rafmacalaba/datause-encoder-data huggingface dataset:
_9_

**Figure 2(a) and 2(b). EFT-POS terminals and transactions per capita with cash to GDP ratios, 1993**

<!-- Start of picture text --> Figure 2(a) Figure 2(b)<br>100 40<br>.35- 0o Finland<br>c 80 Finland<br>* France 30-<br>8 E<br>60 25 France<br>* Belgium DenmarkK<br>E S NDenmark 2 ~~~~~~~~~~~~~20<br>. Norway .* Norway<br>40<br>U.K. 15 Belgium<br>a U.K.<br>Canada<br>10<br>3 20 Nether ds<br>Italy * SWitze nd Netherland Swteland<br>Germany Canada<br>U.S. Japan O U.S. Germany<br>0% 2% 4% 6% 8% 10% 0% 2% 4% 6% 8X 10%<br>Cash to CDP ratio Cash to CDP ratio<br><!-- End of picture text --> _Source: BIS: Payment Systems in the G- 10 Countries, 1994_ While debit and credit card transactions on POS terminals can directly substitute for cash, payments, ATMs make it easier to obtain cash: this can have two opposite effects on cash holdings. By making cash more accessible, ATMs would permit consumers to increase their use of cash for smaller value transactions and so may raise cash holdings. Alternatively, the greater convenience of ATMs as a way of obtaining cash may mean that consumers withdraw smaller amounts each time and make up the difference by visiting an ATM more frequently, and so may on balance reduce average cash holdings. The relationships shown between cash holdings and the number of ATM terminals in Figure 3(a) and the number of ATM transactions in Figure 3(b) is in both cases insignificantly negative or positive (neither R<sup>2</sup> is greater than .02). Thus, in this cross country comparison, ATMs have no significant effect on cash holdings (in contrast to the EFT-POS results where cash holdings are reduced).

**_7_** _Most ATM transactions are cash withdrawals. In the U.S., for example, 86% of ATM transactions are cash withdrawals, 10% are deposits, 3% are account transfers, and 1% are bill payments (Board of Governors of the Federal Reserve System, 1991)._
23 Elbadawi, I. and R. Soto (1997b). "Real Exchange Rates and Macroeconomic Adjustment in Sub-Saharan Africa and Other Developing Countries," in Elbadawi and Soto (op. cit.) _Journal of African Economies_ .

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Ghura, D. and T. J. Grennes (1993). “The Real Exchange Rate and Macroeconomic Performances in Sub-Saharan Africa,” _Journal of Development Economics_ . Vol. 42.

IMF (2005). "The Macroeconomics of Managing Increased Aid Inflows: Experiences of Low-Income Countries and Policy Implications" Available at http://www.imf.org/external/np/pp/eng/2005/080805a.pdf.

Kaminsky, G., and C.M. Reinhart (1999). “The Twin Crises: The Causes of Banking and Balance of Payments Problems. _American Economic Review_ . 89 (3): 473-500.

Kang, S., and J. Meernik (2004). ‘Determinants of Post-Conflict Economic Assistance’. _Journal of Peace Research_ , 41 (2): 149-66.

Krugman, P. 1987. “The Narrow Moving Band, the Dutch Disease, and the Competitive Consequences of Mrs. Thatcher: Notes on Trade in the Presence of Dynamic Scale Economies.” _Journal of Development Economics_ , 27: 41-55 Maeso-Fernandez, F., C. Osbat, B. Schnatz (2002). “Towards the Estimation of Equilibrium Exchange Rates for CEE Acceding Countries: Methodological Issues and a Panel Cointegration Perspective.” European Central Bank, Working Paper Series: 353.

Mengistae, Taye, and Catherine Pattillo (2004). “Export Orientation and Productivity in Sub-Saharan Africa,” International Monetary Fund. _IMF Staff Papers_ , 51 (2): 327.

