rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10656

# **Abstract**

To monitor the evolution of household living conditions during the COVID-19 pandemic, the World Bank conducted COVID-19 High-Frequency Phone Surveys in around 80 countries. Phone surveys are cheap and easy to implement, but they have some major limitations, such as the absence of poverty data, sampling bias due to incomplete telephone coverage in many developing countries, and frequent nonresponses to phone interviews. To overcome these limitations, the World Bank conducted pilots in 20 countries where the Survey of Wellbeing via Instant and Frequent Tracking, a rapid poverty monitoring tool, was adopted to estimate poverty rates based on 10 to 15 simple questions collected via phone interviews, and where sampling weights were adjusted to correct the sampling and nonresponse bias. This paper examines whether reweighting procedures and the Survey of Wellbeing via Instant and Frequent Tracking methodology can eliminate the bias in poverty estimation based on the COVID-19 High-Frequency Phone Surveys. Experiments using artificial phone survey samples show that (i) reweighting procedures cannot fully eliminate bias in poverty estimates, as previous research has demonstrated, but (ii) when combined with Survey of Wellbeing via Instant and Frequent Tracking poverty projections, they effectively eliminate bias in poverty estimates and other statistics.

This paper is a product of the Poverty and Equity Global Practice. 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 kzhang2@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
is likely also a function of _si_ , for example, because sectoral production is characterized by increasing returns to scale and knowledge externalities, so that _yi_ is increasing in _si_ . Alternatively, too much congestion in a sector may lower the sectoral productivity, so that _yi_ is decreasing in _si_ (Fujita and Thisse, 2002). As a descriptive starting point, this could be examined by allowing the rate of poverty reduction to depend only on the change rates in the share of the missing middle and the metropoles, i.e. by estimating an even more reduced version of equation (7): In this equation, β<sup></sup> _U_ and β<sup></sup> _N_ include both the direct effects of the sectoral transformation on poverty through changes in the income distribution and the indirect effects through changes in the income levels. By comparing the coefficients on the change rate of the metropolitan share of the population and the change rate of the share of those living in the intermediate “missing middle” space it can be explored whether and how the patterns of spatial and occupational transformation matter for poverty reduction.

To shed further light on the differences in their effect, the channels through which changes in the sectoral shares affect poverty reduction (economic growth and inequality) will further be explored by linking economic growth and inequality directly to the change rates in the shares. This way, possible growth-equity trade-offs may also be uncovered.

# **3 Poverty, Occupational and Spatial Transformation 1980-2004**

The World Bank’s POVCAL data are used to construct the poverty spells and the rate of poverty reduction.<sup>16</sup> The $1-day and $2-day poverty headcount ratios are taken as measure of

> 16 <u>http://iresearch.worldbank.org/PovcalNet/ April, 2008 (i.e. before the latest revisions of the poverty numbers</u> using the 2005 poverty purchasing power corrections).

11
30 second in the sixth year of the ITAs when the remaining tariffs are removed (this increases this share to 100 percent if duties on MFA products are removed).

The duty-free trade component is the largest in Czechoslovak and Polish exports to the EC. Although industrial products accounted for 93 percent of Romanian imports (see Table 1), the free access for its imports is smaller due to the large share of MFA products which increased from 28.2 percent in 1991 (see Table 7) to 39 percent in 1992. In terms of their share in industrial exports, the VisegrAd countries benefit more from fully liberalized access to EC markets than Romania and to a lesser extent Bulgaria because of the lower proportion of MFN products in their exports.

