rafmacalaba/datause-encoder-data huggingface dataset:
different across countries. The vast majority of existing governance data sources do not report such margins of error, even though measurement error is surely present in them as well, and so it is difficult for users to assess the significance of differences across countries or over time.

# **_<u>Critique 5:</u> The individual indicators underlying the WGI are biased towards the views of business elites, and thus so are the aggregate indicators._**

This concern is raised by both AO and KS. It is apparently based on the observation that several of our data sources are commercial risk rating agencies (whose main clients are businesses), as well as a number of cross-country surveys of firms. In short, they argue, businesspeople like low taxes and minimal regulation, while the public interest demands reasonable taxation and appropriate regulation. Estimates of governance based on the perceptions of businesspeople, and especially the "elite" among businesspeople, will therefore necessarily be biased. We do not think this criticism is particularly valid, for three broad reasons.

- First, we note that our indicators rely on much more than just the views of businesspeople.  In the latest 2005 update of our governance indicators, our data sources include four cross-country surveys of firms, as well as seven commercial risk rating agencies, which one might expect to reflect narrower business interests.  But we also rely on three cross-country surveys of individuals, six sets of ratings produced by government and multilateral organizations (such as the World Bank, the African Development Bank and the US State Department), and finally another 11 data sources produced by a wide range of non-governmental organizations (such as Freedom House, Reporters Without Borders, and many others).  It is therefore simply incorrect to dismiss our indicators as reflecting solely -- or even primarily -- the narrow interests of the business elite.

- Second, it is not at all the case that firm surveys focus exclusively on either foreign investors in a country, or else the "elite" of large domestic firms.  In fact, in the largest cross-country survey of firms that we use, the Global Competitiveness Report,  just 14 percent of respondent firms are foreign-owned in the 2005 round of the survey.  Moreover, 30 percent of all respondent firms are

12
# **A Econometric Techniques**

In this Appendix we briefly describe the econometric techniques that we adopt to produce the results presented in the main body of the paper, in the tables, and in the figures. The nominal commodity price returns are defined as _rm,t_ = ln _Pm,t −_ ln _Pm,t−_ 1, where _Pm,t_ is the nominal price of commodity _m_ at time _t_ .

## **A.1 Qu and Perron (2007)’s Breakpoint Analysis**

We estimate two unrestricted vector autoregressions (VAR) of orders 0 and 1 for the nominal price returns of the eleven commodities over the full sample of data (1970M02-2013M05). Both model specifications satisfy the conventional stability conditions ensuring stationarity. We evaluate the presence of structural breaks at unknown dates in the coefficients of the VAR equations and in the variance-covariance matrices of the error terms.

The errors are assumed to be normally distributed (quasi-maximum likelihood technique). The distribution of the relevant test statistic becomes degenerate as the estimated breaks approach the beginning or the end of the sample, or if any two breakpoints are too close to each other. In order to address this statistical issue, we set a trimming parameter of 15%; that is, 78 months (6.5 years) is the minimum distance between any two breakpoints, between the beginning of the sample and the first break, and between the last break and the end of the sample. The covariance matrices of the errors and the distributions of the regressors in the VAR models are allowed to change from each regime to the next, but no pre-whitening is applied for the construction of the breakpoint intervals.

The multivariate approach that we adopt in this context is consistent with the rest of the analysis.<sup>15</sup> The breaks detected may differ from the breaks in individual bivariate correlation coefficients. Our assumption is that only some parameters of the VAR equations present structural breaks at some point in time and that, when a break occurs, it occurs simultaneously in all model parameters and the variance-covariance matrix of the errors.

Our preferred specification is a VAR(1) model of the eleven nominal price returns. We generally allow for the presence of two breaks, which are estimated to occur in 1979M11 and 1987M07. When we allow for the presence of a third structural break, we generally come across a corner solution, due to the 15% trimming at the two ends of the sample. The implication is that the third structural break is likely to lie to the right of the identified value (corner solution), closer to the end of the sample period. We investigate this possibility by testing for the presence of a structural break only on a restricted sample of data ranging from 1990M01 to 2013M05. By restricting the sample in this way, we reduce the number of trimmed observations from 6.5 years down to 3.5 years, while keeping the trimming parameter constant at 15%. We find an additional structural break in 2007M05, at the onset of several agricultural price spikes.

