer<sup>b,c</sup> , Jan Loeprick<sup>a,c</sup> and Felipe Vallada<sup>c</sup> *

- a. World Bank Group, b. Oesterreichische Nationalbank, c. Vienna University of Business and Economics (WU)

**JEL codes** : F21, F23, H25, H26, H32

**Keywords** : Tax Treaty Policy, Transfer Pricing, Domestic Resource Mobilization, Ukraine

* We thank Eva Eberhartinger, Martin Zagler, Richard Stern, Marijn Verhoeven, Jonathan Leigh Pemberton, Sebastian James, Norbert Roller, Rajul Awasthi, and Adrian Fozzard for their helpful inputs and comments. We would also like to thank the Ukrainian State Fiscal Service and National Bank of Ukraine for guidance and support. The research was financed as part of the technical assistance work under the DFID Trust Fund financing Ukraine Governance Reform Program Technical Assistance
-4-

<!-- Start of picture text --> Piw 1:<br>Fixed Investment as a Percent of GDP<br>24<br>21<br>20 1<br>1S - pm<br>14 12<br>1978-6 I1 13603 1364 351366 16<br>Yew<br>a nwu. + Iswe<br>NOV*: Dta for td 1S hio debt cmstris are takm ham the W's World Dlmic Outook,<br>October 1987.<br><!-- End of picture text -->
Figure 2: Density of total test score for teacher training and books intervention <!-- Start of picture text --> Panel A:- Baseline Survey Panel B:- Follow-up survey<br>0 10 20 30 40 50 0 20 40 60<br>Total test score Total test score<br>Treatment Treatment<br>Control Control<br>.05 .04<br>.04 .03<br>.03<br>.02<br>Density Density<br>.02<br>.01<br>.01<br>0 0<br><!-- End of picture text --> Note: Total test score is the sum of scores in math, reading, writing, listening and Peabody tests.

Extra Teacher Training: As discussed above, the comparison of test scores of students who were treated with in-service teacher training and books against those who received books only is the basis of estimating ATE of teacher training only. This comparison shows that the extra teacher training has weaker impacts on test scores. Due to the extra teacher training intervention, total test scores did not improve (Table 4). Figure 3 presents the kernel density of total test score, which reveals a similar result: both the treatment (training and books receivers) and control (books only receivers) groups of students performed similarly during the baseline and follow-upeven if the mean of total score improved during the follow-up survey for both groups. Out of the ve tests, only score in writing has improved by 15.3 percent of a standard deviation, and this is smaller in magnitude when compared to training, complemented with books.<sup>11</sup> No impact on Peapody, math, reading and listening test scores due to the extra inservice teacher training was found.<sup>12</sup> These ndings lie at the heart of the contentious literature on eectiveness of brief in-service teacher training schemes in improving test scores. Some previous studies nd training improved test score, other do not. For instance, Jacob and Lefgren (2004), employing a quasi-experimental method based on the school reform program in Chicago, established that in-service teacher training had no statistically signicant or academically meaningful impact on reading and math achievement of students in elementary school. Similarly, Zhang et al. (2013) undertook a randomized control trial and documented that short-term in-service teacher training in Beijing's migrant

> 11As discussed in the `identication strategy' section, the impact training only is likely to be overestimated as it might also include any complementarity eects between these inputs.

> 12For students in training only intervention group, the standard deviations of test scores in Peabody, math, listening, reading, writing and total score are 2.14, 2.58, 2.23, 4.15, 2.10 and 8.55, respectively.

19
# _Effects of monetary factors on inflation_

The empirical results suggest that monetary policy and developments in the monetary sector are important determinants of inflation in Ethiopia in the long run. As can be seen in the cointegration residual equation (the ECM term for the monetary sector), 1 excess money supply has a long-term positive impact on the price level even after controlling for the key determinants of money demand, namely GDP and the parallel (mm −pp) −1.04λλ+ exchange rate premium (the latter being a proxy for the opportunity cost of holding liquidity in 0.29ppρρeemm+ 6.46, the domestic currency). In the long run, a 1 percent increase in GDP increases money demand by 1 percent, and a 1 percent increase in the premium reduces money demand by 0.29 percent.

The results show that disequilibria in the monetary sector (as represented by the coefficient on the cointegration equation residual—ECM term) exert inflationary pressure on cereals prices, food prices, and overall price level (all-item CPI), but adjustments to equilibrium seem relatively slow. When there is disequilibrium in the money market, about 2.7 percent of it is removed every month through variations in domestic cereal prices. The speed of return to equilibrium in the food sector is even slower, at only 2 percent every month. For overall CPI, 1.5 percent of the disequilibrium is removed each month.

