rafmacalaba/datause-encoder-data huggingface dataset:
inward FDI by nonresidents and declining portfolio debt and other financial outflows by residents alike.

# **7. Correlates of Capital  Inflows**

To analyze the drivers of capital flows, we estimate regressions in the form of Equation (4), where the dependent variable ���� is capital flows of type i, in country c, in quarter t. As before, flows are normalized by subtracting from each observation the country-specific mean and dividing by the country-specific standard deviation.

# ���� ������ ���� ����� � �� ln������� ������� ��� ����               �4�

Capital flows are regressed on global factors: the Federal Funds Rate and the VIX (converted in log scale).<sup>8</sup> We also include a vector of country-specific variables, �����. Domestic variables include quarterly real GDP growth, capital account openness (the Chinn-Ito Index); financial sector depth (stock market capitalization or bank assets as percent of GDP); and proxies for the business environment (the ICRG rating on investment risk, which is an index ranging from 0 to 12, a score of 12 points equates to very low risk and a score of 0 points to very high risk).<sup>9</sup> We lag these by one quarter (or one year for the variables that are available at annual frequency).<sup>10</sup> Regressions are estimated with country-fixed effects and robust standard errors. Since some of the structural variables are slow-moving, the fixed effects estimates may not be very precise, hence we also estimate the same equations excluding the fixed effects. The results turn out to be very similar, hence we do not report them in order to save space.

The first four columns of Tables 11-14 suggest that FDI is driven mainly by pull factors, but that portfolio flows seem to be driven mainly by push factors, and so-called other flows are driven both by push and pull factors.

Most inflows are not strongly correlated with the federal funds rate, with the prominent exception of portfolio debt inflows (an increase in the US policy rate predictably dampens debt flows). Higher global risk aversion as measured by VIX reduces portfolio capital inflows but not FDI inflows (the coefficient of VIX is negative and significant for all non-FDI flows and largest for portfolio debt and portfolio equity flows). FDI seems to be affected more by domestic than external factors (for example, GDP growth appears to act as a pull factor for FDI). A better investment

> 8 In variations of these regressions we include 10-year US bond yield as an indicator of the US monetary policy. Results are similar to those obtained with the federal funds rate.

> 9 As an alternative to GDP growth, we included one year ahead growth forecast from WEO in the regressions. Its coefficient is insignificant in all regressions.

> 10 To limit multicollinearity we estimate our regressions with a parsimonious set of control variables.

18
more ‘below average’ and more ‘above average’ precipitation and flow (runoff), which is hard to cope with by riparians that are tied to a given water allocation scheme and existing infrastructure that was designed for a given long-term water flow level. We argue that the various basins in our dataset have already experienced changes in water supply variability (flow, precipitation). Thus, a first stage of analyzing the impact of climate change on the stability of intentional water agreements should focus on observing likely effects of past climate changes on past treaty cooperation. If we can show that water supply variability has affected treaty cooperation in the past 150 years, we would expect that further increase in water supply variability would have similar, or even magnified, effects on treaty cooperation. Therefore, by studying the past changes in climate we will be able to extrapolate predictions how future climates may affect future treaty cooperation. The next section develops the theoretical framework with which we will estimate the impact of change in climate on the likelihood of cooperation among international bilateral river basin riparians.

# **3. Theory and Hypotheses**

Pairs of countries sign treaties over water bodies they share for various reasons. The economics and international relations literature suggest that they do it because they either face difficulties they cannot overcome themselves; or that they anticipate externalities relating to pollution, flood control, or hydropower, (Just and Netanyahu, 1998); or for reasons such as economies of scale where parties anticipate being better off acting in a coalition rather than acting alone when facing certain water scarcity situations (Dinar S., 2009).

The economics and international relations literature that applies statistical tools to international water datasets (Brochmann and Hensel, 2009; Espey and Towfique, 2004; Gleditsch et al., 2006; Hensel et al., 2006; Song and Whittington, 2004; Tir and Ackerman, 2009; Toset et al., 2000; Dinar S., 2009) has gone a long way already in developing a theory that explains various aspects of shared water and environmental treaty making and we adopt a number of these general variables in our study.

