rafmacalaba/datause-encoder-data huggingface dataset:
**A1.2 The Wall Street Journal Central European Economic Review (CEER)**

The Central and Eastern Europe Review (CEER) is part of the European branch of the Wall Street Journal (WSJ), an American publishing company. As of May 1999 the CEER can be reached at: http:/linteractive.wsj.com/public/current/summaries/ceer98-2.htm Since 1995, CEER carries out an annual survey of business analysts in Europe and the United States regarding the economic situation in Eastern Europe. Respondents are asked to rate 27 Eastem European countries on their attractiveness as a place to do business over the coming year, based on their perceptl ons of factors including corruption, rule of law and political stability. The factors considered in tShe survey are reported in Table A1.2. To the best of our knowledge, respondents in this surveBy are not provided with definitions of the specific concepts on which they are asked to rate countries.

CEER reports the average response in their December/January issue. We use data from the December/January 1999 issue for political stability and rule of law, and data from the December/January 1998 issue for corruption.

<!-- Start of picture text --> Table A1.2: The Wall Street Journal<br>Central European Economic Review<br>Concepts Measured in 1998 Survey<br>1 Economic Strength<br>2 Balance of Payments<br>3 Business Ethics<br>4 Integration into World Economy<br>5 Liquidity/Ease of Buying Stocks<br>6 Rule of Law<br>7 Price Stability<br>8 Productivity<br>9 Currency Stability/lnvestment Climate<br>10 Political Stability<br>Concepts Measured in 1997 Survey<br>11 Economic Growth<br>12 Price Stability<br>13 Integration into World Economy<br>14 Currency Stability/lnvestment Climate<br>15 Rule of Law<br>16 Ease of Portfolio Position<br>17 Productivity<br>18 Balance of Payments<br>19 Political Stability<br>20 Corruption<br><!-- End of picture text --> 31
and large scale civil wars (PRIO1000) associated with at least 1,000 per year and per incompatibility (see details in appendix). In the baseline, we consider non-humanitarian and nonneutral military interventions that are likely to be implemented before a civil conflict intensifies and therefore alter the balance of power and the winning probabilities of potential warring groups as discussed in the theory section of this paper.

As in Montalvo and Reynal-Querol (2005), the distributional variables are ethnic polarization ( _ETHPOL_ ), ethnic fractionalization ( _ETHFRAC_ ), religious polarization ( _RELPOL_ ), and religious fractionalization ( _RELFRAC_ ). Montalvo and Reynal-Querol (2005) show that the indices of polarization and fractionalization differ, independent of the data source used in their calculations. We choose the World Christian Encyclopedia (WCE) to obtain the ethno-diversity measure, favoring it to the other two sources: the Encyclopedia Britannica (EB), and the ANM (1964). We do so because according to Montalvo and Reynal-Querol (2005) the most accurate description of ethnic diversity is the one in the WCE. It contains details for each country on the most diverse classification level, which may coincide with an ethnolinguistic family or subfamilies. There are also several sources of data on religious diversity. We adopt the _L’Etat des religions dans le monde_ (ET) data, which are based on a combination of national data sources and the WCE, and provide information on the proportions of followers of Animist and Syncretic cults. Montalvo and Reynal-Querol (2005) consider this to be an important factor for the calculation of indices of religious heterogeneity. Since the data used by Montalvo and Reynal-Querol (2005) and Esteban et al. (2012) do not contain information on the distributional variables for Lebanon, we construct the indexes of religious and ethnic polarization and fractionalization based on data from Encyclopedia Britannica Book of the Year 2001.

