rafmacalaba/datause-encoder-data huggingface dataset:
# **A3 Supplementary Tables**

Table A5: Overview of prior literature on management practices, energy, and productivity.

|**Year**|**Authors**|**Country**|**Time Period**|**Outcome Variable**|**Methodology**|**Main Result**|
|---|---|---|---|---|---|---|
|2012|Bloom,<br>Schweiger<br>and<br>Van<br>Reenen|Belarus,<br>Bulgaria,<br>Ger-<br>many, India, Kazakhstan,<br>Lithuania, Poland, Roma-<br>nia,<br>Russian<br>Federation,<br>Serbia,<br>Ukraine,<br>Uzbek-<br>istan|October 2008 to<br>April 2010|operating<br>revenue,<br>proft<br>margin,<br>EBIDTA,<br>returns<br>to total assets|measuring<br>and<br>scor-<br>ing management prac-<br>tices, in areas of oper-<br>ations, monitoring, tar-<br>gets, and incentives|Average<br>management<br>score is positively and sig-<br>nifcantly correlated with<br>total factor productivity.|
|2010|Bloom,<br>Mahajan,<br>McKenzie<br>and Roberts|United<br>States,<br>United<br>Kingdom,<br>Japan,<br>France,<br>Germany, Greece, Poland,<br>Brazil, Colombia, Ecuador,<br>Morocco, China, Indonesia,<br>Philippines, India||sales per employee||Firms in developing coun-<br>tries are often badly man-<br>aged, which substantially<br>reduces their productivity.|
|2012|Bloom,<br>Genakos,<br>Sadun<br>and<br>Van Reenen|Argentina,<br>Australia,<br>Brazil,<br>Canada,<br>Chile,<br>China,<br>France,<br>Germany,<br>Greece, India, Italy, Japan,<br>Mexico,<br>New<br>Zealand,<br>Poland, Portugal, Ireland,<br>Sweden, United Kingdom,<br>United States|2002 to 2012|sales, return on cap-<br>ital<br>employed,<br>sales<br>growth, exit|double-blind<br>survey<br>technique for manage-<br>ment scoring on 18 key<br>practices|Higher management scores<br>are<br>robustly<br>associated<br>with better performance.|
|2010|Bloom,<br>Genakos,<br>Martin<br>and<br>Sadun|United Kingdom|summer 2006|real gross output (sales<br>&<br>inventory<br>changes<br>defated with a sectoral<br>producer price index)|match<br>management<br>data<br>to<br>production<br>and<br>energy<br>usage<br>information|Better managed frms are<br>signifcantly more produc-<br>tive.<br>Management prac-<br>tices that are associated<br>with improved productiv-<br>ity are also linked to lower<br>greenhouse gas emissions.|

**45**
66 Holland, 1990, pp. 3-62.

- Pagano, Marco. "Financial Markets and Growth: An Overview," European Economic Review, April 1993, 32(2-3), pp. 613-22.

- Park, Yun Chul. "The Role of Finance in Economic Development in South Korea and Taiwan," in Financial and Development: Issues and Experience, ed. Alberto Giovannini, Cambridg: Cambridge University Press, 1993, pp. 121-50.

- Patrick, Hugh. "Financial Development and Economic Growth in Underdeveloped Countries," Economic Development Cultural Change, January 1966, 14(2), pp. 174-89.

- Patrick, Hugh T, and Park, Yung Chul. Financial Development in Japan, Korea and Taiwan. New York: Oxford University Press, 1994.

- Petersen, M.A. and Rajan, Raghuram G. "The Benefits of Lending Relationships: Evidence from Small Business Data," Joumal of Finance March 1994,42(1), pp. 3-37.

- Pollard, Sidney and Ziegler, Dieter. "Banking and Industralization: Rondo Cameron Twenty Years On," in ed. Youssef Cassis, Finance and Financiers in European History 18801960 New York: Cambridge University Press, 1992, pp. 17-38.

- Pozdena, Randal J. and Volbert, Alexander. "Bank Structure in West Germany" in Ranking Structures in Mraor Countries, ed. George Kaufman, Boston: Kluwer, 1992, pp. 555-90.

