rafmacalaba/datause-encoder-data huggingface dataset:
and export subsidies. These are (i) value-added taxes on factor inputs to production, (ii) _ad valorem_ production subsidies or excise taxes on production output, and (iii) lump-sum taxes on domestic consumers. In a counter-factual scenario one or more of the tax inst: 'nents adjusts endogenously to balance government (net) tax revenues with expenditures. This equal-yield constraint is accommodated through -i endogenous proportional adjustment of valueadded tax rates or lump-sum transfers. Thus the welfare effects of changes in trade policy explicitly incorporate the appropriate marginal excess burden of raising government revenue from other sources.

Demand functions are uncompensated, so Walras law guarantees that the value of private consumption equals the income from primary factors, taxes, and import and export quota rents. Public consumption is balanced with the value of public endowments and tax revenue.

World market import and export prices are given, and there are no endogenous changes in the terms of trade. In other words, import supplies and export demand are infinitely elastic. The current account balances the value of exports and imports taking into account exogenously-specified capital inflows.

# 2.2 The Turkish SOE Model

The SOE model is relatively easy to implement empirically. One requires a consistent set of Input-Output accounts or a Social Accounting Matrix showing the standard intermediate, final demand and value added transactions. Additional estir-itts of tariff rates, tax rates, or subsidy rates may also be needed, depending on the detail of the Input-Output database. Estimates of elasticities must be assembled for primary factor substitution, import demand, import source, domestic demand, and the transformation of domestic supply into domestic and exported products.<sup>6</sup> We employ a 1985 Input-Output table distinguishing 64 production sectors.<sup>7</sup> We aggregate this to 40 sectors, selecting to aggregate the smallest 24 sectors which account for only _5.1_ % of the value-added of the

> 6 In detail, these elasticities refer to the elasticity of substitution between primary factors of production in each sector, the elstieity of substitution between domestic production and an imports composite in each sector, the elasticity of substitution between imports distinguished by source, also by sector, the elasticity of substitution between domestic consumption of each good (the components of which are, in tum, composites of domestic and imported production); and the elasticity of tmnsformation of domestic production into domestic uses and export.

7 This is the latest Input-Output table available for Turkey as of late 1990.

-6 -
- 12 -

- (e) To encourage (rural) employment; and

# (f) To save foreign exchange.

Of course stumpage fees cannot be raised so high that the wood products are priced out of the market. However, if adequate market intelligence is available, a proper assessment of the commercial value of the available wood resources can be made. Some trees or parts of trees may have a higher value when sold as poles, sawlogs, peeler logs etc. rather than fuel although the latter is usually the dominant end use. Therefore, market intelligence will give a good idea of the amount of effort that can be put into the management of wood resource.

# Cost of Charging for Puelwood Resources

# Costs to Consumers

1.32 Since woodfuel use is concentrated in --he lower income levels of developing country societies, it is important to -valuate the effects that stumpage fees would have on consumers. When estimating the cost to consumers, the following factors have to be considered:

- (a) The relative proportions of fuelwood supply obtained from open access, managed access and private lands;

- (b) The form in which the woodfuel is consumed (i.e., as firewood or charcoal);

- (c) The distance from the consumer to the woodfuel source(s);

- (d) The structure and competitiveness of woodfuels transport and marketing; and

- (e) The elasticities of woodfuels supply and demand.

1.33 Not all the fee burden will be passed directly to the consumers. A portion of it will be borne by the woodfuel producers, transporters, and dealers. A charge for the raw material may provide an incentive to increase efficiency, or it may cut into profits as middlemen reduce their mark-ups.

