rafmacalaba/datause-encoder-data huggingface dataset:
- 23 -

# (3) SB' - (I - a) Vt,., if, + Dt

Naturally, the Central Bank's stock of foreign assets (FA)<sup>will also accumulate,</sup> as it (compulsorily) buys a portion of the proceeds<sup>from the country's</sup> commercial transfers. Algebraically, (4) SFA - _pA_ l TBt., + NISA,.1<sup>]</sup> The newly created money will put upward pressure on<sup>prices via the</sup> instantaneously-clearing money market. The latter's equilibrium will be specified as: (5) (M/P)t - r . y, . exp(-a.idt) Equation (5) simply says that the real money supply (M/P, "P" being the domestic price level) at any time "t" equals the real demand for money.<sup>This is defined</sup> as a function of real output (y), and the domestic, nominal interest<sup>rate (id,);</sup> "r" and "a" are constants". Clearly, by log-differentiating Equation (5) respect to time, a semi-reduced solution for inflation can be obtained:

# (6) **Xrt** - (6M1/^,) - (6y,/y,.,) + a.6id,

where "r" stands for inflation, and "6x" stands for the time derivative of any variable "x". We have already specified the money-supply creation<sup>mechanism in</sup> Equation (1), taking into account the authorities fiscal, financial and foreign exchange policies. Nominal interest rates will also be an exogenously-determined 12 policy variable. It remains, however, to specify the real output formation equation. We have chosen an aggregate-demand-determined mechanism. This implicitly assumes a good deal of idle capacity in the<sup>economy and ignores</sup> IV/ It is possible that, in a financially repressed economy, the true opportunity cost of money holdings is the return from having<sup>real goods (i.e.</sup> expected inflation) ratber than the return from an interest-bearing security **(id).** See, for instance, R. Kitchen, Finance for the Developing<sup>Countries, J.</sup> Wiley & Sons, 1986, Chapter 3. As the stabilization program to be included in our simulation envisages a "flexibilization" of domestic nominal interest rates (in line with inflation), we have preferred to keep the latter as the opportunity cost argument in our demand for money equation.

j-/ Naturally, money supply and interest rates can be both exogenous only if credit market controls are enforceable.
gives the concentration curve for worker participation in the program. This was estimated by locating the families of a random sample of 3,500 Trabajar workers within the national distribution (based on a sample of 22,000 families).'<sup>0</sup> For example, 76% of people living in the households of participating workers had a household income per capita that placed them amongst the poorest 20% of Argentineans nationally.

How does this incidence of income gains compare to other social spending in Argentina? Table 3 gives the concentration curves for the Trabajar program and both aggregate social service spending and social insurance, all on a household basis (to assure that the Trabajar concentration curve is comparable with the numbers in Table 1). Since there is very little variation in the Trabajar wage rate, the concentration curve based on participation is also the benefit incidence curve for gross wage payments." It can be seen from Table 3 that the direct income gains from the program were far better targeted than social insurance and social services as a whole. The program's targeting also appears to be better than any other targeted programs in Argentina. Amongst the programs for which incidence calculations are given in World Bank (1999, Table 3.7), the next best performance was for programs directed at pregnant mothers and children, for which 70% of the benefits went to the poorest quintile of households, which was itself an unusually good performance compared to other programs.

Of course, targeting performance is only one factor in assessing the performance of such programs in reducing poverty (Ravallion, 1999b). A program such as Trabajar is designed to help in one dimension of poverty, while programs directed at health and nutrition of the poor help in quite different dimensions; both types of programs can have important

**10** Identical surveys were used for the program participants and the national sample, and the surveys were implemented at approximately the same time; for details see Jalan and Ravallion (1999). "1 Again this does not net out foregone income, though nor do the standard benefit incidence calculations in Table I take account of behavioral responses. However, as noted above, factoring in foregone income mainly affects the concentration curve below the 20kh percentile (Jalan and Ravallion, 1999).