- Rajan, Raghuram G. "Insiders and Outsiders: The Choice Between Informed and Armslength Debt," Journal of Finance September 1992,42(4), pp. 1367-1400.

- Rajan, Raghuram G. and Zingales, Luigi. "Financial Dependence and Growth," University of Chicago mimeo, May 1996.

- Rebelo, Sergio T. "Long-Run Policy Analysis and Long-Run Growth," Journal of Political Economics, June 1991, 92(3), pp. 500-21.

- Robinson, Joan. "The Generalization of the General Theory," in the Rate of Interest and Other Essays London: MacMillan, 1952.

- Romer, Paul M. "Increasing Returns and Long-Run Growth," Journal of Political Economics, October 1986, 94(5), pp. 1002-37.

- Romer, Paul M. "Endogenous Technological Change," Journal of Political Economics, Oct. 1990, _2a(5,_ Pt. 2), pp. S71-S102.
Table 4: Employment Choice and Land Restrictions: Control functions Results

||Non-Farm|Self-employ.<br>Wa|ge employment i|n|
|---|---|---|---|---|
||Employment|Non-Agriculture<br>Agriculture|Manufacturing|Services|
||(Marginal Effects)|<br>(Marginal|effects)||
|Proportion of Area Under LDO|-1.382***|-0.147<br>0.594***|-0.424**|-0.713***|
||(-5.094)|(-0.950)<br>(2.593)|(-2.450)|(-2.589)|
||<br>(Probit|<br>|||
||Coefficients)|( Multinomial Log|it Coefficients)||
|Proportion of Area Under LDO|-3.817***|-6.430***<br>-2.037|-8.032***|-7.185***|
||(-5.094)|(-3.721)<br>(-1.197)|(-4.183)|(-4.821)|
|First stage Regressions|||||
|Malaria||0.00354***<br>(4.950)|||
|**Relevance of Instruments**|||||
|Kleibergen-Paap/Angrist-Pischke F||24.5|||
|Stock-Yogo 10% max. rel. IV bias||9.08|||
|**Controls**|||||
|Individual characteristics|Yes|Yes<br>Yes|Yes|Yes|
|Household characteristics|Yes|Yes<br>Yes|Yes|Yes|
|Area characteristics|Yes|Yes<br>Yes|Yes|Yes|
|Observations|8,948|8,948<br>8,948|8,948|8,948|

All regressions include individual's age, education, marital status, and dummies for household's religion/ethnicity and a full set of area controls including travel time to cities, disease environment, land and labor productivity indicators and ethnic composition. Robust t statistics in parentheses. Standard errors corrected for clustering at the sub-district level (DSD) * significant at 10%; ** significant at 5%; *** significant at 1% Table 5: Employment Choice and Land Restrictions: Robustness of Control functions Results

||Non-Farm|Self-emplo|y.<br>Wa|ge employment i|n|
|---|---|---|---|---|---|
||Employment|Non-Agri.|<br>Agriculture|Manufacturing|Services|
|1947+ sample|||(Marginal effects)|||
|Proportion of Area Under LDO|-1.436***|-0.164|0.577***|-0.421**|-0.760***|
||(-5.314)|(-1.070)|(2.581)|(-2.510)|(-2.811)|
|Population within 5 hrs of travel<br>time||||||
|Proportion of Area Under LDO|-1.510***|-0.132|0.657***|-0.488***|-0.791***|
||(-5.512)|(-0.951)|(2.891)|(-3.214)|(-2.924)|
|Controlling for Western province||||||
|Proportion of Area Under LDO|-1.225***|-0.141|0.442*|-0.429**|-0.558**|
||(-4.478)|(-0.940)|(1.947)|(-2.310)|(-1.976)|
|Restricted sample: proportion LDO<|0.3|||||
|Proportion of Area Under LDO|-2.904***|-0.464|1.535**|-0.409|-1.821**|
||(-4.069)|(-1.330)|(2.526)|(-0.744)|(-2.526)|