1.34 Studies of charcoal sales in Sub-Saharan Africa have shown that the roadside price of 'harcoal in the areas of production is typically one-third the final retail price of charcoal to urban end users (see Figure 1.1). Therefore, the percentage effects of stumpage fees on consumer prices are diluted by at least this 3:1 ratio. However, as is shown in Figure 1.1, the actual stumpage fee in Zambia is only about five percent of the selling price. To keep pace with inflation the stumpage fee should be about three times what it is, but even a tripling of stumpage will only cause an 8.5 percent increase in the selling price, other things being equal. A similar increase in the stumpage price of firewood would increase the market price by about four percent.
|JCE|6.27|24.25|9.05|28.69|0.106|
|---|---|---|---|---|---|
|MSCE|4.52|20.77|5.28|22.37|0.354|
|Non-Univ Diploma and above|1.18|10.78|1.51|12.19|0.126|
|Highest level of formal education acquired in||||||
|the household||||||
|None|65.42|47.57|56.11|49.65|0.000|
|PSLC|14.29|35.00|18.51|38.85|0.000|
|JCE|11.60|32.02|14.49|35.21|0.246|
|MSCE|7.26|25.95|9.13|28.81|0.248|
|Non-Univ Diploma and above|1.43|11.87|1.76|13.15|0.211|
|Size of the household|5.12|2.39|5.56|2.50|0.000|
|Number of infant (<5yo) in HH|0.82|0.83|0.94|0.89|0.004|
|Number of children (5-14yo) in the household|1.59|1.41|1.75|1.41|0.001|
|Number of prime adults (15-60yo) in HH|2.40|1.39|2.67|1.38|0.000|
|Number of elderly (60yo+) in HH|0.32|0.59|0.19|0.48|0.000|
|<br>Household access to loan (0/1)|13.53|34.21|27.89|44.86|0.000|
|Normalized wealth index|0.05|0.07|0.08|0.11|0.000|
|Normalized TLU index|0.01|0.02|0.01|0.06|0.097|
|Total land area owned by HH in Acres|3.19|49.77|3.97|46.75|0.598|
|Household was affected negatively by some|85.75|34.96|88.61|31.78|0.027|
|<br>income Shock During the last 12 months (0/1)||||||
|Household has a migrant network (0/1)|36.75|48.22|31.66|46.53|0.070|
|Rain - EA level CoV of Dec-Jan rainfall from|0.25|0.04|0.26|0.04|0.002|
|1983/84 - 2012/13||||||
|Average 12-month total rainfall (mm)|853.06|89.51|840.36|87.59|0.012|
|Annual Mean Temperature (∞C * 10)|215.84|19.15|218.01|18.92|0.026|
|<br>HH Distance in (KMs) to Nearest Road|10.11|9.92|8.87|9.54|0.013|
|HH Distance in (KMs) to Nearest Population|38.05|18.05|36.28|17.55|0.343|
|<br>Center with +20,000||||||
|Value of daily_ganyu_wage salary in the EA|548.59|564.61|630.35|710.21|0.005|
|<br>(MKW)||||||
|Price of maize grains in the EA (MKW)|196.70|111.45|205.43|116.73|0.014|
|Price of fertilizer in the EA (MKW)|60.87|39.22|67.36|39.37|0.000|
|EA neighbor’s wage employment participation|14.20|14.52|18.43|15.76|0.000|
|<br>EA neighbor’s self employment participation|19.74|14.32|28.28|16.68|0.000|

Share of total land cultivated in crops (crop 42
Economics”, Annual Review of Economics 1:151-78.

Banerjee, Abhijit, Esther Du‡o, Rachel Glennester and Cynthia Kinnan (2010) “The Miracle of Micro...nance? Evidence from a Randomized Evaluation”, BREAD Working Paper no. 278.

Banerjee, Abhijit and Sendhil Mullanaithan (2010) “The Shape of Temptation: Implications for the Economic Lives of the Poor”, MIT Working Paper no 10-9.

Bauer, Michal, Julie Chytilova and Jonathan Morduch (2010) “Behavioral Foundations of Microcredit: Experimental and Survey Evidence from Rural India”, IZA Working Paper no. 4901.

