rafmacalaba/datause-encoder-data huggingface dataset:
# INFLATION IN CZECHOSLOVAKIA 1985-1991

Zdenek Drabek Kamil Janacek Zdenek Tuma _* The authors are, respectively, Senior Advisor to the Federal and Czech Ministers of Economy and to the Governor of the Central Bank; Head of Department in the Economic Institute of the Czechoslovak Academy of Sciences, and Senior Lecturer at the Centerfor Economic Research and Graduate Education, Charles University, all situated in Prague._ _The paper was written as part of a World Bank research project sponsored by the Socio-Economic Data Division, International Economics Department, and financed partially by the World Bank Research Fund._
|**[6]**<br>**+1)**<br>**average)**|3.292**<br>(1.628)<br>-0.048<br>(0.033)<br>0.070***<br>(0.013)<br>0.573***<br>(0.196)<br>0.087***<br>(0.025)<br>0.347**<br>(0.147)<br>-5.471***<br>(0.802)|577 / 88<br>0.451<br>3.38<br>3.135|
|---|---|---|
|**[5]**<br>**(t**<br>**(3-year**|1.352**<br>(0.661)<br>-0.035<br>(0.042)<br>0.071***<br>(0.017)<br>0.559***<br>(0.170)<br>0.059**<br>(0.028)<br>0.309*<br>(0.175)<br>-5.479***<br>(0.933)|510 / 84<br>0.464<br>3.521<br>3.094|
|**[4]**<br>**DP**|2.862**<br>(1.447)<br>-2.353<br>(2.312)<br>0.307***<br>(0.031)<br>-39.966***<br>(3.051)<br>0.038***<br>(0.006)<br>0.220<br>(0.152)<br>0.062***<br>(0.021)<br>0.214***<br>(0.083)<br>0.076<br>(0.701)|1577 / 88<br>0.436<br>3.519<br>4.165|
|**Table 3**<br>_Sample of 1974-2004_<br>_Dependent Variable: Percentage of Change of the Real Gross Domestic Product_<br>**Variables**<br>**[1]**<br>**[2]**<br>**[3]**<br>**(t+1)**<br>**∆%G**|int1. Index (t)<br>1.269***<br>1.436***<br>(0.270)<br>(0.428)<br>int1. Index_neg (t)<br>-0.37<br>(0.743)<br>int2. Index (t)<br>1.790**<br>(0.810)<br>int2. Index_neg ( t)<br>_Control Variables_<br>Lagged dep. var.<br>0.292***<br>0.292***<br>0.307***<br>(0.032)<br>(0.032)<br>(0.031)<br>LGDP_HP_cycle (t)<br>-38.124***<br>-38.091***<br>-40.016***<br>(3.046)<br>(3.053)<br>(3.038)<br>∆Log(ToT)<br>0.038***<br>0.038***<br>0.038***<br>(0.006)<br>(0.006)<br>(0.006)<br>Population growth<br>0.22<br>0.23<br>0.215<br>(0.147)<br>(0.147)<br>(0.152)<br>Financial account to GDP<br>0.039*<br>0.040*<br>0.062***<br>(0.020)<br>(0.020)<br>(0.021)<br>Trading partners growth<br>0.206**<br>0.206**<br>0.215***<br>(0.083)<br>(0.083)<br>(0.083)<br>GDP (t)<br>-0.17<br>-0.19<br>0.103<br>(0.674)<br>(0.676)<br>(0.700)|Observations / Countries<br>1496 / 84<br>1496 /84<br>1577 / 88<br>R-squared<br>0.442<br>0.442<br>0.436<br>Mean Dep. var.<br>3.59<br>3.59<br>3.519<br>St Dev Dep. var.<br>4.099<br>4.099<br>4.165<br>Note:_y_(t, t+s) corresponds to the average over the period t to t+s of the variable_y_.<br>Except otherwise indicated, all controls are averages for the period over which the dependent variable is measured.|
# **Table 6. Regression results for sharing misinformation** .