All regressions include individual's age, education, marital status, and dummies for household's religion/ethnicity and a full set of area controls including travel time to cities, disease environment, land and labor productivity indicators and ethnic composition. Robust t statistics in parentheses. Standard errors corrected for clustering at the sub-district level (DSD) * significant at 10%; ** significant at 5%; *** significant at 1%
fit the De Soto view, where firms have some potential to grow, yet due to burdensome regulations, they are not able to join the formal sector. Firms above the 75<sup>_th_</sup> percentile threshold could be classified as "parasite" firms as, conditional on their characteristics, they have a level of productivity comparable to or even higher than the top quartile of formal firms but they still remain in the informal sector.<sup>8</sup> Under this framework, we can classify all firms in our sample into four categories: (1) Informal-necessity, (2) Informal-De Soto view, (3) Informal-parasite, and (4) Fully formal. Figure 1, shows the kernel density of this conditional productivity for the four types of firms in our sample, using PCA(5) as the informality index. As expected, the informal firm’s productivity distribution has a heavy left tail compared to formal firms that have a thicker right tail. Yet, there is a huge overlap in the productivity distribution across these firms, suggesting that the reasons for staying informal at the lower end of the distribution might be very different than at the higher end.<sup>9</sup> This also points to the possibility that the shock may not affect all informal firms uniformly.

Figure 1: Distribution of conditional productivity across types of informality <!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>0<br>-5 0 5 10<br>IHS transformation labor productivity<br>conditional on size, sector, age, female ownership, and country<br>Fully formal Necessity<br>De Soto Parasite<br>Informal (all types)<br>Kernel density<br><!-- End of picture text --> Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

8Arguably, our classification hinges on the arbitrary quartile definition, but, in general, firms classified under these three categories are at least similar to the informality view they intend to represent. We also tested a stricter threshold with necessity firms below the minimum of the conditional productivity, and the results (available upon request) do not change much, but standard errors increase significantly due to the smaller number of observations in the necessity category of informality.

9By construction, we observe more overlap between the distribution of De Soto type of firms and the distribution of formal firms, while the density for necessity firms is shifted to the left. The classification is constructed at the country-sector level, and this figure pools all countries and sectors together.

9
Table 10: Health <u>policy (</u> _D_ = 1)

|Year|1990|2000|2010|2020|2030|2040|2050|
|---|---|---|---|---|---|---|---|
|_e_<sup>1</sup><br>_t_|1.000|1.000|1.000|1.000|1.000|1.000||
|_e_<sup>2</sup><br>_t_|0.000|0.067|0.133|0.292|0.550|0.701||
|_λ_<sup>2</sup><br>_t_|3.69|4.29|4.33|4.64|4.95|5.52|6.38|
|_λ_<sup>3</sup><br>_t_|4.52|3.69|4.58|5.00|6.04|7.59|9.39|
|_yt_|2647|2346|2458|2554|2694|3058||
|_Yt_(10<sup>9</sup>)|29.97|37.13|52.24|66.11|79.28|89.84||
|Population (10<sup>3</sup>)|23475|29818|34633|37880|40937|41028||
|Subsidy<sup>_a_</sup>||0.94|1.29|1.82||||

> _<u>a</u>_ in % of the AIDS GDP level without any intervention.

2020 and 2030. The reason for the latter is the reduction in _Q_<sup>2</sup> _t_<sup>andtheincreasein</sup> full income per adult that stem from the cumulative reductions in premature adult mortality up to 2030, since the ending of both spending programmes in 2029 leaves them with identical mortality profiles thereafter. _λ_<sup>3</sup> 140<sup>justexceedsthelevelattained</sup> under the educational programme, while _λ_<sup>2</sup> 140<sup>lies just below its corresponding level (see</sup> Table 10). The lags involved are sufficiently drawn out, however, that _y_ 140 is still 5 per cent lower. The reductions in mortality would lead to a more numerous population in 2040 (by about 10 per cent), and in 2050 new graduates are more productive. It is this consequence of spending on measures to reduce premature adult mortality that make – them socially profitable against the alternative of spending directly on education at least when the time horizon is long enough.