Bigsten, Arne, Paul Collier, Stefan Dercon, Marcel Fafchamps, Bernard Gauthier, JanWillem Gunning, Anders Isaksson, Abena Oduro, Remco Oostendorp, Cathy Patillo, Mans Soderbom, Francis Teal, Albert Zeufack, and Simon Appleton (2000) "Rates of Return on Physical and Human Capital in Africa’s Manufacturing Sector", Economic Development and Cultural Change, 48(4): 801-27.

Bjorvatn, Kjetil, Lars Ivar Oppedal Berge, and Bertil Tungodden (2011) "Human and ...nancial capital for microenterprise development: Evidence from a ...eld and lab experiment", NHH and CMI (mimeograph) Bruhn, Miriam and David McKenzie (2009) “In Pursuit of Balance: Randomization in Practice in Development Field Experiments”, American Economic Journal: Applied Economics 1(4): 200-32.

Brune, Lasse, Xavier Gine, Jessica Goldberg, and Dean Yang (2011). "Commitments to Save: A Field Experiment in Rural Malawi", University of Michigan, May (mimeograph).

Camerer, Colin, Linda Babcock, George Loewenstein, and Richard Thaler (1997). "Labor Supply of New York City Cabdrivers: One Day at a Time", Quarterly Journal of Economics, 112: 407-41.

Charlier, Florence (1999) “Saving or Sharing: The African Households’Dilemma”, Stanford Department of Economics Ph.D. Dissertation.

Deaton, Angus (2010) “Instruments, Randomization, and Learning about Development”, Journal of Economic Literature, 48(2): 424–55 De Mel, Suresh, David McKenzie, and Christopher Woodru¤ (2008). “Returns to Capital: Results from a Randomized Experiment.” Quarterly Journal of Economics 123(4): 1329–72.

De Mel, Suresh, David McKenzie, and Christopher Woodru¤ (2009a) “Are Women more Credit Constrained? Experimental Evidence on Gender and Microenterprise Returns.” Ameri39
**Figure 4.18. Distribution of basic infrastructure connections by quintiles in other urban cities**

(% of households) <!-- Start of picture text --> Q1 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Water District heating Natural gas Electricity Telephone<br>connection connection connection connection connection<br>%<br><!-- End of picture text --> Source: see Table 2.1 Note: average among 20 ECA countries for water connection,19 ECA countries for district heating and telephone connection, 15 ECA countries for natural gas, 10 ECA countries for electricity.

**Poor households are less likely to have access to sanitation or clean fuels for cooking and heating.** Analysis of  households who have an inside toilet and those who use dirty fuels for heating and/or cooking shows that low income households consistently are worse off as can be seen in Figure 4.19 below.  In both capital cities and other urban areas, low income households are much less likely to have adequate sanitation and are much more likely to use dirty fuels.

**Figure 4.19.  Lack of access to inside toilets and use of dirty fuels by quintiles for ECA countries**

<!-- Start of picture text --> Q1 Q5<br>40<br>30<br>20<br>10<br>0<br>Capital Other Urban Capital Other Urban<br>No access to w aste w ater treatment Using dirty fuels<br>%<br><!-- End of picture text --> Source: Table 2.1 The above results are broken down by country in Annex 3. In both capital cities and other urban areas, high income households are consistently more likely to have adequate sanitation and in most countries the differences are substantial. In terms of the use of dirty fuels, the results are a bit more varied. In most countries low income households are more likely to use dirty fuels as would be expected. However, in Georgia, Azerbaijan, Kyrgyz and Turkmenistan, higher income households are more likely. In part, this may reflect the way the indicator was constructed since it was based on households reporting 37
Table C.2, in appendix C, presents the quality indicators that are used in the analysis of the direct observation and household data. The direct observation indicators correspond only to the first antenatal care visits. The household self‐reported indicators correspond to the entire sequence of consultations the women attended during their most recent pregnancy. The descriptive statistics show the variation in the content of antenatal care. Although some physical examinations, such as weighing the pregnant women and measuring their uterine height, are conducted relatively frequently, the rates of diagnostic testing, counseling, and preventive treatments are alarmingly low.

