rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Female-owned activity by distance ring to 7 largest cities

||Share o|f establishm<br>are fem|ents in eac<br>ale-owned|h band that|Share o|f employee<br>f|s in each b<br>emale|and who are|Share|of female e<br>owned es|mployees<br>tablishme|in female-<br>nts|Share o|f male emp<br>estab|loyees in<br>lishments|male-owned|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Distance|2001|2006|2010|Average|2001|2006|2010|Average|2001|2006|2010|Average|2001|2006|2010|Average|
||||||||A.|Unorganized|manufact|uring|||||||
|0-100|0.225|0.393|0.400|0.339|0.222|0.279|0.282|0.261|0.803|0.852|0.917|0.857|0.863|0.874|0.890|0.876|
|100-200|0.298|0.468|0.446|0.404|0.362|0.443|0.421|0.408|0.903|0.961|0.961|0.942|0.758|0.790|0.802|0.783|
|200-300|0.297|0.301|0.383|0.327|0.322|0.306|0.333|0.320|0.854|0.864|0.918|0.879|0.803|0.820|0.858|0.827|
|300-400|0.312|0.461|0.448|0.407|0.369|0.434|0.409|0.404|0.880|0.928|0.955|0.921|0.775|0.813|0.853|0.814|
|400-500|0.310|0.429|0.387|0.375|0.350|0.434|0.338|0.374|0.894|0.950|0.948|0.931|0.802|0.792|0.874|0.823|
|500-600|0.188|0.292|0.248|0.243|0.314|0.327|0.267|0.302|0.909|0.911|0.843|0.888|0.779|0.834|0.860|0.824|
|600-700|0.180|0.177|0.255|0.204|0.327|0.356|0.312|0.332|0.889|0.887|0.945|0.907|0.757|0.727|0.840|0.775|
|700-800|0.229|0.209|0.325|0.254|0.309|0.247|0.321|0.292|0.908|0.924|0.956|0.929|0.795|0.859|0.852|0.835|
|800+|0.086|0.285|0.354|0.242|0.286|0.305|0.282|0.291|0.904|0.945|0.987|0.945|0.763|0.870|0.943|0.859|
|||||||||B. Ser|vices||||||||
|0-100|0.099|0.100|0.110|0.103|0.178|0.157|0.162|0.166|0.766|0.787|0.864|0.806|0.894|0.915|0.916|0.908|
|100-200|0.092|0.100|0.107|0.100|0.209|0.193|0.196|0.199|0.771|0.792|0.874|0.812|0.848|0.876|0.872|0.866|
|200-300|0.089|0.082|0.086|0.086|0.156|0.164|0.157|0.159|0.696|0.742|0.864|0.768|0.893|0.892|0.906|0.897|
|300-400|0.071|0.075|0.082|0.076|0.161|0.174|0.156|0.164|0.721|0.793|0.854|0.789|0.889|0.877|0.903|0.889|
|400-500|0.083|0.094|0.069|0.082|0.152|0.171|0.123|0.149|0.689|0.792|0.811|0.764|0.897|0.890|0.929|0.905|
|500-600|0.060|0.096|0.079|0.078|0.133|0.176|0.152|0.154|0.760|0.814|0.810|0.794|0.919|0.889|0.904|0.904|
|600-700|0.073|0.070|0.068|0.070|0.174|0.154|0.117|0.148|0.807|0.720|0.857|0.795|0.866|0.888|0.927|0.894|
|700-800|0.029|0.054|0.059|0.047|0.122|0.135|0.144|0.133|0.766|0.688|0.753|0.736|0.906|0.906|0.895|0.902|
|800+|0.058|0.081|0.063|0.067|0.093|0.124|0.125|0.114|0.772|0.852|0.819|0.815|0.942|0.930|0.934|0.935|

Notes: See Table 1. Seven largest cities are Mumbai, Delhi, Kolkata, Chennai, Bangalore, Hyderabad and Ahmedabad. Distances measured in km.
# **G Robustness Tests**

Table G.1. Effects of the Intervention on the General Indices Including Additional Controls

||Prosociality<br>Index<br>(1)|Attitudes<br>Index<br>(2)|Beliefs<br>Index<br>(3)|
|---|---|---|---|
|Game [=1]|0.252***|0.318***|0.151*|
||(0.081)|(0.078)|(0.080)|
||[0.003]|[0.001]|[0.038]|
|Video [=1]|0.195**|0.312***|0.013|
||(0.090)|(0.084)|(0.086)|
||[0.023]|[0.001]|[0.172]|
|Diff. p-value|0.491|0.939|0.096|
|YWY joint test|0.003|0.001|0.099|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|
|Observations|815|814|814|