||(1)<br>Sharing<br>Discernment|(2)<br>Misleading<br>Headline Sharing|(3)<br>Non-Misleading<br>Headline Sharing|
|---|---|---|---|
|**_Panel A: Placebo as base group_**||||
|Control|-0.126**|0.068|-0.080|
||(0.059)|(0.061)|(0.058)|
||[0.245]|[0.828]|[0.717]|
|Comprehensive Active|0.232***|-0.304***|-0.027|
|Inoculation|(0.060)|(0.058)|(0.057)|
||[0.000]|[0.000]|[0.629]|
|Brief Active Inoculation|0.062|-0.334***|-0.257***|
||(0.056)|(0.057)|(0.058)|
||[0.258]|[0.000]|[0.000]|
|Passive Inoculation|-0.003|-0.118**|-0.120**|
||(0.055)|(0.057)|(0.055)|
||[0.999]|[0.224]|[0.202]|
|Controls|Yes|Yes|Yes|
|Observations|2,851|2,851|2,851|
|R-squared|0.025|0.057|0.028|
|**_Panel B: Passive Inoculation as base group_**||||
|Control|-0.123**|0.186***|0.040|
||(0.059)|(0.059)|(0.059)|
||[0.233]|[0.016]|[0.909]|
|Placebo|0.003|0.118**|0.120**|
||(0.055)|(0.057)|(0.055)|
||[0.999]|[0.224]|[0.202]|
|Comprehensive Active|0.235***|-0.186***|0.093|
|Inoculation|(0.060)|(0.056)|(0.058)|
||[0.001]|[0.005]|[0.115]|
|Brief Active Inoculation|0.065|-0.216***|-0.137**|
||(0.056)|(0.055)|(0.059)|
||[0.439]|[0.000]|[0.089]|
|Controls|Yes|Yes|Yes|
|Observations|2,851|2,851|2,851|
|R-squared|0.025|0.057|0.028|

_Note._ OLS estimates of treatment effects. Control variables are age, education, gender, and vaccination status for COVID-19. Robust standard errors are shown in parenthesis, and Westfall-Young stepdown adjusted p-values in brackets (1,000 bootstrap replications). _*** p<_ 0.01 _, ** p<_ 0.05 _, * p<_ 0.1.

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as large as that found in Ethiopia. Second, the maximum education within the household significantly mitigates the negative impact of drought. This finding is similar to that of Ethiopia and Bangladesh. In fact, junior high school completion (nine years of schooling) almost offsets the negative effect of the drought.

In Column 4, we include the 2001 flood indicator to know the confounding effect. First, the previous results remain robust. Second, household size significantly mitigated the adverse impact of the 2001 drought. Thus, it appears that both quality (maximum education) and quantity (household size) work to mitigate the adverse impacts of the 2001 drought on school progression in Malawi. Third, the 2001 flood had a weakly negative impact on schooling progression.

# Figure 4 to be inserted

Figure 4 quantifies drought impacts on schooling progression (based on the estimates in Column 3). Case 1 shows that the direct effect (without assets) is a reduction of about 1 year. Cases 2 and 3 suggest that the effectiveness of education is larger than a large family size in Malawi (compared to Ethiopia). The results are comparable to those of Bangladesh.

# **_5.2. Ex-Post Responses_**

This section summarizes our findings on ex-post public assistance and ex-ante asset holdings. We are interested in the effectiveness of ex-post public assistance and whether the possibility of receiving public assistance affects ex-ante actions taken by households.

# Table 8 to be inserted

Table 8 reports estimation results on Bangladesh. In Bangladesh, we highlight three types of assistance: Gratuitous Relief (GR), Vulnerable Group Feeding (VGF) and assistance from non-government organizations since the preliminary analysis showed that these three sources have large shares in the public assistance (see also Quisumbing, 2004). Columns 1 to 4 use disaggregated measures of the flood exposure, interacted with total amounts of each type of public assistance in 1998-1999 and the household asset value. First, the VGF aid is shown to be most effective in mitigating the flood impacts (three out of four cases). Second, the total asset value also significantly mitigates the impacts (two cases). In Column 4, we also found that GR and NGO assistance mitigated the flood impact.

Columns 5 to 8 use a more disaggregated specification of household assets. The VGF aid significantly mitigates the flood impact (three out of four cases). Among the asset measures, maximum education and household size seem to effectively mitigate the disaster effects. However, land size and livestock have only limited roles in Bangladesh. The above results show that the availability of effective public assistance does not substitute for ex-ante actions taken by households: in Bangladesh, both ex-post public actions and ex-ante private actions coexist, playing active roles in mitigating the flood impacts on schooling investments.