In addition, we create three indexes of quality of care, representing the proportion of actions performed among a set of actions recorded in the different data sources. The first index, computed from household data, summarizes the reported content of care recently pregnant women received during any antenatal care visit during the entirety of their pregnancy, among women who had a live birth in the past two years. The second quality index is based on the direct observation data. The third index is based on the procedures reported in the exit interviews. Although we prefer the more objective direct observation data, we have 177 exit interviews with women whose consultations were not observed.<sup>9</sup> In addition, during the exit interviews, we collected data on the prices paid for the consultations. Thus, this larger sample allows us to study the relationship between quality of care and price. As shown in table B.1, in appendix B, the indexes computed with the direct observation and exit interview data are strongly correlated, and the correlation between the two indexes is not significantly affected by wealth.

The indexes assign equal weights to the different components of antenatal care, although of course the clinical importance of the different actions might differ. For example, estimating a pregnant woman’s due date might not be as important as prescribing iron supplements. However, any selection of weights for the different components in the DRC context will be somewhat arbitrary. The results are not dependent on the methodology used to create the indexes. First, we obtain similar results using a principal component analysis to create the quality indexes. Second, tables C.3, C.5, and C.6, in appendix C, present the results of regressions using the individual components of care as dependent variables.

Payments for consultations are paid in lump sum or by service provided. Eighty‐six percent of the women reported paying a flat fee, while the rest reported having paid separately for different components of the consultation (such as laboratory testing and drugs). In the case of itemized

> 9 There is no statistically significant difference in the quality of care reported in exit interviews by women with and without observed consultations. In addition, the two groups of women are not significantly different in wealth, education, marital status, or age.

8
|31|Kenya receives the most aid in health and populations services (China excluded)<br>(2013)||
|---|---|---|
||OECD DAC aid database 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
|32|Ministry of Energy and Petroleum receives most loans from China (2014)<br>Estimates of Development Expenditure Government of Kenya (2014)<br>. . . . . . . . . . . .|33|
|33|Kenya’s debt to China is growing quickly (2010-2014)<br>Kenya National Bureau of Statistics Economic Survey 2015 . . . . . . . . . . . . . . . . . .|37|
|34|Kenya owes most debt to China (2015)<br>The National Treasury of Kenya (2015)<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|38|
|35|Kenya’s debt to China outpaces the rest (2015)<br>The National Treasury of Kenya (2015)<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|38|
|C36|Trade and Distance Kenya and Rest of the World (1948-2014)<br>CEPII Gravity Database 2010 and UN Comtrade 2015||
|C37|Trade and Importer GDP per capita Kenya and Rest of the World (1948-2014)<br>CEPII Gravity Database 2010 and UN Comtrade 2015||
|C38 <br>**st o**|Trade and Importer GDP per capita Kenya and Rest of the World (2014)<br>CEPII Gravity Database 2010 and UN Comtrade 2015<br>**f Tables**||
|1|Estimates of Kenya’s Trade Flows (1948-2014). Dependent variable: Kenya’s exports|14|
|2|Estimates of China’s Trade Flows (1948-2014). Dependent variable: China’s exports .|16|
|3|FDI Projects and related employment (2007-2011)<br>. . . . . . . . . . . . . . . . . . . .|29|
|4|Export Processing Zones sector contribution in 2012 (%) . . . . . . . . . . . . . . . . .|41|
|C5|Estimates of Kenya’s Trade Flows (1948-2014). Dependent variable: Kenya’s exports|50|
|C6|Estimates of China’s Trade Flows (1948-2014). Dependent variable: China’s exports .|53|
|C7<br>C8|Estimates of China’s Trade Flows (1948-2014). Dependent variable: China’s exports .<br>Estimates of Kenya’s Trade Flows (1948-2014). Dependent variable: Kenya’s exports|56<br>59|

# **List of Tables**
The IV regression results are displayed in **Table 8** . The first-stage IV results (column 1) confirm a positive and significant relationship between the WBL index and each of the instruments. The F-statistic for the excluded instruments is significant at the 1% level and well above the minimum recommended level of 10. The over-identification test based on Hansen's J statistic implies that we cannot reject the null hypothesis that the instruments are valid. The second-stage IV results (columns (2), (3) and (4)) confirm that general equality in the law is strongly positively associated with women’s access to financial products.