> 15In fact, the unconditional correlations analyzed in Section 3.2 can be expressed as scalar functions of the VAR parameters.

23
|(8)|(-0.75∗∗∗)|-(0.13)∗∗∗|(-0.67∗∗∗)|-(0.03)∗∗∗|(-Yes∗∗∗)<br>(-No∗∗∗)|(-Yes∗∗∗)|-7484∗∗∗|-0.20∗∗∗|d+silver+livestock) and<br> occupation with direct<br>ousehold-head controls<br>s: ∗∗∗p<0.01, ∗∗p<0.05,|
|---|---|---|---|---|---|---|---|---|---|
|ng Wealth<br>(7)|(-0.62∗∗∗)|-(0.12)∗∗∗|(-0.96∗∗∗)|-(0.03)∗∗∗|(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)|-7484∗∗∗|-0.29∗∗∗|are GSL (gol<br>who report an<br> Household/H<br>ifcance level|
|Non-Housi<br>(6)|)<br>(-0.75∗∗∗)|∗<br>-(0.13)∗∗∗|)<br>(-0.68∗∗∗)|∗<br>-(0.03)∗∗∗|(-Yes∗∗∗)<br><br>(-No∗∗∗)|(-No∗∗∗)|-7484∗∗∗|-0.19∗∗∗|dent variables <br>es households <br>in parentheses. <br>dwelling. Sign|
|(5)|(-0.77∗∗∗|-(0.12)∗∗|(-0.67∗∗∗|-(0.02)∗∗|(-No∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)|-7484∗∗∗|-0.14∗∗∗|two depen<br>mple exclud<br>errors are<br>and type of|
|(4)|)<br>(-3.76∗∗∗)|∗<br>-(0.37)∗∗∗|)<br>(-0.49∗∗∗)|∗<br>-(0.08)∗∗∗|(-Yes∗∗∗)<br><br>(-No∗∗∗)|(-Yes∗∗∗)|∗<br>-12364∗∗∗|-0.08∗∗∗|= β3 = 0. The<br>-debt). The sa<br> Robust standard<br>per household,|
|SL<br>(3)|(-2.78∗∗∗|-(0.38)∗∗|(-0.43∗∗∗|-(0.08)∗∗|(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)|-12364∗∗|-0.19∗∗∗|etting β2 <br>er+livestock<br>of income. <br>er of males|
|G<br>(2)|(-3.76∗∗∗)|-(0.37)∗∗∗|(-0.49∗∗∗)|-(0.08)∗∗∗|(-Yes∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)|-12364∗∗∗|-0.08∗∗∗|ion (2) after s<br>ure+gold+silv<br>main sources<br>old size, numb|
|(1)|(-3.85∗∗∗)|-(0.34)∗∗∗|(-0.30∗∗∗)|-(0.07)∗∗∗|(-No∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)|-12364∗∗∗|-0.01∗∗∗|ients of equat<br>rable expendit<br>of their three<br>cation, househ|
|Variables|Variance of Income (log)||Income (log)||Household Controls<br>Province Fixed Efects|Confict|Observations|R-squared|The table shows the estimated coefc<br>Non-Housing Wealth (income-non-du<br>links to some livestock activity as one<br>include gender, age, marital status, edu<br>∗p<0.1.|

