rafmacalaba/datause-encoder-data huggingface dataset:
Given a set of simplifying assumptions, the external financing needs _EFN_ at time _t_ of a firm growing at rate _g(t)_ is given by:<sup>34</sup> where _b(t)_ is the fraction of the firm's earnings that are retained for reinvestment at time _t_ . Assuming that the firm retains all its earnings, i.e. _b(t)=1_ , the internally financed growth rate _IG(t)_ is the maximum growth rate that can be financed with internal resources only, that is: Demirguc-Kunt and Maksimovic (1998) then regress the percentage of firms in a country that grow at rates exceeding _IG(t)_ on financial development, other country characteristics and averaged firm characteristics in a simple OLS set-up and show, for a sample of 8,500 firms across 30 countries, that the proportion of firms growing beyond the rate allowed by internal resources is higher in countries with better developed banking systems and more liquid stock markets.<sup>35</sup> An alternative approach to assess the impact of access to finance on firm growth is the use of firm-level survey data, as done by Beck, Demirguc-Kunt and Maksimovic (2005), who use firm-level survey data for over 4,000 firms in 54 countries to run the following regression: where _g_ is sales growth of firm _k_ in country _i_ over the period 1996 to 1999, _C_<sup>_(1)_</sup> is a set of firm-level control variables, _C_<sup>_(2_)</sup> is a set of country-level control variables, _o_ is the financing obstacle as reported by the firm and _f_ is a country-level financial development indicator. The financing obstacle is the response by the firm to the question of whether

> 34 The three simplifying assumptions are as follows: First, the ratio of assets used in production to sales is constant. Second, the firm's profits per unit of sales are constant. Finally, the economic deprecation rate equals the accounting depreciation rate.

> 35 Subsequently, this technique has been applied by Demirguc-Kunt and Maksimovic (2002) and Guiso, Sapienza and Zingales (2004), among others.

27
-9-

The other expenditure items are smallers about CFAF 500 million for clandestine private medical practice (estimate based on (341, t351), about CFAF 250 million for total fees charged by health units (estimate based on a survey of all health units, cf. (191, appended .ables), and about CFAF 300 million for health-related transportation expenses.

The order of magnitude of total health-sector ojerating expenditures for 1986 would be as follows (cf. comparative figures in E361 to assess the magnitude of the recent revisions in relation to the first effort of this kind, and cf. (19] for supplementary information on the evaluations used by us):

# CFAF millions

<!-- Start of picture text --> Public financing<br>Government<br>(national budget + regional budgets) 4,200<br>Other public sources 90<br>Development operations 8<br>INPS 380 4,678<br>Family budgets<br>Fees 236<br>Modern drugs 12,000<br>Traditional medicine 2,000<br>Clandestine private fees 500<br>Health-related transportation 300 15,036<br>External aid 542<br>Total 20,256<br><!-- End of picture text --> Families thus now bear three fourths of health expenditure-an expenditure that probably average CFAF 2,700 per capita per year. Free health care is nothing but a fiction.

It must also be borne in mind that current per capita health expenditure is low. The comparable amount was already CFAF 4,000 in Senegal in 1980 (according to [371 p. 162) but only CFAF 2,200 in Burkina Faso in 1981 (cf. [391 p. 17, rather than (401 p. 18, since the latter document was obviously quickly outdated, at least on this point).

It follows that the problems of health financing were clearly evident from the beginning of the 1980s. T' was precisely at that time that a ten-year social and health development plai. sor Mali was drawn up. But while this document bluntly makes no bones about the inability of the government to assume health financing (cf. (381 p. 32), it adopts a typical attitud: first of all, it cites the possibility that wider disseminatlon of health care among the population would afford in opportunity to get patients to share in its financing (cf. (381 pp. 32-33); further on, however, it claims that, already, health care beneficiaries have become accustomed to and have faced up to the fact that they must to bear the bulk of personal health expenditure ([381 p. 157). Wonderful! The problem has disappeared without anybody having had to decide anything at all! And not % word, in this same ten-year plan, not even
and the extent to which they were more or less likely to adopt reforms in the fully specified model of Henisz, Zelner and Guillén (2004) that includes the role of multilateral lenders.