Yet another alternative is to combine the two programmes. Table 11 reports the results from spending half the stream of educational subsidies paid out under the ’pure’ programme _σt_<sup>2=400(seeTable9)onmeasurestocombattheepidemicandtreat</sup> its victims, while reducing the educational subsidy from 400 to 200. This combined programme would cost 0.82 per cent of GDP in 2000, 1.20 per cent in 2010 and 1.79 41
Assessing deprivation with an ordinal variable: theory and application to sanitation deprivation in Bangladesh Suman Seth<sup>_∗_</sup> Gaston Yalonetzky<sup>_†_</sup>

# **1. Introduction**

Around four decades ago, in an influential article titled ‘Poverty: An Ordinal Approach to Measurement’, Nobel laureate Amartya Sen proposed an axiomatically derived poverty measure to avoid some shortcomings of the traditionally used headcount ratio (Sen, 1976). Sen’s approach was ordinal in the sense that his poverty measures assigned an ordinal-rank weight to each poor person’s income, an otherwise cardinal variable. Since then, this seminal article has influenced a well-developed literature on poverty measurement involving cardinal variables within an axiomatic framework (Thon, 1979; Clark et al., 1981; Chakravarty, 1983; Foster et al., 1984; Foster and Shorrocks, 1988a,b; Ravallion, 1994; Shorrocks, 1995).

- _∗_ Leeds University Business School, University of Leeds, UK and Oxford Poverty & Human Development

Initiative, University of Oxford, UK, S.Seth@leeds.ac.uk.

> _†_ Leeds University Business School, University of Leeds, UK, G.Yalonetzky@leeds.ac.uk.
TABLE 4A Panel regression results for subsamples: All sectors

||All sectors<br>Advanced<br>economies<br>only|Advanced<br>economies<br>an<br>EMDEs|d<br>EMDEs<br>only|Commodity<br>importers<br>only|Commodity<br>importers<br>an<br>exporters|d<br>Commodity<br>exporters<br>only|
|---|---|---|---|---|---|---|
|Liner Shipping<br>Connectivity<br>Index|-0.195***<br>[0.0237]|-0.230***[0.0111]|-0.209***<br>†<br>[0.0093]|-0.230***<br>[0.00886]|-0.231*** ‡<br>[0.0109]|-0.198*** ‡<br>[0.0226]|
|Logistics<br>Performance<br>Index|-1.526***<br>[0.107]|-0.298*** †<br>[0.055]|-0.277***<br>†<br>[0.048]|-0.596***<br>[0.0438]|-0.317*** ‡<br>[0.0471]|-0.273*** ‡<br>[0.097]|
|Distance|0.343***<br>[0.0157]|0.221*** †<br>[0.0112]|0.233*** †<br>[0.00993]|<br>0.306***<br>[0.0103]|0.223*** ‡<br>[0.00962]|0.241*** ‡<br>[0.0202]|
|Tariffs|-0.0998<br>[0.239]|0.517*** †<br>[0.0916]|0.203*** †<br>[0.0488]|<br>0.189**<br>[0.0878]|0.453*** ‡<br>[0.0647]|0.238***<br>[0.0682]|
|Regional trade<br>agreement<br>membership|-0.00793<br>[0.0125]|-0.0289***<br>[0.00714]|-0.0850***<br>†<br>[0.0117]|<br>-0.0326***<br>[0.00789]|-0.0545***<br>[0.00844]|-0.0796***<br>[0.0265]|
|Trade policy<br>uncertainty|0.0383***<br>[0.0128]|<br>0.0897***<br>†[0.00706]|0.0583***<br>†<br>[0.00684]|<br>0.0799***<br>[0.00633]|0.0794*** ‡<br>[0.00666]|0.0103 ‡<br>[0.0132]|
|Common border|-0.389***<br>[0.114]|<br>-0.380***<br>[0.136]|-0.453***<br>[0.0334]|-0.327***<br>[0.0581]|-0.494*** ‡<br>[0.0566]|-0.356***<br>[0.0541]|
|Common<br>language|-0.166***|-0.0878*** †|-0.186***|-0.117***|-0.145***|-0.273*** ‡|
|Observations|504|668|102|802|450|408|