31
Table C4: Robustness on Controls: Drivers of Total Debt Inflows, All Sectors - Emerging Markets (Quarterly AHKS data, missing filled from Public Sources)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|log(VIX_t−_1)|-2.261<sup>_∗∗_</sup><br>(0.829)|-1.197<br>(0.745)|-0.824<br>(0.681)|-2.539<sup>_∗∗∗_</sup><br>(0.858)|-2.637<sup>_∗∗∗_</sup><br>(0.948)|0.0743<br>(0.622)|0.554<br>(0.840)|
|GDP Growth_it−_1|0.116<sup>_∗∗∗_</sup><br>(0.0347)|0.0828<sup>_∗∗∗_</sup><br>(0.0252)|0.108<sup>_∗∗∗_</sup><br>(0.0317)|0.114<sup>_∗∗∗_</sup><br>(0.0342)|0.0946<sup>_∗∗∗_</sup><br>(0.0254)|0.0826<sup>_∗∗∗_</sup><br>(0.0234)|0.0836<sup>_∗∗∗_</sup><br>(0.0234)|
|FFR_t−_1||0.796<sup>_∗∗∗_</sup><br>(0.282)||||0.900<br>(0.485)|1.297<sup>_∗∗∗_</sup><br>(0.449)|
|Yield Curve_t−_1|||-1.107<sup>_∗∗∗_</sup><br>(0.313)|||-0.209<br>(0.463)|0.173<br>(0.407)|
|TED Spread_t−_1||||0.524||-1.624|-2.018|
|||||(0.727)||(0.892)|(1.000)|
|Observations|1372|1372|1372|1372|1372|1372|1372|
|_R_<sup>2</sup>|0.071|0.100|0.093|0.071|0.079|0.105|0.106|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|TimeTrend|||||Yes||Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Total Debt is the sum of Portfolio Debt and Other Investment Debt inflow data, constructed by AHKS as described in Section 2. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. FFR is the effective US Federal Funds Rate, lagged one quarter. Yield Curve is the difference between 10 year US Treasury constant maturity rate and 3 month US Treasury constant maturity rate, lagged one quarter. TED Spread is the difference between the 3 month US dollar LIBOR rate and the 3 month US Treasury rate, lagged one quarter. FFR is the effective US Federal Funds Rate, lagged one quarter. Yield Curve is the difference between 10 year US Treasury constant maturity rate and 3 month US Treasury constant maturity rate, lagged one quarter. TED Spread is the difference between the 3 month US dollar LIBOR rate and the 3 month US Treasury rate, lagged one quarter. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01
WPS 3223

# Coordination Failure in Foreign Aid<sup>∗</sup>

Maija Halonen-Akatwijuka<sup>†</sup> University of Bristol February 9, 2004

## Abstract

This paper analyzes the allocation of foreign aid to various sectors in a recipient developing country. Donors tend to favor social sectors over other public expenditure programs. Due to incomplete information, donors may concentrate too much on priority sectors, leaving lower-priority yet important sectors lacking funds. Alternatively there may be gaps in services in priority areas because of the information problem. The more similar preferences the donors have, the more scope there is for coordination failure. Therefore improving information is particularly important when the parties have similar priorities. A joint database on planned projects and budget allocations in each recipient country would provide such information. The point of the paper is that such databases should have both information on current projects and forward-looking information on the planned activities needed to improve aid coordination. It also analyzes the aid fungibility problem in an incomplete information setting, and fi nds that incomplete information reduces the fungibility problem. On the other hand, incomplete information introduces coordination failure and the allocation can be inferior for both the recipient and the donor.

> ∗This paper was prepared as a background paper for the World Development Report 2004: Making Services Work for Poor People. I would like to thank Ritva Reinikka for discussions and Steve Knack, John Mackinnon, Sebastien Mitraille, and In-Uck Park for comments. The fi ndings, interpretations, and conclusions expressed in this paper are those of the author and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Working papers describe research in progress by the author and are published to elicit comments and to further debate.

> †Maija.Halonen@bristol.ac.uk, Department of Economics, University of Bristol, 8 Woodland Road, Bristol BS8 1TN, U.K.

1
concentrated ownership and low valuations and pay low dividends relative to firms from countries with strong legal protection.

Our evidence also includes results from firm surveys of small- and medium-sized private firms, one of the most successful sectors in the Indian economy. We find that alternative financing channels, such as internal financing and trade credits, provide the most important source of funds. We also find that entrepreneurs and investors rely more on informal governance mechanisms, such as those based on reputation, trust and relationships, than formal mechanisms (e.g., courts), to resolve disputes, overcome corruption and finance corporate growth. Our results call for more within-country studies in other regions and countries if we seek to understand better how these informal mechanisms work where formal mechanisms are not available or work imperfectly.

