rafmacalaba/datause-encoder-data huggingface dataset:
9

# Financial Restructurina **of' Eterirjp**

To understand the enterprise restructuring problem. it is useful to categorize enterprises according to profitability. The first class utf enterprise is profitable: the second would be profitable if granted some formi of debt relief. The third class of enterprise would not be profitable even with debt relief. This last class can be further subdivided into firms that are value-subtracting with revenues not adequate to cover the cost of purchased inputs and another class of somewhlat better firms that can cover the costs of non-labor inputs plus somile, though not all, wages. The third class of firms require more drastic restructuring, down-sizing, or liquidation.

In the countries where bank and enterprises restructuring is linked, the banks in their role as creditors, are expected to divide firms into the classes mentioned and to take action accordingly. Profitable firms required no relief. For firms considered viable but over-indebted, banks could either write down the outstanding debt or swap it for equity. For the firms considered non-viable even with debt relief, the banks are to use their power as creditors to force liquidation. Several countries have set up a special conciliation, that is a simplified bankruptcy, process giving banks special work-out powers usually free from the intervention of other creditors.

In CEE/FSU many of the largest loss makers fall in the category of value subtracters. Liquidating these firns, rather than continuing to subsidize their operations, would clearly benefit the economy and free up assets for use by the emerging private sector. Yet these large loss makers are sometimes either the biggest firms or major regional employers where liquidation poses complex social and political problems. 13 In some cases the firms which bring losses but are able to cover costs of non-labor inputs (enterprises with positive value added) are considered to be candidates for temporary wage subsidies; such a selective support targeted towards firms which have some prospects of economic viability and which operate in areas of acute distress could be treated as less expensive than cash transfers to those laid off. In most countries special government agencies have been set up to handle the restructuring decisions about those enterprises that are considered too large or too "strategic" to be managed by the banks.

Triage, coupled with financial workouts, is only the first step. Following that, banks are expected to finance the investments needed to restructure the viable enterprises and to play a role in corporate governance. that is, to provide oversight of decisions taken by enterprise managers.

13/ In many countries, including developed ones, the value-subtracting group contains also utilities. Because of very specific problems of this group the discussion of possible solutions is beyond the scope of this paper.
-23-

basket, labelled the 'Canasta Normativa de Satisfactores Esenciales', CNSE. First, it used the expenditure patterns of households in the seventh income decile of the 1977 IES. Second, it added a few goods deemed essential considering "..the rights that the national laws grant to the population, " their expectations and the objective necessities that society imposes ..

(Coplamar, 1983, p. 133; my translation and emphasis, S.L.). Third, the costs of food and housing were obtained from separate studies also elaborated by Coplamar (1985a, 1985b). In particular, the food component consisted of two parts: (i) the cost of a basket of food that satisfied exogenously given nutritional requirements, and (ii) the cost of additional food items also consumed by households in the seventh decile. The nutritional food basket was labelled the 'Canasta Normativa Alimentaria', CNA, and is composed of 34 food items that satisfy a minimum of 2082 calories and 35.1 grams of protein per day for an adult. In fact, Coplamar constructed fifteen different baskets that satisfied the minimum requirements of calories and proteins, but varied in the number of food items included and the origins of the nutrients (animal vs. vegetal)<sup>30</sup> . The chosen CNA was not the least cost diet, exceeding the minimum by 36X (Coplamar, 1985a, pp. 102-12).

The Coplamar study did not distinguish between moderate and extremepoverty, although other studies (e.g. Hernandez Laos. 1989a) have taken a subset of the CNSE to construct a Canasta Sub--inima, CSN, to set a line of extreme-poverty (thus interpreting the CNSE as the line of moderate-poverty). on the other hand, the Coplamar study distinguished between an urban and a rural basket, but found insignificant cost differences so that only one basket was used for the whole population (op. cit., p. 146).

In this paper I follow a mixed procedure to construct E and z. Consider first the line of extreme-poverty. In principle this line is given by the cost of .the nutritional basket, and the extremely poor are those whose food expenditures are below this cost: these are the individuals who lack 'access to adequate sources of nutrition'. (Differently put, given their preferences and the information at their disposal, the extremely-poor maximize welfare allocating their income across different goods; if the endogenously determined food demanded is less then the nutritional minimum, the individual is

> 30The linear program reached different optimal points because the number of variables (food items) and the number of contraints (mix of foods) was changed in different solutions.
and more likely to work occasionally (Panel B). This is a finding in line with previous results, signaling that mining areas have a higher share of agricultural workers prior to production. The probability of earning cash increases by 7.4 percentage points (0.014 - (-0.060)) with mine opening and this effect is statistically significant. We also see a statistically significant reduced probability of being paid in-kind only or being paid both cash and in-kind. The effects indicate that the labor market opportunities for women change with mining. Mine opening induces a shift from more traditional sources of livelihoods, such as subsistence farming which is seasonal by nature and oftentimes paid in kind, to more cash-based, all year or occasional sectors such as services.