_Crises_ . We combine the coding schemes of (Frankel and Rose, 1996; Detragiache and Spillimbergo, 2001; Hamann and Prati, 2002; Beers, 2003; Kaminsky, 2003), who collectively identify 10 different types of crisis including currency crises, banking crises, stabilization episodes and various forms of default, to construct a dichotomous measure of the existence of a financial or macroeconomic crisis in a given country in a given year. Table 2 provides a listing of the country-years in which a crisis has occurred according to the most inclusive definition.

_Vintage._ We create a variable measuring reform vintage that is initially zero for each country and increases by one for each additional year following the initiation of electricity sector reforms. The initial year of reform is that in which (1) the regulator becomes independent from the ministry, or (2) the regulator becomes independent from the state-owned electricity generating company, or (3) entry by new private generators for resale to final customers is permitted. Table 3 provides a list of the initial reform year for the countries in our sample.

_Checks and balances in established political institutions._ Our measure of checks and balances is the “Political Constraints Index”, ( _POLCON_ ) developed by Henisz (2000).<sup>14</sup> The first step in constructing this measure is the identification of the number of independent branches of government (executive, lower and upper legislative chambers, judiciary and sub-federal institutions) with veto power over policy change in each country. Countries lacking any formal veto points are assigned a score of “0.” For all other countries, the majority preference of each of these branches and the _status quo_ policy are then assumed to be independently and identically

> 14 Data and codebook are available from http://www-management.wharton.upenn.edu/henisz/POLCON/ ContactInfo.html

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**I** -~ **Policy Research**

# **Financial Advisory Services**

WPS 817 This paper is a product of the Financial Advisory Services Group, Cofinancing and Financial Advisory Services Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Yolanda Arellano, room Q5-113, extension 31379 (22 pages). December 1991.

The Brady Plan is a pragmatic approach to debt restructuring that combines the relatively recent feature of debt and debt service reduction and the support of official creditors. The underlying premise of those adopting the Brady Plan is that the existing stock of debt can never be fully serviced, even though the country has embarked on a far-reaching adjustment program.

To date, only a handful of countries (Costa Rica, Mexico, Uruguay, and Venezuela) have successfully concluded their debt reduction negotiations through a Brady Plan with commercial creditors. Others, such as the Philippines, have engaged in Brady-type deb. reduction for part of their outstanding commercial debt.

MukherJee explains what happens when, in response to a country's request, the creditors agree to negotiate to reduce the burden of outstanding commercial debt. She discusses the following questions:

* What factors influence the extent of debt relief that a commercial bank can offer?

* What is a good deal for the banks? How do banks of various nations reconcile their different interests in the country? (Some expect to continue doing business there; others want to cut their losses and exit.) What about a bank's fiduciary responsibility to its depositors and shareholds?

- What about othei creditors of the country, such as holders of its sovereign bonds, or other governments or multilateral agencies? Will or should commercial banks be the only ones to offer relief by t?king losses?

* What is the country's proposed strategy for seeking future financing from private sources, director foreign investment, and international capital markets? How will a debt reduction operation affect the country's access to commercial and private sector finance in the future?

* How much support can be expected from multilateral and bilateral institutions to finance a debt reduction operation? Mukheree summarizes broad guidelines used by the IBRD and IMF.

* What is a good deal for the country? What is the preferred mix (for the country) between debt reduction and debt rescheduling? What considerations should the country take into account?

The Policy Research Work-Ing Paper Scrics disseminaies the findings of work under way in the Bank. An objcctive of the series is to get these ftndings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by **the** Folicy Research Dissemination Center
Finally, compute the overall coverage rate, _C_ ; the D-index; the penalty, _P_ ; and the HOI using the ˆ predicted probability _p_ and sampling weights, _w_ :

# **Appendix 2 Share of Public Spending Captured by Quintiles of Welfare and Circumstance Sets**

**Fig 1 Cote d’Ivoire, 2007 (attending school, ages 6-15)**

<!-- Start of picture text --> Share of public expenditure on Share of public expenditure on<br>education by quintiles of incomes (2007) education by quintiles of probability (2007)<br>24 24<br>22<br>21 21 21<br>20 20 20 20<br>19 20 20 20 20 20 20<br>18<br>17<br>11<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Population (age 6 to 15) Public expenditure Population (age 6 to 15) Public expenditure<br>30 30<br>20 Percentage 20<br>10 10<br>0 0<br><!-- End of picture text -->

