rafmacalaba/data-use-mentions-extended huggingface dataset:
_Fareed M. A. Hassan, ECSPE_ 8 percent for the third one. This means that the first positive tax bracket was rather narrow in relation to the income group it covered.

The second positive income tax bracket of 24 percent covered 78 percent of the fourth income decile, all of the fifth through ninth deciles, and 65 percent of the highest income group in 1992. This tax bracket had the broadest base, covering nearly two-thirds of the total number of households. Potential revenue as a share of total personal incoime was in the range of 4.7 to 16.5 percent, depending on the niumber of households subject to tax in each of the income decile in question.

The next three tax brackets of the 1992 schedule given in Table 2 covered a small part of the households. The 28 percent rate was applicable to only 73 households in the survey or one third of the top income decile. Also, tax rates of 32 and 36 percent covered only three households in the 1992 survey. These brackets were extremely narrow relative to the tax base which they were designed to tap. Perhaps even more striking is the fact that the maximum 40 percent tax rate applied to incomes of over 240, 000 Lev exceeded the maximum income reported in the 1992 household budget survey, indicating that this tax bracket was applied to a nonexistent base! Figure 1 shows the misrnatch between the 1992 income distribution and bracket/rate tax structure.

Total potential personal income tax revenue (TPPITR) can be expressed as follows: TPPITR **=j Ei** (Xij t1 + aj), (2) where: j = 1, 2, 3 ... , 7 (i.e., seven tax rates as given in Table 2); i = 1, 2, 3 ........ 2202 households included in the survey; and Xij tj, and a;j are as defined in equation 2.

The application of equation 2 to household income as recorded in
<br>_Electricity_|positive|Getting Electricity score|
|Infrastructure|GC|_GC Electricity_<br>_Quality_|positive|Electric power transmission and distribution losses as a<br>percentage of domestic supply, from the International<br>Energy Agency|
||ES|_ES Web_|positive|%of firms usinga website to conduct business|
||DB||||
|Internet|GC|_GC Broadband_|positive|Broadband coverage, number of fixed-broadband<br>internet subscriptions per 100 population, from the<br>International Telecommunications Union|
||ES|_ES Skill_<br>_Obstacles_|negative|% of firms reporting skilled labor shortage as an<br>obstacle|
|Human Capital|DB|_WB HLO_|positive|Harmonized LearningOutcome(Angrist et al.,2019)|
||GC|_GC Education_<br>_System_|positive|Quality of education system, from WEF Executive<br>Opinion Survey: how well does the education system<br>meet the needs of a competitive economy?|
||ES|_ES Overdraft _|positive|% of overdraft ability|
|Access to finance|DB|_DB Get Credit_|positive|Gettingcredit score|
||GC|_GC Financial_<br>_Development_|positive|Financial development score, pillar 8|
||ES|_ES Land Access_<br>_Obstacles_|negative|% of firm reporting access to land as an obstacle|
|Barriers to entry/exit|DB<br>GC|_DB_<br>_Construction_<br>_Permit_<br>|positive|Dealing with Construction Permits score<br>|

_Note:_ BE = business environment; DB = _Doing Business_ ; ES = Enterprise Survey; GC = Global Competitiveness index; HLO = Harmonized Learning Outcome; LP = labor productivity; SD = standard deviation; TFP = total factor productivity; WB = World Bank; WEF = World Economic Forum.

27
integration for the rural poor may also depend on targeted investments in a range of public services and infrastructure in remote and ecologically fragile regions, such as extension services, roads, communications, protection of property, marketing services and other strategies to improve smallholder accessibility to larger markets (Barrett 2008; World Bank 2008). For example, for poor households in remote areas of a wide range of developing countries, the combination of targeting agricultural research and extension services to poor farmers combined with investments in rural road infrastructure to improve market access appears to generate positive development and poverty alleviation benefits (Ansoms and McKay 2010; Bellon et al. 2005; Cunguara and Damhofer 2011; Dercon et al. 2009; Dillon et al. 2011; Müller and Zeller 2002; Pattanayak et al. 2003;Yamano and Kijima 2010). In Mexico, poverty mapping was found to enhance the targeting of maize crop breeding efforts to poor rural communities in less favorable and remote areas (Bellon et al. 2005). In the Central Highlands of Vietnam, the introduction of fertilizer, improved access to rural roads and markets, and expansion of irrigation increased dramatically the agricultural productivity and incomes (Müller and Zeller 2002).

# **Conclusion**

The recent focus on structural transformation of developing economies has largely emphasized policies that promote growth of industries and highly commercialized agricultural and service activities (Lin 2011; McMillan and Rodrik 2011; Ocampo et al. 2009; Rodrik 2007 and 2010). This paper has attempted to show that, even if the modern sector is the source of dynamic growth through learning-by-doing and knowledge spillovers, the pattern of labor, land and natural resource use in the rural economy matters to the overall dynamics of structural 20
Gray, Leslie and Michael Kevane. 1999. ―Diminished Access, Diverted Exclusion: Women and Land Tenure in Sub-Saharan Africa,‖ _African Studies Review_ 42(2): 15-39.