In more recent years, DHS has surveyed men based on the same questionnaire used for women’s labor market outcomes. The male sample is, however, much smaller. This sample of 128,135 men (Table 6) indicate that men have higher cash earning opportunities (Panel C) and are less likely to work seasonally after mine openings (Panel D), in line with the results for women. The surveys of men produce very similar results to the partner regressions for the main occupational outcomes (results are available upon request).

# **4.2 Migration**

Inward migration can be spurred by natural resource and mining booms and there is evidence of the creation of mining cities (Lange, 2006 in Tanzania), urban-rural migration (around smallscale mines; Hilson, 2009) as well as work-migration (Corno and de Walque, 2012). Such migration patterns can cause a selection issue where women and their partners have moved to mining areas for work. While urbanization and inward migration are possible channels through which the multipliers work, we are also interested in knowing if the original population benefited from the expansion. The data is repeated cross-sectional data. By restricting the sample to women who have never moved, we try to show that our effects are not driven by women who have migrated inward. The results can be seen in Table 7 and resemble the baseline results both in terms of direction of effects and statistical significance.

We conduct several other robustness tests of our baseline results and these are extensively discussed in Appendix Section A.3. Most importantly, the results are qualitatively unchanged if we restrict the sample to only having control groups closer to the mines or if we control for distance to roads and add fixed effects for mineral and closest mine. The results are also robust to different clusterings and we examine the intensity of mining finding that living closer to several mines magnifies the effects.

17
- 18 -

Given this finding, it is not surprising that a common critique of successful management in poverty-oriented projects is that organizational effectiveness results from charismatic leadership which cannot be replicated or maintained, But there are many important examples of strong leadership that has been institutionalized--for example, with laws and regularized procedures, and where the government has chosen the correct "charismatic" person for the leadership position. This experience is illustrated by the Indian National Dairy Development Board which has been built up to a national program from the model of the Anand Milk Producer's Union of Gujurat State under the direction of one of its original leaders, Verghese Kurien.

Strong leadership, however, is a necessary but not sufficient condition for effective ID. As the NIA study suggests, management and organizational linkages are also necessary components of effective institutional structures. These factors should be incorporated into project design, although in this context design should not be equated with "blueprint" planning. As C scussed below, the most successful poverty programs are often "designed" to ai-ow for flexibility and adaptation.

# _The learning process avproach_

The 1980s have witnessed an evolution of thinking about planning and implementing development projects, away from the blueprint to the more open-ended "learning process" approach. This approach rejects fixed planning and calls for more experimental and adaptive forms of design and implementation.8 This type of incremental decisionimaking is decentralized

> 8 This approach is continually evolving. A professional group known as this Development Management Network meets at least once a year to discuss cases and develop the approach as a concept.
|Task Name|Task Description|
|---|---|
|DCP: Direction, Control, and Planning of<br>Activities|Measures nonroutine cognitive tasks, intended to<br>capture interactive, communication, and<br>managerial skills. This variable captures the<br>extent to which the occupation involves<br>direction, control, and planning of activities. It<br>takes on high values for occupations requiring<br>interpersonal and managerial tasks.|
|GED-MATH|Measures quantitative or analytical reasoning<br>skills.|
|STS: Set limits, Tolerances or Standards|Measures routine cognitive tasks. Measures<br>adaptability to work requiring the setting of<br>limits,tolerances or standards.|
|FINGDEX: Finger Dexterity|Measures routine manual activity. FINGDEX is<br>an abbreviation for finger dexterity.|
|EYEHAND|Measures non-routine manual task requirements.<br>EYEHAND is an abbreviation for eye, hand,<br>foot coordination.|

Notes: Our measure of routine-ness is defined as the sum of the cognitive and manual routine measures divided by all the measures. Routine = (sts + fingdex)/(sts + fingdex + math + dcp + eyehand)

# E. Price of Investment, Total Factor Productivity, and the Real Price of Shipments

The price of investment, which varies by industry and year, is taken from the NBER’s productivity database. Total factor productivity is computed by the NBER for all years through 1996, and was updated through 2002 using data provided to the authors by Wayne Gray. The real price of shipments is taken from this database as well.

# F. Bureau of Labor Statistics Imports and Export Prices

Our data on prices of imports and exports are recorded by the Bureau of Labor Statistics using the Standard Industrial Trade Classification scheme and are weighted by purchases of goods within each industry. These are converted to the 3-digit SIC coding scheme, and then made compatible with the CPS industrial codes using code provided by David Autor. These concordances between SIC coding and CPS coding are available in the online appendix to Autor et al. (1998).