_Notes:_ This table presents OLS estimates derived from Equation 1. The dependent variables are constructed according to the methodology outlined by Kling, Liebman and Katz (2007) for three groups of outcome variables: (i) Prosocial Behavior (detailed in Table 2), (ii) Attitudes (detailed in Table 4), and (iii) Beliefs (detailed in Table 5). All columns control for gender, student status (1 if the person is a student, and 0 otherwise), age, marital status (1 if the person is married, and 0 otherwise), religious affiliation (1 if the person is Catholic, and 0 otherwise), education level indicator variables (primary, secondary, university or more), income, economic strata (1 if the person belongs to a low economic strata, and 0 otherwise), and whether they have children or not. The Diff. p-value reports the p-value of a Wald test of equality of coefficients between the game treatment and video treatment. YWY joint test reports the sharpened False Discovery Rate (FDR) q-values. Significance levels are denoted as:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

55
Brandolini (2001), who show that national statistical agencies differ in their income measures so that cross-country comparability of income inequality is questionable. Moreover, changes in definitions or ruptures within country series may suggest structural shifts in inequality without real significance. Banerjee and Duflo (2003) argue that this type of measurement error may seriously distort causal inference in inequality-growth models. Brock and Durlauf (2001) reject causal interpretations in cross-country studies except under quite exceptional conditions. Their main argument is that causal interpretation requires that estimated parameters can be assumed constant, which is not plausible given the importance of country-specific unobserved information (e.g. regarding policy). Deininger and Okidi (2003) also argue that data used in cross-country studies are national aggregates that are likely to lose valuable region or gender specific information; as a result, they question the relevance of crosscountry evidence for national policy formation – even in case of perfect data.

A related, but often ignored, measurement issue affecting growth and inequality regressions is related to the way the dependent variable is defined. Consider growth over a period _t_ for a country or region _i_ , usually specified as where _y_ is a measure of income or expenditure. This measure is often specified as the logarithm of the mean of per capita expenditure over households _h_ for country/region _i_ , i.e.

5
Policy Research Working Paper 8503

# **Abstract**

The Belt and Road Initiative seeks to deepen China’s international integration by improving infrastructure and strengthening trade and investment linkages with countries along the old Silk Road, thereby linking it to Europe. This paper uses detailed bilateral trade data for 1995–2015 to assess the degree of exposure of Belt and Road economies to China trade shocks. The econometric results reveal that China’s trade growth significantly affected the exports of Belt and Road economies. Between 1995 and 2015, the magnitude of China’s demand shocks was larger than that of its competition shocks. However, competition shocks became more important in recent years, and were highly heterogeneous across countries and industries. Building on these findings, the paper documents the current degree of exposure of Belt and Road economies to China trade shocks, and discusses policy options to deal with trade-induced adjustment costs.

This paper is a product of the Development Research 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/research. The author may be contacted at pbastos@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
# Hosting new neighbors: Perspectives of host communities on social cohesion in eastern DRC<sup>*</sup>

Phuong Pham,<sup>†</sup> Thomas O’Mealia,<sup>‡</sup> Carol Wei,<sup>§</sup> Kennedy Kihangi Bindu,<sup>¶</sup> Anupah Makoond,<sup>||</sup> & Patrick Vinck<sup>**</sup>

**_JEL Codes:_** D74, F22, C83, N47, O15, R23

**_Keywords:_** Displacement, Hosting, Social Cohesion, Surveys, Democratic Republic of Congo

> *Pham and O’Mealia are co-first authors. Acquisition of the data used in this manuscript was supported by the United Nations Development Programme (UNDP). The funder played no role in the analysis, interpretation or writing of the results and decision to submit the manuscript.

This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

†Assistant Professor, Harvard TH Chan School of Public Health and Harvard Medical School, USA ‡Postdoctoral Fellow, Harvard TH Chan School of Public Health, USA §Research Consultant, Department of Emergency Medicine, Brigham and Women’s Hospital, USA ¶Professor, Universit ́e Libre des Pays des Grands Lacs, DR Congo

