rafmacalaba/datause-encoder-data huggingface dataset:
1−β1<sup>′</sup> <u>i2</u> 2−β2<sup>′</sup> <u>i2</u> 1 2) = Φ2 , − (C4) ~~�~~ zz xx zz xx where Φ 2(. ) stands for the bivariate normal cumulative distribution function (cdf), and PP(yyii1 < zz aai yyii2 > zz σσεε1 σσεε2 , −ρρ� 2(. ) stands for the bivariate normal probability density function (pdf). Note that in Equation (1), the estimated parameters obtained from data in both survey rounds are applied to data from the second φφ survey round (x2) (or the base year) for prediction, but we can use data from the first survey round as the base year as well. It is then straightforward to estimate quantity (C3) by dividing quantity 1−β1<sup>′</sup> <u>i2</u> (C2) by Φ , where Φ (. ) stands for the univariate normal cumulative distribution � ~~�~~ zz xx function (cdf). σσεε1 In Equation (4), the parameters and are estimated from Equation (C1), and ρ can be estimated using an approximation of the correlation of the cohort-aggregated household ii εεii ββ σσ consumption between the two surveys ( ). In particular, given an approximation of , where c indexes the cohorts constructed from the household survey data, the partial correlation yycc1yycc2 yycc1yycc2 coefficient ρ can be estimated by ρρ ρρ <u>i1 i2</u> ) 1<sup>′</sup> 2 (C5) ρρyy yy �vvaavv(yyii1)vvaavv(yyii2 −ββ vvaavv(xxii)ββ An alternative way to estimate ρρ= is to further assume that there is a cohort fixed effect in the σσεε1σσεε2 error terms and aggregate all the time-invariant variables to the cohort level and use the following equation ρρ i i i (C6) where the error term i includes a cohort fixed effect and the error i . ii yy = ββ ′xx + εε Note that the standard errors of estimates based on the synthetic panels can in fact be even ii εε ττ νν smaller than that of the true (or design-based) rate if there is a good model fit (or the sample size in the target survey is significantly larger than that in the base survey; see Dang and Lanjouw, 2023, for discussion).

Tables C.1-C.6 present the estimation results using Equation (C1) for all the countries and survey rounds. Tables C.7-C.12 present the descriptive statistics of the estimation sample. These tables show that while most of the time-invariant characteristics show similar distributions across survey rounds (and satisfy Assumption 1), some do not. For example, these include the shares of household heads achieving primary education or secondary education in the Arab Republic of Egypt during 2012-2015 (Table C.7). But the differences are practically very close to 0. Table C.13 presents the estimates for using Equations (C5) and (C6), where are estimated using Equation (C5) for all countries with cohorts being defined by age interacted with household heads’ education. ρρ ρρ We also provide alternative estimates for using Equation (C6). Using these estimates, Figures C.1 and C.2 offer qualitatively similar results to Figures 5 and 6. ρρ 78
# INLATION IN CZECHOSLOVAKIA, 1985-1991'

# **Introduction**

The purpose of this paper is to assess inflation in Czechoslovakia in the period of 1985-1991 and to identify its causes. We shall show that the period has been characterized by inflationary pressures, which were translated partly into open inflation and partly into suppressed and hidden inflation until full price liberalization was introduced in January 1991. Since then, inflationary pressures have been fully channeled into open inflation.

Inflation measurement is dependent on availability and reliability of data. The expertise of Czechoslovak statisticians and statistical offices is reasonable. Czechoslovak price data, however, are problematic, difficult to interpret and not available for certain specific uses.

While the measurement problems are generally known, the way in which Czechoslovak economists and statisticians have addressed these questions is less familiar. The literature is quite plentiful but most, if not all, has been written in Czech or Slovak. In addition, a large number of these studies have appeared in semi-official publications, so that they have not been easily accessible. Given the wealth of material and economic talent, the lack of information about the Czechoslovak literature has been a disappointing gap in understanding economic issues of Central Europe. The paper draws conclusions from a survey of relevant Czechoslovak literature and authors' analyses of limitedly available data.

The period 1990-1991 is analyzed separately in this study. The main reason is the economic reform that followed political changes in November 1989 and led to partial price liberalization in 1990 and full price liberalization in 1991. As a result, inflationary pressures were released much more intensively through open inflation in 1990 and fully in 1991. The increase in price levels in 1990 and 1991 represents partly the size of the monetary overhang accumulated by the end of 1989. Of course, inflation in these two years was fed through other channels as well, as we shall argue in the text. Nevertheless, the highly restrictive macroeconomic policy, which accompanied e reform, was instrumental in minimizing the importance of these channels.

