rafmacalaba/datause-encoder-data huggingface dataset:
# **Annex 1: Countries Included in the Analysis**

|**Middle East and North Africa**|**Latin America and Caribbean**|**Sub-Saharan A**|**frica**|**East Asia and Pacific**|**Europe and Central Asia**|**South Asia**|
|---|---|---|---|---|---|---|
|Djibouti (2013)*<br>Egypt (2013)*<br>Iraq (2011)<br>Jordan (2013)*<br>Lebanon (2013)*<br>Morocco (2013)*<br>Tunisia (2013)*<br>West Bank and Gaza (2013)*<br>Yemen (2013)*|Argentina (2010)<br>Belize (2010)<br>Bolivia (2010)<br>Brazil (2009)*<br>Colombia (2010)<br>Costa Rica (2010)<br>Dominica (2010)<br>Dominican Republic (2010)<br>Ecuador (2010)<br>El Salvador (2010)<br>Grenada (2010)<br>Guatemala (2010)<br>Guyana (2010)<br>Honduras (2010)<br>Jamaica (2010)<br>Mexico (2010)<br>Nicaragua (2010)<br>Panama (2010)<br>Paraguay (2010)<br>Peru (2010)<br>St. Lucia (2010)<br>St. Vincent and Grenadines (2010)<br>Suriname (2010)<br>Venezuela (2010)|Angola (2010)<br>Benin (2009)<br>Botswana (2010)<br>Burkina Faso (2009)<br>Burundi (2014)<br>Cameroon (2009)<br>Cape Verde (2009)<br>Central African Republic (2011) <br>Chad (2009)<br>Congo (2009)<br>Côte d’Ivoire (2009)<br>DRC (2013)<br>Eritrea (2009)<br>Ethiopia (2015)<br>Gabon (2009)<br>Gambia, The (2006)*<br>Ghana (2013)<br>Guinea (2006)<br>Guinea-Bissau (2006)*<br>Kenya (2013)<br>Lesotho (2009)<br>Liberia (2009)<br>Madagascar (2013)<br>Malawi (2014)|Mali (2010)<br>Mauritania (2014)<br>Mauritius (2009)<br>Mozambique (2007)* <br>Namibia (2014)<br>Niger (2009)<br>Nigeria (2014)<br> Rwanda (2011)<br>Senegal (2014)<br>Sierra Leone (2009)<br>South Africa (2007)* <br>South Sudan (2014)<br>Sudan (2014)<br>Swaziland (2006)<br>Tanzania (2013)*<br>Togo (2009)<br>Uganda (2013)<br>Zambia (2013)<br>Zimbabwe (2011)|Cambodia (2016)<br>China (2012)<br>Fiji (2009)<br> Indonesia (2015)<br>Lao PDR (2016)<br>Malaysia (2015)<br>Micronesia (2009)<br>Mongolia (2013)<br>Myanmar (2014)<br>Papua New Guinea (2015) <br> Philippines (2015)<br>Samoa (2009)<br>Solomon Islands (2015)<br>Thailand (2016)<br>Timor-Leste (2015)<br>Tonga (2009)<br>Vanuatu (2009)<br>Vietnam (2015)|Albania (2013)<br>Armenia (2013)<br>Azerbaijan (2013)<br>Belarus (2013)<br>Bosnia and Herzegovina (2013) <br>Bulgaria (2013)<br>Macedonia, FYR (2013)<br>Georgia (2013)<br>Hungary (2013)<br> Kazakhstan (2013)<br>Kosovo (2013)<br>Kyrgyz Republic (2013)<br>Moldova (2013)<br>Montenegro (2013)<br>Romania (2013)<br>Russian Federation (2012)<br>Serbia (2013)<br>Tajikistan (2013)<br>Turkey (2013)<br>Ukraine (2013)<br>Uzbekistan (2013)|Afghanistan (2014)<br>Bangladesh (2013)<br>Bhutan (2015)<br>India (2014)<br> Nepal (2013)<br>Pakistan (2013)<br>Sri Lanka (2011)*|

Note: This table presents the countries included in the analysis using the World Bank Enterprise Survey data. The year in which the survey was implemented is in parentheses. An asterisk indicates that the growth period under consideration in the country is three years, not two. The information was accessed on September 8, 2016.