# <u>Water variability and cooperation</u>

Overall scarcity (or water availability) has become an important explanatory variable in some of these statistical studies. In particular, Dinar, S. 2009 hypothesizes an inverted U-shaped curve 8
||TableD2:|Randomization|Balance,Trimm|edSample|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||Control||<sup>t</sup><br>Testsofd|ifference|<sup>t</sup>|
|_Privateschoollevelvariables_|N|Mean|H*C=0|L*C=0|L<sup>u</sup>*C=0|H*L=0|
|Enrollment|836|154.1|*5.7<br>[0.39]|*13.8<br>[0.14]|*2.0<br>[0.77]|8.1<br>[0.35]|
|Monthlyfee(PKR)|834|221.6|2.5|*20.3|*8.4|22.8|
||||[0.81]|[0.13]|[0.38]|[0.07]|
|Annualexpenses(PKR)|821|65441.7|5,875.8|*5,477.6|*4,902.8|11,353.3|
||||[0.53]|[0.60]|[0.57]|[0.32]|
|Monthlyexpenses(PKR)|832|22293.5|1,061.4|*2,774.9|2,720.2|3,836.4|
||||[0.49]|[0.14]|[0.10]|[0.05]|
|Infrastuctureindex(PCA)|819|*0.141|0.077|0.133|*0.012|*0.056|
||||[0.41]|[0.31]|[0.88]|[0.69]|
|Schoolage(Noofyears)|836|7.9|*0.191|0.615|0.171|*0.806|
||.|.|[0.69]|[0.40]|[0.74]|[0.25]|
|Numberofteachers|834|7.7|*0.045|*0.290|0.316|0.245|
||||[0.88]|[0.44]|[0.31]|[0.47]|
|Averagetestscore|393|*0.242|*0.020|0.074|*0.029|*0.095|
||||[0.81]|[0.48]|[0.75]|[0.34]|
|OverallEffect:p*value|||0.85|0.47|0.94|0.38|

Notes:.*.p<0.1,.**.p<0.05,.***.p<0.01 a).This.table.reproduces.Table.D1,.Panel.B,.using.trimmed.data.to.assess.whether.mean.imbalance.in.Table.D1,.Panel.B,.is. driven.by.large.values.in.the.right.tails..The.trimming.procedure.makes.the.top.2%.of.baseline.values.missing.for.each.variable. Column.1.shows.the.number.of.observations,.and.col.2.shows.the.control.mean..The.remaining.columns.show.tests.of. difference.**.regression.coefficients.and.p*values.in.square.brackets**.between.groups..In.the.bottom.row,.we.report.p*values from.a.test.asking.whether.variables.jointly.predict.treatment.status.for.each.group.

b)..Regressions.are.weighted.to.adjust.for.sampling.and.include.strata.fixed.effects,.with.clustered.standard.errors.at.the.village.level. c).All.variables.are.defined.in.Appendix.C..There.are.fewer.observations.for.test.scores.since.half.of.the.sample.was.tested.at baseline.
- Likewise, to ensure these students are not enrolled in overcrowded rooms – an additional 22,358 rooms will be needed over the next 12 years.

- To provide an augmented service delivery package (enhanced infrastructure, improving quality of service delivery, providing school meals, etc.), the current budget for ECE in Balochistan must be increased from PKR 4.5 billion (US$ 0.02 billion) to PKR 28.5 billion (US$ 0.13 billion) by 2035. This corresponds to an annual increase of 14 percent on average.<sup>15</sup>

- If alternate delivery mechanisms (community construction and vouchers) are used, the budget requirement by 2035 will be substantially reduced to PKR 19.4 billion (US$ 0.09 billion), with an annual increase of 11 percent on average.

The simulation modeling relied heavily on data availability, which varied from province to province. Due to data limitations, several assumptions are made throughout data consolidation and analysis. Population, public enrollment, and unit rates are largely available for the exercise. However, data on human resources and infrastructure was unavailable at the pre-primary level. The figures were, however, available at the primary level. We assume that if a primary school has six classrooms and six teachers, one of them should be for the pre-primary level. So, the total number of teachers at primary level was divided by six to calculate the pre-primary level figures. Similar assumptions were used for baseline education financing data.

The baseline enrollment in private schools was estimated based on private to public enrollment at the pre-primary level ratio using the latest available Pakistan Education Statistics Report 2020-21 (Pakistan Institute of Education, 2022). The data on teacher training was also not available. It was assumed that public school ECE teachers are currently untrained in ECE teaching and must undergo extensive training. Table 1 provides an overview of the baseline data used for simulation modeling.