Merrill Lynch (1998). “A Global Currency and Interest Rate Outlook.” Nabli, K. Mustapha, and Marie-Ange Veganzones-Varoudakis, (2002). “Exchange Rate Regime and Competitiveness of Manufactured Exports: The Case of MENA Countries,” World Bank, Washington DC, Centre National de la Recherche Scientifique, Centre d’Etudes et de Recherches sur le Developpement International, France, (May).

Pesaran, H.; Y. Shin and R. Smith (1999): "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels", _Journal of the American Statistical Association,_ 94:621-634. Radelet, Clements and Bhavnani (2003).
> are negat-iiy **corrieyc** du wiui reut and **financial** sector activity and performance. T nese statistics aiso

> indirectly provide some reassurance about the appropriateness of the timing of the crisis periods. Table 4 presents the pre-crisis levels of our proxies for financial development for our crisis countries. The financial development proxies indicate relatively low levels of financial development in countries like Bolivia, Chile, Columbia, and Mexico, and relatively high levels of financial

> developm.enmt in Hulngar, Japan, and Spain.

# V. Results

First, we investigate the role of financial development on the link between external finance and sector growth for both pre-crisis and crisis periods. To this end, we estimate model (1) both for the pre-crisis period and the crisis period. The regression results are presented in panel A of Table 5. Each "pre-cr.sis" and "crisis" pair of columns Qes a dif,feren^.t r..easij of fin an.cial developm.,e.n.t. sAll standard errors are corrected for heteroskedasticity. Consistent with the findings in Rajan and Zingales (1998), we find for the pre-crisis "normal" period that financially dependent sectors grow on average disproportionally faster in countries with well-developed or deeper financial systems. In our sample, however, this result is statistically significant at the 10 percent level for only the total credit to GDP measure of financial development. This difference could partly be attributed to the fact th.at **,ve hav7e fewver obsei-.ations** th.an **Po2x,i 9mA** Zigae **(199)** since wefo.cus onc.ssw.ti Another reason could be that we use extemal dependence figures on a three-digit level only. Our coefficient estimates for both the value-added share and the interaction term also are somewhat smaller than in Rajan and Zingales (1998).

During crisis periods, we find the opposite relationship, namely, that financially dependent 13
# 6. Household received stove for free plus network.

One treatment is assigned to 6 sites making a total of 6 sites receiving the same treatment. Households that received treatments 1 and 2 received 50 Birr if they used the Mirt stove twice per week during the first monitoring period in addition to receiving the stoves for free. Households that received treatments 3 and 4 paid 25 Birr for the stove. Respondents who paid for stoves did not simultaneously receive incentives for using their stoves, because this combination was judged to be confusing for users. Those in treatment groups 5 and 6 received their stoves for free. Treatments 2, 4 and 6 received the network treatment aspect in addition to one of the three financial treatment aspects.

Table 1 presents the numbers of usable observations by round and treatment. We see that the availability of usable stove use frequency data are quite uniform across monitoring periods and treatments, with treatment 1 being a few percentage points lower than the other treatments. These results suggest that any SUMs failures were not systematic.

Table 1 Available Cooking Event Observations by Treatment and Round

|Treatment Description|Treatment<br>Number|Round 1|Round 2|Round 3|Round 4|
|---|---|---|---|---|---|
|Received 50 Birr usepayment,but no network|1|42|34|49|48|
|Received 50 Birr usepayment, plus network|2|53|51|57|55|
|Paid 25 Birr,but received no network|3|49|49|57|56|
|Paid 25 Birr for the stove, plus network|4|53|55|54|51|
|Received stove for free and no network|5|53|52|51|51|
|Received stove for free, plus network|6|50|46|50|40|
|Total Observatio|ns byRound|300|287|318|301|

It is important that the treatments are randomized across sites. For this randomization we use variables/indicators from community level data collected at the selected sites. These indicators include wealth variance, existence of forest rules and regulations, and percent of biomass change over 5 years, distance to market and percent of households with access to piped water. These variables were derived from the community survey conducted by EEPFE in 2012.

