rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Restaurant Reservations, COVID Cases, and NPIs, United States**

|<br>|(1)<br>Restaurant<br>reservations|
|---|---|
|Close N.E. business<br>|0.818<br>(1.381)|
|Close K-12|2.349|
||(1.720)|
|Stay home/SIP|0.952|
||(1.139)|
|Log cases<br>|-0.678<br>(1.125)|
|Log national cases<br>|-9.775***<br>(0.884)|
|Constant<br>|31.251***<br>(6.388)|
|Time FE|Yes|
|State FE|Yes|
|# of States|49|
|Obs.|1877|
|R-squared|0.958|

Notes: Regression of restaurant reservations (Y/Y percent change) from OpenTable, on NPIs, the log of cases, the log of national cases, state, time fixed effects. Robust clustered errors are in parenthesis. *** p<0.01, ** p<0.05, *<0.1 Fifth, the dominant contribution of voluntary self-restraint along with historical and anecdotal evidence suggests that formal NPIs may be as much coordination mechanisms as repressive measures. For instance, no parent may want to send his/her child to school, but only when schools force all students online can continued safe learning at a distance be realized. As in Japan, no one may want to be seen as the slacker by not showing up at work, but if the government signals that this is the safe thing to do, then all can work from home without stigma.

Sixth, these findings offer both good and bad news. First, they imply that for many countries in the world, selfenforcing dynamics and NPIs can reduce mobility and business activity substantially. That mobility fell almost as much in Sweden, with no NPIs, as the United States dramatically illustrates this point and suggests that the focus on government NPI policy in explaining Sweden’s mortality rate may be justified. The finding that only shutting down public transport has any effect in LICs is consistent with arguments that government capacity may be generally low, and resistance to demobilizing is high where it implies lost livelihoods.

14
reallocation effects of agricultural productivity shocks.

Following a large literature on the importance of rainfall shocks in agricultural productivity variations in Bangladesh, we exploit rainfall shocks (relative to the 30-year average level) across upazilas and over time to understand the effects of productivity increase on employment growth and diversification. We use a two-step empirical approach that focuses on the reduced form regressions of rainfall on the measure of productivity (rice yield per acre) and on the set of outcome variables noted above. The evidence from the reduced form regressions shows that a positive rainfall shock increases employment in all different types of non-farm activities in the small towns, but increases only services activities and small-scale manufacturing in rural areas. The largest impacts are found for construction and transport, education, health and other services, and manufacturing employment in larger scale enterprises located in the small towns, and for educational services provided by larger enterprises in the rural areas.

When interpreted as instrumental variables estimates of the effects of productivity increase, the empirical results suggest that agricultural productivity induces structural transformation within the services sector with employment in skilled services provided by larger firms growing at a much faster pace compared with the services provided by smaller firms/individuals. We find much larger positive effects on non-farm employment growth and transformation in the small towns. In the rural areas, it increases employment mostly in small-scale manufacturing and skilled educational services. The estimates confirm the transformative effect of agricultural productivity increase on nonfarm activities in the small towns. These results suggest that policies promoting agriculture would be beneficial to employment growth and transformation in the small towns in Bangladesh.

# **References**

Adhvaryu, A., A. V. Chari and S. Sharma. 2013, “Firing Costs and Flexibility: Evidence from Firms’ Employment Responses to Shocks in India,” _Review of Economics and Statistics_ , vol. 95, no.3.