Table 12: Shee of Duty-Free Goods in CEE-5 Industial and Total Expors to the EC, 1992-98

||199|2 1993|1994|1995|<br>1096|1997|<br>1998|
|---|---|---|---|---|---|---|---|
||. .- ;--|_(shareci i_|_ndultrial_|_pworts_<br>_in_|_,_<br>_percent)_|||
|Bulgaria<br>|<br>54|<br>54'|57|58|59|60|61|
|_PCSK_|59|<br>63|65|67|71|86|86|
|Hungary|<br>62|<br>64|66|67|_'68_|-78|78|
|Poland|56|<br>63|65|66|72|81|81|
|Romani|a<br>'<br>39|39'|42|44|_46_|48|61|
|CEE-5|58|<br>69|70|70|71|74|80|
|||_(share in to_|_ra mor_|_ts, in per_|_cent)_|||
|Bulgari|a<br>43|<br>43|45|45|46|47|56|
|FCSK|55|<br>59|61|62|66|80|80|
|Hungar|y<br>48|<br>50|51|52|53|60|60|
|Poland|47|<br>53|55|56|61|69|69|
|Romani|a<br>36|<br>36|39|41|43|45|57|
|CEE-5|44|<br>_59_|60|60|61|63|69|

Note: In computing the duty-free shares, the commodity composition of flows in 1992 is applied to the anticipated custom **s** tais of each item in all subsequent years.

# Source: Sce Table 6.

Another factor responsible for the different traniton paths is related to the different provisions for quota/ceiting increases. Until 1995 the share of liberalized products is the largest for Hungary's exports to the EC. In 1993-94 the difference between the FCSK and Poland on the one band, and Hungary on the other, decres etnks to the fister increase in duty-free quotas/ceilings for the former countries. In
of ...nancial dependence for each industry. The expectation is that if ...nancial linkages play a signi...cant role in helping establishments attain greater resilience to credit crunch, the role should be particularly pronounced in industries with greater ...nancial dependence.

The ...nancial dependence variable is measured using the share of capital expenditure not ...nanced with cash ‡ows from operations for the median ...rm in each industry, and captures establishments’dependence on other sources of capital supply such as external loans and intra...rm ...nancing. Similar to the ...nancial linkage variable, the ...nancial dependence measure is computed using the 2000-2004 publicly listed company data from Global COMPUSTAT, with the ranking of industries found relatively stable over time and highly correlated across major countries. Rajan and Zingales (1998) and Braun and Larrain (2005) argue that the measure of ...nancial dependence captures a large technological component that is innate to a sector and is therefore a good proxy for ranking industries in all countries. Industries with the greatest ...nancial dependence according to this classi...cation include, for example, electric machinery, textiles, transport equipment, plastic products, and primary metal.

## 3.4 Other Economic Characteristics

A variety of other establishment-level economic characteristics including sales, labor productivity, age, multi-product status, and exporter status, all lagged by two years, are also taken into account. We use lagged sales to control for establishment size, and labor productivity to proxy for e¢ ciency. We also considered alternative measures such as employment size and market share and found similar results. In addition, we take into account each establishment’s age and multi-product status. The latter variable is an indicator variable that identi...es establishments producing more than one product, and helps control for the e¤ect of product diversi...cation. Finally, we use an indicator variable to separate exporting establishments from establishments that sell only domestically. This variable helps capture the role of previous export participation.

# 4 Econometric Strategy

The primary goal of our empirical analysis is to evaluate, ...rst, the net e¤ect of foreign ownership on establishment performance during the crisis and, second, conditional on foreign ownership how production and ...nancial linkages led to the di¤erential responses of MNC subsidiaries.

An empirical issue that can arise when evaluating the net e¤ect of foreign ownership is the potential endogeneity of foreign ownership status. For example, one can argue that foreign MNCs may have performed better during the crisis because they acquired more productive local establishments or due to unobserved characteristics. If this were the case, the estimated 12
**Table 9.**<sup>**15**</sup> **Regressions for Bank Corruption (with T instrumented by latitude and ethnic diversity)**