# **Fig 2 Zambia, 2009 (attending school, ages 7-18)**

<!-- Start of picture text --> Share of public expenditure on Share of public expenditure on<br>education by quintiles of consumption (2009) education by quintiles of probability (2009)<br>20 20 21 20 20 20 20 21 22 22<br>19 20 20 20 20 20 20 20 20<br>15<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Population (age 7 to 18) Public expenditure Population (age 7 to 18) Public expenditure<br>30 30<br>20 Percentage 20<br>10 10<br>0 0<br><!-- End of picture text --> 37
We designed a short version of the questionnaire to apply at these locations. The questionnaire was four pages in length, consisted of 62 questions, and took a mean time of 7 minutes to answer. Respondents had to be 18 years or older to be interviewed. Interviewing for the São Paulo intercept survey took place between December 9, 2006 and December 20, 2006, while the Curitiba intercept survey took place between March 3 and March 12, 2007.

Consultations with Nikkei community organizations, local researchers, and officers of the bank _Sudameris_ , which provides remittance services to this community, were used to select a broad range of locations. In São Paulo, we chose 9 fixed point locations and 6 events. The 9 fixed locations are: a sports club, a metro station in the Liberdade neighborhood, two _Feiras_ (Sunday open markets), a hospital focused on the Nikkei community, two grocery stores specializing in Japanese foods, a Japanese cultural society which offers language classes and evening events, and outside a branch of the Banco Sudameris in the Saúde neighborhood. The 6 events were: an afternoon Japanese film event organized by the Sociedade Brasileira de Cultura Japonesa, a large cultural festival with music, dancing and _taiko_ -drumming organized by ACAL (Associação Comercial e Assistencial da Liberdade), a Japanese food festival organized by ACESA (Associação Cultural Esportiva de Santana), a Japanese art exposition organized by Fundação Mokiti Okada, a Christmas concert organized by Coral do Bunkyo, Paineiras e Silver Boys, and a music festival organized by Grupo The Friends.

The Nikkei community is less concentrated in Curitiba, with fewer public places where Nikkei are known to gather. Five fixed point locations were chosen: the municipal market, Clube Nikkei (a sports club), the Bunkyo (a Japanese cultural society), a Japanese language school associated with the Bunkyo, and a second Nikkei association, the Associacao Brasileira de Dekasseguis (ABD). The intercept survey was also to include surveying at an event at the Seicho-no-ie church, but this event was canceled.

> 100 Yonsei (fourth generation Nikkei) was carried out in São Paulo. We will not discuss this Yonsei survey in this paper.

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finding credible and easy-to-implement markers of reticent behavior that can be routinely included in surveys that aim to gather sensitive data, as well as deploying novel survey techniques to encourage greater candor (for example using computer-assisted interviewing to reduce the sensitivity of face-toface interactions between respondents and interviewers).<sup>15</sup> This research agenda is particularly important in the case of corruption, where alternatives to self-reported survey-based data are rare.

> 15 Tourangeau and Yan (2007) provide a valuable survey of the results from many different experiments to improve the accuracy of responses on sensitive questions, concluding that "The need for methods of data collection that elicit accurate information is more urgent than ever."

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state level with sample survey data yields parameter estimates that are used to impute incomes to individual households in the population census. These imputed incomes can then be examined to assess poverty at the level of each of the nearly 1000 municipalities in the state. The resultant poverty estimates have been found to line up quite closely to the actual, observed, poverty rates in those municipalities. Moreover, confidence intervals around the point estimates are both moderate in size and encompass the “truth” broadly in accordance with the statistical precision they are intended to reflect. A recent critique of the ELL methodology by Tarozzi and Deaton (2007) uses Monte Carlo simulations to illustrate that there are conditions and circumstances under which the ELL methodology can yield a sense of precision that is far too optimistic. The present study has shown that in an empirical setting that one might have thought, a priori, would fit the conditions for the Tarozzi and Deaton (2007) critique, the ELL methodology performs quite well. The broad applicability of the Tarozzi and Deaton (2007) argument is thus brought into question. Further empirical research into these questions is needed, in different settings and circumstances.