15
bargaining and other union activities (“syndicalization” or trade union premium).<sup>9</sup> The extent to which unionization helps unskilled labor in both sectors is the same.

Skilled wages in the two sectors will be where all the symbols are the same as before except for p = skilled labor premium. The premium too is supposed to be the same across the two sectors. Before reform (liberalization) the _observed_ skill premium π will be and the inter-sectoral wage differential<sup>10</sup> Reforms, as the review of empirical literature has shown, are unlikely to pertain to trade policies alone. They include also direct labor markets changes whether because of political economy considerations or because policy-makers believe that without changes in labor markets trade reforms cannot bear fruit. Reforms involve both a decrease in _t_ and thus in wages in the protected sector;<sup>11</sup> then a decrease in _s_ brought about by “flexibilization” of labor markets and reduced power of trade unions.<sup>12</sup> Both imply an

> 9 For the recent assessment of the union premium in a developed economy context, see Card (1996). 10 We assume that there are only four workers (or differently, the same shares of skilled and unskilled labor in both sectors). In a more complicated model below this assumption will be relaxed by allowing for the difference in skill intensity between protected and unprotected sectors.

> 11 Obviously, the adjustment to the reduction in protection may be through labor-shedding rather than wage cuts. The latter (at least partly and at times fully) seems however to be a common mechanism as argued by Goldberg and Pavcik (2004, p. 22). This is consistent with a noted absence of labor mobility in developing countries following trade liberalization.

> 12 To give a few examples. In 1990 in Colombia, tariff cuts were accompanied by significant reduction of labor rights (elimination of obligatory rehiring of dismissed employees with more than ten years of service and of the severance payment system; see World Bank, 1992). In Nicaragua the 1995-96 tariff liberalization went hand-in-hand with large dismantelment of state sector and “labor flexibilization” (see World Bank, 1997).  In Peru, similarly, the mid-1990’s liberalization of trade occurred together with further labor flexibilization (reduction of severence payments; lengthening of the job probation; extension  of

15
# **Rethinking the Form and Function of Cities in Post-Soviet Countries**<sup>**1**</sup>

Souleymane Coulibaly<sup>2</sup>

**Keywords:** cities, connectivity, Eurasia, planning, greening, subnational finance

## **J.E.L. Classification: N90, P25, P51, R10**

**Sector Board:** Economic Policy

**Acknowledgement:** This paper draws on my contribution to the book “Eurasian Cities: New Realities along the Silk Road” (2012). All the team members of this book are greatly acknowledged.

> 1 The findings reported in this paper are those of the author alone, and should not be attributed to the World Bank, its executive director, or the countries they represent.

> 2 Senior Economist, Europe and Central Asia Poverty Reduction and Economic Management Department (scoulibaly2@worldbank.org).

1
# **References**

Altonji, J. and T. Dunn (1996). Using Siblings to Estimate the Effect of School Quality on Wages. _The Review of Economics and Statistics_ 78 (4): 665-671.

Angrist, J. D. and V. Lavy (1997). The Effect of a Change in Language of Instruction on the Returns to Schooling in Morocco. _Journal of Labor Economics_ 15 (1): S48-76.

Arcidiacono, P., Bayer, P. and A. Hizmo (2010). Beyond Signaling and Human Capital: Education and the Revelation of Ability. _American Economic Journal_ : _Applied Economics_ 2 (4): 76-104. Ashenfelter, O. and C. E. Rouse (1998). Income, Schooling and Ability, Evidence from a New Sample of Identical Twins. _Quarterly Journal of Economics_ 113 (1): 253-284.

Betts, J. R. (1995). Does School Quality Matter? Evidence from the National Longitudinal Survey of Youth. _The Review of Economics and Statistics_ 77 (2): 231-250.

Blau, F. and L. Khan (2001). International Differences in Male Wage Inequality: Institutions versus Market Forces. _Journal of Political Economy_ 104: 791–837.

- Boissiere, M., Knight, J. B. and R. H. Sabot (1985). Earnings, Schooling, Ability   and Cognitive Skills. _American Economic Review_ 75 (5): 1016-1030.