27
# Wages and Employment In the Transition to a Market Economy*

by Simon Commander Fabrizio Coriceli and Karsten Staehr

# Table of Contents

|Section 1 Introduction|2|
|---|---|
|Section 2 Wage Determination<br>under Socialism|4|
|2.1<br>Centrally<br>Planned and Partially Reformed<br>Regimes|4|
|2.2<br>The Market-Based<br>Regime:<br>Poland 1990-91|7|
|2.3<br>Unemployment|8|
|<br>2.4WagesandWage Policy|9|
|Section 3 Wage and Employment<br>Decisions<br>in the Worker-Controlled<br>**Firm**|12|
|3.1<br>The Worker-Controlled<br>Firn|L2|
|3.2<br>A Simple Poliey Game|17|
|3.3<br>A**Single**Period Policy Game|19|
|3.4<br>Concluding<br>Comments|23|
|**Section**4**Tax**Rules and the Worker-Controlled Firm|25|
|**Section 5**Ownership Reform and DecapitalHzatlon|27|
|**S.**1 A Two Period Model with the Worker-Controlled<br>Firm|**28**|
|5.2<br>The Firm is Worker-Controlled|29|
|**5.3**The Firm is Privatized|29|
|5.4<br>The WCF's Problem<br>|**30**<br>|
|**5.5**<br>**Some**Results|30|
|**5.6 Tax**Policy|31|
|5.7<br>Vouchers|32|
|**5.8**<br>Concluding<br>Comments|33|
|**Section**6 Conclusion|33|
|References|36|

# References

* This paper was prepared for the seminar on "Central and Eastern Europe: Roads to Growth," sponsored by the Austrian National Bank and the International Monetary Fund, Baden. Austria, April 1I-18, 1991.
**_19_**

_Price Support at Any Price?_ The quantity control necessary to curb the supply response should be quite severe: the FAST PATH/OUTPUT REDUCTION experiment leads to an output level that is more than 25 % percent below the FAST PAST scenario level (and 3 % below the base run, despite much higher prices).

Table 3.3 presents the macroeconomic impact of the policies applied in the experiments.

# **Tab.3.3 Targeting EU prices Simulation experiments: MACROECONOMIC IMPACT**

||Fast<br>(EU price|path<br>s by 2000)|Fast pat<br>redu<br>(EU pr. by 2|h/output<br>ction<br>000, quotas)|Last momen<br>(50%EU p<br>201|t integration<br>rices by<br>0)|
|---|---|---|---|---|---|---|
||Index<br>2010<br>(base= 100<br>|<br>)|growth<br>rate<br>(aver.)<br>1991-2010|Index<br>2010<br>(base= 100<br>)|growth<br>rate<br>(aver.)<br>1991-2010|Index<br>2010<br>(base= 100<br>)|growth<br>rate<br>(aver)<br>1991-<br>2010|
|**Gross Domestic**<br>**Product**|**96.8**|**4.2%**|**97.1**|**4.2%**|**100.1**<br>**I**|**4.4%**<br>l|
|Fixed Capital Formation|77.0|5.6%|89.1|6.4%|98.0|6.9%|
|Personal Consumption|102.1|3.8%|98.8|3.7%|100.4|3.7%|
|_of which:l_<br>Food products|97.3|1.5%|95.5|1.4%|98.2|1.6%|
|<br>Other goods & services|103.5|4.7%|99.8|_4.5%_|101.1|4.6%|

_Source: Model POLAGR, simulation Jan. 1995_ <!-- Start of picture text --> The impact on fixed capital Targeting EU prices<br>formation is negative in all the I on GDp<br>experiments, while the impact on the Impact on GDP<br>level of personal consumption varies Ddlx DgvigWo to6 frum ntU bm run In % l<br>(the level lower than in the base run 5 Jl<br>only in the FAST PATH/OUTPUT 4 J<br>REDUCTION experiment). While 3 Fast path<br>the FAST PATH experiment mainly 2- _ t r<br>affects investment, the FAST 1 --<br>PATH/OUTPUT REDUCTION 0 - Last moment IntegraIon<br>experiment has a more equally -2<br>distributed negative impact on the -2<br>final demand components. The 10.3<br>macroeconomic policy impact in the 111<br>LAST MOMENT INTEGRATION is<br>not significant (the policy does not Figure 8<br>begin before 2005).<br><!-- End of picture text -->
Policy Research Working Paper 9147