We provide additional controls for potential endogeneity using the lagged values of the explanatory variables to provide an additional way to avoid potential endogeneity problems. As our sample is composed of four waves of data, the use of lagged variables leads us to consider only the 2014, 2017 and 2021 waves, thus substantially reducing the size of the sample.

The results presented in **Table 9** indicate that our key finding is preserved: the coefficients for the interaction terms of interest are positive and significant. This provides support to our claims that endogeneity does not drive our results.

# **5.4. Country-level estimations**

So far, we have performed estimations at the individual level as we link the gender of the individual with her/his level of financial inclusion. We only focused on the beneficial effect of gendered laws on women’s financial inclusion. However, one may argue that improvements in gender-related legislation may be part of a global institutional enhancement that could benefit men too. We should therefore verify that the legal framework contributes to decreasing the gender gap in financial inclusion at the national level. To this end, we perform our estimations using the Global Findex country-level data. We measure the gender gap in access to financial services by considering the ratio of the percentage of women having a bank account to the percentage of men with access to formal account ( _Female to male ratio_ ). The higher the ratio, the lower the gender gap in account ownership. We alternatively test the correlation of each of the eight WBL indicators on this aggregate financial inclusion measure. We include the three country-specific control variables formerly used in the individual-level estimations: the logarithmic value of the GDP per capita ( _lnGDPpercapita_ ), the share of domestic credit to the private sector ( _Domesticcredit_ ) and the natural logarithm of inflation ( _lnInflation_ ).

The results of the country-level regressions are reported in **Table 10** . While our sample size, and, therefore, our statistical power, is much reduced, we find that a greater egality in 18
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2240 The Effects of Land Registration on<br>Financial Development and Economic<br>Growth: A Theoretical and Conceptua<br>Framework|Frank F. K. Byamugisha<br> <br>l|<br>November 1999|E. Gelos<br>37846|
|WPS2241 How Land Registration Affects<br>Financial Development and Economic<br>Growth in Thailand|Frank F. K. Byamugisha<br>|<br>November 1999|E. Gelos<br>37846|
|WPS2242 Confronting Competition: Investment<br>Response and Constraints in Uganda|Ritva Reinikka<br>Jakob Svensson|November 1999|H. Sladovich<br>37698|
|WPS2243 Designing Pro-Poor Water and<br>Sewer Concessions: Early Lessons<br>from Bolivia|Kristin Komives|November 1999|M. Salehi<br>37157|
|WPS2244 True World Income Distribution, 1988<br>and 1993: First Calculations, Based<br>on Household Surveys Alone|Branko Milanovic|November 1999|P. Sader<br>33902|
|WPS2245 Opportunities for Improving<br>Environmental Compliance in Mexico|Susmita Dasgupta|November 1999|Y. D'Souza<br>31449|
|WPS2246 Infrastructure's Contribution to<br>Aggregate Output|David Canning|November 1999|H. Sladovich<br>37698|
|WPS2247 Does Deposit Insurance Increase<br>Banking System Stability?<br>An Empirical Investigation|Ashl DemirgOg-Kunt<br>Enrica Detragiache|November 1999|K. Labrie<br>31001|
|WPS2248 Privatization and Regulation of<br>Transport Infrastructure in the<br>1990s: Successes ... and Bugs to<br>Fix for the Next Millennium|Antonio Estache|November 1999|G. Chenet-Smith<br>36370|
|WPS2249 Argentina's Transport Privatization<br>and Re-Regulation:<br>Ups and Downs<br>of a Daring Decade-Long Experience|Antonio Estache<br>Jose C. Carbajo<br>Gines de Rus|November 1999|G. Chenet-Smith<br>36370|
|WPS2250 Universal Service Obligations in<br>Utility Concession Contracts and<br>the Needs of the Poor in Argentina's<br>Privatizations|Ornar Chisari<br>Antonio Estache|November 1999|G. Chenet-Smith<br>36370|
|WPS2251 Will the Euro Create a Bonanza<br>For Africa?|Daniel Cohen<br>Nicolai Kristensen<br>Dorte Verner|November 1999|H. Vargas<br>37871|
Ilzetzki, E., C. M. Reinhart, and K. S. Rogoff. 2020. “Will the Secular Decline in Exchange Rate and Inflation Volatility Survive COVID-19?” NBER Working Paper w28108, National Bureau of Economic Research.