> 15 These calculations assume that the baseline year is 2022. Estimated figures are projected up to 2035 – resulting in 14 periods. The compounding formula used in this case is ((28.5/4.5)^(1/14))-1.

6
# Table of Contents

|I.|Introduction ....................................................<br>l|
|---|---|
|II.|Financial Development,<br>Growth, an.d the Sources of Growth:|
||Contemporaneous<br>Associations ....................................<br>3|
||A. Data: The financial indicators<br>.<br>.<br>4|
||B. Growth indicators:<br>Measurinca orowth and its sources<br>..<br>6|
||C. Simole correlations<br>..<br>8|
||D. Contemporaneous<br>rearessions:<br>1960-1989<br>..<br>9|
||E. Sensitivity<br>analvses<br>.<br>.<br>.<br>.10|
|III.|Initial Financial Development,<br>Growth, and its Sources ......... 12|
||A. Initial values ..............................................<br>12|
||B. Instrumental<br>variables ......................................<br>15|
||C. Sensitivity<br>analyses ........................................<br>15|
|IV.|Conclusions<br>.....................................................<br>17|
||References .....................................................<br>19|
||Tables .........................................................<br>21|
||Appendix Tables ................................................<br>32|

We received helpful advice from William Easterly, Mark Gertler, Lant Pritchett, Nouriel Roubini, Andrei Shleifer, and Mark Watson. Sara Zervos provided excellent research assistance. Support was received from the World Bank research project "How Do National Policies Affect Long-Run Growth?"
|PPR|Working Paper Series|||
|---|---|---|---|
|Title|Author|Date|Contact|
|WPS11O Impact of Lower Oil Prices on<br>Renewable Energy Technologies|E. Terrado<br>M. Mendis<br>K. Fitzgerald|||
|WPS111 Recent World Bank ActivitiesioEnergy|Industry<br>and Energy<br>Department|||
|WPS112 A Visual Overview of the World Oil<br>Markets|Kay McKeough<br>Jose Escay<br>Sompheap Sem|||
|WPS113 Public Sector Pay and Employment<br>Reform|Barbara Nunberg|October 1988|J. Cheeseman<br>61703|
|WPS114 Africa Region Population Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodoifo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS115 Asia Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswori<br>31091|
|WPS116 Latin America and the Caribbean<br>Region Population<br>Projections||||
|1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswori<br>31091|
|WPS117 Europe, Middle East, and North Atrica<br>(EMN) Region Population<br>Projections||||
|1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswor;<br>31091|
|WPS118 Contract-Plans<br>and Public<br>Enterprise<br>Performance|John Nellis|October 1988|R. Malcolm<br>61707|
|WPS119 Recent Developments in Commodity<br>Modeling: A World Bank Focus|Walter C. Labys|October 1988|A. Daruwala<br>33716|
|WPS120 Public Policy and Private Investment<br>in Turkey|Ajay Chhibber<br>Sweder van Wijnbergen|October 1988<br>|A. Bhalla<br>60359|
the country’s fiscal system in 2019 on poverty and inequality, with an eye on vulnerable populations, such as elders and children. We show that the effects of some fiscal interventions do improve poor people’s lives, while there are still many others that reinforce their vulnerability.

Leveraging the CEQ methodology and both household survey and administrative data, we find that both poverty and income inequality decrease from the pre-fiscal income to different concepts of post-fiscal income. Extreme poverty reduces by 5.1 p.p. and moderate poverty by 0.6 p.p. Income inequality, in turn, declines by 6.4 Gini points from MIPP to consumable income. Despite the relatively low reduction in the poverty headcount ratio, we demonstrate that the Brazilian fiscal policies do alleviate the situation of the poor, as both the poverty gap and the squared poverty gap reduce by a third and a fifth in terms of extreme poverty, respectively. Notwithstanding the large impacts of fiscal policies, Brazil remains one of the most unequal countries with a Gini coefficient of 0.521.