# Source: World Bank.

Note: * p<0.05, ** p<0.01, ***p<0.001,. Robust standard errors are shown in parentheses. † indicates statistically significant difference of the coefficient estimate from the coefficient estimate for a sample of advanced economies only. † indicates statistically significant difference of the coefficient estimate from the coefficient estimate for a sample of commodity importers only. The table shows estimated coefficients from a gravity panel regression estimated for up to 95 countries using annual data for 2007-18 where the dependent variable is the log of bilateral trade costs. The regression includes time fixed effects. Standard errors are clustered by country pairs.
**Table 2. Socioeconomic Costs of Violence: A Typology**

|Direct monetary<br>costs|Value o<br>•<br>•<br>•<br>•<br>•<br>•|f goods and services used in treating or preventing violence:<br>Police<br>Criminal justice system<br>Medical<br>Psychological counseling<br>Damage to physical infrastructure (housing, etc.)<br>Socialservices|
|---|---|---|
|Non-monetary costs|Pain an<br>•<br>•<br>•<br>•|d suffering:<br>Increased morbidity<br>Increased mortality via homicide and suicide<br>Abuse of alcohol and drugs<br>Depressive disorders|
|Economic multiplier<br>effects|Macroe<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•|conomic, labor market, intergenerational productivity effects:<br>Decreased labor market participation<br>Reduced productivity on the job<br>Lower earnings<br>Increased absenteeism<br>Intergenerational productivity impacts via grade repetition and<br>lower educational attainment of children<br>Decreased investment and savings<br>Capital flight<br>Decreased tourism<br>Decreased government revenue<br>Impact onpolicy-making by distorting government spending|
|Social multiplier<br>effects|Impact<br>•<br>•<br>•<br>•<br>•<br>•|on interpersonal relations and quality of life:<br>Intergenerational transmission of violence<br>Erosion of human capital<br>Erosion of social capital and the social fabric<br>Reduced quality of life<br>Erosion of the state’s credibility<br>Reduced participation indemocratic processes|

_Source_ : Adapted from Morrison, Buvinic and Shifter (2003).

In addition to these direct costs, crime and violence have significant “multiplier” effects on the economy by depressing savings, investments, earnings, productivity, labor market participation, tourism, and ultimately growth. A recent study for Colombia suggests that for every additional 10 homicides per 100,000 residents, the level of investment falls by approximately 4%.<sup>49</sup> Put another way, if homicide rates in Colombia had remained unchanged since the 1960s, total annual investment in Colombia today would be around 20% higher. Over time, violence and its costs can become nearly self-fulfilling, with violence leading to lower investment and a deteriorating economy, which in turn can unleash new bouts of aggression and crime. As mentioned in the introduction, a study on Colombia by Rubio (1996) concludes that the cumulative effect of “lost growth” as a result of crime and violence has been such that the country would today have a per capita

> 49 Buvinic and Morrison (2000).

9
# Figure B4. Papers with Publication Biases used Ad Hoc Surveys to Evaluate R&D Additionality

