rafmacalaba/datause-encoder-data huggingface dataset:
8 The decline was particularly surprising in the case of the Central European troika.<sup>9</sup> The FCSK, once a renowned exporter of machino tools and other high quality industrial products, became increasingly specialized in agricultural products and ores and nonferrous metals. Poland, thanks to Western credits, had a relatively modern industrial base on the eve of the 1980s, but experienced the second largest loss among the CEE-5 in share of OECD markets for manufactured products. Hence its investment drive in the 1970s had no discernible impact on its international competitive position. Hungary, a country much praised for its reform efforts in the 1980s, tended to shift away from manufactures and food products to minerals and raw materials, although its relative competitive position feli the least among the CEE-5 in the 1981-89 period.

# B. Two Distinct Phases: Breakdown and Precarious Recovery

An examination of the CEE-5 OECD export performance in the 1980s suggests the existence of two distinct periods. The first, between 1980 and 1983, can be called a "breakdown" period; the contraction in OECD import demand resulted in a much larger fall in exports from the CEF-5. The second, between 1984 and 1989, can be described as a period of stagnation and "progressive marginalization" of the CEE-5 region in OECD markets. Despite the expanding import demand in OECD countries, especially for manufactured products, communist governments' efforts to boost exports produced -Ited results.

The first period coincided with OECD recession and with deterioration in East-West political relations. Total OECD imports fell at an annual average rate of around 4 percent in the 1981-84 period, reaching their lowest point in 1982 when they fell by 6.2 percent. However, the fail in CEE-5 exports was significantly larger than the decline in OECD import demand overall.'° The latter fell by around 12 percent in aggregate over this period, while CEE-5 exports declined by nearly 30 percent.

As can be seen from Table 3, during 1981-83 all exports with the exception of mineral fuels contracted much more than OECD imports. The most affected were manufactures: the ratio of the average annual (negative) growth rate of CEE-5 exports to the OECD import (negative) growth rate was 7.12. There were only a few bright spots: all CEE-5 economies recorded a lower fall in exports of mineral fuels (as a result, their market shares for fuels increased); Czechoslovak exports of agricultural products increased; Hungary substantially expanded its exports of raw materials and mineral fuels; and Romania increased Its exports of ores and nonferrous metals. Bulgaria's and especially Poland's exports collapsed in all product 9 The worst performer was Bulgaria, whose share in 1989 in total OECD imports of manufactured goods was 5'.3 percent lower than in the peak year of 1980. Romana's share dropped by 41 percent in comparison with its peak year in 1980, Poland's by 40.4 percent, Czechoslovakia's by 27 percent, and Hungary's by 28.3 perment.

10 For this reason, the data for 1983 or 1984 is used as the base in the inter-temporal comparative analysis.
|**Table 2: Assessment**|**of the p**<br>Favoriti|**revalence**<br>sm (%)|**of differe**<br>Bribe|**nt forms**<br>ry (%)|**of corrupt**<br>Embezzle|**ion accord**<br>ment (%)|**ing to the**<br>Fraud|**private**<br>(%)|**and publi**<br>Extorti|**c sector**<br>on (%)|Obs.|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Prev.|Com.|Prev.|Com.|Prev.|Com.|Prev.|Com.|Prev.|Com.||
|Single proprietor|67.8|13.8|53.9|25.7|54.6|27.0|26.3|28.3|17.1|21.7|152|
|Partnership|59.8|20.1|56.7|22.6|61.0|20.7|28.7|31.7|17.7|26.8|164|
|Foreign corporation<br>|66.7|17.6|68.6|21.6|51.0|31.4|29.4|25.5|13.7|37.3|51|
|Local company<sup>1</sup>|73.6|13.9|69.9|17.6|58.3|25.5|31.9|27.8|20.4|28.7|251|
|_All private sector_|67.6|16.0|61.9|21.4|57.5|25.0|29.3|28.8|18.2|27.1|618|
|_Allpublic sector_|52.1|21.5|34.8|33.4|39.8|30.4|25.8|28.2|14.5|23.3|480|
|_Total_|60.8|18.4|50.1|26.6|49.8|27.4|27.8|28.5|16.6|25.4|1098|

<u>1 Includes others; Pre. = prevalent; Com. = Common.</u> Source: Authors’ own calculations based on the Institutional Module of the Uganda Second National Integrity Survey 2002.