Bandyopadhyay, Sushenjit & Skoufias, Emmanuel, 2012. Rainfall variability, occupational choice, and 29
For our data on geographical and historical linkages, we use a standard source (the CEPII distance dataset). The data include the international distance between countries (in logarithms), and dummy variables equal to unity for countries that are geographically contiguous (share a common border), have a common official language, or were in a colonial relationship. In addition to these exogenous sources of trade costs, we are also interested in capturing the effects of policy. We therefore include a dummy equal to unity for countries in the same regional trade agreement (RTA) covering services. The variable was constructed for this paper using information from the WTO.<sup>6</sup> As in van der Marel (2011), we expand on the Chor (2010) model when it comes to the data on Heckscher-Ohlin forces. Specifically, we specify the endowment/intensity interaction terms as follows: As in van der Marel (2011), we treat ICT capital separately from non-ICT capital. In both cases, as noted above, we are interested in the interactions between country- and industry-specific factors.<sup>7</sup> To calculate the country-specific factors for labor, we take the proportion of high-skill (tertiary educated) employees in the population aged 25 and over (in logarithms). These data are sourced from the Barro and Lee (Forthcoming) dataset, with interpolation where necessary. These data give us measures of

> 6 In additional results, available on request, we show that our results are robust to the use of a variable capturing RTAs in goods sectors instead of services.

> 7 Typically, the inclusion of interaction terms in the model would also necessitate including each variable in levels. However, the fixed effects used here absorb the country- and industry-specific factors when they are not interacted, so separate specification is unnecessary.

15
Policy Research Working Paper 8423

# **Abstract**

This paper studies the production and trade linkages between a selected group of economies belonging to the Belt and Road Initiative (BRI). After defining a group of Belt and Road Economies, the paper uses three standard trade databases to analyze trade and production linkages among these economies. With the help of state of the art economic decompositions of input-output tables, coupled with standard international trade statistics, the analysis quantifies the amount of production sharing between the economies of the area. The main finding is that trade integration among Belt and Road Economies has largely increased: Intraregional exports went from 30.6 percent in 1995 to 43.3 percent in 2015. Since the increase in gross exports was driven mostly by intermediate goods, the study investigates the evolution of regional production networks across Belt and Road Economies.

This paper is a product of the Macroeconomics, Trade and Investment 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/research. The author may be contacted at mauro_boffa@outlook.com.

_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
**Figure 1.  Tracking subjects in 1969-77 data as of 2002-4**

<!-- Start of picture text --> Traceable, living in Guatemala<br>272<br>(11%) 1113<br>Died between (60%)<br>1969 and 2002 Original Villages<br>Migrants<br>2393 1856 154<br>Subjects in 2019 (77%) (8%)<br>69-77 Traceable in 2002 Stayed in Guatemala Nearby Villages<br>study<br>419<br>(23%)<br>In or nearby<br>102 163 Guatemala City<br>(4%) (8%)<br>Untraceable Left the<br>170<br>Country<br>(9%)<br>Elsewhere in Guatemala<br><!-- End of picture text --> 15
# **Table 7: GDP per capita and consumption per capita growth with household and nonfinancial corporation credit**

||growth_|growth_|growth_|
|---|---|---|---|
|**Dep. Var:**|gdppc|gdppc|cons.pc|
||2SLS|2SLS|2SLS|
||(1)|(2)|(3)|
|hhcredit|0.0627|0.0901|0.0805|
||(0.324)|(0.664)|(0.630)|
|hhcredit<sup>2</sup>||-0.0006|-0.0005|
|||(0.764)|(0.743)|
|nfccredit|0.1683***|0.3244***|0.2002***|
||(0.004)|(0.000)|(0.002)|
|nfccredit<sup>2</sup>||-0.0009***|-0.0005***|
|||(0.000)|(0.003)|
|fdi|-0.4300*|0.1688|0.0819|
||(0.082)|(0.474)|(0.631)|
|enrolment|0.0342|-0.0288|-0.0277|
||(0.557)|(0.594)|(0.546)|
|initialgdppc|-10.00***|-9.99***|-8.24***|
||(0.000)|(0.000)|(0.000)|
|Constant|92.0477***|90.8370***|81.0123***|
||(0.000)|(0.000)|(0.000)|
|# of Obs.|197|168|157|
|Prob > F|0.000|0.000|0.000|

*: in 2SLS, legal origin and religion are used as instruments.