||Research Manager, Harvard Humanitarian Initiative, USA

**Assistant Professor, Harvard TH Chan School of Public Health and Harvard Medical School, USA
Figure 6: Real Consequences in the U.S. of U.S. Deficit Reduction: Permanent versus Tenmpoarary versus Anticipated <!-- Start of picture text --> U.S, Real GOP U.S. Private Consumption<br>1.2 .<br>1 '3⁄4 .,.,..... . ,<br>0.8 3~~~~~~~~~~~~~~~~~~~~~~~~~~~~.8<br>-0.4 0.2<br>Ij t -TsmprarG t _ Ea tE<br>-1.4 6 -- -- &0.6<br>1924 1890 1220 2000 20 2v04 2WiO 2005 2010 2012 I1i4 Ie fl1a z.goo 2902 MI4 2C00 lCCI 2010 2212<br>Year Year<br>US 1penTrgg Cut 41.6 of G O.P) U X Endlng Cut I.6 ofd G0 P.)<br>U.S. Private Investment U.S. Trade Balance<br>j 0.4 06<br>0.4<br>5 0.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.<br>_,- El -I m4ez 0.0 D~ L|'~=5 fl.O - 3" W L 6 -Thminnest<br>-0.2 1894 1994 168 200 2002 2DJT054 200tl 20105 203 202 - 1 9J10 1994 aeis22 3e002 2004 2900 2006 081C0 2Q1<br>Yccr Year<br>U.S. 598M9,q Cut (3.62 Of 0.0.9'.) U.2. 59611rg1 043 43.5i 63 G.O.P.)<br><!-- End of picture text -->
**Table 2: Sources of Growth at firm and national levels, 2002‐05**

|TFP growth contribution to output<br>growth|Macro level<br>0.22|Firm<br>Level<br>0.96|
|---|---|---|
|Capital growth contribution to output<br>growth|0.28|0.04|
|labor growth contribution to output|‐0.08|0.00006|
|growth|||

Note: 1) the contribution is calculated as covariance between input growth and output growth over variance of output growth. 2) Due to data availability limit, the above contribution is calculated using firm level survey data in 9 countries: Brazil (814 firms), El Salvador (8 firms), Guatemala (5 firms), Honduras (2 firms), India (846 firms), Madagascar (72 firms), Mauritius (59 firms) and South Africa (389 firms)

**(a) TFP growth and output growth.** At the firm level, the median output growth is 2.88 percent per year while median TFP growth is 1.41 percent per year.  This may imply that TFP accounts for about 49 percent of the output growth.  Such a conclusion ignores the fact that TFP growth may be inducing decisions to invest in physical and human capital.  In other words, inputs are endogenous to TFP and output growth.  Klenow and Rodriquez-Clare (1997) have an ingenious way of disentangling this effect as they calculate the contribution of TFP growth to additional output (over and above the average growth rate).  Their calculation is based on the covariance between TFP growth and output growth divided by the variance of output growth.  In our case, TFP’s contribution turns out to be not 49% but 96% on firm level.

The main findings so far are that:

**1.** TFP is important both at micro and macro levels, and therefore macro-level productivity growth are linked to micro-level efficiency to allocate inputs and output across businesses (in line with Syverson 2009).  However, it is less important at the macro level and more important at the micro level, implying that there is not a one-to-one linkage or closer link as found in the literature using US industry data.

**2.** Capital accumulation is important both at micro and macro levels – more important at macro level than at micro level.

**3.** The contribution of TFP to output growth depends crucially on the share of physical capital in real output (alpha).  The higher the alpha, the lower is the contribution of TFP to growth because decreasing alpha lowers the contribution of physical capital (K) and increases the contribution of labor (L).

**4.** Alpha of 0.87 across our sample countries is very high than estimates found in other studies, which is normally betweens 0.35 to 0.40, especially for industrial countries.  This higher alpha confirms the lower contribution of TFP to growth in developing countries compared with industrial countries.  It also aligns with other studies that found large left tail of poorly managed firms in developing countries.

10
<!-- Start of picture text --> 74<br>:; COAH<br>° \ NL ° \i<br>% of establishments Totes % of labor \ AMPS<br>that participate sy : . ; that Participatesin wv at .<br>in GVCs by state \, et oe ; — ) in weby stateby state state aw?wv <) ).<br>0.00 to 0.05 Me gto2 Pt<br>0.050.10 0. 1520 to to to 0.15 0.10 0.2 50 , <PoucrNesVt- eh de eenNIban , 21046t08810 toto 10126t08810 toto 1012810 toto 101210 toto 1012 toto 1012to 1012 101212 : <n ON peasy .<br>0.25 to 0.30 12 to 14<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>; COAH<br>° \i<br>% of labor \ AMPS<br>that Participatesin weby stateby state state aw?wv at <) ). )<br>gto2 Pt<br>: <n ON peasy .<br>21046t08810 toto 10126t08810 toto 1012810 toto 101210 toto 1012 toto 1012to 1012 101212<br>12 to 14<br><!-- End of picture text -->
# **Tables**