'We would like to thank Mr. I. Sujan and Mrs. Kocianova (Federal Statistical Office), and and Mr. E. Sip (Federal Ministry of Finance) for their assistance in providing data and helpful suggestions. Thanks are also due to Richard Porter, John Flemming, Jong-Goo Park, participants of The World Bank workshop and the anonymous reviewer for the helpful comments on the first draft of the paper. The usual disclaimer holds - none of these persons is responsible for any shortcomings which may still remain and the views expressed in the paper do not represent the views of the World Bank or its member countries.
**Table 11.  Consolidated Financial Statements of KKVS SHGs**

|_Particulars_|_1998-99_|_1999-00_|_2000-01_|
|---|---|---|---|
|**Income**||||
|Interest|1,015,950<br>|1,879,625<br>|3,811,767|
|Service charges/Fees|71,137<br>|32,017<br>|3,332|
|Donations|200,875<br>|1,890<br>|1,615|
|Sundry Income|23,654<br>|143,596<br>|412,670|
|Total|1,311,616<br>|2,057,128<br>|4,229,384|
|**Expenditure**||||
|Interest on loan|207,990<br>|587,839<br>|1,388,234|
|Interest on savings<br>|175,101<br>|362,425<br>|714,156|
|Service charges/Fees|46,494<br>|268,505<br>|548,237|
|Travel|31,492<br>|42,141<br>|93,637|
|Administration|80,576<br>|59,591<br>|16,081|
|Printing and Stationery|42,449<br>|20,077<br>|56,147|
|Training|2,520<br>|31,078<br>|66,811|
|Audit fee|12,110<br>|23,325<br>|30,841|
|Specified Funds<br>|20,713<br>|154,404<br>|315,414|
|Donations<br>|201,949<br>|22,710<br>|-|
|Sundry expenses|21,787<br>|256,259<br>|449,028|
|Profits|468,435<br>|228,774<br>|550,798|
|Total|1,311,616<br>|2,057,128<br>|4,229,384|
|**Equity and Liabilities**||||
|Reserves and Surplus|331,596|329,814|497,749|
|Member Deposits|2,718,404|5,122,666|9,042,217|
|Loans|2,730,153|6,307,886|14,966,253|
|Specified Funds|325,484|557,793|1,005,299|
|Current liabilities|147,739|88,818|90,048|
|Total|6,253,376|12,406,977|25,601,566|
|**Assets**||||
|Land and Fixtures||||
|Deposits|461,064|1,093,644|1,704,265|
|Loans and Advances|5,693,377|11,205,109|23,575,939|
|Cash|98,935|108,224|321,362|
|Total|6,253,376|12,406,977|25,601,566|

Item heads in original statements reorganized and relabeled for easier comparison.

- 33 -
# Table 7

Per Capita Expenditures in Provinces Classified According to Income Categories (1993) (Eh. dongs)

|Expenditure|High Income'<br>Provinces (Per<br>Capita GDP) ><br>450 th. Dongs|Middle Income:<br>Provinces (Pcr<br>Capita GDP) ><br>300 - 450 th. Dongs|Low Income'<br>Provinces (Per<br>Capila GDP <<br>300 th. Dongs|All<br>Provinces|
|---|---|---|---|---|
|1991 (Actual)|||||
|1. Capital expenditure|22.4|10.0|12.2|15.9|
|2.Currentexpenditure.of|70.9|47.4|49.0|58.2|
|which|||||
|(i) Education|13.4|11.3|10.9|12.2|
|(ii) Health|8.8|4.6|11.9|6.5|
|3. Total expenditure|93.3|57.4|61.2|74.1|
|1992 (Actual)|||||
|1. Capital expenditure|34.6|14.9|20.8|25.0|
|<br>2. Current cxpenditure. of|126.3|73.6|79.7|98.1|
|which|||||
|(i) Education|22.0|17.3|17.4|19.4|
|(ii)Health|1337|7.8|7.8|10.4|
|3. Total expenditurc|160.9|88.5|100.5|123.1|
|1993 ( Budget Estimates)|||||
|1. Capital cxpenditure|27.0|14.0|19.9|19.9|
|2<br>Working capital and|12.4|2.8|2.4|5.6|
|subsidy to SOES|||||
|3. Current expenditurc. of|139.2|78.9|82.5|100.6|
|which|||||
|(i)<br>Economic services|40.4|11.1|13.7|21.9|
|<br>(ii)<br>Education|26.5|21.8|23.3|23.0|
|<br>(iii) Hcalth|14.7|9.3|4.4|11.3|
|(iv)Training & scientific|6.2|4.0|9.8|4.8|
|rescarch|||||
|(v)<br>Others|51.4|32.7|31.3|39.6|
|4. Total expenditure|178.6|95.7|104.7|126.6|

