rafmacalaba/datause-encoder-data huggingface dataset:
Table 5.13 <!-- Start of picture text --> Stock Market Capitalizations<br>(US$ billion)<br>Market 1995 1996 1997 1998 1999 2000<br>New York (Dow Jones Industrial) 5,950 7,210 8,900 9,654 13,400 16,755<br>Tokyo (Nikkei) 3,440 3,150 2,285 2,375 3,560 3,795<br>London (FT100) 1,380 1,410 1,780 2,070 2,750 2,665<br>Hong Kong (Hang Seng) 301 428 384 353 509 515<br>Taipei (Weighted Price) 176 287 313 264 300 417<br>Seoul (Composite) 198 144 53 75 272 252<br>Singapore (STI Ind Index) 169 184 127 110 207 173<br>Kuala Lumpur (Composite) 214 302 97 92 140 159<br>Manila (Composite) 56 80 35 31 37 19<br>Bangkok (SET) 139 105 25 32 52 40<br>Jakarta (Composite) 66 93 36 17 29 41<br>Total Southeast Asia 644 764 320 282 465 432<br>SE Asia as percent of Dow Jones 10.8 10.6 3.6 2.9 3.5 2.6<br>SEAsia as percent of HKSE 214.0 178.5 83.3 79.9 91.4 83.9<br>Source: Freeman (2000: Table 1, 4)<br><!-- End of picture text --> The above table shows that as of June 2000, the aggregate capitalization of Southeast Asia's five stock markets was US$432 billion, which was only about 3 percent of the Dow Jones, 16 percent of the London Stock Exchange, and 3.6 percent larger than 216 the exchange in Taipei.

Table 5.14

|S|tockMarketC|apitalization||
|---|---|---|---|
||(percent <br>|ofGDP)<br>||
|||Jan.1990|Jan.1995|Jul.2000|
|Indonesia|n.a.|22.8|33.8|
|Malaysia|n.a.|248.8|165.0|
|Thailand|n.a.|85.6|40.3|
|Singapore|122.9|236.3|261.9|
|Korea|54.8|41.2|66.4|
|China|n.a.|n.a.|8.1|
|Taiwan|154.8|84.3|119.9|
|HongKong|110.1|218.1|336.7|
|Australia|45.9|61.8|35.6|
|U.S.|50.6|62.5|184.5|
|Source:Wilso|n(2002,Table|2.3,26)||

Table 5.14 puts the capitalization level into perspective (showing stock market capitalization as a percent of GDP) and also shows changes over time. The degree of capitalization has increased and Wilson (2002) attributes this to a greater degree of 82
transmission process from growth to poverty may be significantly different between urban and rural areas.<sup>36</sup> To account for this specificity of transition economies, and particularly Latvia, we include an estimate of unemployment ratio in the set of regressors, as well as income inequality, which is listed as one of the main causes of rapid increase of poverty in the transition period. We account for this by including in the regressor a proxy for income inequality estimated as the absolute deviation from the median income at the household level.

The model is used sequentially, drawing on the stepwise procedure to investigate the poverty determinants at the national level and across urban and rural areas. The poverty determinants are represented as a function of host of variables, conditioning variables, and the parameters are estimated from the model in each period. In particular, the spatial location and other socioeconomic variables are conditioned upon the poverty rates prior to parameters estimation to account for possible effects of initial conditions and persistence in poverty. Hence, the function _Dt_ (•) which captures the determinants of poverty at time _t_ can be represented by the following equation:

> where the conditioning variable _P_<sup>ˆ</sup> (α, _t_ ) is known, and the objective is to estimate the

> regression parameters β<sup>ˆ</sup> _t_ , so as to maximize the log of the probit likelihood function. A relatively large number of regressors are considered in the initial model and then gradually reduced through a calibrated stepwise procedure in the process of maximization of the likelihood function.<sup>37</sup>

# **Empirical Results**

The probit analysis identifies a number of variables as determinants of poverty in Latvia in the late 1990s. These variables include unemployment ratio, household amenities and facilities, ownership of assets and durable goods, and income inequality, spatial location of households, and the burden of age dependency. Unemployment ratio appears to be strong and consistently significant across urban and rural areas. This consistency corroborates previous studies, which supports the correlation between unemployment and welfare in most former Eastern European countries during the first phase of the transition (World Bank [2000a, 2000b]). That first phase was indeed characterized by rapid increase in unemployment, due in part to shrinking demand for labor and high rate of job destruction. And the significance of this variable in the late 1990s may point to its relative stability over time.<sup>38</sup>

> 36 For further details on the labor market structure in Sub-Saharan African countries, see Agénor, Izquierdo and Fofack [2002] and for the profile of poverty in Sub-Saharan Africa (Bigman and Fofack [2001]).