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

The other variables have the expected signs, though enrollment is never significant, and FDI is only significant in some specifications. Consumption per capita growth rates tend to be less robustly associated with household credit, particularly after controlling for financial crises. There is no positive relationship in the linear versions, or in the quadratic specification except in the OLS and GMM basic specifications. On the other hand, firm credit retains its quadratic relationship to consumption per capita growth.

22
- 14 -

Figure 15. Quality Improvements in Telecommunications Services in ECA <!-- Start of picture text --> 10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>Source: BEEPS.<br>Changes in telecommunications service interruptions (days) Uzbekistan Georgia Kyrgyz Armenia Russia Azerbaijan Macedonia Kazakhstan Moldova Romania Estonia Belarus Slovakia Bulgaria Poland Czech Lithuania Ukraine Turkey Slovenia Croatia Hungary Albania<br><!-- End of picture text -->

# **III. ESTIMATION RESULTS AND POLICY IMPLICATIONS**

Six seemingly unrelated regressions (SUR) are performed; the results are shown in Table 3. The coefficients are broadly consistent with economic theory. The coefficient of output _Y_ is positive and significant, and the operating cost increases with unit labor costs (wages). Note that each model includes only one proxy of infrastructure quality, because the infrastructure variables in the sample tend to be correlated with each other.<sup>6</sup> In addition, by construction it makes little sense to include two dimensions, i.e., annual frequency and hourly duration of service interruptions, in the model. The reason is that the numbers of hours needed to resume suspended services are unconditional in our data, meaning that they include zero hours if firms do not experience any outages. Hence, no annual experience of interruptions means that the average interruption per day is zero hours, and vice versa.

From the industrial organization point of view, one interesting implication of the estimated cost function is economies of scale in production. The output elasticity evaluated at the sample means is about 0.8, regardless of the selection of infrastructure variables. The estimate is strongly significant in a statistical sense. This means that the estimated cost function exhibits economies of scale; firms could expect more production at relatively small additional operating costs.

> 6 Estimating the similar model with more than one infrastructure variable has been found to generate theoretically unreliable results.
34

# **References.**

- Ahuja, V., B. Bidani, F. H. G. Ferreira and M. Walton (1997): _Everyone’s Miracle?: Revisiting Poverty and Inequality in East Asia_ (Washington, DC: The World Bank).

- Amadeo, E. and J. M. Camargo (1997): “Brazil: Regulation and Flexibility in the Labor Market” in S. Edwards and N. Lustig (eds.) _Labor Markets in Latin America_ (Washington, DC: The Brookings Institution).

- Azzoni, C. (1994): “Crescimento Econômico e Convergência das Rendas Regionais: o caso brasileiro”, _Anais do XXII_<sup>_o_</sup> _Encontro Nacional de Economia (ANPEC)_ , **1** , pp.185-205.

- Barros, R. P., R. Henriques and R. Mendonça (2000): “A Estabilidade Inaceitável: desigualdade e pobreza no Brasil”, Ch. 1 in R. Henriques (ed.) _Desigualdade e Pobreza no Brasil_ (Rio de Janeiro, IPEA).

- Blejer, M. and I. Guerrero (1990): “The Impact of Macroeconomic Policies on Income Distribution: An Empirical Study of the Philippines”, _Review of Economics and Statistics_ , **LXXII** (3), pp.414-423.

- Bonelli, R and G. L. Sedlacek (1989): “Distribuição de Renda: evolução no último quarto de século”, in Sedlacek, G. L. and R. Paes de Barros, _Mercado de Trabalho e Distribuição de Renda: Uma Coletânea_ , (Rio de Janeiro: IPEA; Série Monográfica #35).

- Bourguignon, F. (1979): "Decomposable Income Inequality Measures", _Econometrica_ , **47** , pp.90120.