Table 1: User Perceptions of Buses & _Pink Slip_ Scheme

|||(1)||(2)|t-test|
|---|---|---|---|---|---|
|Variable|N|Mean/SD|N|Mean/SD|(1)-(2)/SE|
|_Panel A. Nonusers vs Users_||||||
||N|onusers||Users|Difference|
|Affordability and availability of|184|3.196|1,106|3.914|-0.718***|
|free commute||(0.786)||(0.820)|(0.065)|
|Safety against accidents, crashes,<br>threats, and thefts|184|1.630<br>(0.640)|1,106|4.052<br>(0.878)|-2.422***<br>(0.068)|
|Connectivity|184|2.772<br>(0.798)|1,106|4.046<br>(0.919)|-1.274***<br>(0.072)|
|Bus frequency, waiting time, travel<br>time, and unnecessary stops|184|2.484<br>(0.992)|1,106|3.238<br>(1.197)|-0.754***<br>(0.093)|
|Accessibility to bus stops|184|2.424<br>(1.053)|1,106|3.167<br>(1.091)|-0.743***<br>(0.086)|
|_Panel B. New users vs continuous users_||||||
||N|ew users|Contin|uous users|Difference|
|Affordability and availability of|182|4.071|924|3.883|0.189***|
|free commute||(0.828)||(0.816)|(0.066)|
|Safety against accidents, crashes,|182|3.945|924|4.073|-0.128*|
|<br>threats, and thefts||(0.884)||(0.876)|(0.071)|
|Connectivity|182|4.187|924|4.018|0.168**|
|||(0.878)||(0.924)|(0.074)|
|Bus frequency, waiting time, travel|182|3.214|924|3.242|-0.028|
|<br>time, and unnecessary stops||(1.177)||(1.201)|(0.097)|
|Accessibility to bus stops|182|3.132|924|3.174|-0.042|
|||(1.048)||(1.100)|(0.089)|

_<u>Notes:</u>_ Respondents evaluate their perception of a particular aspect of buses on a scale ranging from 1 (highly unsatisfactory) to 5 (highly satisfactory). In the last column, we test the differences between treated and control areas using a t-test with equal variance. * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

27
**Figure 1:  Poverty rates for China and Africa, 1981-2004**

<!-- Start of picture text --> % of population living below $1 a day<br>70<br>60<br>Sub-Saharan<br>50<br>Africa<br>40<br>30 Developing world<br>outside China<br>20<br>10<br>China<br>0<br>1980 1990 2000<br><!-- End of picture text --> <u>Source: Chen and Ravallion (2007).</u>

**Figure 2: Income inequality in China**

<!-- Start of picture text --> Gini index of income inequality (%)<br>45<br>40<br>35<br>30<br>25<br>20<br>1980 1990 2000<br><!-- End of picture text --> <u>Source: Ravallion and Chen (2007).</u> 28
introduced such innovations before. Firms that had a website in 2019 were 55% more likely to start or increase business activity online than firms that did not have a website.

Fig. 11. Firms’ responses during the COVID-19 crisis by firm characteristics <!-- Start of picture text --> Start/increase online business activity<br>40%<br>30%<br>20%<br>10%<br>0%<br>GVC Innovator High productivity Foreign licensed Website Well managed<br>technology<br>no yes<br>Start/increase remote work<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>GVC Innovator High productivity Foreign licensed Website Well managed<br>technology<br>no yes<br><!-- End of picture text --> _Source:_ the authors’ calculations based on COVID-19 Follow-up Enterprise Surveys.

More productive firms were also adapting better during the pandemic. They were less likely to close down both permanently and temporarily; they also suffered smaller sales and employment losses. Furthermore, more productive firms were more likely to start or increase business online and more likely to start or increase remote work. For example, high productivity firms (top 10 percentile of the productivity distribution) were 55% more likely to increase online business activity and were more than three times are likely to introduce remote work compared to low productivity firms in the bottom decile.

Firms with links abroad – through foreign ownership, trade or use of foreign technology – were on average more likely to adapt, in particular they were more likely to introduce remote working arrangements.<sup>15</sup> Firms that were majority foreign owned were 11% more likely to start or increase remote work during the pandemic than domestic firms. Trading has a similar effect to foreign ownership: global value chain participants (firms who both import and export at least 10% of their sales) were about 9% more likely to start or increase remote work than firms who did not import or export.