> 37 For further details on the properties of MLE estimates of probit models, see Greene [1997].

> 38 Iin the early 1990s, the rate of job destruction in Central Europe averaged 12 to 16% annually, and open unemployment was in the range of 10 to 15% in the region, with higher rates recorded in a number of countries (World Bank [2000b]).

25
<!-- Start of picture text --> Figure 6A: Poverty Rate, Growth and Shared Prosperity Figure 6B: Poverty Headcount, Growth and Shared Prosperity<br>45<br>40 240M<br>35 220M<br>30 200M<br>£2<br>e<br>ray 25 [3]3 180M<br>s 3<br>a5 20 =x<br>160M<br>15<br>140M<br>10<br>5 120M<br>0 100M<br>2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030<br>Year Year<br><!-- End of picture text --> Source: International Futures v7.04 Scenario i Base Case i Constant Inequality ® Lower Inequality i Faster Growth i Growth and Shared Prosperity Source: International Futures v7.04

# Scenario

i Base Case i Constant Inequality @ Lower Inequality i Faster Growth i Growth and Shared Prosperity
19 an accentuation of unemployment problems in the future, although high flexibility of relative wages could, in principle, mitigate this problem.<sup>21</sup> For some time to come, urban labor markets may therefore continue to exhibit characteristics similar to Arthur Lewis’s (1954) model of “unlimited supply of labor” in urban areas, although at gradually rising, rather than constant, real wages for low-skilled workers (in the connection with higher labor productivity).<sup>22</sup> Largely as a result of the huge public ownership of financial assets, it is reasonable to say that financial markets in China are less developed than labor markets. In particular, this holds for securities’ markets. For instance, only very few non-state firms are listed on the Shanghai and Shenzhen stock exchanges, and the bulk of the shares of incorporated state firms are not available for purchase.<sup>23</sup> The efficiency and flexibility of the stock market are also constrained by the poor financial transparency of listed companies, although the transparency is higher for SOEs that have recently issued shares (often in the form of Initial Public Offerings, IPOs) in Hong Kong and on foreign stock markets (mainly New York). Moreover, the bond market is very thin, only constituting about 20 percent of GDP, and bond issues by the government and SOEs dominate the market. Indeed, the bulk of the stock of bonds is also held by government agencies, partly because of the near absence of insurance companies and private pension funds. Moreover, for bonds to play an important part in Chinese financial markets, deregulations of the bond market would be necessary, including a lifting of interest ceilings and a removal of _de facto_ bond issue controls.

In contrast to the thin securities market, the volume of bank assets is quite large in China, mainly because of the huge household saving in the form of bank deposits. While in other East-Asian countries banks tend to intermediate 30-40 percent of the total financial capital in the national economy, the corresponding figure in China seems to be above 70 percent (McKinsey Global Institute, 2006). Since large firms in China, in fact, get their main financing from banks rather than from the poorly developed securities markets, small and

> 21Indeed, the UNDP (2000, p. 58) makes already now the judgment that the “explosive increase in unemployment has become the most challenging issue in China’s economic and social development”. 22 The Harris and Todaro (1970) model of urban unemployment seems less relevant for China since the unemployment rate for migrants to cities is lower than the unemployment rate for permanent residents; see Giles et. al.(2005)