# _3.4 Data Management_

The SUMs generate an enormous amount of temperature data for each household, but our interest is only in the frequency of stove use, which is defined as the number of times that cooking

**13 |** P a g e
# **References**

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Enami, A., Lustig, N., & Aranda, R. (2017). Analytical foundations: Measuring the Redistributive Impact of Taxes and Transfers. In N. Lustig (Ed.), _Commitment to Equity Handbook: Estimatingthe Impact of Fiscal Policy on Inequality and Poverty._ Washington DC: Brookings Institution Press and CEQ Institute, Tulane Unviersity.

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Hernández Oré, M., Sánchez, L., Sousa, L., & Tornarolli, L. (2018). _Fiscal and Welfare Impacts of Electricity Subsidies in Central America._ Washington DC: World Bank.

Inchauste, G., & Lustig, N. (2017). _The Distributional Impact of taxes and Transfers. Evidence from Eight Low‐ and Middle‐Income Countries._ Washington DC: The World Bank.

International Labour Office. (2016, November ). Mongolia: Child Money Programme. _Building Social Protection Floors. Country Note Series._ .

Kakwani, N. (1977). Measurement of Tax Progressivity. An International Comparison. _The Economic Journal, 87_ , 71‐80.

Koplow, D. (2009). _Measuring Energy Subsidies using the Price‐Gap Approach: What does it leave out?_ IISD Trade, Investment and Climate Change Series. Winnipeg International Institute for Sustainable Development.

Lustig, N. (Ed.). (2017). _Commitment to Equity handbook. Estimating the impact of fiscal policy on inequality and poverty"._ Washington DC: Brookings Institution Press and CEQ Institute, Tulane Unviersity.

Lustig, N., & Higgins, S. (2013, September). Commitment to Equity Assessment (CEQ): Estimating the Incidence of Social Spending, Subsidies and Taxes. Handbook. _CEQ Working Paper_ (1).

Lustig, N., & Higgins, S. (2017). The CEQ assessment: measuring the impact of fiscal policy on inequality and poverty. In N. Lustig (Ed.), _Commitment to Equity Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty._ Washington DC: Brookings Institution Press.

18 MONGOLIA Distributional impact of taxes and transfers / November 2018
10 rebound from the expenditure cuts that were made in response to the debt crisis in 1982. By 1994, however, total spending on education had risen to 5.4 percent of GDP, or almost a full percentage point above its previous peak of 4.5 percent reached in 1982.

<!-- Start of picture text --> Figure 3<br>MEXICO<br>Education Spending per Student Capita<br>3500.00 -<br>8 3000.00 Prvate<br>o 2500.00<br>Z2000.00-<br>1500.00- Muipa<br>aC<br>1000.00<br>O 500.00 Federal<br><!-- End of picture text --> Source: Informe de Gobiemo, 1989 and 1997.

Figure 3 describes the evolution of funding for education in Mexico since the early 1980s. Private spending represenited less than 10 percent of total expenditures in education in 1987, and has fallen to below 5 percent in recent years. (This figure somewhat underestimates total private costs, which, with some adjustments noted in the Annex could be around 27% of total education spending.) Similarly, the share of state and municipal spending peaked at 17% of total spending in 1990, but since then also declined to less than 10 percent of total spending. The federal government, on the other hand, currently accounts for 87 percent of total sector spending. These figures are not far out of line with those from an OECD review of education expenditures, which indicated that in 1994, about 19 percent of spending on education in Mexico came from private sources, compared to a country average of 13 percent for the whole of the OECD.<sup>9</sup> Mexico does not stand out particularly in the preceding comparison. As in most countries, the public sector directly or indirectly finances the lion's share of education expenditures. Mexico does stand out in contrast to its OECD partners, however, when these expenditures are disaggregated by level of instruction. As shown in OECD (1998, pg. 97) the ratio of spending per student at the tertiary level compared to spending at the primary level exceeded 500 percent in 1994 and was the highest among OECD countries. (The ratio in Korea, for example, is close to the OECD average of 264 percent, or less than half the Mexican ratio.)