|Study<br>ID||**ES (95% CI)**|**%**<br>**Weight**|
|---|---|---|---|
|Mixed data||||
|04AUT01||**4.06 (3.54, 4.65)**|**0.56**|
|10TUR11||**1.26 (0.99, 1.59)**|**0.19**|
|10ITA17||**1.37 (1.09, 1.71)**|**0.21**|
|10SER18||**1.15 (1.01, 1.30)**|**0.69**|
|10JAP20||**1.05 (0.99, 1.11)**|**3.51**|
|09ECA26||**1.27 (1.12, 1.44)**|**0.67**|
|10MEX34||**1.26 (0.99, 1.59)**|**0.19**|
|Subtotal (I-squared = 98.2%, p = 0.000)||**1.25 (1.20, 1.31)**|**6.02**|
|Survey||||
|05DEU02||**1.43 (1.13, 1.80)**|**0.19**|
|10WBC04||**1.19 (1.08, 1.30)**|**1.19**|
|04BEL05||**1.03 (1.01, 1.05)**|**22.73**|
|11EUC06||**1.05 (1.03, 1.08)**|**16.53**|
|10RUS08||**1.11 (1.06, 1.16)**|**4.77**|
|10ARG10||**1.36 (1.00, 1.87)**|**0.11**|
|10DNK12||<br>**1.33 (0.98, 1.80)**|**0.11**|
|10CAN13||**1.10 (1.07, 1.13)**|**16.27**|
|10EUC14||**1.23 (1.05, 1.44)**|**0.43**|
|10TUR15||**1.22 (1.18, 1.25)**|**12.22**|
|10AUT16||**1.35 (1.25, 1.47)**|**1.64**|
|10EUC22||<br>**3.00 (1.11, 8.16)**|**0.01**|
|09EUC24||**. (0.00, .)**|**0.00**|
|08TUR27||**1.36 (0.96, 1.94)**|**0.08**|
|08FIN28||**1.45 (0.94, 2.24)**|**0.06**|
|08GER29||**1.35 (1.22, 1.50)**|**0.98**|
|06EUC30||**2.00 (0.98, 4.10)**|**0.02**|
|07HRV31||**. (0.00, .)**|**0.00**|
|06BGR33||**. (0.00, .)**|**0.00**|
|05FIN35||**2.47 (2.36, 2.59)**|**5.10**|
|11ITA37||**1.21 (0.93, 1.58)**|**0.15**|
|Subtotal (I-squared = 98.5%, p = 0.000)||**1.16 (1.14, 1.17)**|**82.59**|
|Not specified||||
|08SVK03||**1.31 (0.95, 1.81)**|**0.10**|
|Subtotal (I-squared = .%, p = .)||**1.31 (0.95, 1.81)**|**0.10**|
|no data used||||
|11ECA07||**1.03 (0.94, 1.14)**|**1.10**|
|10CEE21||**. (0.00, .)**|**0.00**|
|Subtotal (I-squared = 0.0%, p = 0.317)||**1.03 (0.94, 1.14)**|**1.10**|
|Administrative records||||
|10USA09||**1.14 (0.35, 3.66)**|**0.01**|
|10UKR19||**1.18 (1.00, 1.39)**|**0.41**|
|09CEE23||**1.67 (1.09, 2.58)**|**0.06**|
|09FIN25||**1.28 (0.96, 1.72)**|**0.12**|
|07MUC32||**1.35 (0.93, 1.97)**|**0.07**|
|10ESP36||**1.36 (1.32, 1.41)**|**9.52**|
|Subtotal (I-squared = 0.0%, p = 0.525)||**1.36 (1.31, 1.40)**|**10.19**|
|Heterogeneity between groups: p = 0.000||||
|Overall (I-squared = 97.9%, p = 0.000)||**1.18 (1.17, 1.19)**|**100.00**|
|1.0e-08|**1**|1.0e+08||

Source: Own estimations 37
generally, have been difficult to detect. This paper has focused exclusively on within-country variations to trade policy changes while carefully disaggregating factor endowments. Overall, the results suggest that changes in inequality are correlated with changes in tariffs which are quite robust to inclusion of various controls and to changes in sample periods.

Three patterns emerge from these conditional correlations. First, as suggested by factor-proportions theories of international trade, increases in inequality are positively correlated with trade liberalization in countries well-endowed in highly skilled workers and with workers that have very low education levels. Decreases in inequality are positively correlated with trade liberalization in countries that are well-endowed with primary-educated labor. Increases in inequality are positively correlated with trade liberalization in countries relatively well-endowed in mining and fuels production, assets which are very unequally distributed. Similar results are apparent when decile data are used instead of the usual Gini coefficient. Thus, if one extends the factor-proportions theory of trade to include a non-traded sector where those with minimal education are most likely to be employed, trade liberalization in poor countries where the share of the labor force with little education (workers that have not finished primary school) is high is likely associated with increases in inequality as is often pointed out by critics of globalization. Trade liberalization is also associated with increases in inequality in capitalabundant and high-skill abundant countries so that trade liberalization only reduces inequality in countries abundant in unskilled labor.

Second, the results on the pattern of signs are quite robust, and the addition of control variables yields plausible results. We find no evidence of reverse causality. Controlling 24