> 23 The International Finance Corporation (IFC, 2000) reports that among 976 listed companies only 11 were non-state firms in 2000.
Appendix 2: Variable Definitions and Data Sources

|Variable|Definition|Source|
|---|---|---|
|Headcount index|Headcount index is the percentage of population<br>living in households with consumption or income per<br>person below the poverty line ($1 per day). It is also<br>called poverty incidence.|PovcalNet|
|Poverty gap|Poverty gap is a ratio of the distance of mean<br>shortfall from the poverty line ($1 a day or less) and<br>the poverty line. It characterizes how far below the<br>’|PovcalNet|
||poverty line the average poors income lies.||
|Income of the poorest<br>quintile|Defined as the share of income earned by the poorest<br>quintile times average income divided by 0.2;<br>however, when the first quintile share is not|Dollar and Kraay<br>(2002),<br>WIDER database|
||available, it is assumed that the distribution of<br>income is lognormal, and the share of income<br>accruing to the poorest quintile is obtained as the 20<sup>th</sup><br>percentile of this distribution, using Gini coefficient.|PovcalNet|
|GDP per capita|Nominal gross domestic product divided by the size<br>of the population|IMF database|
|Private credit over GDP|Private credit by deposit money banks to GDP.|Financial Structure<br>Database (2007), and<br>International<br>Financial Statistics<br>(2007)|
|M3/GDP|Liquid liabilities of the financial system as a percent<br>of GDP.|WDI database|
|Property rights|An index measuring the ability of individuals to<br>accumulate private property, secured by clear laws<br>that are fully enforced by the state.|Heritage Foundation<br>database (2007)|
|Inflation|Annual change in consumer price index (CPI)|WDI database|
|Trade openness|Sum of imports plus exports as share of GDP (in<br>percentage)|WDI database|
|Legal origin|A dummy variable taking 1 when a country’s law is<br>based on English common law; and 0 otherwise.|La Porta and others<br>(1998)|
|Rainfall|Average annual rainfall.|Ciccone (2011) and<br>WDI database|
|Creditor rights|The index measures the legal rights of creditors<br>against defaulting debtors in different jurisdictions,<br>and ranges from 1 to 4, with a higher value indicating<br>stronger creditor rights.|Djankov, McLeish,<br>and Shleifer (2005)|
|Rule of law|<br>A score measuring the extent to which agents have<br>confidence in and abide by the rules of society, and in<br>particular the quality of contract enforcement, the<br>police, and the courts, as well as the likelihood of<br>crime and violence|Governance Matters<br>(World Bank)|

21
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**Table** 10: Deposits (as a percsitage of assets)

||1970|1975|1980|1985|1990*|
|---|---|---|---|---|---|
|UK|2|3|2|3|6|
|us|1|4|5|a|6|
|Germany|3|3|2|1|2|
|Japan|-|-|-|-|-|
|Canada|3|4|4|4|1|
|Netherlands|1|2|1|1|3|
|Sweden|0|0|0|0|0|
|Switzerland|4|4|3|1|1|
|Denmark|-|-|-|-|-|

* 1989 for Canada 1987 for Denmark

Table 11t Bonds **(as a** percentage of assets)

||1970|1975|1980|1985|1990*|
|---|---|---|---|---|---|
|UK|32|24|24|20|14|
|uS|45|42|41|40|36|
|Germany|19|18|24|32|25|
|Japan|12|34|51|49|47|
|Canada|53|50|50|49|47|
|Netherlands|15|13|10|19|23|
|Sweden|76|76|74|77|84|
|Switzerland|25|24|28|31|29|
|Denmark|72|72|63|67|67|

* 1989 for Canada 1987 for Denmark

27c
# **Figure 7 Productivity Commission index of regulatory restrictions in banking services**

<!-- Start of picture text --> Malaysia<br>China<br>Vietnam<br>Brazil<br>Tunisia<br>Morocco<br>Thailand<br>Chile<br>Russia<br>Latvia<br>Serbia/Montenegro<br>Zambia<br>Australia<br>Bulgaria<br>Lithuania<br>Moldova<br>Macedonia<br>Estonia<br>Croatia<br>Albania<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>Restrictiveness index<br>Domestic Foreign<br><!-- End of picture text --> Data source: Based on Dee (2005) and McGuire and Schuele (2000).

29
isolated from the direct and most severe impact of the war. Mechanically, we can infer nonmilitary deaths by subtracting recorded military deaths from the total. However, it should be noted that these two series come from different sources, and measurement errors may increase as a result of this calculation. While acknowledging this concern, the results in Column (2) of Table 2 separate military deaths from non-military. This suggests that it is non-military losses that account for the correlation with COVID-19 deaths. Nonetheless, given the measurement concerns, we will focus on total deaths in our robustness tests.