Jurado, K., S. C. Ludvigson, and S. Ng. 2015. “Measuring Uncertainty.” American Economic Review 105 (3): 1177-1216.

Kabukçuo ğ lu, A., and E. Martínez-García. 2018. “Inflation as a Global Phenomenon— Some Implications for Inflation Modeling and Forecasting.” Journal of Economic Dynamics and Control 87 (C): 46-73.

Kabundi, A. N. and F. Ohnsorge. 2020. “Implications of Cheap Oil for Emerging Markets” Policy Research Working Paper 9403, World Bank, Washington, DC.

Kose, M. A., Otrok, C. and Whiteman, C.H., 2008. “ <mark>Understanding the Evolution of World Business Cycles.” Journal of international Economics 75 (1): 110-30.</mark> Kose, M. A. and M. Terrones. 2015. Collapse and Revival: Understanding Global Recessions and Recoveries. International Monetary Fund, Washington, DC.

Kose, M. A., H. Matsuoka, U. Panizza, and D. Vorisek. 2018. “Inflation Expectations: Review and Evidence,” in Inflation in Emerging and Developing Economies: Evolution, Drivers, and Policies, edited by Ha, J., M. A. Kose, and F. Ohnsorge, World Bank, Washington, DC.

Lenza, M. and G. E. Primiceri. 2022. “How to Estimate a Vector Autoregression After March 2020.” Journal of Applied Econometrics 37(4): 688-699.

Mat ě j ů , J. 2019. “What Drives the Strength of Monetary Policy Transmission?” International Journal of Central Banking 15 (3): 59-87.

Melolinna, M. 2015. “What Has Driven Inflation Dynamics in the Euro Area, the United Kingdom and the United States?” ECB Working Paper 1802, European Central Bank, Frankfurt am Main.

Miranda-Agrippino, S. and H. Rey. 2020. “US Monetary Policy and the Global Financial Cycle.” The Review of Economic Studies 87 (6): 2754-76.

O’Brien, D., C. Dumoncel, and E. Gonçalves. 2021. “The Role of Demand and Supply Factors in HICP Inflation During the COVID-19 Pandemic–A Disaggregated Perspective.” Economic Bulletin Articles 1/2021.

Peersman, G., 2005. “What Caused the Early Millennium Slowdown? Evidence Based on Vector Autoregressions.” Journal of Applied Econometrics 20 (2): 185-207.

Rogoff, K. 2003. “Globalization and Global Disinflation.” Federal Reserve Bank of Kansas City Economic Review 88 (4): 45-78.

Rogoff, K. 2006. “Oil and the Global Economy.” Manuscript, Harvard University.

Rogoff, K. 2007. “Impact of Globalization on Monetary Policy” in Federal Reserve Bank of Kansas City, The New Economic Geography: Effects and Policy Implications.

Rogoff, K. 2019. “Is This the Beginning of the End of Central Bank Independence?” G30 Occasional Paper.

Rogoff, K. 2021a. “Are Inflation Fears Justified?” Project Syndicate, 1 March 2021.

22