- I The high income provinces are: Ba Ria-Vung Tau. Ho Chi Minh city. Ha Noi. Dong Nai. Kien Giang. Quang Ninh . Khanh Hoa. Hai Phong. Hoa Binh. Vinh Long. Tra Vnh. Long An. An Giang. Can Tho, Minh Hai and Dac Lac

2. The middle income provinces are: Ticn Giang. Yen Bai. Soc Trang. Bac Thai. Tay Ninh. Ben Tre. Song Be. Lam Dong. Dong Thap. Quang Nam. Hai Hung. Vinh Phu, Thanh Hoa, Nam Ha. Thai Binh. Kon Tum. Lai Chau. Phu Yen and Ha Tay.

3. The low income provincres are: Long Son. NinL. Binh. Tuyen Quang. Ha Bac. Ninh Thuan, Gia Lai, Thua Thien Hue. Quang Binh, Binh Dinh, Binh Thuan. Cao Bang. Quang Tri. Nghe An. Quang Ngai. Ha Tinh. Ha Giang, Lao Cai and Son La.

Source: Ministry of Finance and State Planning Committee, Government of Viet Nam.

13
<!-- Start of picture text --> Figure 9<br><!-- End of picture text --> <!-- Start of picture text --> United States<br>Percentage reporting use of cocaine<br>(Ages 12 and older)<br>4<br>3,5<br>3<br>2,5<br>2<br>1,5<br>1<br>0,5<br>0<br>Current use (past month)<br>Ocasional use (1 to 11 days in past 12 months)<br>* change in methodology: from paper-and-pencil to computer assisted inteviews.<br>** methodological changes in the survey<br>*,** : results prior to 1999 and the ones after 2002 are not comparable to other years.<br>Source: ONDCP.<br>Figure 10<br>30-day prevalence of cocaine use among 12th graders in the<br>US<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1985 1988 1990 1991 1992 1993 1994 1995 1996 1997 19981999* 2000* 2001*2002** 2003**<br>1991199219931994199519961997199819992000200120022003200420052006<br>prevalence rate<br><!-- End of picture text --> <mark>Source: M</mark> onitoring <mark>the Future Study (University of Michigan)</mark> 56
- **Bail, Christopher A., Friedolin Merhout, and Peng Ding** , “Using Internet Search Data to Examine the Relationship between Anti-Muslim and pro-ISIS Sentiment in U.S. Counties,” _Science Advances_ , 2018, _4_ (6).

- **Bandyopadhyay, Subhayu and Javed Younas** , “Poverty, Political Freedom, and the Roots of Terrorism in Developing Countries: An Empirical Assessment,” _Economics Letters_ , 2011, _112_ (2), 171–175.

- **Baum, Matthew A and Philip BK Potter** , “Media, Public Opinion, and Foreign Policy in the Age of Social Media,” _The Journal of Politics_ , 2019, _81_ (2), 747–756.

- **Bellemare, Marc and Casey Wichman** , “Elasticities and the Inverse Hyperbolic Sine Transformation,” _Oxford Bulletin of Economics and Statistics_ , 2020, _82_ (1), 50–61.

- **Benmelech, Efraim and Esteban F. Klor** , “What Explains the Flow of Foreign Fighters to ISIS?,” _Terrorism and Political Violence_ , 2018, _0_ (0), 1–24.

- **Berger, J.M.** , “Tailored Online Interventions: The Islamic state’s Recruitment Strategy,” _CTC Sentinel_ , 2015, _8_ (10), 19–23.

- **and Jonathon Morgan** , “The ISIS Twitter Census: Defining and Describing the Popula-

- tion of ISIS Supporters on Twitter,” _The Brookings Project on US Relations with the Islamic World_ , 2015, _3_ (20), 4–1.

- **Bhuller, Manudeep, Tarjei Havnes, Edwin Leuven, and Magne Mogstad** , “Broadband Internet: An Information Superhighway to Sex Crime?,” _Review of Economic Studies_ , 2013, _80_ (4), 1237–1266.

