rafmacalaba/datause-encoder-data huggingface dataset:
Jorgenson, eds., _Handbook of Computable General Equilibrium Modeling._ North Holland, Eksevier V.B., pp. 227-301.

Devarajan, Shantayanan, and Delfin S. Go. 2012. "The Simplest General-Equilibrium Model of a Small, Open Economy." Chapter in a book in honor of Ernȍ Zalai, 2013 _._

- _______. 1998. "The Simplest Dynamic General Equilibrium Model of an Open Economy." _Journal of Policy Modeling_ 29(6): 677-714.

- _______. 2003. "The 123PRSP Model." In François Bourguignon and Luiz A. Pereira da Silva, _The Impact of Economic Policies on Poverty and Income Distribution._ Washington, DC: The World Bank and Oxford University Press.

- Devarajan, Shantayanan, Delfin S. Go, and Hongyi Li. 1999. "Quantifying the Fiscal Effect of Trade Reform: A General Equilibrium Model Estimated for 60 Countries." Policy Research Working Paper 2162. World Bank, Development Research Group, Washington, DC.

- Devarajan, Shantayanan, Delfin S. Go, Jeffrey D. Lewis, Sherman Robinson, and Pekka Sinko. 1997. "Simple General Equilibrium Modeling." In Joseph F. Francois and Kenneth A. Reinert, eds., Applied Methods for Trade Policy Analysis: A Handbook. Cambridge: Cambridge University Press.

- Devarajan, Shantayanan, Delfin S. Go, John Page, Sherman Robinson, and Karen Thierfelder. 2008. "The Macroeconomic Dynamics of Scaling Up Foreign Aid." In Delfin S. Go and John Page, eds. _Africa at a Turning Point? Growth, Aid, and External Shocks._ Washington, DC: The World Bank.

- Devarajan, Shantayanan, Delfin S. Go, Sethaput Suthiwart-Narueput, and John Voss. 1997b. "Direct and Indirect Fiscal Effects of the Euro-Mediterranean Free Trade Agreements." Paper presented at the Mediterranean Development Forum, Knowledge and Skills for Development in the Information Age, Marrakech, Morocco, May 12-17, 1997.

- Devarajan, Shantayanan, Jeffrey D. Lewis, and Sherman Robinson. 1990. "Policy Lessons from Tradefocused, Two-sector Models." _Journal of Policy Modeling_ 12(4): 625-57.

- _______. 1993. "External Shocks, Purchasing Power Parity, and the Equilibrium Real Exchange Rate." World Bank Economic Review 7(1): 45-63.

- Devarajan, Shantayanan, Yazid  Dissou, Delfin S. Go, and Sherman Robinson. 2017. "Budget Rules and Resource Booms and Busts – A Dynamic Stochastic General Equilibrium Analysis," _World Bank Economic Review_ (2017) 31(1):71-96. https://doi.org/10.1093/wber/lhv059.

- Devarajan, Shantayanan. 1997. "Real Exchange Rate Misalignment in the CFA Zone." _Journal of African Economies_ 6(10): 35-53.

- _______. 1999. "Estimates of Real Exchange Rate Misalignment with a Simple General-Equilibrium Model." In Lawrence E. Hinkle and Peter J. Montiel _, Exchange Rate Misalignment: Concepts and Measurement for Developing Countries_ . Washington, DC: World Bank and Oxford University Press.

- Faini, Ricardo, Lant Pritchett, and Fernando Clavijo. 1992. "Import Demand in Developing Countries." _<mark>International Trade Modelling</mark>_ <mark>, 279-97.</mark>

21
**Table 2: Costs of a Plain Vanilla Domestic Bond Issue (in US$)**

|||**Face Valu**|**e Issued (10**|**00s US$)**||
|---|---|---|---|---|---|
|**Cost Category**|10,000|20,000|50,000|100,000|200,000|
|Investment Banking Fees|200,000|400,000|500,000|1,000,000|2,000,000|
|Legal Fees|50,000|50,000|50,000|50,000|50,000|
|Regulatory fee|5,576|6,336|6,336|6,336|6,336|
|Taxes|160,800|321,600|804,000|1,608,000|3,216,000|
|Stock exchange registration|2,498|4,784|6,367|7,200|7,200|
|Bond Market Rep|11,520|11,520|11,520|11,520|11,520|
|Printing|8,576|8,576|8,576|8,576|8,576|
|Road Show|8,640|8,640|8,640|8,640|8,640|
|Rating Agency (2 in total)|27,960|27,960|27,960|35,000|35,000|
|**Total Costs**|475,570|839,416|1,423,399|2,735,272|5,335,272|
|**% of Issue Size**|**4.76%**|**4.20%**|**2.85%**|**2.74%**|**2.67%**|