40
Policy Research Working Paper 8222

# **Abstract**

This paper documents to what extent firms from developing countries borrow short versus long term, using data on corporate bond and syndicated loan markets. Contrary to claims in the literature based on firm balance sheets, firms from developing countries borrow through bonds and syndicated loans at maturities similar to those obtained by developed country firms. The composition and use of financing matters. Firms from developing countries borrow shorter term in domestic bond markets, but the differences in international issuances (accounting for most of the proceeds) are significantly smaller. Developing country firms borrow longer term in syndicated loan markets, which they partially use for infrastructure projects. However, only large firms from developing countries (similar in size to those from developed ones) issue bonds and syndicated loans. The short-termism in developing countries is partly explained by a lower proportion of firms using these markets, with more firms relying on other shorter-term instruments.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group and the Strategy and Operations Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at jcortinalorente@ worldbank.org, tdidier@worldbank.org, and sschmukler@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
i.e., _OEW_ = γ _y_ +η +ν with an innovation error term ν . The restriction that γ ≥ β seems plausible; if anything the income effect would be lower for _SWL_ . The test equation becomes:

> (where ε * = ε − βν / γ ).  Thus the bias arising from heterogeneous FOR effects is attenuated using subjective economic welfare as the regressor, and it is eliminated entirely when the income effect for _SWL_ is the same as for _OEW_ ( β = γ ).

# _Endogeneity of comparison-group welfare_

In common with other tests of RD theories in the literature, we treat our measures of the economic welfare of friends and neighbors as exogenous.<sup>23</sup> This is unlikely to be as strong an assumption in this setting as in developed countries. Poor people in developing countries clearly do not have the same degree of freedom to choose their location — and hence their neighbors — as in a developing country. In rural areas, poor people have typically lived in the same village or nearby for most of their lives. (We look at some evidence on this point for Malawi later.) It can also be argued that the externalities at issue stem from pre-existing relationships, which facilitate repeated interaction and trust. Exogeneity is a plausible assumption for risk sharing; people are unlikely to enter an informal risk-sharing arrangement with strangers.<sup>24</sup> That is more contentious for the social comparisons than generate relative deprivation. If RD is an important factor then it may prompt people to change their friends and neighbors.

If poor people were free to choose their friends and neighbors, what would we expect to find in our data? With sufficient continuity, we can think of their problem as that of choosing

> 23 It is difficult to imagine defensible instrumental variables in this case; any variable that would influence one’s choice of friends and neighbors would presumably also be a direct influence on one’s overall welfare, invalidating the exclusion restriction required for identification.

> 24 There is evidence on this point in Fafchamps and Gubert (2005) who studied risk-sharing relationships amongst rural Filipinos.

16
# FIGURE 24: SHARE OF GOVERNMENT, HEALTH, EDUCATION AND SCIENTIFIC SERVICES IN EMPLOYMENT: SOVIET REPUBLICS AND OECD

<!-- Start of picture text --> 40%-<br>30%-_<br>AR GE,<br>10%- _<br>0% I<br>7.5 8 8.5 9 9.5 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text --> <!-- Start of picture text --> Ul<br><!-- End of picture text -->
# 5.2 Beyond Labor Income

The above results find no evidence of poverty traps in Mexican labor income. One potential concern is that some of the households with very low or zero labor income could be non-poor due to other sources of income, such as autoconsumption, transfers, rent and interest. As these households are not poor, we would not expect to find them in a poverty trap. However, their inclusion among other households with similar labor income levels who rely much more on labor income in the estimation of income dynamics is likely to reduce the chance of detecting poverty traps for this latter group.

We therefore examine the robustness of our results to the use of more comprehensive measures of household welfare. We use data from Mexico’s National Income and Expenditure Survey, the Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH). This survey has been taken biannually since 1992, and we use the six rounds from 1992-2002.<sup>11</sup> To allow direct comparison with the urban employment survey, we use only data from cities of population 100,000 and above. As with the ENEU data, we restrict our analysis to households with heads aged 25 to 49, and define cohorts based on five year birth intervals of the household head within three education groups. This results in a sample size of between 3,000 and 4,000 households in each round. Further restricting the number of observations per cohort in a round to be at least 100 results in a pseudo-panel of 19 cohorts observed over 6 time periods.<sup>12</sup> We consider four measures of household welfare. The first measure is total household labor income from the principle occupation of each member, which corresponds to the measure we have used in the ENEU. The second measure is total monetary income of the household, which includes household income from

> 11 Earlier rounds of this survey were also conducted in 1989 and 1984. The inclusion of these surveys would require estimating income dynamics over unequally-spaced time periods. While in principal the methods of McKenzie (2001) could be extended to cover the measurement error case, we choose to focus on the equally-spaced surveys for this paper.

> 12 There are 82 cohort-wave observations, since some cohorts are not observed in every round, either due to insufficient observations, or due to the cohort being out of the 25-49 year age range for the household head.

21
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