31
In Figure 3, we begin to show that migration flows between origin and destination countries could be associated with corruption levels. Although most country-pairs bilateral migration is relatively low, high migration outflows are observed for high levels of corruption in sending countries, while much lower outflows are observed from countries that experience low levels of corruption. Preliminary analysis also suggests that higher migration flows are observed from relatively more corrupt to relatively less corrupt countries. Corruption appears to be both a push and a pull factor for migration decisions.

# **2.3 Control variables**

The control variables that we use in the analysis are summarized in Table 1.

First, following the literature on gravity models, we measure the geographical distance between countries by including a dummy variable equal to one when countries share a common border, as well as indicators for the difference in time zones, the distance (weighted by population), and the area of the country. To control for cultural and economic distances, we include variables measuring whether countries share a common language, a common religion, and a common currency. All these variables come from the CEPII Gravity Database.

Second, to control for differences in income levels, labor markets, and openness, we include GDP per capita levels, inflation rates, and the importance of agriculture and services in the local economy. These variables come from the World Bank World Development Indicators.

Third, to measure differences between countries in legal barriers to mobility, employment legislation, and the generosity of the welfare system, we consider OECD measures on: _i_ ) employment protection legislation; _ii_ ) product market regulation; _iii_ ) social expenditure; and _iv_ ) the right to work.<sup>12</sup>

> 12The OECD employment protection legislation is a synthetic indicator of the strictness of regulation on the dismissal of workers on regular contracts and the hiring of workers on temporary contracts. It ranges from 0 to 6, where 6 measures the strictest regulation. Then, a larger number indicates more job security. The OECD product market regulation measures the degree to which policies promote or inhibit competition in markets. It ranges from 0 to 6, where 6 captures policies that most inhibit competition. The OECD social expenditure indicator is presented as a share of GDP, and it includes social expenditure on old age, survivors, incapacity-related benefits, health, family, active labor market programmes, unemployment, housing, and other social policy areas. When looking at 2010 data on migration policies from the Immigration Policies in Comparison (IMPIC) database (Helbling et al. 2017), the previous indicators correlate in the expected direction. For example, social expenditure tends to be higher in countries with less restrictive labor migration policies (e.g., on work permit validity, labor market tests, loss of employment, or age limits).

6
our tests is the possibility of systematic (non-random) reporting biases. If RD is a source of disutility then it may well influence the answers given to interviewers. Could this undermine our tests? It is instructive to consider a simple, yet seemingly plausible, model of that bias. Suppose that utility depends on own-economic welfare and comparison-group welfare and that RD matters, but to ‘save face’ the respondent hides the true RD from the interview. More precisely, assume that the responses are biased in amounts that are directly proportional to the difference between (true) own welfare and that of the comparison group; if the respondent feels poorer than her comparison group then she over-states her own economic welfare and/or under-states that of her comparison group. Evidently then the true parameters are positively-weighted linear combinations of the estimated parameters based on the reported data.<sup>25</sup> The key implication is that (even though we do not know the bias parameters) if we find that both own-welfare and comparison-group welfare have positive coefficients then this must also be the case for the true parameters; reporting biases cannot be hiding a true RD effect when no such effect is observed empirically.

# **4. Data and methods for Malawi**

We use the 2004 round of the Malawi Integrated Household Survey (MIHS). This is a comprehensive, nationally-representative survey of households administered between March 2004 and April 2005 by the Government of Malawi’s National Statistical Office with assistance from the World Bank, under the Living Standards Measurement Study. A two-stage stratified sample is based on the 1998 Malawi Population Census and is drawn from 564 enumerating

> 25 Suppose that _u_ = α _y_ + β _y n_ + ε for a measure of own-economic welfare _y_ and comparison-group welfare _y n_ .  The reported values are related to the true values according to: _y_ ˆ = _y_ −φ 1( _y_ − _y n_ ) and _y_ ˆ _n_ = _y n_ +φ 2 ( _y_ − _y n_ ) where φ _i_ ∈ []1,0 ( _i_ =1,2). Then α = _a_ 1( −φ 1<sup>)</sup> + _b_ φ 2 and β = _b_ 1( −φ 2<sup>)</sup> + _a_ φ 1 .