Bowles, S., Gintis, H. and M. Osborne (2001). The Determinants of Earnings: A Behavioral Approach. _Journal of Economic Literature_ 39 (4): 1137-1176.

Card, D. and A. Krueger (1992). Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States. _Journal of Political Economy_ 100 (1): 1-40.

- Case, A. and M. Yogo (1999). Does School Quality Matter? Returns to Education and the Characteristics of Schools in South Africa. NBER Working Paper 7399, National Bureau of Economic Research, Cambridge, MA.

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# **1. Introduction**

Agriculture is an activity heavily affected by climatic conditions. The impacts of changes in climate on agricultural activities have been shown to be significant for low input farming systems in developing countries in Africa (Rosenzweig & Parry 1994; Reilly et al. 1996; Reilly & Schimmelpfennig 1999; Kates 2000; McGuigan et al. 2002). Furthermore, tropical regions in developing countries are usually characterized by poor marginal soils that cover extensive areas, making them unusable for agriculture, leaving the developing countries particularly vulnerable to potential damage from environmental changes (Mendelsohn & Dinar 1999).

The impacts of climate change on agricultural activities in terms of yield losses and increasing water needs have been studied for the last decade in Egypt. The results have shown Egypt to be particularly vulnerable to climate change because of its dependence on the Nile River as the primary water source. Its large traditional agricultural base is undergoing intensive development and its long coastline is being eroded. Climate change could do severe damage to agricultural productivity if no adaptation measures are taken (El-Shaer et al. 1997). Previous research on the impact of climate change on agricultural sectors has revealed that yields and water use efficiency will be decreased in comparison with current climate conditions, even when the beneficial effects of CO2 are taken into account.

Climate change could decrease the national production of rice by 11% and soybeans by 28% by the year of 2050, compared with their production under current conditions (Eid & EL-Marsafawy 2002). It could reduce national maize production by about 19% (Eid et al. 1997b) and barley grain by 20% (Eid et al. 1995). Cotton seed yield would increase by 17% if the temperature increased by 2 _°_ C and by 31% with a 4 _°_ C increase (Eid et al. 1997a). By the year 2050, climate change could increase water needs by up to 16% for summer crops but decrease them by up to 2% for winter crops (Eid & El-Mowelhi, 1998).

However, despite all the extensive research on the effect of climate change on crop production there has been no research on the economic impacts on the agricultural sector. Heavy economic dependence on agriculture in Egypt means that the effects of climate change are likely to threaten the welfare of the population and hamper economic development. Economic adjustments, such as improving the overall water use efficiency of the agricultural system, soil drainage and conservation, land management, and crop alternatives are essential measures for overcoming the adverse impacts of climate change. If appropriate measures are taken, the negative effects of climate change on agricultural production and other major resource sectors (water and land) may be lessened.

# **_1.1 The objectives of the study_**

The study objectives follow the general objectives as set by Global Environmental Facility (GEF) project document. However, they were redefined to focus specifically on the Egyptian conditions. These redefined objectives are as follows: 4
TABLE OF CONTENTS (CONTINUED)