In the short run, money growth strongly affects prices in the food and non-food sectors, but it has no impact on cereal price inflation. The effect of a shock on money growth occurs in the third month after the shock for food prices and during the twelfth month after the shock for non-food prices. This latter delayed reaction may give a false impression of a disconnect between money supply and the different components of inflation. With respect to the magnitude of the shortterm response of prices to money growth, econometric results suggest that a 1 percent increase in money growth increases food prices by 0.13 percent and non-food prices by 0.05 percent. This response may appear weak but, considering the large increases of money supply particularly over the last ten years (by over 20 percent per year according to the NBE data), the impact on prices is indeed very strong.

Even though these results are in line with economic theory, they are different from those of some previous influential studies that did not find any long-term effect of excess money supply on inflation in Ethiopia, notably Durevall et al. (2013). The reason might be that this study covers a longer sample period. The behavior of monetary aggregates as presented in Section 3 suggests that they were stable during most of the period covered by the Durevall et al. study (January 2000 to December 2009). The longer and more updated sample period covered by this study (up to September 2020) captures a period of rapid increase in money stock and money velocity relative to the previous period.

17
West Nile, Mid-North and Karamoja regions. From this list, sub-counties that had an Airtel Money agent were dropped, based on an agent database provided by the Bill and Melinda Gates Foundation. Moreover, all EAs without Airtel network coverage were removed. A random sample of 1,200 (EAs) was drawn from all rural EAs in the remaining 249 sub-counties using population proportional to size sampling stratified by sub-county.

In September and October 2015, a household listing exercise was conducted in the EAs selected for the study to generate a sampling frame for the baseline survey. At the same time, all businesses in each EA were listed to gain information on potential mobile money agents in the EA.

To minimize potential spillovers of agents to control group EAs, the selected EAs were mapped and grouped into clusters. A 0.5km buffer was drawn around the boundary of each EA and EAs whose buffers overlapped were grouped. Clusters were thus at least 1km apart from each other.

In this process, some EAs were dropped from the sample since either the listing exercise could not be conducted (for logistical or security reasons), the listing did not yield any businesses and thus no potential agents, or the maps were missing from the software to group EAs into clusters. As a result, there were 929 EAs that were grouped into 658 clusters.

Using a computer assisted stratified randomization approach, we assigned 329 clusters to a treatment group and 329 clusters to a control group. The treatment clusters formed the list of EA clusters for Airtel Money agent rollout. No such agents were to be rolled out in control clusters.

The randomization strata were based on three variables, resulting in 16 strata. First, a variable equal to one if the cluster included more than one EA (which was the case for 16 percent of clusters) and equal to zero if the cluster included only one EA. Second, a variable equal to one if the distance to the nearest bank branch was greater than the median distance across all clusters (25.2km) and equal to zero 7
<!-- Start of picture text --> B t − B*t ⎡ B t − B ty ⎤ ⎡ B ty − B*t ⎤<br>* = ⎢ * ⎥ + ⎢ * ⎥<br>Y Y Y<br>t ⎣ t ⎦ ⎣ t ⎦<br>14243 14243<br>discretionary policy automatic component<br><!-- End of picture text --> A shortcoming in this methodology lies in attributing to discretionary policy all changes in the budgetary position that are not associated with the business cycle. Therefore, results should be taken with a grain a salt. Figure 5 does provide support in favor of the stated conjecture. As shown in Figure 5, there is a negative correlation between the two components of fiscal policy in 16 out of the 17 LAC countries in the sample. Within the developed world positive correlations are more commonly observed though, in a number of these countries, discretionary fiscal policy also exhibits a procyclical pattern.

Finally, an additional intuition about the strength of automatic stabilization or the degree of flexibility embedded in the tax system can be gained by looking at the responsiveness of the automatic fiscal revenue stabilizers to changes in cyclical conditions. To that end, a variant (including terms of trade) of the policy rule used by Galí and Perotti (2003) is estimated on a country-by-country basis: where CY represents the output gap measured by the Hodrick-Prescott cyclical t component of log-GDP;<sup>P stands for the terms of trade (in level, in log or in log</sup> t difference, depending on unit root tests); D is the stock of outstanding public debt in t ratio to potential GDP and where the potential GDP is in turn obtained from the HodrickPrescott trend component. The public debt time series for each country is estimated by using information on the debt-GDP ratio at a given point in time and using time series data on the overall budget balance and GDP along with the law of motion of government debt to reconstruct the entire path. The government debt-GDP ratio at a given point in time is retrieved from the FitchResearch database and Moody’s. The coefficient β measures the size of automatic movements in the revenue ledger accounts of the government budget in response to changes in cyclical conditions. The simultaneity bias associated with the potential problem of endogeneity of the output gap variable is addressed by instrumenting for it with its lagged value and the first lag of the U.S. output gap (or the first lag of the three major European economies’ output gap for the U.S. regression).