Table 3 provides the coefficients on WW2 deaths for various changes in specifications. Column (1) in Table 3 shows the extended specification of the regression in column (1) of Table 2. Column (2) in Table 3 shows the same specification as Column (1) but with controls for communist regimes and axis of power. The specification in row (1) adds dummy variables of whether the communist party was dominant in a country before 1991 and whether a country belonged to the axis powers. None of these extra variables had a significant effect on COVID-19 death rates, and the coefficient on WW2 deaths changes little.<sup>8</sup> A systematic COVID-19 death underreporting could alter our results. Such underreporting might be especially pronounced in several countries of the former Soviet Union, such as Belarus, Tajikistan, and Uzbekistan. (Turkmenistan reports no deaths from COVID-19 and thus is not included in our sample.) To test the sensitivity of our results to such underreporting, we triple the officially reported rates for these countries. The results of that simulation are shown in Table 3 and are consistent with our main specification.

Another concern could be related to the endogeneity of losses during WW2 to the death toll of COVID-19. One could argue that the most altruistic and socially and politically active people would be more likely to die during the war. A society with high levels of social capital might suffer disproportional losses. Then the argument that the exposure to war violence increases social capital, which, in turn, helps to reduce COVID-19 mortality is reversed. The fact that WW2 ended 75 years before the 2020 pandemic clearly reduces the concerns for such nonrandom selection. Nevertheless, to partially address this potential bias, we estimate our model on

> 8 Estimations of the cumulative COVID-19 infection rates on the same set of covariates produce no significant results. We also estimated specifications with other governance indicators from WGI dataset: Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. None of these variables shows significant coefficients in estimations. These results are available from the authors.

11
We use an instrumental variables (IV) approach to address the possible endogeneity of the share of migrants in a given destination region. We follow a consolidated approach in the literature, and construct a shift-share Bartik instrument to control for the current migrants’ share in a given region.<sup>14</sup> The instrument consists of both a “share” and a “shift” component. To build the instrument, we use the spatial distributions of immigrants in 2002 across European NUTS regions (the initial “share” prior to the period of analysis), interacted with the aggregate growth rate of the stock of immigrants at the EU level between 2002 and the year of study (the “shift”).<sup>15</sup> The “share” component provides predictive power to the instrument. It relies on the fact that new immigrants’ settlement decisions are partly determined by the presence of earlier immigrants, mainly through network externalities (Gross and Schmitt, 2003). Recent migrants learn of opportunities in those locations from the network of immigrants, and they may prefer the benefits of living near their conationals.<sup>16</sup> As a result, the past distribution of immigrants across European regions can help predict their current location. The “shift” component provides exogeneity to the instrument if the events influencing the flow of migrants to the EU as a whole are arguably exogenous to changes to the labor market conditions in each NUTS region. These criteria will be satisfied as long as each region is small relative to the EU and business cycles of the regions are not highly correlated.

Formally, our first Bartik instrument– denoted PS, hereafter – for the share of immigrants in each region can be expressed as the following: where _Share_migs,2002_ is the share of migrants in NUTS2 destination region _s_ in 2002; (1+ _g_mig_ )t,2002 is the growth rate of working-age migrants in EU15 countries between 2002 and year t; and (1+ _g_nat_ )t,2002 is the growth rate of working-age natives in all EU15 countries between 2002 and year t.

> 14 For prior studies using Bartik-type instruments to study the impact of migration shocks see Jaeger (2006); Edo et al. (2019), Mitaritonna, Orefice, and Peri (2017), Peri and Sparber (2009). Jaeger, Ruist, and Stuhler (2018) provide a detailed review of this literature and offer potential caveats about the validity of this instrument.

> 15 The earliest year for which information on workers’ country of origin is available in the EU-LFS is 2002. Our instrument, however, does not distinguish the lagged immigrant shares by migrants’ region of origin as some destination countries in the EU-LFS do not provide the region of origin breakdown.