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**Table  4.2: Bond Issuance Process in the Eurobond Market for Frequent Issuers: Book-Building Method**

|_Date_|_Action_|_Communication_<br>_medium_|
|---|---|---|
|_X_− 1 or 2 days|Lead manager requests syndicate members to conduct<br>premarketing and may transmit to them a premarketing<br>sheet with relevant publicinformation.|Telephone and fax|
|_X_|Based on premarketing feedback, lead manager launches<br>the transaction.|Telephone|
|_X_|Lead manager instructs syndicate members to log onto<br>the Web site (incase ane-book-building system is used).|Telephone|
|_X_|Lead manager transmits syndicate members the official<br>price-talk sheet via e-mail or Bloomberg message. The<br>syndicate members submit to lead manager their orders<br>either at a predetermined daily time or as the orders are<br>taken.|E-mail, Bloomberg,<br>or Web site<sup>a</sup>|
|Throughout book-<br>building process|Lead manager accepts protection requests. A preliminary<br>prospectusisissued to syndicatemembers.|Telephone and fax|
|_X_+ 2or3 days|Leadmanagerprices theissue.||
|Same day as pricing|Lead manager transmits pricing to syndicate members<br>and confirms theiracceptance ofpricing.|E-mail, Bloomberg,<br>orWeb site|
|Within 1 day after<br>pricing|Lead manager transmits final allotment notice.|E-mail, Bloomberg,<br>orWeb site|
|As soon as possible<br>afterpricing|(Final securities registration statement supplement is<br>filed,if registration isrequired.)||
|Before closing|Final prospectus supplement is issued, and its receipt is<br>acknowledged by the exchange.|E-mail, Bloomberg,<br>orWeb site|
|Before subscription|Other issuematerials are distributed byhand orbyfax.||
|_X_+5 days|Subscription||
|_X_+7 or more days<br>_Source_: Author and his i|Closing<br>nterviews with Euro market practitioners.||

_Notes_ : a. Captive or open e-bookbuilding system. ‘Bookrunner’ intranet screen of IPMA is an open system

# **_4.2 Regulatory Focus_**

The regulatory focus of corporate bond issuance has been placed on the protection of the public in many emerging markets. This regulatory stance assumes that each issue of corporate bonds may attract many financially unsophisticated or uninformed retail investors. Thus, the regulation of corporate bond issuance ensures the protection of the public through a regulator-approved prospectus describing the issuer and the offered bond. The regulator attempts to ensure that the disclosure in the prospectus is comprehensive, accurate, understandable, and timely.<sup>24</sup> As a result, the issuer has to spend many months satisfying the regulator about these aspects before the regulator signs off on the prospectus.

> 24 Some trade-off occurs among the four factors—comprehensiveness, accuracy, understandability, and timeliness—of disclosure. For example, if the issuer and the regulator try to make sure that a disclosure is comprehensive and accurate, the disclosure document may become less easy to understand or less timely to use

20
Recession might have prevented local labor markets from adjusting and potentially absorbing laid-off workers. Even if local businesses wanted to expand, the paralyzed banking sector during the early period of the Great Recession could not supply sufficient credit to businesses (Chodorow-Reich, 2014).

Table 6.2: Employment loss multiplier during the Great Recession

|2008-2010|Change in number of|non-tradablejobs|
|---|---|---|
||[Reduced form]|[IV]|
|Bartik|1.532***<br>[0.148]||
|△tradable jobs||1.606***<br>[0.098]|
|Constant|Y|Y|
|Time fixed effect|Y|Y|
|County fixed effect|Y|Y|
|Observations|8,848|8,848|
|R-squared|0.138||
|Number of county|3,077|3,077|

Robust standard errors in brackets, *** p<0.01, ** p<0.05, *p<0.1 Note that this multiplier could nest the effect of fiscal stimulus. During the Great Recession, the U.S. government signed two major stimulus acts: the Economic Stimulus Act in 2008 of about 152 billion dollars and the American Recovery Reinvestment Act in 2009 of about 787 billion dollars. To the extent that the stimulus could flow to areas more exposed to tradable sectors, the job loss multiplier here could include the positive effect of the fiscal stimulus programs. In other words, the job loss multiplier estimated here may have underestimated the true multiplying effect of job losses. The fact that we still find a large and significant job loss multiplier suggests that the negative job loss spillover outweighs potentially positive effects of the fiscal stimulus programs.