18
# **Cash Transfers and Temptation Goods:**

# **A Review of Global Evidence**

David K. Evans Anna Popova World Bank World Bank Keywords: Cash Transfers, Temptation Goods, Impact Evaluation, Review, Consumption JEL Codes: E20, I38, O12 Acknowledgments: The authors are very grateful for comments and guidance from Javier Baez, Chris Blattman, Julio Elias, Francisco Ferreira, Mario Macis, and Norbert Schady. The findings, interpretations, and conclusions are entirely those of the authors. Corresponding author: David Evans (devans2@worldbank.org).
# **4 Discussion**

The extremely high social and economic cost of the HIV/AIDS epidemic motivates the continued search for innovative prevention approaches. This study is the first rigorous impact evaluation of an intervention aimed at sexual behavior change to show a significant decrease in HIV incidence, the ultimate objective of any HIV prevention intervention (Galárraga et al., 2009; Pettifor et al., 2012). The study shows that financial incentives, when implemented in the form of giving individuals a chance of a high reward conditional on negative results of periodic screenings for incident sexually transmitted infections – an objectively measured marker for risky sexual behavior – can be a powerful tool for HIV prevention.

We are not aware of any large scale randomized trial to assess the impact of these kinds of financial schemes. The use of lotteries as part of a public health intervention is not unprecedented, however, not even in the area of HIV prevention. In South Africa, for example, a growing number of programs for HIV screening use cash lottery prizes to encourage people to get tested, with the HIV screening lottery in the Western Cape Province being the most notable example.

Our study did not have an intervention arm where participants were given the expected value of lottery for certainty conditional on STI status. Thus, we cannot rule out that similar effects would have been achieved with a conditional financial cash transfers program that paid out the expected value instead of a lottery ticket with a chance to win. To the extent that we can learn from comparing similar experiments implemented in other contexts, however, the available evidence suggests that conditional financial incentive programs with certain cash transfers of the same (expected) magnitude do not lead to significant behavioral changes. The RESPECT study in Tanzania (de Walque et al., 2012, 2014) shared a number of similarities to the Lesotho trial, including financial incentives tied to periodic (every four month) screening of STIs, a low and a high financial transfer arm, free STI treatment of all trial participants, and similar inclusion criteria (males and females, aged 18-30 years). The prevalence rates for any of syphilis and trichomonas at baseline were also similar in the two trials (13.8% and 13.5%, respectively).<sup>20</sup> However, the transfers were higher in the Tanzania project – participants were eligible for $10 or $20 per testing round, or $30 or $60 for the trial period, compared to $3.3 or $6.6 in expected terms per testing round, or $20 or $40 for the trial period in the Lesotho project – and participants received the full cash reward for certainty conditional on being STI negative 20We are grateful to the RESPECT study team for providing us with summary statistics on baseline and end of trial prevalence of syphilis and trichomonas.

19
12 In the last few years, a number of studies have sought to quantify global inequality, and to investigate its dynamics. One of the most ambitious was a paper by Bourguignon and Morrisson (2002), who constructed a time-series of world inequality estimates for the period from 1820 to 1992. For all but the last ten to twenty years of that series, disaggregated household survey data are not available for many countries. The authors thus grouped countries into 33 ‘blocks’, the composition of which changed over time, depending on data availability (see Bourguignon and Morrisson, 2002, for details). The distributions are constructed in such a manner that all the members of a ‘bloc’ are assumed to have the same distribution as a country for which data are actually available in the relevant time-period. The authors construct a distribution based on decile (and some ventile) shares, and on GDP per capita figures. Individuals are assumed to have the same incomes within tenths (or twentieths) of the distribution, where that income corresponds to the group’s share of GDP per capita. This set of strong assumptions allowed the authors to construct a long time series covering most of the 19<sup>th</sup> and 20<sup>th</sup> centuries.<sup>17</sup> The main finding of the study is that world inequality rose almost continuously from the onset of the industrial revolution until the First World War. During that period, the world’s Gini index rose from 0.50 to 0.61. Although inequality was also rising within most countries for which data were available, the real driving force for this increase in global disparity was inequality _between_ countries, i.e., international inequality (see Figure 4).

Between the two World Wars, and until around 1950, a decline in within-country inequality was observed, but the rise in inequality across countries continued apace and proved to be the dominant force.<sup>18</sup> The world Gini index rose further to 0.64. From the middle of the twentieth century onwards, the rise of global inequality slowed, as Japan and parts of East Asia started growing faster than Europe and North America. This process became particularly pronounced after the take-off of China in the 1980s. Broadly speaking, global inequality changes in the second half of the last century are much less significant than in the 130 years that preceded

> 17 Given the long-run perspective of this exercise, however, it is likely that some of the problems associated with using means from the National Accounts had only limited importance. In particular, the estimated evolution of GDP per capita over such a long period is likely to be very strongly correlated with any measure of household welfare.