- **Bloom, Mia, Hicham Tiflati, and John Horgan** , “Navigating ISIS’s Preferred Platform: Telegram,” _Terrorism and Political Violence_ , 2017, _31_ (6), 1242–1254.

- **Brockmeyer, Anne, Quy-Toan Do, Cl ́ement Joubert, Mohamed Abdel Jelil, and Kartika Bhatia** , “Transnational Terrorist Recruitment: Evidence from Daesh Personnel Records,” _The Review of Economics and Statistics_ , forthcoming.

18
assessments indeed seem to increase the employability of out-of-job workers by giving them a certificate to prove their skills.

The end-line survey also asked those who became newly employed in the past six months (that is, since the baseline survey) how they found those jobs. Broadly, two job search channels are available to job seekers to find employment: formal and informal. Going through formal job search channels means searching and finding jobs through the publicly open job-posting information, traditionally in newspapers and increasingly on websites. Informal channels are through personal networks—typically family and relative networks. Using such informal job search channels is extremely common in Bangladesh, especially for micro and small companies. Naturally, formal job search channels tend to be more competitive and rely on proper documents of applicants’ qualifications. Going through informal channels may give job seekers lesscompetitive, flexible access to jobs, but the downside is that it keeps wages lower than they would otherwise be.<sup>9</sup> The survey results show that recent successful job seekers in the treatment group are significantly more likely to use formal channels to find jobs than their control group counterparts (see figure 3.2). It may well be because they are more confident and competitive in the open job market thanks to their RPL certificates. Also, among the RPL participants who found new jobs in the past six months, most (81 percent) said in the interview that the RPL assessment helped them a lot to find the job. These findings concur with the notion that RPL assessments and certificates enhance workers’ employability and confidence in skills, which would lead to improved employment outcomes for RPL participants.

**Figure 3.2 Job Search Channels Used by RPL Applicants Recently Employed at End-line**

<!-- Start of picture text --> Informal channel Formal channel<br>(personal networks) (digital/paper job postings)<br>88%<br>73%<br>27%<br>12%<br>Treatment group Control group<br><!-- End of picture text --> _Source:_ Original figure for this publication.

_Note:_ The difference is statistically significant at the 5 percent level. The number of observations with data on job search channels is 73 for the treatment group and 49 for the control group.

# Impact on Quality of Employment

## **Wage Levels**

Next, we examine the impact of RPL on wage levels among those who are in wage employment. In the labor economics literature, wage functions usually take academic qualification or years of schooling as one of the main influencing factors. However, that is generally limited to verifiable formal education qualifications. Another primary variable is years of work experience (or age as an alternative), usually

> 9 Graduate tracer studies in Bangladesh consistently find that wages for those who found jobs through informal channels are lower than wages for those who found work through formal channels.

17
Table 8 Gender

|Males Females|
|---|
|Treatment 0.0384**-0.0031|
|(2.39) (0.14)|
|# applications 0.0357***0.0060|
|(4.16) (1.41)|
|Treatment * # applications -0.0197*0.0228**|
|(1.99) (2.22)|
|R squared 0.1513 0.1135|
|N obs 1381 395|

*** 1%, ** 5%, * 10% significance. Numbers in parentheses are absolute t values using robust standard errors. The sample consists of job seekers who were in the system more than 30 days at the time of taking the test and applied for less than 30 positions during the tracking period.

Table 9 Personality traits by gender

|Male Female|
|---|
|Agreeableness (% Cooperative) 69.38 73.14|
|Conscientiousness (% Organized) 69.24 70.93|
|Openness to experience (% Imaginative) 65.66 60.51|
|Emotional stability (% Calm) 72.35 69.35|
|Extraversion (% Quiet) 51.95 49.29|

22
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addressing cost-risk trade-o¤s in ...nancing decisions, most importantly decisions on debt composition. This indicator may appear to be perfectly correlated with GDP per capita, in fact the correlation is estimated to be �0:55. Although the correlation can be regarded as high an exclusion of the degree of concessionality from the regression for strategies was rejected. We use the ratio of concessional debt to total external debt to approximate this indicator. This measure was obtained from the GDF & WDI Central database of the World Bank.