||Ordered<br>Probit|Ordered<br>Probit|Ordered<br>Probit|Ordered<br>Probit|IV Provit|IV Probit|IV Provit|IV Provit|
|---|---|---|---|---|---|---|---|---|
|T_245|-.17***|-.07*|-.09**|-.11***|-1.11***|-1.14***|-1.09***|-1.57***|
||(-4.62)|(-1.92)|(-2.43)|(-2.95)|(10.82)|(-5.6)|(-5.78)|(-13.5)|
|Gvt_yn|-.11|-.02|-.03|-.17|-.26**|--.27**|-.26**|-.10|
||(-.97)|(-.26)|(-.3)|(-1.57)|(-2.22)|(-2.14)|(-2.07)|(-.97)|
|Frk_yn|-.13**|-.12**|-.114*|-.18***|-.09|-.10*|-.09|-.09*|
||(-2.43)|(-2.14)|(-2.0)|(-3.6)|(-1.62)|(-1.82)|(-1.64)|(-1.87)|
|Exp_yn|-.11**|-.18***|-.114***|-.18***|-.18***|-.23***|-.18***|-.25***|
||(-2.3)|(-3.73)|(-2.82)|(-3.98)|(-3.53)|(-4.5)|(-3.48)|(-5.89)|
|manuf|.09|.04|.05|.03|.11*|.10*|.11*|.12**|
||(1.55)|(.81)|(.99)|(.58)|(1.88)|(1.74)|(1.85)|(2.58)|
|service|.03|-.04|-.02|-.02|-.01|-.03|-.01|.04|
||(.62)|(-.90)|(-.47)|(-.57)|(1.47)|(-.62)|(-.18)|(.86)|
|lsale|-.005|-.006|-.003|-.004|.01|.008|.01|.03***|
||(-.72)|(-.82)|(-.49)|(-.61)|(1.47)|(1.08)|(1.33)|(4.3)|
|lncomp|.14*|.15*|.14*|.03|-.0005|-.01|.005|-.10|
||(1.82)|(1.91)|(1.77)|(.47)|(-.01)|(-.12)|(.06)|(-1.49)|
|Inflat_9599|.0002|.004*|-.0003|.007***|-.07***|-.0003|-.004|.01***|
||(.09)|(1.77)|(-.11)|(2.8)|(-3.55)|(-.13)|(-1.63)|(3.86)|
|Growth_9599|.04***|.03*|.025|.05**|.33***|-.07***|-.07***|-.01|
||(2.93)|(2.00)|(1.61)|(2.5)|(6.56)|(-2.72)|(-2.91)|(-.86)|
|Lpriv9599|-.05|.04|.079**|-.25***|.34***|.29***|.32***|.16***|
||(-1.61)|(1.16)|(2.01)|(-7.33)|(6.56)|(5.7)|(6.3)|(3.1)|
|Suppower_pc|.20***||.201||.19***||.19***||
||(8.01)||(.02)||(8.37)||(7.79)||
|Pmindex_pc||-.29***|-.29***|||.04|.02||
|||(-6.83)|(-6.77)|||(.46)|(.24)||
|apmindex||||.04***||||.34***|
|||||(2.67)||||(13.04)|
|Constant|||||4.63***|4.97***|4.61***|4.19***|
||||||(8.92)|(4.57)|(4.56)|(12.4)|
|# obs|2850|2850|2850|3380|2850|2850|2850|3380|
|Pseudo R<sup>2</sup>|.04|.04|.05|.04|||||

Note: _1) The first four ordered probit models are run with standard MLE with heteorskedasticity-robust standard errors. The dependent variable for ordered probit is Bank corruption, which is the response to the question “How problematic is corruption of bank officials for the operation and growth of your business?”. Answers vary between 1 (no obstacle), 2 (minor obstacle), 3 (moderate obstacle), and 4 (major obstacle).The dependent variable for the next four IV probit models is binary response based on Bank Corruption – with 1(no obstacle) recoded as 0, and all the other values recoded as 1(obstacle to various degree). See Appendix 2 for variable description and sources._ _(2): Numbers in parentheses are t statistics._ _(3): Coefficients with * denote significance at .10 level, ** at .05 level and *** for.01 level._ _(4): The Wald endogeniety tests for IV probit models all show that the IV regression is valid (in the sense that it is significantly different from the non-IV models)._ 15 Using muslim and catholic share of the population and other combinations gives similar results.