In contrast, state-owned enterprises were significantly less likely to introduce changes in responses to the crisis – in line with earlier studies that documented that they are less likely to innovate.<sup>16</sup> Nonetheless, state-owned firms were less likely to close and experienced smaller drops in sales than domestic private firms, reflecting more widespread government support (controlling for their other

> 15 See also EBRD (2021a) and Kodama et al. (2018).

> 16 See EBRD (2021b) and references therein.

18
The counterpart work requirement is likely to entail implicit targeting to the poor, assuming that they tend to have lower reservation wages.<sup>4</sup> However, given the weak capacity to organize, supervise and enforce the work requirement at local level in such a large program, it is not clear how effective the _Jefes_ work requirement was in practice compared to _Trabajar_ . The history (whereby the work requirement appeared as something of an afterthought), the rapid scaling up, and the circumstances of the crisis, may well have made it hard to enforce the work requirement. And, of course, the self-targeting aspect of the work requirement materializes only insofar as the participants have to comply with it to obtain the transfer.

The behavioral responses to such a crisis, and to such a large public program as _Jefes_ , are clearly of interest. There are various responses that could be expected — both to the crisis (such as through efforts to smooth consumption by income smoothing) and to the policy intervention. It has been argued that the entire participation in _Jefes_ entailed a commensurate reduction in unemployment (INDEC, 2003). This clearly ignores possible behavioral responses to the program through labor supply decisions, either to participate in the workforce or change the total number of hours worked.

Household family composition could well also respond to such a shock, by delaying the formation of new households (Foster and Rosenzweig, 2002) or by changing living arrangements as a response to receipt of a public transfer (Duflo, 2000). Household division (in the form of children sharing) has been anecdotally reported as a potential response to the _Jefes_ program.

Behavioral responses are also relevant to assessing impacts on poverty. Following common practice, INDEC (2002b) calculated the program’s poverty impact by subtracting the _Jefes_ payment from the incomes of participants. Thus the poverty rate in the absence of the

> 4 Supportive evidence on this assumption for Argentina can be found in the results of Jalan and Ravallion (2003) on the _Trabajar_ program that preceded _Jefes_ .

8
- 25 -

Table 4 The Incidence of Taxes in Malawi (1983)

||Percentage of|Household E|xpenditures|
|---|---|---|---|
|Income Group (Kwachas)|Commodity<br>Taxes a/|Income<br>Taxes|Total<br>Taxes|
|High (K>4,800)|13.8 (17.9)|14.4|28.2|
|Medium (K 1,200-4,799)|14.3(17.5)|4.5|18.8|
|Low (K 480-1,199)|13.1 (12.8)|1.0|14.1|
|Lowest (K<480)|9.1(8.8)|1.0|10.1|

Source: Table 2.6, Tax Policy in Malawi, World Bank, November 1986.

a/ The column in parentheses under Commodity Taxes indicates the anticipated pattern of tax burdens under the proposed<sup>commodity</sup> tax reforms.
Can taxes help ensure a fair globalization?<sup>_∗_</sup> François Langot<sup>_†_</sup> Rossana Merola<sup>_‡_</sup> Samil Oh<sup></sup>

> _∗_ Funding from the World Bank-ILO Research Fund is gratefully acknowledged. The work greatly beneted from helpful comments by Stéphane Adjemian, Thomas Brand, Matteo Cacciatore, Valerie Cerra, Ralph Chami, Olivier Charlot, Ekkehard Ernst, Aurelien Eyquem, Miguel Foguel, Fabio Ghironi, Jinill Kim, Stefan Kühn, Gladys Lopez-Acevedo, Hugo Ñopo, Pierella Paci, Ugo Panizza, Stela Rubinova, Raymond Saner, Thepthida Sopraseuth, Christian Viegelahn, Sergio Vieira, the DYNARE team, as well as participants in seminars and conferences at the World Bank, the ILO, the Graduate Institute of International and Development Studies in Geneva, the International Labour and Employment Relations Association (ILERA) 2018 Congress, the 2018 Asian Meeting of Econometric Society, the 2019 Congress of the Swiss Society of Economics and Statistics and the EcoMod 2019 Conference. However, all errors remain our own. The views contained here are ours own and not necessarily those of the ILO and the World Bank Group.

> _†_ Le Mans University (Gains-TEPP & IRA), Paris School of Economics & Cepremap. E-mail: angot@univ-lemans.fr

> _‡_ ILO, International Labour Oce, Research Department. E-mail: merola@ilo.org

> Bank of Korea, Research Department. E-mail: samil.oh@bok.or.kr