> 16 A large body of literature documents the role played by migrants’ networks and diasporas is affecting the location choices of new migrants. See for example, Munshi and Rosenzweig (2006), Beine, Docquier and Ozden (2011), Beaman (2012), Patel and Vella (2013) and Kramarz and Nordström Skans (2014), among others.

22
Table 5: Panel regressions of GDP per capita<sup>_†_</sup>

||(_P1_)|(_P2_)|(_P3_)|(_P4_)|(_P5_)|(_P6_)|
|---|---|---|---|---|---|---|
|||_Sec_|_ond stage inc_|_ome equatio_|_n_||
|Investment|0.162|0.152|0.111|0.126|0.031|0.139|
|share|(0.05)<sup>_∗∗∗_</sup>|(0.04)<sup>_∗∗∗_</sup>|(0.08)|(0.08)|(0.11)|(0.10)|
|Net rate of|-0.098|0.043|0.337|0.349|0.733|-0.612|
|depreciation|(0.13)|(0.13)|(0.18)<sup>_∗_</sup>|(0.19)<sup>_∗_</sup>|(0.28)<sup>_∗∗∗_</sup>|(0.46)|
|Human|0.409||||||
|capital|(0.11)<sup>_∗∗∗_</sup>||||||
|Alternative||0.323|1.503|1.546|2.183|-0.937|
|human capital||(0.05)<sup>_∗∗∗_</sup>|(0.44)<sup>_∗∗∗_</sup>|(0.47)<sup>_∗∗∗_</sup>|(0.72)<sup>_∗∗∗_</sup>|(1.12)|
|Constant|8.263<br>(0.39)<sup>_∗∗∗_</sup>|8.101<br>(0.37)<sup>_∗∗∗_</sup>|||||
|||_First _|_stage human _|_capital equat_|_ion_||
|Family|||0.029|0.309||0.011|
|resources|||(0.11)|(0.11)||(0.09)|
|School|||-0.253|-0.261|-0.180||
|resources|||(0.08)<sup>_∗∗∗_</sup>|(0.08)<sup>_∗∗∗_</sup>|(0.06)<sup>_∗∗∗_</sup>||
|Governance|||-0.047||-0.030|-0.081|
||||(0.06)||(0.04)|(0.06)|
|Broad||||-0.032|||
|governance||||(0.07)|||
|_F_|9.018<sup>_∗∗∗_</sup>|21.990<sup>_∗∗∗_</sup>|6.173<sup>_∗∗∗_</sup>|5.980<sup>_∗∗∗_</sup>|5.478<sup>_∗∗∗_</sup>|1.261|
|Anderson _LR_|||13.012<sup>_∗∗∗_</sup>|12.015<sup>_∗∗∗_</sup>|12.627<sup>_∗∗∗_</sup>|4.395<sup>_∗_</sup>|
|Cragg-Donald _F_|||4.356|4.017|6.342|2.188|
|Hansen _J_|||3.256|4.024|2.407|1.294|
|_N_|511|1175|435|435|658|536|

> _†_ Notes: All variables were transformed to logarithmic form. Heteroskedasticity and autocorrelation-robust (asymptotic) standard errors reported in parentheses. With the exception of the pooled specification, regressions included country and time fixed effects.

> _∗_ indicates significance at 10 percent level, _∗∗_ indicates significance at 5 percent level, and

> _∗∗∗_ indicates significance at 1 percent level.

smaller, it is still economically significant: A 1 percent increase in human capital leads to a 1 _._ 5 percent increase in per capita income. This decline is probably due to the inclusion of country fixed effects, which would capture a good deal of idiosyncratic country-specific variation.

In columns ( _P4_ )–( _P6_ ), we make several minor perturbations to this benchmark. Specification ( _P4_ ) replaces government effectiveness with the broad measure of governance, while columns ( _P5_ ) and ( _P6_ ) limit the instrument set by dropping, respectively, family and school inputs as instruments. While dropping family inputs as an instrument or using the broad measure of governance does not affect our results in any qualitative fashion, the instrument set is weakened considerably by the absence of school inputs. Specification ( _P6_ ) satisfies the relevance condition only marginally, and the utility of the model—as given by the _F_ test—is very low. While we report the estimates in this final model for completeness, we are inclined to heavily discount them in our analysis.

21