29
Gorton, Gary and Andrew Metrick (2012). Securitized banking and the run on repo. _Journal of Financial Economics_ , 104(3), 425-451.

Gourinchas, Pierre-Olivier, Philippe Martin, and Todd E. Messer (2018). The Economics of Sovereign Debt, Bailouts and the Eurozone Crisis. Mimeo.

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Jorgensen, Erika, and Jeffrey Sachs. (1989). Default and Renegotiation of Latin American Foreign Bonds in the Interwar Period. In Eichengreen, Barry and Peter H. Lindert (ed.) _The International Debt Crisis in Historical Perspective_ , Cambridge, MA: MIT Press, pp. 48-85.

J.P. Morgan (1999). Introducing the J.P. Morgan Emerging Markets Bond Index Global (EMBI Global). Kaminsky, Graciela L. and Pablo Vega-García (2016). Systemic and Idiosyncratic Sovereign Debt Crises, _Journal of the European Economic Association_ , 4(1), 80-114.

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Lizarazo, Sandra (2013). Default Risk and Risk Averse International Investors. _Journal of International Economics_ , 89(2), 317-330 Lorenzoni, Guido and Iván Werning (2018). Slow Moving Debt Crises. Mimeo.

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47
Policy Research Working Paper 6485

# **Abstract**

Competitive bidding is an important policy tool to procure goods and services from the market at the lowest possible cost. Under traditional public procurement systems, however, it may be difficult to purchase highly customized objects, such as energy efficiency services. This is because not only prices but also other nonmonetary aspects need to be taken into account. Multidimensional auctions are often used to evaluate multidimensional bids. This paper examines the bidding strategy in multidimensional auctions, using data from public energy service company projects in Japan. It shows that multidimensional auctions work well, as theory predicts. The competition effect is significant. In addition, strategic information disclosure, including walk-through and preannouncement of reserve prices, can also promote energy savings and investment. Risk sharing arrangements are critical in the energy service company market. In particular, the public sector should take regulatory risk.

This paper is a product of the Sustainable Development Departmen, Africa Region. 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 author may be contacted at aiimi@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
**Table 11. Advice/Treatment sought as a result of a child having a fever in the past two weeks**

||_No/Self_<br>_treatment_|_Public,_<br>_higher_<br>_level_|_Public,_<br>_lowerlevel_|_Private,_<br>_medical_|_Private,_<br>_commercial_|_Traditional_|_Total_|
|---|---|---|---|---|---|---|---|
|_WesternandCentral_|_Africa_|||||||
|QuintileI(poorest)|64.2|5.2|9.7|8.6|11.1|1.4|100|
|Quintile2|50.2|8.6|16.2|6.9|15.9|2.2|100|
|Quintile3|36.5|12.7|20.5|9.7|19.9|0.7|100|
|Quintile4|27.5|16.6|18.3|17.5|19.4|0.8|100|
|Quintile5(richest)|23.0|24.5|14.0|16.6|21.8|0.1|100|
|Total|43.0|12.2|15.7|10.9|17.0|1.2|100|
|_EasternandSouthern_|_Africa_|||||||
|QuintileI(poorest)|40.5|8.4|28.6|13.1|7.2|2.2|100|
|Quintile2|37.3|5.8|32.1|11.2|11.2|2.4|100|
|Quintile3|34.4|8.1|28.7|16.5|11.9|1.3|100|
|Quintile4|28.9|10.5|30.6|14.9|13.9|1.3|100|
|Quintile5(richest)|21.3|17.3|25.4|25.0|10.6|0.5|100|
|Total|33.2|9.5|29.2|15.6|10.9|1.6|100|

_Source:_ Author's calculations from pooled and weighted DHS data.

The use of lower-level public facilities is greater than that of higher-level facilities, especially for the poorest groups in both regions. Perhaps surprisingly, in Western and Central Africa the poorest use lower-level public facilities substantially less than people from the upper quintiles-the differential is on the order of 4 percentage points. This is not the pattern in Eastern and Southern Africa where the use of lower-level public facilities is fairly constant at around 30 percent, falling slightly in the richest quintile.