Interestingly, the flood impact was smaller among girls than boys. There seems to be some qualitative difference between genders. One possible answer is that boys may 15
# **E.16 Surveillance**

The WHO defines contact tracing as: “identification and follow-up of persons who may have come into contact with a person infected with the Ebola virus.”<sup>A6</sup>

**Table E.16:** Contact Tracing among Confirmed Patients by Treatment

||||Proportion a|mongContacts|
|---|---|---|---|---|
|Treatment|Total Traced|Proportion Traced|Family|Outside|
|Control|17|0.59|0.50|0.50|
|CM|55|0.22|0.61|0.39|
|NFA|28|0.24|0.57|0.43|

_Notes_ : Total Traced (column 2) counts the number of cases subject to contact tracing across the treatment arms. Proportion traced then divides this number by the total number of confirmed cases. In the final two columns, we restrict attention to cases subject to contact tracing and compute the proportion of contacts from the patients’ family or outside their family. Family here includes individuals within the nuclear family, e.g., parents, children, siblings.

**Table E.17:** Effect on Surveillance Proxies

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Pr(Lab Test)|0.926|0.012|0.018|0.008|0.01|144|
||(0.099)|(0.016)|(0.02)|(0.018)|(0.02)||
|Delay: Report - Lab|5.029|-2.874|-3.072|-2.72|-0.352|160|
||(16.915)|(1.978)|(2.419)|(2.259)|(2.441)||
|Log(Case Workers + 1)|1.672|0.125|0.134|0.117|0.016|160|
||(0.952)|(0.18)|(0.221)|(0.206)|(0.223)||

_Notes_ : Treatment effects estimated using OLS including matching-triplet fixed effects. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01

> A6 `https://www.who.int/csr/resources/publications/ebola/contact-tracing-guidelines/en/`

A49
# **Table 3. Dynamic Error Correction Models for Changes in Log Spreads: Fixed Effects, Non-crisis periods, 1991M1-2004M6**

|**Coe**<br>|**fficient values**<sup>**16**</sup>**(abso**<br>|**lute t-ratios)**<br>||
|---|---|---|---|
|**explanatory variables**<br>|**Actual US interest**<br>**rate changes only**<br>**(1)**<br>|**Actual and forecast**<br>**US interest rate**<br>**changes (2)**<br>|**Parsimonious**<br>**model**<br>**(3)**<br>|
|**(1) lagged residual from**<br>**Column 4 of Table 1**|**-.1040 (9.33)**|**-.1037 (9.12)**|**-.1035 (9.13)**|
|**(2) changes in US Interest**<br>**Rate Variables**||||
|**US T Bills**|**.0338 (1.66)**|**.00092 (.04)**|**..**|
|**10 year Treasuries**|**.0653 (3.41)**|**.0674 (3.46)**|**.0671 (3.66)**|
|**US hi-low corporate**<br>**spread**|**.1274 (16.9)**|**.1280 (16.6)**|**.1288 (17.0)**|
|**Forecast US T Bills**|**..**|**.0660 (1.36)**|**.0735 (1.96)**|
|**Forecast 10yr Treasuries**|**..**|**.0114 (.21)**|**..**|
|**Forecast US hi-low**<br>**spread**|**..**|**.1619 (4.26)**|**.1513 (4.92)**|
|**(3) changes in country**<br>**specific variables**||||
|**trade openness**|**.1016 (.96)**|**.0982 (.93)**|**..**|
|**debt/gni**|**.00888 (2.49)**|**.00894 (2.49)**|**.00974 (2.74)**|
|**reserves/debt**|**-.0219 (3.35)**|**-.0220 (3.35)**|**-.0237 (3.63)**|
|**short-term/total debt**|**-.0281 (3.55)**|**-.0302 (3.77)**|**-.0304 (3.81)**|
|**(4) contagion dummy**|**.00288 (.97)**|**.00444 (1.47)**|**..**|
|**(5) statistics**||||
|**no. of obs.**|**1471**|**1441**|**1443**|
|**R2**|**Within .2218**<br>**Between .3108**<br>**Overall .1960**|**Within .2369**<br>**Between .3161**<br>**Overall .2119**|**Within .2368**<br>**Between .3080**<br>**Overall .2122**|
|**p-value for zero**<br>**coefficients**|**.0000**|**.0000**|**.0000**|
|**p-value for test all u(i)=0 **|**.0009**|**.0033**|**.0038**|

> 16 Separate country intercepts are not reported.

21
# **INTRODUCTION**

1. On January 1, 2007, Romania became a member of the European Union.  This achievement was in part made possible by a substantial reform effort that has allowed Romania to make impressive progress towards long-term stability and sustained growth in the last six years.