24
Figure 18: Probability of digital or innovation response by firm size <!-- Start of picture text --> (a) Increase use of digital tech. (b) Investment in digital solutions<br>60 80<br>48 47<br>45<br>41 60 54<br>40 39<br>34 34<br>42<br>40<br>33 32<br>21 28<br>20 23<br>20 16 17<br>0 0<br>Micro (0-4) Small (5-19) Medium (20-99) Large (100+) Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>Wave 1 Wave 2 Wave 1 Wave 2<br>Probability of investing in digital<br>Probability of starting/increasing use of digital<br><!-- End of picture text -->

- (c) Probability of introducing new products

<!-- Start of picture text --> 30<br>23<br>20<br>20<br>16<br>14<br>10<br>0<br>Micro Small Medium Large<br>(0-4) (5-19) (20-99) (100+)<br>Probability<br><!-- End of picture text --> _Fourth_ , uncertainty about the future dissuades firms from making investments in digital technologies and product innovation even though there could be potential rewards in aligning with the changes in market demand and supply conditions (Figure 19).<sup>30</sup> While governments cannot predict the surge of potential new contagion waves, uncertainty could still be addressed through predictable policy interventions.

> 30See García-Quevedo et al. (2017) for a review of the impact of uncertainty and future demand on innovation, and Cirera et al. (2021b) show that uncertainty about future demand is an important deterrent in the adoption of technology in Vietnam.

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**Figure 3.2. Sectoral output growth between 1990 and 2000**

(Average annual % growth) <!-- Start of picture text --> Percent<br>-25 -20 -15 -10 -5 0 5 10<br>Czech<br>Estonia<br>Hungary<br>Latvia<br>Lithuania<br>Poland<br>Slovak<br>Slovenia<br>Albania<br>Bulgaria<br>Croatia<br>Moldova<br>Macedonia<br>Romania<br>Armenia<br>Azerbaijan<br>Kazakhstan<br>Kyrgyz<br>Tajikistan<br>Turkmenistan<br>Uzbekistan<br>Belarus<br>Russia<br>Ukraine<br>Agriculture Industry Manufact. Services<br>EU Accession<br>Balkans/EE<br>Caucasus<br>Central Asia<br>Slavic<br><!-- End of picture text --> Source: WDI 2002

# **3.2. Large disparities in national income and national income poverty**

There are large disparities in GDP per capita across countries in the region. In 2001 GDP per capita ranged from nearly $1,000 in Tajikistan to above $8,000 in Lithuania. And 11 out of 16 countries for which data are reported were below the ECA average, i.e. GDP per capita of around $6,500. Although a wide divergence in incomes was evident even before transition, the uneven economic recovery and reform process has tended to widen the gap between the less developed and the more developed countries in the region. Most of the countries that experienced recovery or growth in real GDP since 1989 as shown on Figure 3.1, are in the upper middle income ranking (WDI), with the exception of Uzbekistan, while the poorest performers 13
11 The overall gender-differential in pay is not particularly low in Latin America and female relative (to male) wages in the region compare favorably with female relative wages in industrialized countries -- the latter being typically between 65 and 75 percent.<sup>2</sup> " In fact, given that the present comparison refers in some cases to weekly, even monthly, earnings the pay differential in Latin America may be lower than that in advanced economies. However, unless evidence to the contrary is presented, we do not believe that this finding suggests that women are less "under-paid" (relative to men) in Latin America than women in industrialized countries. There are two reasons for this belief. First, it is likely that women's earnings in the formal sector are more represented in our data sets than earnings of women engaged in activities in the informal sector. For example, in some countries the number of women reporting positive hours of work but no earnings was more than one-third than the number of women reporting work and positive earnings. Second, and related to the former point, many women employed in the formal sector work in the public sector. As already noted, the public sector is not a discriminating employer. The following table 4 reinforces this point.