||G.12<br>G.13|National Food Marketing Agency (BANASUPRO) ............<br>31<br>Honduran Forestry Development Corporation (COHDEFOR)<br>and Honduran Banana Corporation (COHBANA) .............<br>31|
|---|---|---|
||G.14|Honduran Coffee Institute (IHCAFE)<br>.................... 32|
||G.15<br>G.16|National Agrarian Institute (INA)<br>.<br>.<br>32<br>Central Bank<br>..<br>33|
||H.<br>Over<br>H.1<br>H.2|all Conclusions .33<br>The Main Thrust of a Propsed Action Program ........... 33<br>Proposed Package of Measures to Reduce the Fiscal<br>Deficit<br>.....<br>34|
||H.3|Risks of The Suggested Package of Measures<br>..<br>34|
||H.4|Conclusion ............................................<br>35|
|Tables|||
||Table 1|Central Government Current Revenues, 1982-87 ............3|
||Table 2|Rate of Value Added Taxes by Selected Countries, 1987...4|
||Table 3|Parameters for the New Tariff Regime, 1987 ..............6|
||Table 4|Rates of Surcharges, 1988 ...............................<br>6|
||Table 5|Central Government Expenditures by Function, 1981-87 ... 10|
||Table 6|Central Government Current Transfers to the Rest of<br>the Public Sector, 1984-86 .............................<br>11|
||Table 7|Central Government Capital Transfers to the Rest of<br>the Public Sector, 1984-86 .............................<br>12|
||Table 8|Central Government Finances, 1981-87 ................... 14|
||Table 9|Debt Stock of the Public Sector, 1984-86 ............... 15|
||Table 10|Banking System Financing of the Public Sector,<br>As End of Year, 1983-87 ................................<br>16|
||Table 11|Budgeted Expenditures by Sector, 1988 .................. 1?|
||Table 12|Public Sector Investment Expenditures, 1981-87 ......... 19|
||Table 13|Planned Investment Program by Sector, Share of Ongoing<br>and New Projects In Investment Program, 1987-90 .20|
||Table 14|Finances of Public Sector Enterprises, 1981-87 .23|
Policy ReseaRch WoRking PaPeR 4588

# **Abstract**

As part of the recent health reform effort, the government of Georgia launched a Medical Assistance Program in June 2006 to provide health insurance to its poor population. So far the program covers slightly over 50 percent of the poor and provides benefit coverage for outpatient and inpatient care. This paper estimates initial impact of the Medical Assistance Program and assesses whether the benefits have reached the poorest among those eligible, using utilization data from June 2006 to December 2006. Based on the analysis using a regression discontinuity design and a three-part model, the paper presents two main findings. First, the Medical Assistance Program has significantly increased utilization of acute surgeries/inpatient services by the poor. Second, the benefits have successfully reached the poorest among the poor. These two findings indicate that government efforts to improve the poor’s access to and utilization of health services are yielding results. The paper emphasizes that the initial dramatic increase in surgeries must be interpreted with caution, given the possible misclassification or misreporting of acute surgeries in the data. The paper also stresses the need to continue monitoring implementation of the Medical Assistance Program and further improve program design, particularly the targeting mechanism, to achieve better efficiency, effectiveness and overall equity in access to health care services.

This paper—a product of Human Development Department and South Caucasus Country Department, Europe and Central Asia Region— is part of a larger effort in the Bank to support the Government of Georgia in its poverty reduction and improving access to social services by the poor. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at xhou@worldbank.org and schao@worldbank.org.

_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
Figure 4.4 <!-- Start of picture text --> Trade Intensity Index - Selected Regions<br><!-- End of picture text --> <!-- Start of picture text --> 6.00-i _<br>5.00- __<br>3400- I ii1 iig -<br>1980 1990 199s 1998 1999 2000<br>ElASEAN *ASEAN +3 DAJI East Asia 3 EU NAFTA|<br><!-- End of picture text -->

See Endnotes for calculation of index.

**All** East Asia Includes ASEAN plus Japan, China, South Korea, Hong Kong and Taiwan. No Taiwan data for 1980 and 1990. Source: IMF DOTS Yearbooks 1985, 1992, 2000 **&** 2001; IMF DOTS Quarterly Updates; and World Bank, World Development Indicators 2002.