# **Abstract**

Tourism is an important source of foreign exchange and employment across developing economies. A scant literature has explored the relationship between tourism and the advent of the internet. This paper contributes to the tourism-trade literature and studies the empirical relationship between international tourism and the adoption of digital technologies that facilitate search about tourism opportunities across countries. It links foreign visits with the spread of the use of the internet in sending countries and the level of development of business-to-consumer digital tools in host countries. The paper estimates a well-specified gravity model of tourist arrivals between country pairs with panel data. The results indicate that frictions affecting bilateral tourism flows have been attenuated by the advent of digital tools. The absolute value of the effects of bilateral geographic distance, language differences, and border-contiguity seem to be reduced by the use of the internet by potential tourists and the business sector in host countries. The results are robust to alternative proxies for internet use for tourism search proxied by data from Google trends. The paper also presents simulations of the potential impacts of advances in the adoption of digital tools over time, linking the adoption process to mechanisms of technology adoption that are commonplace in the literature.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at jlopezcordova@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
score was the highest in the North East, but the mean values are quite similar in all regions. A CES-D index of above 10 is regarded as a threshold level for significant depressive symptoms. In our sample, 28 percent of the respondents have a score above this level, so poor mental health is indeed a common issue in the regions studied.<sup>16</sup> Looking at the demographic characteristics in table 1, panels a and b, we can see that household size is largest in the North East and smallest in the South South, and the fraction of female household heads is lowest in the North East and highest in the South South. Similarly, household heads are least educated in the North East and most educated in the South South. Twenty-one percent of all households in the sample are polygamous, with the highest share of polygamous households being in the North East and the lowest in the South South. These descriptive statistics illustrate the regional differences between Nigeria’s poorer north and relatively wealthier south.

# 4.   Empirical framework 4.1.   Panel data sets

For the analysis, we created two panel data sets: one for the consumption measures, including poverty, and one for food insecurity as outcome variables. The outcome variables were measured at different points in time. Figure D.1 in appendix D illustrates the timing of the data in terms of the outcome variables as well as the telephone survey.

For consumption, we have data from three waves that have two visits each; one visit is in the autumn (post-planting), and the other is in the spring (postharvest). The consumption aggregate is the median of the consumption level of those two visits. Hence, we have a measure for consumption at three points in time (2010–11, 2012–13, and 2015–16). Our food insecurity measure, the CSI, comprises the seven-day recall questions administered during each visit of the GHS—that is, altogether six times during the GHS panel (autumn 2010, spring 2011, autumn 2012, spring 2013, autumn 2015, and spring 2016). For simplicity, in referring to this panel in the paper, we talk about _rounds_ to refer to the wave-visit frequency. Given that in our estimation strategy we are interested in the lagged conflict events to alleviate concerns of reverse causality, we are dropping the CSI measured in 2010 from the analysis because no conflict information exists for 2009.<sup>17</sup> Figure D.2, panels a and b,

> 16 See Jamison et al. (2018) for more analysis with this data.

> 17 That is, for the postharvest visit of GHS wave 1 (spring 2011), we include events in 2010. For GHS wave 2, visit 1 (autumn 2012), events in 2011 were included, and for wave 2, visit 2 (spring 2013), events in 2012 were

13
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- 11 I

inefficiently. The tax roster was allowed to deteriorate during most of the 1980s; additionar:', data were collected manually with a high percentage of error. In order to increase the efficiency of tax collection, a new computer software was developed in an increasingly large number of agencies. The tax authorities also attempted to compensate for the absence of automated systems for collection and taxpayer auditing through several local initiatives, some of which had notable success such as the lottery based on VAT receipts that increased registration of transactions. Legal modifications have allowed more flexibility in the application of CAcal secrecy, permitting DGI to publish in newspapers lists of defaulting taxpayers, which has also curtailed fraud. Finally, taxpayer compliance increased significantly when a considerable simpliScation of tax forms was introduced in January 1992.

The low yield and high cost of taxpayer inspections and audits have been a prominent feature of Argentina's tax administration during the 1980s.'4 DGI management proved unable to generate from within the necessary changes to improve productivity. Since 1989, however, many of the problems that plagued the institutional infrastructure of tax administration have been identified. First, an extensive audit of industrial promotion beneficiaries was launched as well a census of VAT taxpayers. This operation provided an impressive amount of information about potential taxpayers; moreover, the presence of DGI officials all over the country had a deterrent impact on VAT noncompliance. Second, the strategy also focused on the improvement of DGI's audit capacity. This included the development of a computer program and the use of new tax audit procedures for selected economic firms in different sectors. The administration has been able to detect interruptions in the VAT chain and to develop indicators of critical inputs to estimate taxable bases.