> 18 The increase in inter-country inequality between 1914 and 1950 took place _during_ each of the two World Wars, and most markedly during the Second World War. The inter-war period properly defined (1919-1939) actually saw a reduction in inter-country inequality. On the association between wars and rising international inequality, and between crises and its decline, both during this period and in 1890-1895, see Milanovic (2006).
|**Table 8. Inter-temporal deflators**|
|---|

|**Country**|**Year**|**2011 PPP**|**Intertemporal**<br>**deflator **|
|---|---|---|---|
|Afghanistan<sup>15</sup>|2012|n.a.|n.a.|
|Bangladesh|2010|24.85|0.90|
|Bhutan|2012|16.96|1.11|
|India|2011/12|13.78|1.04|
|- Rural|2011/12|12.91|1.03|
|- Urban|2011/12|15.69|1.04|
|Maldives|2009|10.68|0.87|
|Nepal|2010|25.76|0.93|
|Pakistan|2011/12|25.41|1.05|
|Sri Lanka|2012/13|42.22|1.11|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). All countries use CPI, except for Bangladesh, that uses BNPI.

# **5. Assessing differences to estimate national consumption aggregates**

# _Standardized vs. national consumption aggregates_<sup>_16_</sup>

Because of the wide variation in the way consumption data are collected by each country, the World Bank developed a standardized data set of consumption aggregates. The standardization of the consumption aggregates was constructed by reclassifying expenditure items into the categories adopted by the International Comparison Program (ICP) - which has 110 basic headings, 91 classes, 43 groups and 13 categories, and designing strict data cleaning procedures to ensure consistency in the standardization.<sup>17</sup> However, this standardization has limitations (Dupriez, 2007). Although consumption aggregates are standardized in the sense that they use a common data dictionary, the method of data collection and questionnaire designs affect the standardization directly and cannot be addressed.

Table 9 shows the average per capita consumption using both the standardized and national consumption aggregates. The table shows that, for the most part, there is not a large difference in expenditure per capita between the two aggregates. One exception is housing expenditure in urban India. Using the national consumption aggregates, imputed rent amounts to 70 cents per day (in USD 2011 PPP), but when imputed rents for homeowners are included in the standardized consumption aggregate, the housing expenditure jumps to USD 1.6 (in 2011 PPP). The standardization of the consumption aggregates decreases housing expenditure in Bhutan and Sri Lanka. Sri Lanka also sees a fall in food, and non-food non-durable expenditures due to standardization process.

> 15 Afghanistan is not included in Povcalnet because it lacks a PPP deflator and is not comfortable using a regression-based PPP. 16 As mentioned previously, we use the term national consumption aggregates to refer to the consumption aggregates created by the statistical offices of the respective countries, while the standardized consumption aggregates refer to the consumption aggregates obtained from the standardized consumption data sets created by the World Bank.

> 17  A detailed description of the standardization of the consumption aggregates in South Asia is available in Dupriez (2007).

14
**Figure 2. Contribution to Change in Machinery Exports (1993-94 to 2003-04)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Indonesia Malaysia Philippines Thailand China Developing<br>EA<br>Components Finished<br><!-- End of picture text --> Source: UN-COMTRADE

**Figure 3. Contribution to Changes in Machinery Imports (1993-94 to 2003-04)**

<!-- Start of picture text --> Indonesia Malaysia Philippines Thailand China Developing<br>EA<br>Components Finished<br><!-- End of picture text --> Source: UN-COMTRADE