2. Nonetheless, the income gap with the new member states of the EU remains large. In order to sustain growth and improve competitiveness, a second generation of reforms has been put in place to help the country’s successful integration in EU and global markets. A cornerstone of this reform agenda is the implementation of less restrictive product market policies that enable firms to put resources, both capital and labor, to their most efficient use.

3. This paper presents a comparative analysis of Romania’s performance in product market regulation (PMR). The approach used relies on a methodology developed by the OECD (Conway, Janod and Nicoletti 2005), that measures the degree to which domestic policies inhibit or promote competition. The data – which are derived from a selfreported survey – is policy-focused as opposed to perception based (or based on market outcomes). It is available for OECD members and Brazil. Data for Romania were first collected in 2002 in the context of an OECD country study on Romania (OECD 2002a) and then again in early 2006.

4. The key finding is that Romania’s product market policies appear to be less restrictive of competition than most direct comparators from the region and not far from the OECD average (as estimated on the basis of data collected in 2003). This result is particularly impressive when compared to its earlier score: OECD (2002a), employing an earlier version of the PMR methodology, found that product market policies in Romania were among the most restrictive compared to OECD countries. This implies that over the past few years Romania has engaged in a comprehensive reform effort across a wide array of product market policies. Such striking improvement is in line with the findings of other surveys such as Doing Business, which shows Romania to have improved its ranking from 71 out of 155 countries in 2005 to 49 out of 175 in 2006.

5. There are, however, two important caveats to this otherwise impressive achievement. The first is that the PMR approach measures officially adopted policies. It does not capture implementation and enforcement. In fact, interviews with business association suggest a significant gap between officially adopted policies, on the one hand, and implementation and enforcement, on the other. While Romania has achieved impressive results in terms of having laws that are more conducive to private sector development, the lack of a greater effort in terms of implementation and enforcement would substantially reduce the impact of the policies adopted in recent years that could hurt the competitiveness of the Romanian economy in the longer run.

6. The second is that the analysis compared Romania in 2006 with OECD countries in 2003 (the latest year in which a PMR survey was conducted). This does not detract 1
significant at the 1 percent level.

Table 2: VIIRS Nighttime Lights: Cross Section and Temporal Relationships

||N|ight light|growth (yo|y)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|GDP growth (yoy)|0.318*|0.320|0.495***|0.467***|
||(0.176)|(0.198)|(0.129)|(0.138)|
|Country fxed efects|-|Yes|-|Yes|
|Date fxed efects|-|-|Yes|Yes|
|Obs|2957|2957|2957|2957|
|Adjusted _R_<sup>2</sup>|0.00438|0.00543|0.277|0.286|

_Note_ . This table presents the regression results of nighttime light growth on GDP growth, incrementally controlling for time and country fixed effects. Throughout, standard errors are clustered by time and in parentheses.yoy=year-over-year.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

# **4.3 Relationship for Different Country Groups**

In table 3, we further examine the relationship between nighttime light growth and GDP growth across different country groups. We group countries by their income status as discussed above.

Table 3 presents the results of regressions of nighttime light growth on GDP growth controlling for country and year fixed effects. Column (1) shows that nighttime light growth is weakly correlated with GDP growth for AEs. Columns (2) and (3) indicate that the relationship is much stronger for EMs and especially LIDCs. Grouping EMs and LIDCs together in column (4), the coefficient on GDP growth is statistically significant at the 1 percent level. Column (5) shows that the coefficient is slightly smaller when estimated using the entire sample, albeit still statistically significant at the same level.

While table 3 provides insightful information on the relationship between nighttime light growth and GDP growth, the estimated coefficients cannot be used directly as the elasticity of nighttime lights with respect to GDP, because both variables contain measurement errors. In the next section, we develop a framework to estimate the elasticity properly.

11
than incapacitating workers. As the capital-worker ratio drops, this mechanism would suggest a growth expansion in industry.

# **3. Estimation Methodology**

The point of departure is a standard growth regression equation designed for estimation using (cross-country, time-series) panel data: Where the subscripts _i_ and _t_ represent country and time period, respectively; _y_ is the log of output per capita, _CV_ is a set of growth control variables, and _ND_ represents natural disasters;  _t_ and  _i_ denote unobserved time- and country-specific effects, respectively; and  is the error term. The dependent variable ( _yi,t-yi,t-1_ ) is the average rate of real output growth (i.e., the log difference of output per capita normalized by the length of the period).