For a long time, DGI management was unable to plan or carry out reforms to improve productivity. Past union contracts demanded a flat compensation curve, as well as ensuring job security, which made it difficult for management to improve the quality of the work force. Qualified staff were difficult to attract and retain, morale was low, and staff usually held more than one job to compensate for low wages. This was aggravated by high turnover in the position of Director General of DGI. As part of the Government's strategy, a new general organizational structure in line with modern managerial concepts was approved in January 1992. At the same 14 Between 1986 and 1989, collected revenue per audit fell from US$126 to US$34, while the average cost per audit increased from US$278 to US$838.
- 21 -

||1989|1990|1991|1992|1993|194|1995|<br>1996|
|---|---|---|---|---|---|---|---|---|
|ExportGrowthRate4/|5.0X|<br>16.01|-3.6X|3.11|<br>2.1X|<br>1.4X|7.2X|5.4X|
|Experts/GDP 5/|23.46|22.5X|21.1X|22.0X|22.4X|23.41|23.31|23.7X|
|ImportGrowthRate 4/|-9.31|-10.4.|**_T.11_**|-8.6"|9.5X|-6.3X|12.31|6.60|
|1 arts/GOP 5/|29.31|27.41|28.91|27.81|29.61|28.81|30.11|31.41|
|Curr. Ace. Def. 6/(ineL. grants)|337.2|339.3|655.8|418.1|489.5|542.2|600.5|<br>833.0|
|Curr. Ace.De..7/(exct. grants)|454.3|517.6|788.8|621.1|754.S|581.2|730.5|883.0|
|Curr. Acc.Oef./GDPCexcI.grants)|7.41|<br>6.11|8.41|<br>7.01|<br>8.31|6.51|<br>7.51|<br>8.31|
|Curr. Ace.Oet./GOP(lncl.grants)|4.1X|4.02|6.91|4.71|5.41|6.1X|6.01|7.81|
|Net I'tl<br>Reserves Stk CUSS.)|-87.9|-218.1|*526.8|-501.2|-540.7|-433.7|-296.6|*111.8|
|Clnmonths inports)|-0.46|-1.12|-2.31|-2.43|-2.43|-2.03|-1.19|-0.40|
|_Change_in Ilt% Resrves Stk|49.2|-130.2|-308.6|25.6|-39.5|107.0|137.1|184.8|
|TotaLExternaL Debt**Ratio**|60.91|**62.21**|**63.21**|**70.31**|**73.51**|78.0x|**72.6**|**72.o0**|
|Pubtic Debt Ratio|**73.91**|78.01|**85.01**|**94.41**|102.31|108.91|106.71|108.51|
|**Memo:**<br>Residual PSIR|-532.4|109.4|27.8|65.9|**97.8**|122.2|149.0|**4SO.?**|
|External<br>Financing<br>Gap (USSm)|CS49.4)|80.0|819.8|s57.9|S67.9|S119.8|S136.9|S159.5|

- 1/ Includes aLl lnvestmn ts other than central government's. 2/ Historical fiscal data are from the IMF but GOP Cin denomfnator) Is from GOK. Itf IMFs GDP figure is used, Rev/GDP would be 23.31 in 1989, while Exp/GOP equals 30.41 The deffcit/GDP would then be 7.2 percent excludin **g** rants.

- 3/ Includes adjustments for flow-of-funds consistency. 4/ Goods and nonfactor services in historical accounts. 5/ Growth rates In 1989 are 10.11 and s.1 percent if the historical implicit trade deflators C1982u1D0) from the national accounts are used. The model calculates deftlators which are linked explfcitly to exchange rate movements and comeodity price movements (198a7100).

- 5/ Growth rates In 1989 are 10.11 and s.1 percent if the historical

- 6/ Ratio for 1989 exeludes adjustments for consistency with national accounts.

- 7/ Ratio for 1989 fncLudes an adjustment for consistency with national accounts.

- 8/ Publie expenditure contains an adjustment factor whfch Inftlates the residual pubife sector borrowing requirement (albeit without violating flow of funds consistency). The reason Is heuristic: the policy solutions can be more elearly illustrated.
may facilitate their acceptance for their daughters, wives, mothers, and sisters to engage in market work (Cunningham et al 2010).

To invest in girls’ health, emerging evidence is finding that unconditional cash transfers can change girls’ behaviors (mostly by delayed marriage and childbearing, see Baird et al 2010), but supply-side interventions may also be effective. Re-orienting health delivery systems to provide adolescent girls with services that are accessible, customized, confidential, and nonjudgmental could provide girls with information to make good sexual health choices, better support them to prevent unwanted circumstances, and screen for reproductive and sexual health risks such as domestic violence and unintended pregnancy (Cunningham et. al. 2008b).