> <sup>21</sup>
|Father‘s schooling|-0.386|-0.0897|0.225|0.0521|
|---|---|---|---|---|
||(0.246)|(0.149)|(0.140)|(0.0971)|
|Mother‘s schooling|0.270|0.0745|0.255|0.425***|
||(0.279)|(0.227)|(0.183)|(0.153)|
|Father rural|0.329|0.0238|-0.0932|0.0946|
||(0.262)|(0.122)|(0.164)|(0.111)|
|Mother rural|-0.399|-0.00812|0.0434|-0.212*|
||(0.259)|(0.118)|(0.160)|(0.116)|
|Log hh size|0.0667|-0.174**|-0.309***|-0.176**|
||(0.0890)|(0.0752)|(0.0719)|(0.0756)|
|Log cell size|-0.458***|-0.349***|-0.104|-0.202**|
||(0.108)|(0.0880)|(0.0918)|(0.0862)|
|Share of cell aged<5|-1.079**|-0.484**|-0.293|-0.580**|
||(0.425)|(0.232)|(0.455)|(0.241)|
|Share of cell adults|0.246<br>(0.307)|0.334<br>(0.203)|0.815***<br>(0.311)|0.716***<br>(0.205)|
|Has formal schooling|0.189|0.446***|0.327***|0.446***|
||(0.124)|(0.114)|(0.101)|(0.0827)|
|Fostered|0.270*|-0.0535|0.350**|0.0617|
||(0.151)|(0.174)|(0.169)|(0.125)|
|Fostered young|-0.301|0.228|-0.293|0.172|
||(0.221)|(0.203)|(0.218)|(0.166)|
|Age at first marriage|0.00190<br>(0.00881)|0.0273***<br>(0.00760)|-0.0114<br>(0.00697)|0.0111<br>(0.00799)|
|Inherited land|0.00869|0.113|-0.156|-0.0340|
||(0.121)|(0.113)|(0.147)|(0.111)|
|Inherited house|-0.165|-0.0896|0.150|0.0746|
||(0.111)|(0.110)|(0.110)|(0.0822)|
|Other inheritance|0.259***|0.0293|0.176|-0.0160|
||(0.0982)|(0.0796)|(0.122)|(0.110)|
|Constant|11.31***|11.65***|9.912***|11.94***|
||(1.124)|(0.627)|(0.733)|(0.563)|
|Observations<br>|733|904|686|842|
|R<sup>2</sup>|0.297|0.321|0.385|0.395|

Note: Robust standard errors in parentheses, clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. The variables ̳First same gender‘, ̳First of siblings‘ and ̳First born is male‘ all refer to children of the same father, same mother. Regressions also contain regional (department) dummies. The reference variables are Wolof ethnicity, all other religions, occupation ̳inactive‘, share of cell members 5-15, no and non-formal schooling.

33
# **Why Firms Avoid Cutting Wages: Survey Evidence from European Firms**

Philip Du Caju, Theodora Kosma, Martina Lawless, Julián Messina and Tairi Rõõm<sup>*</sup> JEL Codes: J30, J32, J33, J51, C81, P5 Keywords: labour costs, wage rigidity, firm survey, wage cuts, European Union

> * Author affiliations: Philip Du Caju  (National Bank of Belgium, <u>philip.ducaju@nbb.be), Theodora Kosma (Bank of</u> Greece, <u>tkosma@bankofgreece.gr),  Martina Lawless (Economic and Social Research Institute and Central Bank of</u> Ireland, martina.lawless@gmail.com), Julian Messina (World Bank and IZA, jmessina@worldbank.org), and Tairi Rõõm (Eesti Pank, Estonia, tairi.room@eestipank.ee ).

The work was conducted within the framework of the Wage Dynamics Network coordinated by the European Central Bank. We would like to thank all the participants of the meetings of the Wage Dynamics Network for their helpful comments and work on compiling the country data. We also thank the participants of the Irish Economic Association meeting and a Bank of Greece seminar for their comments. The opinions expressed in this paper are solely those of the authors and do not necessarily reflect the views of their institutions.
||||Dependent variable:|||
|---|---|---|---|---|---|
||||Single bidding|||
||(1)|(2)|(3)|(4)|(5)|
|Procedure type (negotiated)|0.611"**||||0.534"**|
||(0.015)||||(0.017)|
|Procedure type (non-open)|2.047°**||||1.059°**|
||(0.022)||||(0.028)|
|Decision period (5 to 8 days)||0.447°**|||0.422***|
|||(0.023)|||(0.023)|
|Decision period (1 to 4days)||1.544°**|||1.263"**|
|||(0.018)|||(0.018)|
|No call for tenders|||1.488"**||0.758"""|
||||(0.017)||(0.029)|
|Submission period (from 5 to 8 days)||||0.258""*|-0.014|
|||||(0.016)|(0.018)|
|Submission period (missing)||||1.105"**|-0.095"**|
|||||(0.014)|(0.022)|
|Observations<br>|193,980<br>|193,980<br>|193,980<br>|193,980<br>|193,980<br>|
|LogLikelihood|-102,755.100|-102,283.300|-103,566.100|-104,590.800|-98962.630|
|Akaike Inf. Crit.<br>|206,210.200|205266.600|207,830.200|209,881.700|198,635.300<br>|
|Note:|||||*p<0.1; “*p<0.05; ***p<0.01|
||Includedcontrolsnotshow|nare:Buyerlocation,Buye|rtype,Supl.location,Contr|acttype,YearFE, Mar|ketFE,Contractvaluedeciles|
# **Appendix for Online Publication**