While legal fees can range according to any complications that arise, most parties quoted an approximate $50,000 for completing a straightforward deal. In cases of structuring deals, legal fees can be another 1-1.5% of the face amount. In total, the costs of domestic issuance for a plain vanilla bond range from 2.67%-4.76% of the issued amount. Costs of nearly 5% for issuance are quite high, and are unlikely to be incurred unless a company is seeking non-material benefits from issuance, such as reputational effects. In practice, it is unusual for companies to issue domestically in amounts less than US$40 million. Most market participants conclude that cost is not the primary factor keeping companies out of the market, but rather the high credit rating requirement.

Costs of issuing equity are slightly more difficult to estimate given the fact that there have been only two IPOs in the last six years. There is less data to narrow the ranges of legal and investment banking fees, but the costs presented in Table 3 are indicative of what fees were charged during the last period of high activity, which are consistent with the fees levied in the most recent few deals (one in 2003). Interestingly, investment banks in Chile charge similar fees for helping companies issue debt or equity. In terms of direct outlays, the chief benefit of issuing equity instead of debt is the avoidance of the 1.6% tax. On aggregate, the fees for a US$200 issue fall around 1.6%, and for small issues of US$10 still only come to around 2.5% of face value. All else equal, cost considerations would suggest companies would favor equity over bond issuance.

**Table 3: Costs of an Ordinary IPO (in US$)**

|||**Face Value**|**Issued (10**|**00s US$)**||
|---|---|---|---|---|---|
|**Cost Category**|10,000|20,000|50,000|100,000|200,000|
|Investment Banking Fees|150,000|300,000|750,000|1,500,000|3,000,000|
|Regulatory fee|5,576|6,336|6,336|6,336|6,336|
|Stock Management Fee|15,000|15,000|15,000|15,000|15,000|
|Stock exchange registration|2,498|3,240|5,402|7,200|7,200|
|Legal Fees|50,000|50,000|50,000|50,000|50,000|
|Marketing / Disclosure|20,000|20,000|20,000|20,000|20,000|
|Rating Agency|27,960|27,960|27,960|40,000|65,000|
|**Total Costs**|256,034|407,536|859,698|1,623,536|3,148,536|
|**% of Issue Size**|**2.56%**|**2.04%**|**1.72%**|**1.62%**|**1.57%**|

10
In the case of Eknan and Metell's study the expenditure increase is marginally (but probably insignificantly) less than one - so that they conclude that aid causes a slight reduction in non-aid financed expenditure. In fact, we cannot be sure about this statement. If the budget constraint they give is correct then expenditure in the excluded sectors must fall in the face of the aid inflow, since the increase in expenditure in other sectors plus the fall in revenues is slightly greater than one. But the budget constraint is written excluding borrowing (printing money) - Pack and Pack state that in Indonesia budgets have been balanced, but the topic is not mentioned in the Kenyan study." The observed decrease may be accommodated by a fall in borrowing - but borrowing also may have risen to allow a further rise in expenditure in the excluded sectors: we simply cannot say. But we can say that inappropriate imposition of a balanced budget budget constraint assumes that aid has no effect on public savings, when this effect is one of the key issues under examination.

Therefore one modification to the approach should be to include borrowing in the budget constraint (which may take the value of zero if there is none). Second, as explained, the rationale for excluding certain sectors is unclear. It would be preferable to include all sectors.

Gupta's (1993b) study of India both includes the full budget constraint (although the treatment of non-project aid is unclear) and does not exclude any sectors. in his model, however, borrowing is determined as a residual, so that same comments as before apply. The results sbow an increase in development expenditure which is almost identical with the aid flow, but also an increase in non-developmental expenditures, almost entirely financed by increased borrowing. These results are difficult to understand: we find neither fungbility between developmental and non-developmental expenditures, nor that aid crowds in government developmental expenditures. Why should the govermnent borrowing to finance defence be considered a response to the aid inflow when the aid cannot in any sense be being said to be being used for the defence expenditures?

The approach described here is only a partial one for reasons already discussed. The expenditure equationrs include GDP as a regressor, hence holding income constant rather than allowing for multiplier effects. To the extent that aid does increase GDP the fill effect of aid on expenditure is being under-stated. To capture the full effect of the aid inflow requires estimation of an economy-wide model.

_57_
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12 unions, organized special interest groups) or with ethnic, geographic or other identity-based ties to elites in power.

Finally, corruption is intricately linked to these systems. It is facilitated by the combination of creation of rents that are controlled by state (or sometimes nonstate) actors. It is also linked to both forms of rent-sharing, manifest in high-level corruption in major business deals, and the widespread petty corruption faced by citizens in their daily lives. It is also linked to patterns of inequality, since it typically benefits political and business elites in the sharing of rents, and, at a lower level, officials in public sector jobs.

These deeply embedded political economy and institutional drivers of accountability are being subject to incremental changes through at least two powerful forces developing in Africa—increasing political competitiveness, and greater organization and communication among citizens. We discuss each of these trends in turn, to examine how they might be leveraged for greater civil society action for accountability.

# _2.3 Accountability and democratization in Africa_

Data from the Polity IV effort, which uses expert opinion of political scientists to categorize the extent of autocracy and democracy of a regime, can be used to illustrate the nature of the democratic change moving across Africa over the past 20 years. There is a continuum of regimes, from completely autocratic to a fully consolidated democracy, with a large range of intermediate types (that they refer to as “anocracies”) that have elements of both autocratic and democratic processes. Their synthetic classification uses a numerical system, in which -10 to -7 are classified as autocratic, -6 to +6 as intermediate, and +7 to +10 as democratic.

Figure 2 illustrates both the size of the change between 1985 and 2009, and the current classification for most of the countries. In the mid-1980s, 31 out of a total of 40 in the data base were fully autocratic, with only two full-fledged democracies (Gambia and Mauritius), and 6 intermediate regimes, most of which had a heavy weight of autocracy. By end-2009, the Polity IV classification has 12 fully democratic regimes (Benin, Botswana, Comoros, Ghana, Kenya, Lesotho, Mali, Mauritius, Senegal, Sierra Leone, South Africa and Zambia) and only 3 fully autocratic (Eritrea, Somalia and Swaziland<sup>8</sup> ) with a majority being classified as 8 Eritrea and Somalia are not included in the figure, as data was not available for the earlier year.
60 "Eurostat." _Database ‐ Eurostat_ . N.p., n.d. Web. 03 July 2017 <u>http://ec.europa.eu/eurostat/data/database</u> Faist, T. (1999) The Crucial Meso level. In Hammer, T., Brochmann, G., Tamas, K. and Faist, T. (eds.) _International Migration, Immobility and Development_ , Berg, Oxford/New York, 187‐ 217.

Gerver, M. (2017). Paying Refugees to Leave. _Political Studies_ , 0032321716677607.

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Gibson, J., & McKenzie, D. (2011). The microeconomic determinants of emigration and return migration of the best and brightest: Evidence from the Paciac. _Journal of Development Economics, 95_ (1), 18–29. doi:http://dx.doi.org/10.1016/j.jdeveco.2009.11.002 Gmelch, G. (1980). Return migration. _Annual review of anthropology_ , _9_ (1), 135‐159.

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<mark>Harild, N., Christensen, A., & Zetter, R. (2015). Sustainable Refugee Return.</mark> Hatton, T. J., & Williamson, J. G. (1998). _The age of mass migration: Causes and economic impact_ . Oxford University Press on Demand.

Hunt, J. (1992). The impact of the 1962 repatriates from Algeria on the French labor market. _ILR Review_ , _45_ (3), 556‐572.

Houte, M. V., & Davids, T. (2014). Moving back or moving forward? Return migration, development and peace‐building.

International Monetary Fund “Refugee Surge in Europe: Economic Challenges”, January 2016 International Organization for Migration, Reintegration‐ Effective Approaches (2015) International Institute for Strategic Studies (IISS) Armed Conflict Survey 2017 Kindleberger, C. P. (1967). _Europe's postwar growth: The role of labor supply_ . Harvard University Press.
PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA share of own generation. The share of manufacturing in GNI was positively associated with the degree of own generation.<sup>17</sup> The World Bank’s enterprise surveys offer clues to why firms elect to generate their own power. Because deficiencies in the provision of public power impede business operations, firms install their own generating equipment to protect themselves against power outages.<sup>18</sup> Power outages have a significant effect on business operations. Firms that experienced few power outages (fewer than 30 days per annum) tended to declare that electricity was not an obstacle or was only a minor obstacle for the operation and growth of their businesses. On the other hand, firms that suffered frequent outages (more than 60 days per annum) tended to believe that electricity was a major or very severe obstacle for the operation and growth of their businesses.

Frequent power outages result in significant losses for enterprises in terms of forgone sales and damaged equipment. Firms experiencing frequent power outages (more than 60 days per year) lose 10–12 percent of their sales (figure 17). This is about twice as high as the figure for firms that have few outages (less than 15 days per year). Equipment damage traceable to power outages is about twice as high for firms that suffer frequent outages as for firms that suffer few outages (1 percent of sales compared to 0.5 percent of sales) (figure 18).

**Figure 17  Sales lost owing to power outages**

Percent

**Figure 18 Equipment losses resulting from power outages**

<!-- Start of picture text --> Percent<br>14.00 1.20<br>12.00 1.00<br>10.00<br>0.80<br>8.00<br>0.60<br>6.00<br>0.40<br>4.00<br>2.00 0.20<br>0.00 0.00<br>< 5 days 5 - 15 15 - 60 60 - 120 >120 days < 5 days 5 - 15 days 15 - 60 60 - 120 >120 days<br>days days days days days<br>Power Outages (days p.a.) Power Outages (days p.a.)<br>Sales)<br>Power Outages (% of Sales)<br>Loss from Power Outages (% of<br>Lost or Damaged Equipment from<br><!-- End of picture text --> Source: World Bank, Enterprise Survey Database.

The size and the scope of own generation is higher for firms that experience frequent power outages. Firms that experience frequent power outages are about twice as likely to have their own generator as firms that have few power outages (figure 19). The share of electricity produced in-house increases sharply among firms that have frequent power outages (8–14 percent), compared with other firms (4–6 percent).

> 17 The estimated coefficients reflect correlations, not causalities. Further analysis of the estimated relationship is impeded by the simultaneity of the choice of own-generation and the explanatory variables.

> 18 Statistics from the Enterprise Surveys on power outages and the adoption of own-generation appear in appendix 1, tables 2a to 2f.

13
**Figure 9. Infrastructure stocks in India and China, 1998 and 2003**

<!-- Start of picture text --> 300<br>India China<br>200<br>100<br>0<br>1998 2003 1998 2003 1998 2003<br>electricity generation, km of paved roads per Number of fixed lines<br>watts per person 100,000 hab per 1000 people<br>Source: World Development Indicators .<br><!-- End of picture text --> Figure 9 compares India and China. In 1980 India had higher infrastructure stocks— power, roads, and telecommunications—but China has rapidly overtaken India. From 1990 to 2000, China’s installed power capacity increased by 136 percent, compared with 51 percent in India. China has been investing around 8–10 times India’s level in highways since the mid1990s. Just for India to catch up to China’s present per capita overall infrastructure stocks would take an investment of 12.5 percent of GDP per year through 2015.<sup>40</sup> The 1996 India Infrastructure Report called for an increase in investment to around 8 percent of GDP by 2005/6. Recent estimates produced by the Bank suggest that India might need to invest 8 percent or more of GDP over the period 2006–10 to sustain annual GDP growth at near 8 percent and replace old capital stocks. Over the past decade, however, estimates suggest India has invested only 3–4 percent of GDP in infrastructure. There are also significant regional and income-related differences in access to much-needed infrastructure, which the government is seeking to address and which also require investment.

The greatest pressures on Indian infrastructure are in electricity, roads and urban infrastructure. The average manufacturer in India loses 8.4 percent in sales annually on account of power outages, as opposed to less than 2 percent in China and Brazil. The frequency and average duration of outages are such that generators are standard industrial equipment in India, accounting for as much as 30 percent of a business’s power consumption in many cases. Almost 61 percent of Indian manufacturing firms own generator sets; the comparable figures are 20 percent in Malaysia, 27 percent in China, and 17 percent in Brazil.<sup>41</sup> Infrastructure constraints in India are the most binding on manufacturing, particularly export-oriented manufacturing. The share of exports and imports in India’s GDP today is less than half that of China’s, and at 2.5 percent, India’s world market share in the trade of goods and services is a small fraction of China’s (10.5 percent). Nonetheless, India exports an impressive

> 40 World Bank (2004b).

> 41 World Bank (2004c).

23
than the global average in each case. With universal coverage of primary and secondary education, women achieve relatively high levels of education in all three counties (figure 1), making such notable gender pay gaps particularly striking.

**Figure 1: Summary measures of educational attainment in Central Asia**

<!-- Start of picture text --> Expected years of schooling Gross tertiary enrollment rate (%)<br>18 80<br>15.8<br>16 15.1 70 68<br>14 12.6 12.2 11.5 13.2 12.7 60 55<br>11.9<br>12 10.7 10.9 47<br>50<br>10<br>37<br>40 35<br>8 27 27 29<br>30<br>6<br>20 16<br>4 13<br>2 10<br>0 0<br>TJK UZB KAZ TKM KGZ TJK UZB KAZ TKM KGZ<br>Women Men Women Men<br><!-- End of picture text --> _Source: World Development Indicators and National Statistical Offices._ Taking Uzbekistan as an example that illustrates challenges across the region, the gender gap in labor force participation is strongly linked to both educational attainment and the number of children living in the household (figure 2). Only 6 percent of women with general secondary education or below, and 12 percent of women with upper secondary education, are active in the labor market compared to 18 percent and 45 percent of men, respectively. However, 52 percent of women with bachelor’s degrees or higher are active in the labor market, which is more comparable to the 60 percent labor participation rate for men with bachelor’s degrees. Furthermore, women (ages 16+) in the bottom two wealth quintiles were more than 50 percent likely to be inactive or unemployed, whereas 39 percent of men in the lowest wealth quintile and 32 percent of men in the second lowest quintile were either inactive or unemployed. Having young children also affect female and male labor force participation differently. Having young children has distinct effects on female and male labor force participation. In the case of men, having more young children is associated with a higher likelihood of working. For women, it is the reverse, women are much less likely to work outside the home if more children are present.

4
-13-

We therefore modeled the shift as a break in the level of income rather than in the coefficient of the time trend. The results of this regression are summarized in equ. (3): (3) log(PERYP) - 4.47 + .058 TIME - 0.10 DUMMY 2 - 0.97, D.W.-0.47 Temporary income TMPYP is defined as the excess of actual income over trend: TMPYP - YP/PERYP YP is actt'al disposable income, and PERYP the permanent component.

With these data definitions, the private consumption regression yields the following estimates: (4) log(CONKP) - -1.54 - 0.82 LOG(l + RDEP) - 0.77 CPIinf (1.92) (2.12) (2.37)

**+** 1.35 log(PERYP) - 0.19 log(TMPYP) (7.91) (0.32)

R2 - 0.96, DW - 1.72, Sample Period 1970-1986, TSLS The impact of the real after-tax deposit rate RDEP on private consumption is negative, and significantly so. In addition, private consumption depends negatively on inflation, with an almost equal coefficient. This has also been found in consumption analysis for some developed countries: in particular see Bean (1986) for similar evidence on the UK. Finally, the effect of permanent income on consumption is strongly positive, as expected, with a coefficient close to one. The coefficient on temporary income 'q low and insignificant (a t-statistic of only 0.32; significance requires a value of 2 or more). All these results fit in well with accepted theory of consumer behavior.

ChhibberDisk/chhibber-Turkey Paper/ac :07-28-88: 1c