- Bourguignon, F., F.H.G. Ferreira and N. Lustig (2005): _The Microeconomics of Income Distribution Dynamics in East Asia and Latin America_ (Washington DC: The World Bank and Oxford University Press).

Cardoso, E. (1992): “Inflation and Poverty”, NBER Working Paper No. 4006 (Cambridge, MA).

Cardoso, E., R. Paes de Barros and A. Urani (1995): "Inflation and Unemployment as Determinants of Inequality in Brazil: the 1980s", Chapter 5 in Dornbusch, R. and Edwards, S. (eds): _Reform, Recovery and Growth: Latin America and the Middle-East_ ; (Chicago: University of Chicago Press for the NBER).

- Corseuil, C.H.L. and M.N. Foguel (2002): “Uma Sugestão de Deflatores para Rendas Obtidas a partir de Algumas Pesquisas Domiciliares do IBGE”, IPEA Texto para Discussão #897, Rio de Janeiro.

- Cowell, F.A. (1980): "On the Structure of Additive Inequality Measures", _Review of Economic Studies_ , **47** , pp.521-31.
Policy Research Working Paper 7951

# **Abstract**

On October 15, 2015, World Bank Group President Jim Yong Kim announced the World Bank Group’s commitment to support the 78 poorest countries to implement a multi-topic household survey every three years between 2016 and 2030, for monitoring progress toward ending extreme poverty and boosting shared prosperity. This paper estimates the resource requirements to achieve the objectives of implementing 390 surveys across 78 International Development Association countries from 2016 to 2030, and providing direct technical assistance to the national statistical offices on all facets of survey design, implementation, and dissemination toward timely production of quality household survey data. The approach to the costing exercise is unique, as it makes use of detailed data on actual survey implementation and technical assistance costs from a group of countries, unlike previous attempts at costing household survey data gaps. The required total budget, in accordance with the survey design features recommended by the World Bank Household Survey Strategy, is estimated at US$945 million for the period of 2016-2030. Of this, US$692 million is projected to cover the survey implementation costs across 78 countries, and US$253 million is projected to cover the costs of direct technical assistance to be provided to the national statistical offices.

This paper is a product of the Development Data Group and the Poverty and Equity Global Practice Group. 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 tkilic@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
_4_ foreign as well **as** domestic investors (unrestricted) versus stocks which can only be owned by domestic Investors (restricted). Some other papers have applied these tests to developing countries. Errunza and **Losq** (1985) find tentative empirical support fo. a hypothesis of mild' market segmentation. Errunza, Losq and Padmanabhan (1992) find that many emerging markets are neither completely integrated with nor completely segmented from industrial countries.

For imperfectly accessible stocks, Stulz and Wasserfallen (1992) test their model for Swiss stocks and find that a relaxation of investment barriers lowers substantially the value of the shares available to foreigners only relative to the value of the shares available to all investors. Bailey and Jagtiani (1992) use this model to investigate differential pricing of restricted and unrestricted stocks for Thailand. They find that cross-sectional difference in the severity of foreign ownership explains some of the variation in the premiums of unrestricted shares over restricted shares, leading to a mildly segmented **capital** market.

# **II. The Data**

The raw data we have cover 20 emerging markets. The price and rate of return data are generally available from _1975_ on. Table 2 and 3 provide some basic statistics for the rates of return on the IFC indexes and other market data in these emerging ma.kets over the period 1989-1992. Annex 1 describes the criteria used for creating the indexes.

As can be observed from Table 2, the IFC indexes have in general increased, for some countries by multiple factors (e.g., Argentina). There is also a great variation in the market capitalization across countries.6 The rates of return in emerging markets are in general high, but so are the standard deviations (Table 3). The highest rate of return is for Argentina, more than 100% on an annual basis. However, Argentina also has the highest standard deviation, almost 130%, and the highest range. In general, the rates of return and standard deviations for the emerging markets are much higher than those for the industrial countries. Table 3 also provides the skewness and kurtosis measures, which indicate that the rates of return are not likely drawn from normal distributions. Jarque-Bera tests for normality bear this out: for most markets it rejects normality (see further Claessens, Dasgupta and Glen, 1993).