Internal macroeconomic management - the standard deviations of CPI in‡ation and GDP growth are used to capture quality of internal macroeconomic management. Since price stability is the basic objective and goal of monetary policy we ...nd the standard deviation of in‡ation indicative of the quality of internal macroeconomic management. Also, the monetary and ...scal policies aim at smoothing ‡uctuations in economic performance, the economic growth cycle. We again draw the link from sound and successful macroeconomic policy of a government to its likely engagement in sound practice regarding public debt management. However, also in this case the argument can be posed di¤erently. Namely, that the success of a sound macroeconomic policy will depend on the institutional set up of the economy, such as e.g. wage negotiation mechanisms or capital adequacy requirements for ...rms, that determines the pass-through and size of domestic shocks. Although, the government can in‡uence the institutional set up in the long run, facing larger domestic shocks can lead to adoption of more advanced instruments for public debt management. The CPI in‡ation and GDP growth series were obtained from the GDF & WDI Central database of the World Bank.

Flexibility of exchange rate regimes - we use the standard deviation of the change in the exchange rate to approximate this indicator. The lower the standard deviation the lower the ‡exibility of an exchange rate regime. However, varying volatility of exchange rates across countries is also attributable to varying impacts or sizes of external shocks. This would be certainly the case if one dealt only with ‡oating exchange rate regimes. In order to condition on the external shocks we employ other variables such as volatility of current account or the terms of trade, see below. The standard deviation of exchange rate is computed using the exchange rate series from 18
Table 2: Household Income and Expenditure in INR

||Treatment|Control|
|---|---|---|
|Total income|5965.6|5140.5|
||(524.9)|(383.8)|
|Wage income|4344.2|4549.9|
||(386.6)|(363.6)|
|Total expenditure|4266.4|2833.0|
||(136.7)|(183.9)|
|Food expenditure|1713.7|1404.0|
||(65.18)|(46.69)|
|Nonessential expenditure|322.3|112.7|
||(17.55)|(24.53)|
|Number of households|3403|3214|

Note: Summary statistics are based on the period June 2017 to May 2018. Total income; wage income; and total, food and nonessential expenditures are expressed in terms of per household member. Standard deviations are in parentheses.

Our baseline household sample comprises 6,617 households, of which 51.4 percent are in Kerala and the rest are in Tamil Nadu (34.8 percent) and Karnataka (13.7 percent). Table 2 reports the summary statistics for the key household level variables (in logs). Treated households (those in Kerala) earn and spend on average more than those in the control districts (those in Tamil Nadu and Karnataka).

# **5. Impact on Aggregate Economic Activity and Credit**

This section examines the impact of the disaster on aggregate economic activity, consumer demand, and credit. We examine the impact on monthly nighttime light intensity, monthly ATM transactions, and outstanding quarterly credit by estimating the following equation: The left-hand side represents the log of the dependent variables. The cross-section unit _i_ refers to districts for nighttime light intensity and credit, and to pin codes for ATM trans15
|to which total foreign deposits can be allocated to individual<br>tistics. The frst column shows our main sample of 22 highly<br> for which relevant data are available. The frst line shows the<br>w the fraction of this total can be allocated to specifc banking<br>gory ”Other non-havens” includes Australia, Brazil, Denmark,<br> is the diference between 100% and the sum of the percentages|||
|---|---|---|
|e shows how the extent <br> Locational Banking Sta<br>r countries in the world <br>ntry. The next lines sho<br>he Isle of Man. The cate<br> category ”Unallocated” <br>Banking Statistics.|||
|**ta.** The tabl<br> from the BIS <br> shows all othe<br>unterpart cou<br>uernsey and t<br> the U.S. The <br>s, Locational|**Other**<br>17,097|22%<br>9%<br>5%<br>5%<br>3%<br>42%<br>36%|
|**blic deposit da**<br>licly available data <br> the second column <br> total deposits by co<br>” includes Jersey, G<br>rlands, Sweden and <br>national Settlement|**High Aid**<br><br>200|29%<br>14%<br>3%<br>8%<br>5%<br>56%<br>15%|
|**rage of pu**<br>ased on pub<br>ntries while <br>statistics on <br>ory ”Jersey<br>apan, Nethe<br>nk for Inter|eposits|posits<br>rland<br>bourg<br>m<br>etc.<br>en deposits<br>ted|
|**ve**<br>s b<br>cou<br>le <br>teg<br>d, J<br> Ba|gn d|n de<br>tze<br>em<br>giu<br>ey<br>av<br>oca|
|**Table 6: Co**<br>banking center<br>aid-dependent <br>publicly availab<br>centers. The ca<br>Finland, Irelan<br>above. Source:|Forei|Have<br>- Swi<br>- Lux<br>- Bel<br>- Jers<br>Non-h<br>Unall|

28