Table 4 Female Wages (in local currency) and Female Relative to Male Pay (percent) in the Private and Public Sectors (selected countries)

|Country (pay reference)|Educational<br>Level|Fem<br>Priv|ale wazes<br>ate Public|F/M pa<br>Private|y ratio<br>Public|
|---|---|---|---|---|---|
|Guatemala (quetzals/hour)|Primary|1.03|2.11|69|128|
||Secondary|2.03|4.07|79|127|
||Tertiary|3.90|4.26|72|96|
|Panama (balboas/hour)|Primary|0.62|1.28|56|77|
||Secondary|1.52|2.13|85|77|
||Tertiary|1.03|1.83|58|71|
|Uruguay (pesos/hour)|Primary|_551_|695|79|91|
||Secondary|646|853|71|94|
||<br>Tertiary|1211|1268|56|89|
|Costa Rica (colon/month)|All levels|10928|24954|66|91|

Source: Respective country studies in Psacharopoulos and Tzannatos (1991).

**21** See Gunderson (1989).
# **1 Introduction**

While coronavirus (COVID-19) is primarily a health crisis, its ramifications extend to social and economic aspects of countries. Trade collapsed and demand and supply shocks became widespread after pandemic-induced measures were imposed globally to contain transmissions. Supply-side shocks on exports arose from COVID-19 related deaths and infections, and lockdown policies such as border closures, slow clearance of goods and workplace closures that reduced labor participation resulted in a slump in overall firm and country production capacity. Demand shocks then compounded the supply shocks from declines in aggregate demand due to job redundancies, business closures, cautionary spending and investments. These shocks were in turn transmitted and augmented through disruptions in the global supply chains (Hayakawa and Mukunoki, 2021a; Hayakawa and Mukunoki, 2021b).

In this context, this paper assesses the trade effect of the pandemic on the largest economy in Southeast Asia, Indonesia. The study investigates the broad effects of lockdown measures (domestic, i.e., imposed by Indonesia, and external, i.e., imposed by Indonesia’s trading partners) on Indonesia’s imports and exports, including the survival of products in import and export markets. Then, we differentiate between sea and air trade as well as assess the role of specific non-tariff measures (NTMs) on the effect of lockdown measures on trade performance in Indonesia. Additional analyses are also conducted to establish the sensitivity of our results to the type of lockdown policy and the effect of lockdowns on export and import prices. Lockdown is a binary composite indicator variable where one indicates the month any of the following measures were imposed by Indonesia and its trading partners: workplace closure, the closure of public transport, stay-at-home requirements, restrictions on internal movement, and international travel controls. This is done using a monthly series of productby-country data for the period 20 months before and 20 months after January 2020, i.e., May 2018 to August 2021, in survival analysis and event-study designs. For imports, domestic lockdowns represent demand factors and external lockdowns represent supply factors. For exports, the inverse applies.