Harrower, Sarah and John Hoddinott, 2004. ―Consumption Smoothing and Vulnerability in the Zone Lacustre, Mali,‖ Food Consumption and Nutrition Division Discussion Paper no. 175, International Food Policy Research Institute, Washington, DC.

Hogan, Margaret, Kyle Foreman, Mohsen Naghavi, Stephanie Ahn, Mengru Wang, Susanna Makela, Alan Lopez, Rafael Lozano, and Christopher Murray. 2010. ―Maternal Mortality for 181 Countries, 1980–2008: A Systematic Analysis of Progress towards Millennium Development Goal 5.‖ _The Lancet_ 375 (9726): 1609–23.

Horrell, Sara and Pramila Krishnan. 2007. ―Poverty and Productivity in Female-Headed Households in Zimbabwe,‖ _Journal of Development Studies_ 43(8):1351-1380.

Hurd, Michael and David Wise. 1989. ―The Wealth and Poverty of Widows: Assets Before and After the Husband‘s Death,‘ in David Wise (ed.) _The Economics of Aging_ , University of Chicago Press.

Jensen, Robert. 2005. ―Caste, Culture, and the Status and Well-Being of Widows in India,‖ in David Wise (ed.) _Analyses in the Economics of Aging_ , University of Chicago Press.

Kevane, Michael. 2004. _Women and Development in Africa: How Gender Works_ , Lynne Rienner Publishers, Inc., Boulder Colorado.

Kevane, Michael and Bruce Wydick. 2001. ―Social Norms and the Time Allocation of Women's Labor in Burkina Faso,‖ _Review of Development Economics_ 5: 119-29.

Kirwen, Michael. 1979. _African Widows_ , Orbis Books, New York.

Lanjouw, Peter and Martin Ravallion. 1995. "Poverty and Household Size," _Economic Journal_ 105: 1415-1435.

Lilja, Nina, John Sanders, Catherine Durham, Hugo De Groote and Issiaka Dembélé. 1996. ―Factors Influencing the Payments to Women in
has long known that the contribution of services to world commerce has been underestimated. Trade in services is not only growing faster than trade in goods, but also creating value far beyond what national accounts measure, more so than trade in goods (McKinsey, 2019). The sheer importance of commercial presence - Mode 3 - as a means of delivering services to markets implies that the notion of investment facilitation, as currently discussed in rule-making circles, relates predominantly to steps taken to facilitate foreign investment in services. In the same vein, discussions of trade facilitation in services chiefly (if not exclusively) relate to commercial presence.<sup>5</sup> Statistics measuring the value of cross-border exchanges of services (as defined under trade agreements) could long only be found in countries’ balance of payments (BoP) data, even though these only capture a limited share of world services trade. Indeed, the principal means of supplying services internationally, which is through a commercial presence abroad, is not captured in BoP statistics.<sup>6</sup> 5 While Mode 3 is central to any coherent discussion of trade facilitation in services, the Government of India has to date chosen not to take part in the Joint Statement Initiative on investment facilitation that pursues a number of aims closely aligned to those proposed by the Indian proposal on TFiS.

6 BoP statistics focus on transactions between residents and non-residents, and do not capture services that are supplied within the country through business establishments owned or controlled by foreigners.

3
Figure 3 Summary Statistics of indicators used to predict consumption in survey-to-survey imputations, by GHS wave <!-- Start of picture text --> 90.00%<br>80.00%<br>70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00% GHS 2010/11<br>20.00% GHS 2012/13<br>10.00%<br>0.00% GHS 2015/16<br>GHS 2018/19<br><!-- End of picture text --> Note: the figure shows the average value of each indicator in each wave of the GHS. These non-monetary indicators are used to develop the consumption model used in the survey-to-survey imputations for each wave.

First, we present the results of the imputation into the 2018/19 GHS. We do this to verify that the NLSS and GHS surveys are indeed comparable and to check whether, using GHS data, we can replicate – or at least get close to – the official NLSS-based poverty headcount rate for 2018/19 of 39.1 percent.<sup>19</sup> The results show an imputed poverty headcount rate of 41.9 percent. The imputed headcount rate at the US$3.20 poverty line shows a similar difference of 2 percentage points (72.3 percent in the imputed results vs. the official estimate of 71.0 percent), while the imputed Gini coefficient is very similar to the official NLSS estimate (34.9 vs. 35.1). The small gap between the imputed GHS-based and actual NLSSbased poverty estimates can be explained by differences in some of the non-monetary variables used in the consumption model (reported in Table 2), which are likely driven by the two surveys having different data collection schedules. Yet, we argue that the difference is small enough and, in a context of
# I. INTRODUCTION

Turkey has, alone among the high-debt countries, managed to maintain a high growth rate after rescheduling its debt. Its real GNP grew by 5% on average since 1980. By comparison, countries with recent debt-servicing problems, grew at only 1.2% since 19811 , almost a four percentage point difference on average. At the same time, