rafmacalaba/datause-encoder-data huggingface dataset:
**Table 16:** Profitability rules <u>by country, April 2007</u>

|**Country**|**Description of profitability regulations**|
|---|---|
|Argentina|Each administrator must guarantee that the average real rate of return in the last 12 months is<br>equal to at least 70 percent the system’s average or this same profitability minus two<br>percentage points (whichever is the lowest).|
|Chile|Each administrator must guarantee that the average real rate of return in the last 36 months is<br>not lower than the lesser of (a) the average real return of each fund minus 2 percentage<br>points for funds C, D, and E and 4 percentage points for funds A and B, or (b) 50 percent of<br>the average real return of all the funds.|
|Colombia|Each administrator must guarantee that the average real rate of return in the last 36 months is<br>equal to at least 70 percent the system’s average.|
|El Salvador|Each pension fund administrator must guarantee that the average real rate of return in the<br>last 12 months is equal to (a) at least the system’s average minus 3 percentage points, or (b)<br>80 percent of the average real return of all the funds (whichever is the lowest).|
|Uruguay|Each administrator must guarantee an annual average real rate of return, calculated every<br>month,that is the lowest of 2percent or the system’s average minus 2percentagepoints.|

Sources: SAFJP, SAFP, _Superintendencia Financiera de Colombia_ , El Salvador’s Pension Supervisor and _Banco Central del Uruguay_ .

Overall, rates of return have been similar across different pension fund managers since the inception of the reformed pension systems. This similarity of returns across managers reflects the similar composition of their portfolios – the herding behavior that has been well documented in the literature.<sup>54</sup> In Colombia, modest increases in the dispersion of real rates of return across pension fund administrators are due to increases in portfolio diversification, especially with respect to foreign-issued instruments, but they remain relatively low indicating herding behavior in the industry (Figure 16). For Argentina and Peru, the averages and standard deviations of real rates of return have been more volatile than in the case of Colombia and the low coefficient of variation across administrators points to possible herding behavior (Figure 17 and Figure 18).

# **IV.3.2. Under-Developed Capital Markets**

Portfolio allocation of pension funds in emerging markets is also related to relatively small, illiquid, and concentrated domestic capital markets. The growth of assets managed by pension funds, due to both reasonably high rates of return and mandatory contributions, has been higher than the development of domestic capital markets, especially in LAC (Figure 19). This resulted in a situation of “pension funds in search of additional assets,” which has constrained asset allocation and portfolio diversification.

> 54 See, for example, Queisser (1999), and Srinivas et al. (2000).

31
Hirschman’s project summarizes the discouragement within the Bank at the end of their collaboration: I was one of the original supporters of World Bank cooperation with Professor Hirschman because of my conviction that the Bank still has a good deal to learn about project preparation and evaluation and because I thought that a fresh look by a perceptive and objective observer could add significantly to our knowledge of this important subject. However, I don’t believe the manuscript does this. It is well written and contains a number of interesting observations. But by and large it does not contain any operationally useful analysis of the merits and priority of the particular projects observed by Professor Hirschman or of the kind of reshaping or rethinking of the projects which might have made them better. In short, I for one gained no significant new insights into the process of project preparation and evaluation.<sup>73</sup>

# **6. The Roots of the Disagreement**

Hirschman’s attempt to establish a qualitative approach to project appraisal had as its cornerstone the detailed historical reconstruction of the “personal profiles” of projects as well as their larger political and social context. It aimed at underscoring “the element of the unknown, the uncertain and the unexpected,” as Hirschman put it, in order to understand what deflected the projects from their originally charted course. Finally, its goal was to assess the broader impact of a project, such as its effects on the distribution of wealth, income and power in the affected population. Hirschman’s approach to project appraisal was a natural evolution of his previous work, which had marked an increasing distance from the early debates in development theories. The “failure of several of the earlier ideas as _practical_ policy solutions,” as Tony Killick put it (Killick 1978, p. 27, italics in the original), prompted Hirschman’s detailed inquiries into the mechanisms of economic policy-making, as well as the early evaluation studies that the Bank undertook in the 1960s and the convergence of the Bank and Hirschman on the need to develop a more systematic method for appraisal.<sup>74</sup> But the shared perspective on what was needed did not become a shared perspective on how to meet this need. Hirschman tried to transform the _Weltanschauung_ of the Bank and the Bank’s approach to project design, management and appraisal. The Bank, instead, expected Hirschman to analyze the Bank’s projects without any revolution in perspective, and to work

> 73 Richard H. Demuth to Mr. Robert E. Asher, September 13, 1966, World Bank Hirschman Folders Vol. 2. 74 As Killick noted, among the most renowned “casualties” of this counter-revolution was also the strategy of import-substituting industrialization (ISI), of which Hirschman had been a strong supporter. Hirschman, however, questioned the “exhaustion” of the usefulness of ISI in Hirschman 1968b.

27
goods tends to underestimate poverty rates in rural areas, while estimating a more accurate poverty rate for urban areas. Dropping ownership of durable goods improves rural poverty rates, but underestimates urban poverty rates.

Finally, we compared the poverty estimates of all three approaches: Subsample Imputation, Subsample Direct Estimation, and Full Sample Imputation. Although poverty rates from the Full Sample Imputation were estimated from a slightly different set of households in the 2005 HIES, we directly compared the poverty rates of all three approaches; table 8 contains the results. For this comparison, the subsample size was 640 households, and the Full Sample Imputation approach excluded ownership of durable goods from both urban and rural consumption models. All three approaches estimated rural poverty rates very well, while the subsample approaches appeared to show better estimations of urban poverty rates.

The Full Sample Imputation approach was worst, underestimating the urban poverty rate by more than 4 percentage points. As a result, the urban poverty rate estimated by the Full Sample Imputation approach was outside the 95 percent CI of that of the Full Sample Direct Estimation. Finally, the Subsample Direct Estimation and Full Sample Imputation both suffered large SEs compared with those from the Subsample Imputation. In all levels, the SEs from Subsample Imputation were smaller than those from the Subsample Direct Estimation and Full Sample Imputation. In particular, the urban poverty estimate in Subsample Direct Estimation and the national and rural poverty estimates in Full Sample Imputation were large. All in all, the best performer was the Subsample Imputation approach using a subsample size of 640 households.

**Table 8: Comparison of Three Approaches with the Full Sample Direct Estimation**

|Approach|Area|Poverty<br>rate|SEs|
|---|---|---|---|
|Sbl|National|39.4|(2.0)|
|usampe<br>Imputation<sup>a</sup>|Rural|43.1|(2.5)|
||Urban|28.2|(3.1)|
|Subsample|National|40.2|(2.7)|
|Direct|Rural|43.8|(3.2)|
|Estimation<sup>a</sup>|Urban|29.0|(4.5)|
|Fll Sml|National|38.9|(3.7)<sup>d</sup>|
|u ape<br>Imputation<sup>b</sup>|Rural|43.1|(4.6)|
||Urban|24.3|(3.6)|
|Full Sample|National|40.0|(1.1)|
|Direct|Rural|43.8|(1.3)|
|Estimation<sup>c</sup>|Urban|28.4|(1.9)|

_Source_ : Authors’ estimations using data from the 2005 HIES. a. The size of subsample is 640 households.

b. Consumption models without durables are shown here. c. Full sample includes all households included in the 2005 HIES data.

d. This SE is estimated assuming there is no correlation between urban and rural poverty rates.

# **IV. Price Data Analysis**

Although the Subsample Imputation approach performs well, there is one more challenge to overcome to implement it in Bangladesh, where poverty lines are estimated and updated for 16 strata separately. When updating the poverty lines, the government calculates an average of food price inflation and nonfood price inflation weighted by averages of the budget shares between two survey years. An important point here is 15
# **1. Introduction**

A number of important policy and research issues concern the relationships of banks with nonfinancial firms. Theory suggests that such relationships may play key roles in resolving information problems and mitigating financial market imperfections more generally, and empirical analysis has shown important benefits from strong banking relationships.

Much of the research focus is on the nature of relationships formed by different types of banks and on the rationale for multiple banking relationships. To our knowledge, however, existing studies of relationship forms and multiple relationships have not directly addressed the potentially important role of bank ownership types – foreign, state-owned, and private domestic banks. In addition, very few of the empirical relationship studies are applied to data on developing nations, where relationships may be particularly important because of financial system inadequacies.

This paper directly addresses these under-researched issues by formulating hypotheses about the effects of bank ownership type on relationships and testing these hypotheses using data from India, one of the largest and most important developing nations. In particular, we test for patterns in firms’ main banking relationships across ownership types, and investigate the impact of bank ownership type on the number of banking relationships and the likelihood that firms diversify across ownership types.

The hypotheses are based on the extant theory and knowledge about the effects of bank ownership type on bank-firm relationships. Theory suggests a number of reasons why ownership type might affect relationships. Information-based theories of banking relationships (e.g., Stein 2002) suggest that foreign banks – by virtue of their inability to process “soft” information about opaque local firms and market conditions – may be more likely to use their advantages in “hard” information and serve as the main relationship bank for transparent firms. In contrast, state-owned banks often have direct mandates to serve certain types of businesses, such as small firms with limited access to private-sector credit, stateowned firms, firms in rural areas, or those in “priority sectors.” Other research emphasizes that multiple banking relationships might be a consequence of firms’ need to protect themselves from premature withdrawal of services from financially fragile relationship banks (e.g., Detragiache, Garella, and Guiso 2000). We argue that the concept of fragility may be broadened to describe the banking relationship itself. In particular, there are reasons to expect that the
# **TABLE A13:  Global E-Governance (EGV)**

## **A13: Global E-Governance**

http://www.insidepolitics.org/egovt04int.pdf The Global E-Governance Index is compiled by Prof. West of Brown University's Center for Public Policy. Official websites are evaluated for the presence of various features dealing with information availability, service delivery, and public access. Features assessed included online publications, online database, audio clips, video clips, nonnative languages or foreign language translation, commercial advertising, premium fees, user payments, disability access, privacy policy, security features, presence of online services, number of different services, digital signatures, credit card payments, email address, comment form, automatic email updates, website personalization, personal digital assistant (PDA) access, and an English version of the website.

Range for the E-Government index- 0 (bad)-100 (good) based on availability of publications and databases (72 points) and number of online services (28 points).

In the table below we list the variables included in each of the governance indicators. In this paper, we use data from 2002 to 2005.

|**Table A13: Global E-Governance(192**|2005<br>**developed and deve**|2004<br>**lopin**|2003<br>**g cou**|2002<br>**ntries**|2000<br> **)**|1998|1996|
|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||
|NA|..|..|..|..|..|..|..|
|**Political Stability and Lack of Violence**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|Global E-governance Index|X|X|X|X|..|..|..|
|**Regulatory Quality**<br>||||||||
|NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|NA|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||
|NA|..|..|..|..|..|..|..|

61
# **<u>Table 3. Structure of Exports and Imports</u>**

|Agriculture & hunting|EXP<br>132|EXP%<br>6|EXPIN%<br>21|IMP<br>18|IMP%<br>1|IMPIN%<br>4|
|---|---|---|---|---|---|---|
|<br>Forestry|1|0|9|1|0|13|
|Fishery|1|0|3|0|0|2|
|Coal & lignite|92|4|48|1|0|4|
|Crude oil & gas|1052|47|85|68|4|28|
|<br>Metal extraction|45|2|21|5|0|4|
|Other mineral resources|13|1|22|3|0|10|
|Food products|38|2|12|69|4|23|
|Tobacco|3|0|6|1|0|5|
|Textiles|4|0|9|14|1|29|
|Clothing & furs|1|0|11|7|0|43|
|Leather products|0|0|8|4|0|63|
|Wood products|1|0|5|20|1|80|
|Paper & pulp|1|0|7|21|1|68|
|<br>Publishing|35|2|2|12|1|31|
|Oil products|20|1|6|74|4|26|
|Chemicals|51|2|85|119|6|94|
|Rubber & plastic|2|0|5|43|2|78|
|<br>Other non-metallic products|74|3|1|27|1|36|
|<br>Basic metals|354|16|53|89|5|24|
|Metal products|9|0|10|40|2|54|
|Machinery|13|1|20|183|10|84|
|Office equipment|0|0|6|15|1|98|
|Electrical equipment|18|1|73|249|14|98|
|<br>Communication equipment|2|0|43|34|2|92|
|Medical equipment|2|0|23|27|1|80|
|Vehicles & trailers|0|0|0|81|4|98|
|<br>Other transport equipment|0|0|1|41|2|57|
|<br>Furniture & other industry|1|0|7|20|1|67|
|Processing of raw materials|||||||
|<br>Heat & power & gas|6|0|2|6|0|3|
|Water|||||||
|Construction|3|0|0|116|6|17|
|Repair of vehicles|||||||
|<br>Wholesale trade|0|0|0|0|0|0|
|Retail trade|||||||
|Hotels|||||||
|Restaurants|||||||
|Food delievery|||||||
|<br>Land and pipeline transport|121|5|15|82|5|11|
|Water transport||||2|0|46|
|Air transport||||11|1|26|
|<br>Communication|9|0|8|7|0|7|
|Financial intermediation|2|0|2|7|0|6|
|Insurance|0|0|3|12|1|54|
|Real estate services|||||||
|IT services|0|0|0|2|0|9|
|Research and development|||||||
|<br>Public administration|28|1|14|6|0|4|
|Education|||||||
|Health & social services|||||||
|Waste services|||||||
|Public associations|||||||
|<br>Recreation|0|0|0|2|0|3|
|Other services|107|5|14|297|16|32|
|EXP: Value of exports|||||||
|EXP%: Sector exports as a percentag|of aggregate ex|orts|||||
|<br>EXPIN%: Sector exports as a percent|<br>age of domestic o|<br>utput|||||
|<br>IMP: Value of imports|||||||
|IMP%: Sector imports as a percentag|e of aggregate im|ports|||||
|<br>IMP%: Sector imports as apercentag|<br>e of aggregate co|<br>nsumption|||||

22
- [69] Wooldridge, Jeffrey. 2002. _Econometric analysis of cross section and panel data_ . Cambridge, MA: MIT Press.

- [70] World Bank. 2009. _World Development Report 2009: Reshaping economic geography_ . The World Bank: Washington D.C.

- [71] World Bank. 2011. _Africa’s transport infrastructure. Mainstreaming maintenance and management_ . The World Bank: Washington D.C.

- [72] Yang, Dean and HwaJung Choi. 2007. “Are remittances insurance? Evidence from rainfall shocks in the Philippines.” _World Bank Economic Review_ 21(2): 219-248.

- [73] Yepes, Tito, Justin Pierce and Vivien Foster. 2009. “Making sense of Africa’s infrastructure endowment. A benchmark approach.” World Bank Policy Research Working Paper 4912.

43
# **Figure 2 Structure of Latvia’s Manufacturing Exports in 2001, as % of Total**

<!-- Start of picture text --> Chemicals and Wood and wood<br>plastics products<br>8% 34%<br>Transport<br>equipment,<br>metal industry<br>and engineering<br>21%<br>Textile and<br>textile products<br>Other<br>15%<br>manufacturing Food and food<br>13% products<br>9%<br><!-- End of picture text --> _Source_ : Ministry of Economy of Latvia, 2002.

- Between 1993 and 1999, productivity in these three sectors increased more slowly than overall manufacturing productivity.  In other words, Latvian exports are concentrated in sectors where productivity is growing at a relatively slow pace. This is not a recipe for long term prosperity or rising standards of living.

**Figure 3 Productivity gains in selected manufacturing sectors compared to average manufacturing productivity growth in Latvia, 1993-1999**

<!-- Start of picture text --> Rubber and plastic products<br>Fabricated metal products<br>Non-ferrous metals<br>Paper, printing and publishing<br>Wood and wood products<br>Food and food products<br>Textile and leather<br>Iron and steel<br>Machinery<br>Transport equipment<br>Chemicals<br>-15 -10 -5 0 5 10<br><!-- End of picture text --> _Source:_ UNIDO, 2003 13
are more likely to experience spells of unemployment as they search for the best job following graduation. Since the determinants of university enrollment and graduates’ job search patterns are not well understood and likely depend on unobserved factors, we focus on results for groups over 25.

Examining recent changes in the returns of young male vocational graduates, particularly those between 25 and 35 who have generally completed their education, paint a more pessimistic picture. For example, while Table 5 shows a higher formality rate among all male vocational graduates, Figure 8 shows that the middle cohort drives this positive formality rate in their youth, and that the premium has disappeared for the youngest cohort. This is consistent with the dramatic fall in the effect of vocational education on men’s wages shown in Figure 10. After enjoying a smaller wage premium at the age of 21, individuals in the youngest cohort face an increasingly large wage penalty. Estimates of the wage penalty for the youngest cohort of men reached 30 percent in 2000 and an 43 percent in 2007, both of which are statistically significant.<sup>20</sup> These results indicate a steep decline in returns for recent male public vocational graduates.

One possible explanation for this decline relates to recent changes in the structure of the Indonesian economy. Since the financial crisis of 1998, the economy has increasingly relied on the service sector to generate growth. Annual growth in the industrial sector has fallen dramatically, from 9 percent from 1990 to 1997, to 4.3 from 1999 and 2007. During the same two periods, annual service sector growth remained strong, falling slightly from 7.0 to 6.3. More recently, employment in the service sector has grown rapidly. From 2003 to 2007, service sector employment grew at roughly 4 percent per year while industrial sector employment grew at 2.5 percent per year (World Bank, forthcoming). The increasing prominence of the service sector could disproportionately affect vocationally trained males because they tend to choose technical majors, as shown in Figure 3. Women, on the other hand, tend to choose to study business management or tourism skills, for which demand may have remained stronger.

Another potential explanation for the recent decline in male vocational returns is deterioration in the quality of vocational training for men. For example, technical

> 20 Significance is at the 95 percent level. The penalty in 2000 is robust to the use of median regression, although the estimated penalty in 2007 is not robust. The OLS estimates are preferred, however, as they are trimmed to reduce the influence of outliers and because they include controls at the district level for location of lower secondary school.

26
diminishing economies of scale. Thus, while current density of MFIs has a positive effect on household net worth, there is a negative wealth effect from past density of MFIs, showing diminishing returns to MFI density. But household accrues benefits in terms of higher income from past incidence of higher village level MFI density.

# <u>8. Conclusions and policy implications</u>

Household-level panel data enables the use of a household-level fixed-effects method to treat program endogeneity without complicated estimation techniques and thus offers an alternative to investigate whether the results obtained from the analysis of cross-sectional data hold. Panel data, especially long-panel data, also offers a clear substantive advantage over cross-sectional data in estimating microcredit impacts: it helps analyze dynamic issues such as whether credit effects change over time (or decline for that matter), whether market saturation with village diseconomies is a possibility for microfinance expansion in a country such as Bangladesh which has a heavy microfinance program concentration, and whether the phenomenon of multiple program membership is a consequence of the credit expansion and an indication of income diversification due to village diseconomies. Panel data, however, is not a panacea in estimation as it has its own problems in estimation.

Using panel household survey data carried out at three points (1991/92, 1998/99 and 2010/11) over 20 years in 87 villages of rural Bangladesh, this paper investigates various dimensions of microcredit effects on a set of behaviors to validate earlier results obtained with cross-sectional data or short panel data. In particular, this paper provides separate estimates of female and male credit from all sources of microcredit on household per capita income, expenditures, poverty, nonland assets, household net worth, male and female labor supply, and schooling of children. It 27