Table 4 provides some cross-sectional information on the monthly rates of return of the individual stocks for each market (the methodology used for creating the individual stocks' rates 'Defined as a situation where the industrial countries' security markets are well integrated and developing country investors can invest in all these (foreign) security markets but foreign investors can not vice-versa invest in developing countries.

6It is important to note that the IFC indexes cover only a subset cf all stocks listed on the various exchanges, varying between 39% (Turkey) and 90% (Colombia) in terms of market capitalization. Typically, because of its selection criteria, the IFC index will be weighted towards the larger market capitalization and more liquidly traded stocks.
**Figure 7: Road Density in Terms of Population (road-km/1000 people)**

<!-- Start of picture text --> 20<br>17.3<br>15<br>10 9.2<br>7<br>6.7<br>6.1<br>5<br>3.3<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

**Figure 8: Paved Roads** **<u>(% of total roads)</u>**

<!-- Start of picture text --> 100<br>82<br>80<br>60 57<br>52<br>50<br>40<br>25<br>21<br>20<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text --> 14
Figure 1: Attributes of globally engaged firms (exporters) <!-- Start of picture text --> (a) Size (b) Labor Productivity<br>80 30 28<br>26<br>23<br>60<br>20<br>44<br>40<br>29<br>10<br>20 16<br>0 0<br>Micro and small (<20) Medium (20-99) Large (100+) Low Middle High<br>P-value of test that medium and large firms are more likely to be exporters than micro & small firms: P-value of test that middle and high productivity firms are more likely to be exporters than<br>Medium firms: 0.000; Large firms: 0.000 low productivity firms: Middle: 0.000; High: 0.000<br>(c) Management (d) Technology readiness<br>20 20<br>15 14<br>11 11<br>10 10<br>8<br>8<br>0 0<br>Low Medium High Low Medium High<br>P-value of test that medium and high scoring firms are more likely to be exporters than P-value of test that medium and high scoring firms are more likely to be exporters than<br>low scoring firms: Medium firms: 0.038; High firms: 0.000 low scoring firms: Medium firms: 0.000; High firms: 0.000<br>Probability of being an exporter Probability of being an exporter<br>Probability of being an exporter Probability of being an exporter<br><!-- End of picture text --> _Notes:_ The sample includes 33,336 firms in 47 countries in panel a, 17,949 firms in 33 countries in panel b; 10,939 firms in 10 countries in panel c and 13,794 firms in 13 countries in panel d.

# **5. The Impact of COVID-19 on Globally Engaged Firms: Some Stylized Facts**

## **5.1 Trade Outcomes**

Analysis based on the customs data and the BPS data with the sub-sample of globally engaged firms only brings forth two main findings. _First_ , the pandemic led to a strong decline in quarterly firm-level exports and imports in the first six months of 2020 but the negative impacts dramatically weakened in the rest of 2020, indicating modest adaptation of globally engaged firms, especially exporters, to the pandemic (Bricongne et al., 2012; de Lucio et al., 2020; Pimenta et al., 2021). Firm exports and imports in customs data declined by 41% and 29% in the second quarter (Q2 2020) relative to that of 2019 (Figure 2, panel a). These trade outcomes had already declined in the very early stages of the pandemic (Q1 2020) that began to adversely affect China, a key global destination and source country for trade. Firms were also affected by the pandemic through a substantial decline in their number of products and partner countries in the second quarter of 2020 (Appendix Figure C5, panels a and b). These negative impacts dampen during the rest of 2020 (Q3 and Q4 2020) relative to corresponding values in 2019. Consistent with the analyses of the customs data, the BPS data also confirms a sharp decline in firm exports (as a share of sales) and imports (as share of 12