1
Table 3: Estimation of Health Technology: OLS and IFE

|Variables||Dependen|t Variable: L|(Individual’s|weight for h|eight)|
|---|---|---|---|---|---|---|
||(1)<br>Male,|(2)<br> HEIO|(3)<br>Male, no|(4)<br>t HEIO|(5)|(6)<br>Female|
||OLS|FE|OLS|FE|OLS|FE|
|L calorie|0.0513***<br>|0.0067***<br>|0.1097***<br>|0.0054**<br>|0.0760***<br>|0.0054***<br>|
|intake|(0.009)|(0.002)|(0.008)|(0.002)|(0.006)<br>|(0.002)<br>|
|preg|||||0.1197***|0.0333***|
|nant|||||(0.010)|(0.004)|
|lact|||||0.0653***|-0.0077**|
|ating|||||(0.008)|(0.004)|
|L mcapx|0.0827|0.0049|0.1384*|0.0078|0.0570|-0.0239|
||(0.076)|(0.019)|(0.076)|(0.024)|(0.057)|(0.015)|
|(L mcapx)<sup>2</sup>|-0.0044|-0.0003|-0.0085|-0.0007|-0.0024|0.0017|
||(0.006)|(0.001)|(0.006)|(0.002)|(0.004)|(0.001)|
|L land|0.0108|0.0229*|0.0142|-0.0140|-0.0141|-0.0050|
|per capita|(0.012)|(0.012)|(0.015)|(0.024)|(0.011)|(0.011)|
|L household|0.0453***<br>|0.0295**<br>|0.0319**<br>|-0.0079<br>|0.0098<br>|-0.0040<br>|
|size|(0.012)|(0.013)|(0.014)|(0.020)|(0.011)|(0.012)|
|share of|-0.0798*|-0.0142|-0.2174***|-0.0464|-0.1576***|-0.0589**|
|boys|(0.043)<br>|(0.026)<br>|(0.047)|(0.031)|(0.040)<br>|(0.026)<br>|
|share of|-0.0748*|-0.0565**|-0.0330|-0.0178|-0.2523***|-0.0673***|
|girls|(0.043)|(0.024)|(0.050)|(0.032)|(0.039)|(0.025)|
|share of|-0.0033|-0.0309|-0.0891*|0.0031|-0.1077***|-0.0700***|
|madol|(0.047)|(0.025)|(0.048)|(0.029)|(0.040)|(0.024)|
|share of|-0.0062|-0.0490**|-0.0750|-0.0011|-0.0870**|-0.0063|
|fadol|(0.041)|(0.021)|(0.047)|(0.028)|(0.035)|(0.020)|
|share of|0.0425|0.0084|0.1949***|0.0418|-0.0003|-0.0129|
|madlt|(0.046)|(0.023)|(0.052)|(0.031)|(0.044)|(0.024)|
|head’s|0.0017||0.0033***||0.0012||
|schooling|(0.001)||(0.001)||(0.001)||
|wife’s|0.0011||0.0025||0.0046***||
|schooling|(0.002)||(0.002)||(0.002)||
|head’s|-0.0087***||0.0019||0.0028||
|age|(0.003)||(0.004)||(0.003)||
|head’s<br>|0.0001***||-0.0000||-0.0000||
|_age_<sup>2</sup>|(0.000)||(0.000)||(0.000)||
|wife’s|0.0059*||-0.0067||-0.0046||
|age|(0.003)||(0.004)||(0.003)||
|wife’s<br>|-0.0001**||0.0001||0.0000||
|_age_<sup>2</sup>|(0.000)||(0.000)||(0.000)||
|L hvpwed|-0.0005||-0.0022||-0.0007||
||(0.002)||(0.002)||(0.002)||
|L wvpwed|0.0073*||0.0003||0.0007||
||(0.004)||(0.005)||(0.004)||
|Constant|-5.6971***|-8.3137***|-3.8445***|-2.2452***|-3.6609***|-2.5293***|
||(0.344)|(0.919)|(0.278)|(0.115)|(0.198)|(0.112)|
|Adj._R_<sup>2</sup>|0.370|0.125|0.821|0.128|0.753|0.146|
|Observations|2,815|2,815|4,565|4,565|7,305|7,305|
|Individuals|858|858|1436|1,436|2152|2,152|

Note: HEIO, high energy intensive occupation; L natural log, mcapx, monthly per capita expenditure; madol, male adolescent; fadol, female adolescent; madlt, male adult; hvpwed, 1+head’s assets; wvpwed, 1+wife’s assets (both in 000; taka); additional controls are log age and its square; survey rounds and site dummies; robust standard errors are in parentheses clustered at individual level; *** significant at 1%, ** at 5%, and * at 1% level.