Our results also indicate that direct transfers and public services benefit mostly the individuals from the first quintile of the pre-fiscal income distribution. Programs such as BPC, Bolsa Familia, and rural pensions are key examples of such direct transfers that mainly go to the poorest. We also show that the Brazilian fiscal system tackles more poverty among households with elderly people, while seems to have the opposite effect on those with children. Importantly, we also document that the current policies lead to a higher poverty reduction for Afro-Brazilians than whites among the elderly. However, poverty remains high among Afro-Brazilians at 41 percent (compared to 19 percent among whites).

Such analyses hold the promise of providing actionable information for policy makers and fostering datadriven insights for researchers on possible reforms to the fiscal system that could improve its capacity of shortening gaps and promoting welfare-enhancing interventions. The recent move towards increases in transfers to children residing in low-income households seems like a step in the right direction. Coupled with an integral sustainable fiscal strategy, such efforts can boost the poverty reducing power of the Brazilian fiscal system.

Certain assumptions of the current methodology can and should be addressed in future research. One of the most relevant ones is that of the uniform impact of government expenditures on educational or health services. As a work on the varying quality of learning has demonstrated (World Bank 2022a), it could be important to allow varying effects of expenditures that help effectively attain higher levels of human capital. On the health side, literature suggests the Brazilian public system encumbers patients with long waits for some health services that could be translated into economic costs that are not being appropriately accounted (Marinho, 2009). Information contained in indicators such as the World Bank’s Human Capital Index may provide a sensible _proxy_ measure to account for differences in quality.

39
Anthoff, A. and Hahn, R., 2010. Government failure and market failure: on the inefficiency of environmental and energy policy. Oxford Review of Economic Policy 26 (2), 197-224

- Aronsson, T. and K-G Löfgren eds. 2010. Handbook of Environmental Accounting. Cheltenham: Edward Elgar.

Arrow, K. J., 1962. The economic implications of learning by doing. The Review of Economic Studies 29 (3), 155-173.

Asheim, G.B., and M.L. Weitzman, 2001. Does NNP Growth Indicate Welfare Improvement? Economics Letters 73:2, pp. 233-39.

Barbier, E., 1997. Environmental Kuznets Curve special issue: Introduction”. Environment and Development Economics 2, 369–381.

Baumol, W. and W. Oates, 1988. The Theory of Environmental Policy, (2nd edition) Cambridge University Press, Cambridge.

Baumol, W., 1972. On Taxation and the Control of Externalities, American Economic Review, 62(3), 307321.

Betz, R., and M. Sato, 2006. Emissions trading: lessons learnt from the 1st phase of the EU ETS and prospects for the 2nd phase. Climate Policy 6(4), 351–359.

Blanchard, O. J., Gali, J., 2010. The Macroeconomic effects of Oil Price Shocks: Why are the 2000s so Different from the 1970s?

Bolt, K.; Ruta, G. and M. Sarraf, 2005, Estimating the Cost of Environmental Degradation, The World Bank.

Bourguignon, F. and Fields, G. 1990. Poverty Measures and Anti-poverty Policy. Recherches Economiques de Louvain, 56(3-4), pp. 409-427.

Bovenberg, A. L. and Sjak Smuldters. 1996. Transitional Impacts of Environmental Policy in an Endogenous Growth Model, Int. Econ. Rev. 37:4, pp. 861-95.

Bowen, A., 2011. Green growth, green jobs, and labour market. Background paper for the SDN Flagship Report on Green Growth. The World Bank, Washington, DC.

Brock, W.A., and Taylor, M.S. 2005. Chapter 28 Economic Growth and the Environment: A Review of Theory and Empirics Aghion, P. & Durlauf, S. N. (ed.) Elsevier, Vol. 1, Part 2, pp. 1749-1821 Brock, W., and Taylor, M. 2010. The Green Solow model , Journal of Economic Growth, Springer Netherlands, Vol. 15(2), pp. 127-153-153.

Camerer, C. and H. Kunreuther, 1989. Decision Processes for Low Probability Events: Policy Implications. Journal of Policy Analysis and Management, 8, 565-592.