> 9These figures comprise total expenditures on education, including privately purchased materials, whereas the earlier figures shown in Table 3 are identical to the what the OECD expenditure review refers to as direct spending on educational institutions.
# **Table 3. IPO Average Issuance Size**

|||Full sample|||Full sample||Low- and M|iddle-Incom|e Countries|Low- and|Middle-Incom|e Countries|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Stock Market Capitalization<br>(% of GDP)|-0.610|||-0.660|||-0.760|||-0.754|||
||(0.430)|||(0.415)|||(0.652)|||(0.646)|||
|Stock Market Total Value||-0.087|||-0.072|||0.557|||0.581||
|Traded (% of GDP)||(0.465)|||(0.459)|||(0.646)|||(0.591)||
|Stock Market Turnover|||0.168|||0.203|||-0.223|||-0.209|
|Ratio|||(0.463)|||(0.455)|||(0.324)|||(0.326)|
|Foreign Direct Investment,|-0.006|-0.005|-0.005|-0.003|-0.004|-0.003|-0.217|-0.265|-0.292|-0.218|-0.251|-0.290|
|net inflows (% of GDP)|-0.011|-0.011|-0.012|-0.01|-0.011|-0.011|(0.296)|(0.287)|(0.301)|(0.297)|(0.290)|(0.304)|
|Political Stability and<br>Absence of Violence|0.356|0.155|0.169||||0.123|-0.177|-0.307||||
||(0.825)|(0.783)|(0.722)||||(1.430)|(1.303)|(1.296)||||
|Investment Profile||||-0.306|-0.154|-0.248||||-0.004|0.164|0.022|
|||||(0.237)|(0.262)|(0.231)||||(0.368)|(0.358)|(0.338)|
|Constant|22.29***|20.13***|19.36***|25.36***|21.51***|21.52***|23.47***|19.41***|21.32***|23.40***|18.23***|21.32***|
||(1.755)|(1.364)|(1.552)|(2.788)|(2.785)|(2.500)|(2.695)|(1.877)|(1.507)|(3.834)|(3.195)|(2.857)|
|Country Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Time Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Observations|295|240|261|292|237|258|110|103|108|110|103|108|
|R-squared|0.052|0.047|0.043|0.060|0.049|0.049|0.104|0.107|0.110|0.104|0.110|0.109|

_Note:_ The table reports fixed effects panel regressions using 5 years of data. The outcome variable is IPO average issuance size in millions USD. FDI net flows as percentage of GDP, political stability and absence of violence/terrorism, as well as investment profile are used as controls. This analysis includes both country and year fixed effects. One-year lags (t-1) are applied to all the explanatory variables and controls. Markings *, ** and *** denote significance at 10%, 5% and 1%, respectively. Robust standard errors are presented in parentheses. All variables are described in Table A-7 and Table A-9 in the annex.

14
making comparisons within countries over time. For those countries where the 1996 and 2023 confidence intervals do not overlap, we color the marker for the country red and show the confidence intervals in 1996 (2023) as horizontal (vertical) thin lines.

For most countries, confidence intervals for the two periods overlap, indicating that the observed changes in estimates of governance between 1996 and 2023 are not statistically significant. However, for some countries, the confidence intervals do not overlap, signaling statistically significant changes. These include cases like improvements in Voice and Accountability in Nigeria and Timor-Leste or declines in Rule of Law in Eritrea and Venezuela.

The frequency of significant over-time changes naturally depends greatly on the time horizon under consideration. For example, looking across all annual changes for all countries in all six indicators over the entire time span covered by the WGI, only 0.2 percent of the one-year changes are statistically significant in the sense of 90 percent confidence intervals in the two periods not overlapping. For fiveand ten-year changes, the share of statistically significant changes increases to 3 percent and 7 percent respectively. Looking at the longest possible 27-year change from 1996 to 2023 for all countries across all six aggregate indicators, just 13 percent of changes are statistically significant. The prevalence of significant changes also depends on the standard of significance. For example, if we focus on 68 percent confidence intervals (i.e. the point estimate of governance plus or minus one standard deviation) which may be sufficiently high standard of evidence for many purposes, the share of statistically significant 27year changes increases to one-third.