Prominent in this figure is that ASEAN, in all years shown, had a higher intensity index than any of the other groups. (See Table S.8 in appendix for intensity index data table.) Its index of 4 to 6 between 1980 and 2000 was well above that of the EU, for which the index was only between 1.7 and 1.9. Although NAFTA's index was higher (about 3.0 in all years), this was still below that of ASEAN. This tells us that ASEAN has had a higher degree of intraregional trade than the EU or NAFTA in 1980 and the 1 990s. This is the opposite of the findings based on the trade share analysis presented earlier. (See Tables 3 and 4.) If the ASEAN group is widened to include China, Japan, and South Korea (ASEAN-Plus-Three), the intensity drops considerably; i.e., to about 2 or slightly above. The reason for this could be the low level of trade between ASEAN and China relative to China's large share of exports to the U.S. and EU. Whereas if the "All East Asia" group is considered, the intensity goes up somewhat, probably because of the inclusion of Hong Kong which trades heavily with China. Both of these groups (ASEAN-Plus-Three and "All East Asia") have indices greater than that of the EU, but less than that of NAFTA.

Although these indices have not changed dramatically, there was an overall decline in intensity with some fluctuation over the 20-year period for the East Asian groups. However, for ASEAN most of the decline occurred between 1980 and 1990.

16
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 1*** 0.97***<br>H<br>Kendall tau-b 0.89*** 1*** 0.89***<br>Spearman 0.95*** 0.97*** 0.93***<br>A<br>Kendall tau-b 0.89*** 0.89*** 0.83***<br>Spearman 0.98*** 0.98*** 0.97***<br>M0<br>Kendall tau-b 0.94*** 0.94*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>C12. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All<br>indicators included. Alternative scenario: Victim of crime indicator removed (weights within the Social Cohesion<br>dimension were scaled from 6.25% to 8.33% so all weights add up to 1).<br>Peru<br>C12.1. FOSD: Counting Vector C12.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C12.3. SOSD: Social Sustainability Index C12.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.94*** 1*** 0.98***<br>H<br>Kendall tau-b 0.82*** 0.99*** 0.9***<br>Spearman 0.98*** 0.86*** 0.82***<br>A<br>Kendall tau-b 0.9*** 0.67*** 0.7***<br>Spearman 0.97*** 0.99*** 0.97***<br>M0<br>Kendall tau-b 0.89*** 0.96*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6<br>-.05<br>CCDF<br>.4 Difference<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C12. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Victim of crime indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C12.1. FOSD: Counting Vector C12.2. Dominance Analysis 61
stagnant backcasted trend over the decade and in an estimated poverty headcount rate of 44.6 percent in 2009; the 2015/16-2018/19 GIC accurately captures the fact that richer Nigerians’ consumption is more sensitive to Nigeria’s growth. Overall, therefore, it appears that relaxing the assumption of a flat passthrough rate across the distribution shifts the backcasted estimate for 2009, but not enough to reproduce anything like the 17.3-percentage point drop implied by using 2009/10 HNLSS poverty estimate directly.

_Figure 5 Backcasting poverty rates assuming different pass-through rates across the distribution of household consumption_ <!-- Start of picture text --> 60.0<br>56.4<br>55.0<br>50.0<br>47.9<br>46.2<br>45.0<br>44.6<br>40.0<br>39.1<br>35.0<br>30.0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>HNLSS 2009/10 GIC 2010/11; 2018/19<br>GIC 2015/16; 2018/19 GIC 2010/11-2015/16<br><!-- End of picture text --> Note: the figure shows backcasted series using different values of the pass-through rate at different deciles of the consumption distribution. Household consumption data from the 2018/19 NLSS is matched to sectoral GDP growth rates (MFM-Tool World Bank) based on household head’s sector of employment. The backcasted poverty rates are calculated at the US$1.90 poverty lines. Different values of the decile-level pass-through rate are calculated using imputed household consumption data from three waves of GHS data (2010/11,2015/16, and 2018/19).

Lastly, we test the robustness of the results to using different methods to map the growth rates in sectoral GDP to the 2018/19 NLSS. In the main results, the mapping of households to sectors is based only on information about the household head’s employment sector. To check whether the results are sensitive to this particular micro-macro mapping approach, we use the sector of employment of (1) the oldest working household member or (2) the household member closest to 40 in age as alternative variables to map the household data to the sectoral GDP series. This has virtually no impact on the results (see Figure 7 in the Appendix).

19