19
**<u>Table 2: Key characteristics by farm size group, 1982, 1999, and 2008</u>**

|||**1982**|||**1999**|||**2008**||
|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**Small**|**Med.**|**Large **|**Small**|**Med.**|**Large **|**Small**|**Med.**|**Large **|
|**Household characteristics**||||||||||
|Head’s age (years)|48.64|49.91|52.75|47.77|49.78|54.08|49.75|52.19|54.60|
|Head’s education (years)|2.03|2.18|2.28|3.98|4.25|4.84|4.76|4.75|5.86|
|Household size|5.90|6.80|8.45|5.89|6.25|7.98|5.26|5.65|6.62|
|# members >14 years old|3.80|4.33|5.29|3.81|4.22|5.50|3.59|4.05|4.85|
|Share of prime males|48.07|48.83|48.59|47.37|46.86|46.16|43.92|44.41|43.80|
|<br>Share of prime female|44.51|43.82|42.69|43.55|43.18|42.53|43.89|42.43|41.50|
|<br>Share of elderly male|4.10|4.46|5.08|4.56|5.42|6.06|6.07|6.83|7.43|
|Share of elderly females|3.32|2.89|3.65|4.52|4.54|5.25|6.12|6.33|7.28|
|<br>**Income and endowments**||||||||||
|Area owned (acres)|1.07|3.73|16.13|1.21|4.04|14.80|1.08|3.71|13.09|
|<br>Area cultivated (acres)|1.75|4.54|15.98|2.25|5.70|17.11|2.09|5.88|18.72|
|Owns machinery|0.01|0.03|0.13|0.14|0.17|0.33|0.18|0.38|0.52|
|...if yes, value (‘000 Rs./acre)|1.34|1.53|7.62|8.50|31.37|106.35|22.944|43.88|122.87|
|Total income (‘000 Rs)|14.50|21.85|45.30|38.80|55.39|123.11|46.539|74.49|161.55|
|Share from agriculture|50.67|71.87|94.37|40.34|63.55|77.23|34.48|58.01|80.52|
|**Labor use**||||||||||
|Using any labor for agric.||||||||||
|production (%)|96.42|99.13|99.32|92.87|99.88|99.55|93.46|99.89|99.80|
|<br>Using family labor for ag. (%)|96.11|97.86|97.97|92.76|99.71|99.36|92.40|98.29|96.11|
|Using hired labor for ag. (%)|52.96|67.30|79.72|58.36|78.18|83.92|65.88|84.16|91.30|
|<br>Worked as agric. labor (%)|47.66|29.77|11.22|34.28|17.28|6.09|29.50|17.44|6.14|
|Worked as nonagric. labor (%)|7.17|6.65|4.39|20.55|11.98|5.81|29.21|15.20|8.90|
|Worked as nonagric. self-||||||||||
|<br>employment (%)|7.63|5.70|6.49|9.13|6.52|7.99|18.33|17.71|15.56|
|**Cultivation details**||||||||||
|Agric.labor (days/acre)|158.98|79.72|48.80|91.55|37.75|19.86|82.50|36.56|17.71|
|Own labor (days/acre)|107.37|39.68|17.96|79.66|30.37|14.31|63.98|24.91|10.47|
|Male labor||||51.41|19.24|9.12|44.64|17.39|7.69|
|Female labor||||20.98|8.05|3.47|18.30|7.21|2.70|
|Hired labor (days/acre)|52.11|40.46|31.22|11.89|7.38|5.55|18.52|11.65|7.24|
|<br>Male labor||||7.64|4.32|3.59|11.83|7.55|4.73|
|Female labor||||3.72|2.69|1.71|6.45|3.86|2.34|
|Rents or owns machinery|44.08|49.64|54.50|55.30|66.78|65.03|69.63|76.30|79.43|
|<br>... if yes, value (Rs./acre)|192.46|109.04|92.47|644.70|485.76|369.08|1413.15|795.49|555.44|
|<br>**Village level variables**||||||||||
|<br>Agricultural wage (Rs/day )|15.66|14.51|15.14|45.19|45.22|45.81|46.83|47.93|48.57|
|Nonagric. wage (Rs/day )|15.73|15.25|16.78|58.16|56.85|57.42|66.39|67.81|70.50|
|Number of observations|825|1,363|1,558|1,698|1,719|1,101|2,078|1,869|977|

_Source:_ Authors’ computation from 1982, 1999, and 2008 ARIS-REDS surveys. Note: Large, medium, and small farms are defined as having more than 5, between 2.5 and 5, and less than 2.5 acres of land, respectively.

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