# 4.3 Discussion

A key feature of the cross-country and over-time comparisons discussed above is the importance of considering not just point estimates of governance, but also the ranges of likely values defined by the confidence intervals. These non-trivial confidence intervals reflect the reality that measuring governance is difficult, and that different data sources will come to different conclusions about countries’ relative positions on the aspects of governance that they measure. We note that this problem is not a consequence of our use of perceptions data to construct the WGI. Even if we were to rely on a set of objective indicators to construct the aggregate WGI measures, these data sources would also differ in their assessments of governance across countries, again leading to margins of error. Indeed, most indicators of governance or the investment climate, or economic indicators more broadly, are subject to measurement error. A key feature of the WGI is that we transparently disclose this measurement error 18
_Services PTAs_

# **Contents**

|**1**|**Introduction**|**3**|
|---|---|---|
|**2**|**Related literature**|**9**|
|**3**|**Data**|**13**|
||3.1<br>Services Policy and PTA Data . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>13|
||3.2<br>Services Trade Data . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>17|
||3.3<br>Sourcing of Value-Added . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>18|
|**4**|**Methodology**|**20**|
||4.1<br>Estimation strategy . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>20|
||4.2<br>Aggregation of services provisions . . . . . . . . . . . . . . .|. . . . . . . . . .<br>23|
|**5**|**Results for Cross-Border Services Trade**|**28**|
||5.1<br>Benchmark Overall PTA Efect . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>28|
||5.2<br>Impact of Policy Confgurations . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>34|
|**6**|**Results for Sourcing of Value-Added**|**37**|
||6.1<br>Redirection of Services VA Inputs . . . . . . . . . . . . . . .|. . . . . . . . . .<br>37|
||6.2<br>Linkages to Investment and Mode 4 . . . . . . . . . . . . . .|. . . . . . . . . .<br>40|
|**7**|**Conclusions**|**43**|
|**8**|**Appendix**|**50**|

2
We use the HBS data for the period 2011-2019 to construct and value the consumption basket of each household in the sample. Households are sorted into deciles 1 (lowest) to 10 (highest) according to their per capita consumption.<sup>8</sup> Unfortunately, the survey is only representative at the national level, making it impossible to examine differences in inflation experience across regions or between urban and rural areas. Moreover, the HBS was not carried out in 2020 due to the disruptions created by the Covid-19 pandemic. The implications for not having the 2020 survey when analyzing the inflation incidence in 2020 are discussed in the following section.

The SILC is a nationally and regionally representative survey similar to the HBS, but it is more focused on capturing detailed information on income sources while collecting less detailed data on household expenditures. TUIK has been conducting the Turkish SILC survey since 2006, which is analogous to the EU-SILC administered by Eurostat since 2004 to monitor income, social inclusion and living conditions in all EU countries plus Iceland, Norway and Switzerland. The sample size of the survey in Turkey has been expanding over time, increasing from 17,562 households in (65,472 individuals) in 2012 to 24,068 households (81,178 individuals) in 2018. Starting in 2014, the SILC survey became geographically representative at the NUTS2 level, corresponding to 26 sub-regions.<sup>9</sup> The income data in SILC is more suitable for our analysis compared to the data available in HBS. Rather than asking respondent households to report their income for the 12-month period preceding the survey, SILC collects income data for the same reference period (survey year) for all households in the sample. Consequently, it is possible in the SILC data to infer individuals earning the minimum wage in the same year. Additionally, the SILC survey includes questions on household non-labor income, such as public and private transfers and income from properties and other assets. Detailed data on employment also allows breaking employment type across employees, employers and self-employed as well as labor intensity (part and full time workers). This study uses SILC data for the period 2011-2018.<sup>10</sup>

> 8 The World Bank uses the consumption data available in the HBS to calculate and monitor the evolution of monetary-based poverty rates in Turkey.

> 9 NUTS stands for Nomenclature of Territorial Units for Statistics, a system of geographic divisions developed by Eurostat for statistical purposes (https://ec.europa.eu/eurostat/web/nuts/background).

> 10 The reference period for the income data collected in the SILC survey is the year preceding the field work.

9