G. Panel Data Set of Current Population Survey Workers We construct a panel data set from the CPS Merged Outgoing Rotation Groups by matching individuals surveyed in two consecutive years between 1983 and 2002. Each month, one quarter of survey respondents are asked questions about their job including wage, occupation and industry. One year later, half of these individuals are again asked these same job-related questions. We use Madrian and Lefgren's (2000) matching algorithm to first match an individual based on their
adults and may engage more readily in sexual intercourse. It is also possible that their recent “circumcision” make them more vulnerable to infection.

## [Insert figure 2]

Figure 2 reports the percentage of males circumcised by age. The table shows that the proportion of boys circumcised increases with age. This result confirms that in Lesotho male circumcision takes place during the teenage years and not during childhood, like in many other African countries. It should be noted that the above explanation about the surprising relationship between male circumcision and HIV infection in Lesotho relies more on anecdotal than on scientific evidence. An in-depth study of initiation schools and the practice of male circumcision in Lesotho might be useful, especially now that the message that male circumcision is proven to be protective is more and more likely to be disseminated.

# **4 Empirical Analysis**

This section analyzes HIV prevalence and sexual behaviors using a multivariate analysis. Compared to the bivariate results included in table 2, multivariate regressions allow considering the effect of a variable “ceteris paribus”, i.e. holding the other variable constant. Contrary to what is frequently done in the epidemiological literature, we have chosen not to enter self-reported sexual behaviors as controls in the HIV status regressions. In a cross-section analysis, the estimates derived from such regressions would suffer from reverse causality or from endogeneity. For example, condom use could prevent HIV infection (possible negative association) but on the other hand, HIV positive people or high-risk people may be more likely to use condoms because of their higher exposure (possible positive association).

Therefore, we run two separate sets of regressions. The first one uses HIV status and the probability of being tested for HIV as dependent variables. The second set of regressions uses as dependent variables sexual behaviors and other attitudes related to HIV/AIDS epidemic such as condom use, extra marital sex, abstinence, virginity and age at sexual initiation. Specifically, we tested attitudes related to the so10
Some pnce data were estimated from quantity and expenditure data. Table I in Annex shows the distribution of the sample and size of sub-sanples.

# 2. Matching of Quality

29. As mentioned above, for some goods such as foods, quality can be assumed to be the same, at least in terms of equivalence in use.

30. In tobacco (01.400), the three American brand cigarettes that are mnost popular in China were selected. In clothing and footwear (02.000), we consulted with experts in these industies and then decided on the selection of goods. After the first author arrived in the United States, he re-amined the issue of quality matching in these categories and found the matching of quality was reasonably satisfactory.

31. In furiure, furnishing, household equipment and operations (04.000), different approaches dalng with quality matching were employed in various detailed categories. In fnitur, fixtures, carpets and other floor covering (04.100), for example, the principle of equivalence in use was followed, so those goods which are very often imported from abroad or those goods considered as luxury goods by Chinese standard were selected. In heating and cooking appliances, and major household appliances (04.300), the criterion of equivalence in quaLly was used.

32. The situation in transport and communication (06.000), was similar to that in furnitur etc. Those goods wlich are either imported or produced on an imported assembly line were selected. The exprts in tansport and communication were consulted to make sure that the quality was matched.

3. Some **Topics in the Biltral Comparison** 3.1 Gross Rent Comparson 15
29 Thus, the impact of federal transfers on fiscal decentralization at the regional level was mixed. On one hand, federal aid was by no means concentrated in regions with the highest degree of fiscal decentralization, but on the other, larger federal transfers to regions resulted in larger transfers from regions to local governments, and thus indirectly promoted some decentralization. In the preceding section, we identified two distinct patterns of fiscal decentralization prevalent in Russian regions of different administrative type. In oblasts and krais, the downward shift of expenditure responsibilities is generally financed through increases in the share of local governments in consolidated regional revenues - "true decentralization". In autonomous okrugs and republics, however, relatively more funds for local budgets are provided by regional governments via interbudgetary transfers - "decentralization through transfers". Consequently, the results of the correlation analysis suggest that federal transfers to regions promoted the process of fiscal decentralization in autonomous okrugs and republics and slowed down it in oblasts and krais.

# Section 7. Decentralization and Economic Performance.

The panel-data regression analysis conducted in this section addresses the following specific issues related to the nature and impact of fiscal decentralization within Russian regions:

- What are the main social and economic determinants of the degree of fiscal decentralization within Russian regions?