The regression equation is dynamic in the sense that it includes the level of output per capita ( _yi,t-1_ ) at the start of the corresponding period in the set of explanatory variables. This poses a challenge for estimation given the presence of unobserved periodand country-specific effects. While the inclusion of period-specific dummy variables can account for the time effects, the common methods of dealing with country-specific effects (that is, within-group or difference estimators) are inappropriate when a regression is dynamic in nature.

The second challenge is that most explanatory variables are likely to be jointly endogenous with economic growth, so we need to control for the biases resulting from simultaneous or reverse causation. Although natural disasters are exogenous–and treated as such in the econometric estimation<sup>4</sup> —their effects would be incorrectly estimated if the endogeneity of the remaining variables in the model is ignored.

> 4 The measure of external shocks (i.e. growth rate of terms of trade—see below)  is also considered exogenous.

9
**Table 15. Illustrative investment targets for infrastructure in Uganda**

||Economic target|Social target|
|---|---|---|
|ICT|Install fiber-optic links to neighboring capitals and submarine<br>cables.|<br>Provide universal access to GSM signal and public broadband<br>facilities.|
|Irrigation|Develop additional 445,041 hectares of economically viable<br>small scale irrigation.|n.a.|
|Power|Develop 1,258 MW of new generation capacity and 537 MW<br>inter-connectors (no-trade scenario).|<br>Increase electrification to 27.2 percent (100 percent urban and<br>15.1 percent rural).|
|Transport|Achieve regional (national) connectivity with good quality 2-<br>lane (1-lane) paved road.|Provide rural road access to 27 percent of the highest-value<br>agricultural land, and urban road access within 500 meters.|
|WSS|n.a.|Achieve Millennium Development Goals, clear sector<br>rehabilitation backlog.|

_Source:_ Mayer and others 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others 2009. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology; GSM = global system for mobile communications. n.a. = Not applicable.

_Meeting these illustrative infrastructure targets for Uganda would cost $1.4 billion per year over a decade._ Capital expenditure would account for 85 percent of this requirement. The power sector has the highest spending needs and will require an estimated $0.4 billion per year; the water and sanitation sector has the second-highest spending needs at about $0.3 billion per year to meet the MDGs. Around $0.2 billion per annum will be required to meet needs in each of the ICT, irrigation, and transport sectors (table 16).

**Table 16. Indicative infrastructure spending needs in Uganda, 2006–15**

|US$ million per year||||
|---|---|---|---|
|Sector|Capital expenditure|Operations and<br>maintenance|Total needs|
|ICT|143|18|161|
|Irrigation|231|1|232|
|Power (nontrade)|390|57|447|
|Transport|137|84|221|
|WSS|293|56|348|
|Total|1,194|216|1,410|

_Source_ : Mayer and others 2009Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others 2009. Derived from models that are available online at www.infrastructureafrica.org/aicd/tools/models. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology.

42
least one foreign bank. The data set includes 108,016 domestic syndicated loans and 117,325 international syndicated loans.

Firms are classified into financial and non-financial corporations according to their Standard Industry Classification (SIC) code. Firms with SIC codes between 6000-6800 are classified as financial corporations. The data set includes 40,591 equity, 184,236 bond, and 45,403 syndicated loan issuances by financial firms (or 20, 63, and 20 percent of the total issuances of equity, bonds, and syndicated loans, respectively).

To examine the comparative characteristics and performance of issuing and non-issuing firms in the Arab countries, we match the data set on security issuances from SDC Platinum with 20032011 firm-level balance sheet information from the Orbis (Bureau van Dijk) database. The latter covers publicly listed companies, providing a rather homogeneous sample of firms. By omitting unlisted firms from the analysis using the matched data, the sample excludes firms that are (1) relatively small and sometimes informal, (2) likely to have different accounting standards, and (3) less likely to issue in capital markets. The final matched data set covers 1,462 firms from 12 Arab countries. In particular, our sample includes firms from Algeria, the Arab Republic of Egypt, Bahrain, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, and the United Arab Emirates.

Moreover, we classify firms as issuers or non-issuers based on whether they issued equity, bonds, or syndicated loans at any point during our sample period. Because firm-level balance sheet information is only available from 2003 to 2011, we classify a firm as an issuer if it had at least one issuance during that period. We further classify whether firms are equity, bond, or loan issuers depending on whether firms issued any equity, bonds, or syndicated loans, respectively. The sample of non-issuing firms is held fixed throughout the paper. Non-issuing firms are those that did not have 7