Seeking treatment from private sources, either medical or commercial, is about 25 percent. In both regions the use of private facilities increases over most of the wealth distribution, although it falls for commercial sources at the upper quintiles in Eastern and Southern Africa. This fall is (more than) made up for by an increase in the use of private medical facilities and government hospitals however. Overall in this region the use of all private facilities for treatment and advice increases from 17 percent in the poorest quintile to 28 percent in the richest. In Western and Central Africa the use of private facilities increases from 20 percent in the poorest quintile to 38 in the richest quintile.

# _Multivariate analysis_

Again, multivariate analysis can help sort out some of the confounding factors. The approach used here is to estimate a Multinomial Logit (MNL) model for the sample that reports having had fever in the past two weeks, i.e. an estimate of the correlates of treatment choice conditioned on the sample who were ill. The approach allows one to investigate the partial association between treatment choices and household wealth, after conditioning on the same set of variables as in the incidence analysis, i.e., observed _23_
linkage. However, since Chile’s FDI regime was liberal since the 1980s, lobbying by manufacturing industries for services liberalization would have occurred well before our sample period and is thus unlikely to be source of bias for our coefficient of interest.<sup>25</sup> On the other hand, the productivity growth of manufacturing plants in Chile in the 1990s may have been a driving force for services MNCs to invest in Chile in expectation of strong demand for services. This reverse causality could lead to an upward-biased coefficient on the services FDI linkage. While ECLAC (2004) argues that foreign investors were attracted to the sound performance of recently privatized services firms in Chile, there is no clear evidence that the performance of the manufacturing users of those services was a driving force for FDI. Nevertheless, strong productivity growth in some manufacturing industries in Chile may have provided an additional incentive for MNCs to invest in the country’s services sectors. Our choice of a services FDI linkage measure based on services FDI _stocks_ instead of FDI _inflows_ helps to mitigate this potential reverse causality. Moreover, our final empirical specification includes the services FDI linkage lagged two periods, instead of being lagged one period as in (4). The question that arises is whether the two-period lagged services FDI linkage is exogenous to current plant productivity growth. This would not be the case if plant productivity growth was serially correlated over time. While plant productivity _levels_ tend to exhibit strong serial correlation in micro datasets, this is not expected for plant productivity _growth_ . Indeed, our tests for first-order autocorrelation in plant productivity growth based on the BaltagiWu locally best invariant (LBI) test show no evidence of serial correlation. We therefore

> 25 In fact, one may even question whether such type of lobbying played a role given that the privatization of services firms starting in the late 1980s was partly motivated by the need to solve a public deficit problem (Bitran and Saez, 1994).

19
first enter information about the terms and conditions at which new debt would be contracted from each of the creditors.

If run as a "requirements" model, the DM computes values (or equations) for the interest rates on existing and new debt and the stock of existing debt for each year of the projection period. These values (or equations) are then exported to the RMSM-X model where they are used to construct the following foreign credit supply schedule:<sup>7</sup> (3.1) i*t =i e(F*e,_,/F *t,-) + i n*[(F*t,_l-F *el_)/F*t,_l] where i*t* i*e and **i*nl** are the implicit interest rates on total, existing and new debt, respectively; and F *t and F*e are the stocks of total and existing debt. Throughout this chapter the subscripts e, n and t, stand for "existing", "new" and "total" debt. Equation (3.1) is used in the RMSM-X model to solve the foreign credit market simultaneously with the rest of the macroeconomic model. In this way we obtain values for i*t and F*t, as well as for interest payments. The values obtained for F*t are fed back to the DM, where detailed projections of gross (GD) and net (ND) disbursements, commitments (C), amortization payments (AP), debt stocks (F) and interest payments (IP), are produced. To assemble 7 Unless otherwise specified all variables are defined in the current period (T). When the variables refer to a period other than T we use a time subscript relating the date of the variable with T. for instance, the total stock of debt in year T will be denoted F t, while the same variable in year T-1 will be denoted by F 1 40