<!-- Start of picture text --> July 25, 2018<br>March 24, 2020 September 1,<br>Residency permit (Permiso Especial de President Duque 2020<br>Permanencia – PEP) for irregular announced a nationwide<br>refugees registered in the RAMV is nationwide lockdown<br>announced quarantine ended<br>December<br>21, 2018 October 2020 February 2021 October 2021 February 2022<br>March 6,<br>April 6, 2018RAMV registrystarts June 8, 2018RAMV registryends August 2, 2018PEP programstarts PEP programends 2020First case ofCoronavirus Surveycollectionstarts Surveycollectionends Surveycollectionstarts Surveycollectionends<br>2018 SAMPLE 2020 5 MONTHS 2021 2022<br>OF<br>LOCKDOWN Wave 1 Wave 2<br>442,462 refugees registered in Around 281,307 3,455 Households 2,307 Households<br>395 municipalities in Colombia people received the<br>(35% of the territory) PEP document<br>COVID-19 Pandemic<br><!-- End of picture text --> Figure A.1. Registry in PEP. Program Roll-out and the COVID19 Pandemic: Timeline
Using the consumption data from the GHS-Panel, dietary diversity indicators from the harvest round of the data are constructed. The indicator is constructed by classifying food items from the consumption module into 12 distinct food groups. The food groups are delineated according to guidelines from the Food and Agriculture Organization (Kennedy et al, 2011). Dietary diversity is an important nutritional indicator of household calorie availability. In a review of the nutritional literature on validation studies, Ruel (2003) documents a consistent set of results in developing countries which illustrate this positive correlation between dietary diversity measures and nutrient adequacy. For this reason, the present study uses household dietary diversity as its primary nutritional measure. In addition to dietary diversity, calorie intake from food groups is included in the descriptive analysis. Calorie intake and production was estimated using the consumption and agricultural production data in the GHS-Panel and applying calorie conversions for each item from the U.S. Department of Agriculture's National Nutrient Database for Standard Reference<sup>1</sup> . The item level calorie estimates were then aggregated to the food/crop group and household levels for use in the analysis.

The two measures of production variability used in the analysis that follows are a count of the number of crop groups harvested and the value of agricultural output (agricultural revenue). Both measures are calculated using information from the post-harvest round of the GHS-Panel. To construct the count of harvested crop groups, food crops were separated into 5 groups that correspond to 5 of the 15 groups that comprise the dietary diversity measure. Harvested nonfood crops were excluded. Agricultural revenue was calculated using farmer estimates of the total harvest value for each crop.

8
highest growth firms and products (Melitz, 2003). Pakistan’s performance in this respect is considerably deteriorating over time. The rates of survival after 2004 increased. At the same time, the number of new exporters each year steadily decreased. These two results together are a potentially worrisome signal of a decrease in firm dynamism, whereby the greater persistence in export relationships is due to less competition in the domestic market. This may contribute to explain why Pakistan’s export performance in the past decade was positive but more moderate than the one of other Asian economies.

**Figure 28 Survival rates after 1 year of export activity**

<!-- Start of picture text --> 1-Year Exporter Survival Rate<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2004 2005 2006 2007 2008<br>PAK BGR COL ECU PER ZAF<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

To investigate further, whether the lower dynamism of firms’ exports has had a detrimental effect on export growth, the next sub-section will decompose the year-on-year contribution of the extensive and the intensive margin to aggregate export growth.

Exports growth is computed using the mid-point growth rate (Davis and Haltiwanger 1992 as applied to firm-level exports by Bricogne et al 2011). The main advantage of this approach is that it allows the computation of the contribution of the extensive and the intensive margin to export growth. Other methodologies only allow a static assessment of the extensive margin (Hummels and Klenow 2005).

Table 11 that provides the decomposition reads as follows. In each of the columns the average annual growth relative to the indicated period is reported. The aggregate figure for all trade flows is reported in the last row. The remainder of the table reports the contribution to aggregate growth by each of the margins. For example, looking at annual growth over the year 2002-2003, the first row indicates that export growth due to entry of new firms into exporting equaled 9.3%. Since the corresponding loss in export growth due to the exit of firms equaled 7.8%, the net contribution of firm churning was 1.5%.

The next set of rows accounts for the decision of existing exporters to enter new foreign markets (country entry) or to exit existing foreign markets (country exit). In this case entry into new markets accounted for 9.8% of export growth over 2002-2003, which was partially offset by 37