32
# **C. DIRECT NET BALANCE OF PAYMENTS IMPACT OF BULGARIA’S EU ACCESSION**

Upon accession Bulgaria and Romania will join a group of so called “cohesion countries” and will become net beneficiaries from the EU budget, i.e. the inflow of funds from the EU budget would be higher than the outflow of resources from Bulgaria to the EU. All flows of funds between the country and the EU are taken into account in order to estimate the net balance of payments effect for Bulgaria’s EU accession. Payment appropriations are used in calculating the net effect of the country’s EU accession, since it is only actual payments that have a macroeconomic impact on the recipient country.<sup>6</sup> Based on the updated financial envelope presented above, net transfer of EU funds to Bulgaria is estimated to be on average 3.7 % of the projected GDP in 2007-2009. The positive impact picks up significantly as a percentage of output in the first three years of accession. This is due to a gradual phasing-in of most of the EU funds and a higher rate than the expected real GDP growth (see Table 3). Net transfers to the country start at 2.6 percent of GDP in 2007 and build up to 4.2 percent of GDP in 2009. Each step in arriving at this result is outlined below.

**Table 3: Net Balance of Payment Effect for Bulgaria (% of GDP)**

|**Share in GDP**|**2007**|**2008**|**2009**|**2007-09**|
|---|---|---|---|---|
|**Inflow of funds**|||||
|Post-accessionappropriationsforpayments|2.3%|4.0%|4.3%|3.6%|
|Pre-accessionappropriationsforpayments|1.5%|1.2%|1.1%|1.3%|
|_Total inflow of funds_|3.8%|5.2%|5.4%|4.8%|
|**Outflow of funds**|||||
|Own Resources|1.2%|1.2%|1.2%|1.2%|
|Contribution to the EIB|0.007%|0.007%|0.013%|0.009%|
|Payment to the RFCS|0.000%|0.000%|0.006%|0.002%|
|_Total outflow of funds_|1.2%|1.2%|1.2%|1.2%|
|**Net impact on Bulgaria**|2.6%|4.0%|4.2%|3.7%|

_Source_ : Own calculations based on the Commission Communication of 10 February 2004, EU Accession Treaty for Bulgaria and Romania of April 2005 and Bulgarian Ministry of Finance estimations on the preaccession funds disbursements.

# **1. Inflows of EU Funds to Bulgaria**

Total payments from the EU budget to Bulgaria comprise of total post-accession appropriations for payments and total appropriation of pre-accession aid.

> 6 Hallet (2004) and Hallet and Keereman (2005) use the same approach in their work, too.

5
**<mark>Tax compliance also can influence how firms react to minimum wage policies</mark>** <mark>. When income underreporting is prevalent around the minimum wage levels, non-compliant firms may convert some of their undeclared wages into official wages to comply with the new minimum wage policy, thereby staying off the tax authorities' radar. This could be important for employment impacts,  as unreported wages can serve as a cushion to mitigate the impact of minimum wage increases (Tonin, 2011). Empirical findings for Latvia (Gavoille & Zasova, 2023) indicate that in firms significantly affected by the minimum wage reform, the employment response can be three times greater for compliant firms compared to those evading taxes in the year following the minimum wage increase. While there are some estimates of tax compliance gap across sectors for Romania (Robayo-Abril, Balaban & Wronski), there are no estimates of employment elasticities for compliant and noncompliant firms. Therefore, we did not consider tax compliance status in our simulations.</mark>

**<mark>The aggregate and distributional impacts may depend on whether dismissed workers transition to unemployment or inactivity vs. other formal or informal sector jobs</mark>** <mark>.  For example, if dismissed workers move to unemployment or inactivity, the earning losses are at the highest. If they move to other job opportunities with some higher or lower wages than the initial wage, the earning losses are of lower magnitude. As there is no panel or retrospective labor market information in household surveys or the tax administrative data to estimate these labor market transitions, we work with different scenarios that give us upper and lowerbound estimates of the losses.</mark>

# **Then, we use the following algorithm for the microsimulation:**

- Estimate an expected wage increase (in percentage) for the employed population, age groups, and employees affected by the minimum wage increase.

- Use the different elasticity values and wage increases estimated in (1) to estimate the probability of job loss.

- To select "job losers" for each individual, we generate a random variable that takes the value of "1" with the probability computed in (1); in other words, we assign non-employment status randomly.

- Compute simulated earnings and unemployment.

- Repeat the procedure 200 times and compute average earnings and job losses across simulations.<sup>25</sup>

We conduct policy simulations based on two distinct scenarios for the minimum wage increases:

> 25 The number of replications was limited due to the size of data set. However, Pattengale et al. (2010) discuss the number of bootstrap replications needed and conclude that at least 100 may be necessary.

34