# **Count girls**

Providing programs is not sufficient since entry to programs often depends on recognition that girls exist. This requires an effort by governments to register all newborns and provide birth certificates to ensure access to health services and education (Cunningham et al, 2008b). And once the girls (and boys) are older, it is necessary to furnish them with government-issued identification cards so that they may continue to access educational opportunities, jobs, and health services.

To monitor program success, it is necessary to collect data on adolescents and disaggregate it by age and gender to assess whether programs are reaching adolescent girls. By tracking program beneficiaries by age, gender, marital status, location, family income and school enrollment status in all programs and sectors, program managers and governments can better assess whether programs are reaching adolescent girls— especially the most vulnerable. Regularly reporting results will increase accountability, share learning, target solutions, demonstrate success, and catalyze more resources.

# **Advocate for girls**

Finally, governments could better advocate for girls at two levels. First, governments could make the law work better for adolescent girls by repealing laws that discriminate against girls in the workplace, schools, or family and ensure equality of access to health services, education, jobs and earnings, credit, and property ownership.

24
Maddala, G. S. (1983), Limited Dependent and Qualitative Variables in Econometrics, Econometric Society Monographs #3, Cambridge University Press, Cambridge. Morduch, Jonathan (1994) "Poverty and Vulnerability” American Economic Review 84(2):221-225.

Pagan, A. and F. Vella (1989), "Diagnostic tests for models based on individual data: A survey", Journal ofApplied Econometrics, 4, S29-S59.

- Powell, J. L. (1984), "Least Absolute Deviations Estimation For the Censored Regression Model", Journal of Econometrics, 25, 303-325.

(1986), "Censored Regression Quantiles", Journal of Econometrics, 32, 143-

# 155.

Ravallion, Martin (1988), "Expected poverty under risk-induced welfare variability", Economic Journal, 98, 1171-1182.

_

- (1993), Poverty Comparisons, Harwood Academic Press: Fundamentals

of Pure and Applied Economics, Chur, Switzerland.

Sen, Amartya (1976) "Poverty: An Ordinal Approach to Measurement", Econometrica 46:437-446.

Slesnick, Daniel T. (1993) "Gaining Ground: Poverty in the Postwar United States", Journal of Political Economy 101: 1-38.

Watts, H.W. (1968), "An Economic Definition of Poverty", in D.P. Moynihan (ed.), On Understanding Poverty, New York: Basic Books.
||(0.088)|(0.061)|(0.188)|(0.099)|(0.165)|(0.096)|(0.409)|(0.136)|(0.362)|(0.099)|
|---|---|---|---|---|---|---|---|---|---|---|
|Purchased fixed<br>assets Y/N|0.170***|0.139***|0.083|0.052|0.101|0.199***|0.182|0.302***|-0.035|0.195**|
||(0.049)|(0.037)|(0.140)|(0.083)|(0.112)|(0.077)|(0.197)|(0.107)|(0.158)|(0.081)|
|Experienced<br>power outage|0.151**|0.111**|0.303*|0.053|0.256|0.264**|-0.257|-0.117|-0.321**|-0.113|
|Y/N|||||||||||
||(0.067)|(0.044)|(0.169)|(0.100)|(0.218)|(0.105)|(0.232)|(0.091)|(0.129)|(0.109)|
|Experienced|||||||||||
|losses due to<br>crime Y/N|0.109*|-0.018|0.329**|-0.184*|0.125|0.024|0.098|-0.098|-0.427*|-0.152|
||(0.059)|(0.050)|(0.151)|(0.102)|(0.116)|(0.082)|(0.184)|(0.217)|(0.220)|(0.186)|
|Website Y/N|0.227***|0.351***|0.131|0.397***|0.441***|0.482***|0.081|0.064|0.442**|0.170|
||(0.058)|(0.040)|(0.152)|(0.099)|(0.139)|(0.078)|(0.180)|(0.093)|(0.180)|(0.125)|
|_cons|15.187**<br>*|15.087***|17.099***|15.565***|9.735***|10.313***|12.099***|11.953***|12.834***|13.272***|
||(0.358)|(0.241)|(0.802)|(0.547)|(0.568)|(0.504)|(1.020)|(0.410)|(1.487)|(0.850)|
|_N|13,009|30,506|1,899|5,087|2,469|6,729|535|3,926|897|5,552|
|Adjusted R2|0.833|0.797|0.827|0.804|0.778|0.749|0.870|0.838|0.825|0.601|

Note: .01 - ***; .05 - **; .1 - *. Coefficients from the estimation of an OLS model are reported. Standard errors are clustered at the level of the strata; rescaled weights are applied in the regressions. The model also includes controls for the within-country region in which the firm is located, the sector in which it operates and the year in which the survey was carried out.

22