- Is there a link between regional fiscal decentralization and public expenditures policy (measured through the structure of the consolidated budget expenditures)?

- Does the degree of fiscal decentralization have an influence over regional economic performance?

In the initial stages of our empirical investigation of these problems, we have considered the following set of social and economic variables as potential determinants of the degree of fiscal decentralization within Russian regions:

# A. Geographic and administrative characteristics:

- Geographic location (regions divided in 11 zones, so called "economic regions", based on their location)

- Territory of region as percent of the total territory of the Russian Federation (%)

- Population density in 1994 (persons per square kilometer) * Administrative type (republic, oblast and krai, autonomous okrug)

# B. Social variables:

- Share of the poor population (%) - 1994 and 1995 data only

- Money income as percent of the subsistence minimum

- Unemployment rate (%) - 1994 and 1995 data only

- Share of rural population in 1994 (%)

- Rate of infant mortality (number of babies per 1000 live births, who died before reaching the age of one)

- Infant mortality in 1990 - used as a reference variable that reflects specific social characteristics of Russian regions prevalent before the transition to market economy

- Life expectancy (years)
Policy Research Working Paper 10680

# **Abstract**

Surveys frequently rely on annual recall to capture individuals’ labor activities over the preceding year. This paper uses a panel of rural households in Malawi for a survey experiment to test the effect of a long, annual recall window on reported labor supply relative to a set of quarterly interviews. The paper documents large losses in reported labor participation using the long recall window with reductions of over 20 percent of reported activities and months worked and a 2.5 times greater incidence of reported unemployment relative to the shorter window. These losses are greater for activities further in the past and especially for individuals whose labor supply is reported by other family members, reaching up to 50 percent for some outcomes. The profile of households’ primary respondents, predominantly male and older, and differential effects by age further suggest that long recall may cause meaningful biases in the resulting data for women and younger household members.

This paper is a product of the Development Impact Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sherskowitz@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

_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
**Table A3.IR:  Father's Education and Educational Expenditure on Children**

|||**RURAL**<br>**IHDS**|**INDIA**<br>**2005**||
|---|---|---|---|---|
||**(6-22) Year**|**s Children**|**(8-23) Year**|**s Children**|
|Father's Sch.|153.9|145.4|169.2|158.7|
||(9.08)***|(8.77)***|(11.0)***|(10.6)***|
||[16.3]***|[15.5]***|[19.8]***|[18.5]***|
||{23.5}***|{21.5}***|{262.4}***|{23.8}***|
|Father's Sch. * Daughter|-22.8|-21.9|-24.0|-22.4|
|Dummy|(11.4)**|(11.3)*|(13.9)*|(13.7)|
||[13.0]*|[12.8]*|[15.4]|[15.1]|
||{8.69}**|{9.11}**|{9.63}**|{10.3}**|
|Daughter Dummy|-182.5|-137.1|-211.0|-134.0|
||(43.5)***|(43.7)***|(52.3)***|(52.7)**|
||[47.7]***|[49.1]***|[58.6]***|[60.9]**|
||{38.1}***|{42.5}***|{48.5}***|{56.7}**|
|Intercept|772.3|1585.4|877.6|1949.3|
||(33.3)***|(54.7)***|(39.8)***|(67.9)***|
||[60.9]***|[101.5]***|[69.7]***|[125.9]***|
||{106.3}***|{214.7}***|{118.4}***|{256.0}***|
|No. of Children||-274.4||-361.9|
|||(17.2)***||(21.8)***|
|||[32.4]***||[42.4]***|
|||{52.7}***||{65.7}***|
|No. Observations|23058|23058|19002|19002|
|||**NSS**<br>|**1995**<br>||
||**(6-18) Year**|**s Children**|**(8-18) Year**|**s Children**|
|Father's Sch.|35.2|36.0|36.5|37.5|
||(1.425)***|(1.453)***|(1.535)***|(1.567)***|
||[2.444]***|[2.408]***|[2.501]***|[2.477]***|
||{4.779}***|{4.739}***|{4.678}***|{4.647}***|
|Father's Sch. * Daughter|-3.7|-3.8|-3.4|-3.5|
|Dummy|(2.083)*|(2.081)*|(2.285)|(2.281)|
||[2.115]*|[2.094]*|[2.236]|[2.210]|
||{2.097}*|{2.068}*|{2.100}|{2.053}*|
|Daughter Dummy|-74.1|-69.2|-65.2|-58.5|
||(8.694)***|(8.719)***|(9.700)***|(9.729)***|
||[9.920]***|[9.122]***|[9.454]***|[8.760]***|
||{13.59}***|{13.40}***|{15.46}***|{15.11}***|
|Intercept|426.9|463.7|461.0|507.7|