Figure 6 depicts country-specific estimates of the output gap coefficient of the fiscal reaction function. Without exception, all industrial countries exhibit a statistically significant response from the revenue budget items to automatically counteract domestic cyclical fluctuations. In Latin America very few countries exhibit a significant response. When it is significant, it is much weaker than in the developed country sample. In general, the region as a whole tends to have a barely acyclical non-discretionary fiscal policy.

10
Figure A5: Rainfall and prices, Mali and Niger (rainy season months) <!-- Start of picture text --> Mali Niger<br>Average monthly rai nfall Maize Average monthly rainf all Beans<br>Millet Rice_Imported Maize Millet<br>Rice_Local Sorghum Rice_Imported Sorghum<br>Source: Authors’ calculation using VAM and TRMM data<br>Figure A6: Rainfall and prices, Nigeria and Senegal (rainy season months)<br>Nigeria Senegal<br>Average monthly rainf all Maize<br>Average monthly rainf all Maize Maize_Imported Millet<br>Millet Sorghum Rice_Imported Sorghum<br>2003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7 2000m72001m72002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>2000m72001m72002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>2002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>500 100 600<br>150<br>80 500<br>400<br>100<br>60 400<br>300<br>Rainfall(mm) Rainfall(mm)40 300<br>50<br>200<br>Average nominal price(XOF/KG) Average nominal price(XOF/KG)<br>20 200<br>100<br>0 0<br>100<br>300 300 300<br>400<br>250 250<br>250<br>200 300<br>200<br>200<br>Rainfall(mm)150<br>Rainfall(mm)<br>150<br>200<br>150 100 Average nominal price(XOF/KG)<br>Average nominal price(XOF/KG)<br>100<br>50<br>100 100<br>50<br><!-- End of picture text -->

# Source: Authors’ calculation using VAM and TRMM data

Figure A6: Rainfall and prices, Nigeria and Senegal (rainy season months) 28
aggregated into basic or ‘low-level’ indicators by assigning subjective weights to the various regulatory requirements. Given the normalization of the basic data, all the lowlevel indicators also have a scale of zero to six, reflecting increasing restrictiveness of regulatory areas.<sup>5</sup> Basic indicators are then aggregated into broader regulatory domains. Higher-level indicators are calculated as weighted averages of their constituent lower-level indicators. The attribution of lower-level indicators to each higher-level indicator, and the weights used in the aggregation, are based on principal component analysis (Nicoletti et al., 1999). At the highest level of aggregation the overall indicator of product market regulation summarizes the restrictiveness of the regulatory framework in the product market. The structure of the PMR system, with progressive levels of aggregation, has the advantage of allowing a decomposition of higher-level indicators, with an increasing degree of detail, into the values of the more disaggregated indicators, each corresponding to specific regulatory provisions.

Data refer to the beginning of 2006 for Bulgaria and Romania, to end-2003 for OECD countries and 2004 for Brazil.

As to benchmarks, the most obvious ones for Bulgaria are Romania, for which estimation of PMR indicators has been carried out in parallel with Bulgaria and refers to 2006, and the countries of Central and Eastern Europe that are also OECD members: these include Czech Republic, Hungary, Poland and the Slovak Republic and we refer to them as the OECD CEE. The fact that the data for the OECD CEE are from 2003, when these countries were at about the same point as Bulgaria in 2006 relative to their accession to the EU, makes them interesting benchmarks. Nevertheless, when assessing Bulgaria’s relative performance, it should be kept in mind that the OECD CEE countries have made further substantial progress in various areas of product market regulation since EU accession in 2004.

Extension of the benchmarking exercise to Brazil, Mexico and Turkey offers a broader perspective on other middle income countries (MICs) with different historical experiences. Finally, comparison with the OECD or high income EU15 countries helps identify longer term objectives for policymakers.<sup>6</sup> These benchmarks are used in the graphs. However, Bulgaria’s score relative to the full sample of countries (30 OECD members plus Brazil) is shown in Appendix I for all PMR indicators.

> 5 The calculation of low-level indicators, including the weights used, is based on Conway et al. (2005). 6 EU15 countries are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom.

8