38
8 Table 1: Basic OLS growth accounting regression, dependent variable per annum growth of GDP per worker.

||Barr<br>educat|o-Lee<br>ion data"|Nehru-Swa<br>educatio|nson-Dubey<br>n data2)|
|---|---|---|---|---|
||Basic|with initial GDPPW|Basic|with initial GDPPW|
|Physical capital per<br>worker|.524<br>(12.8)|.526<br>(12.8)|.501<br>(9.54)|.501<br>(9.49)|
|Educational capital per<br>worker|-.049<br>(1.07)|-.038<br>(.795)|-.104<br>(2.07)|-.117<br>(2.04)|
|Ln (Initial GDPPW)||.0009<br>(.625)||-.0008<br>(.491)|
|Number of countries|91|91|79|79|
|R-Squared|0.653|0.655|0.557|0.561|

Notes: Absolute values of t-statistics in parenthesis constants are included but not reported.

I) Uses King-Levine data on physical capital stocks.

2) Uses Nehru-Dhareshewar data on physical capital stocks.

Two columns in table I add the initial level of GDP per worker to the basic regressions, for two reasons. First, on a theoretical level, much attention has been given to conditional convergence which implies TFP growth should be higher the lower the initial level of income (Barro and Sala-i-Martin, 1995)14. On a more pragmatic level, perhaps poorer countries have more rapid percentage growth of human capital or the measurement error is associated with level of development, either of which could bias the results. However, adding the initial level of GDP per worker has no impact on the negative estimates of the effect of education.

The results for both physical and human capital are robust to the sample used. The negative coefficient on schooling growth persists if: a) only developing countries are used, b) the observations from Sub-Saharan Africa are excluded or c) regional dummies are included. Moreover, as a test for the robustness of the results to outliers, individual observations identified as influential were deleted 14 Although conditional convergence is of theoretical interest, it is obvious that absolute divergence in per capita incomes is obviously the prime empirical phenomena of the modem era (Pritchent 1995).
The evidence comes from a few studies. The cross-country regressions take the log of GDP per capita explained by average schooling and additional control variables. The micro studies refer to individual log wage explained by individual years of schooling, average years of schooling in a relevant geographical area, and additional control variables. The social returns equal the sum of the two schooling coefficients. Heckman and Klenow (1997) estimate the externality by comparing the schooling coefficient from cross-country regressions with those from cross-individual regressions. When they take into account-differences in technology, social returns become similar to private returns. Rauch (1993) looks at the effect of average education on workers' wages and finds significant externalities. However, average and own education may *be highly correlated. Acemoglu and Angrist (2000) correct for this by using instrumental variables. A few studies in Africa have focused on estimating external benefits of education in agriculture using the education of neighboring farmers. A one year rise in the average primary schooling of neighboring farmers is associated with a 4.3 percent rise in output compared to a 2.8 percent effect of own farmer primary education in Uganda (Appleton and Balihuta 1996, reported in Appleton 2000). Another study finds 56 percent and 2 percent figures for Ethiopia, but seems rather too high (Weir 1999, reported in Appleton 2000). The results overall are inconclusive.

# **V. Policy Issues**

Not only has the academic literature on returns to schooling increased, as is evidenced here, both in quantity and quality, but the policy implications have changed, too. No longer are returns to education seen as prescriptive, but rather as indicators, suggesting areas of concentration. A good example is the impact of technology on wage differentials, which led to a huge literature on changing wage structures (see, for example, Krueger 1993; Patrinos 2001).

At the same time, the importance of returns to education is seen in their adoption as a key indicator by the OECD (2001a) in their annual _Education at a Glance_ series and other policy documents (OECD 2001b; OECD 1997). Increasingly, governments and other agencies are funding studies of returns to education along with other research, to guide macro policy decisions about the organization and financing of education reforms. This was the case in the United Kingdom's higher education reforms as well as the Australian higher education financing reforms.

Innovative use of rate of return studies is being used to both set overall policy guidelines and to evaluate specific programns. Examples include the Indonesia school building program (Duflo 2001), India's blackboard project (Chin 2001) and Ethiopia's major sector investment program (World Bank 1998).

Above all, returns to schooling are a useful indicator of the productivity of education and incentive for individuals to invest in their own human capital. Public policy needs to heed this evidence in the design of policies and crafting of incentives that both promote investment and ensure that low-income families make those investments.

4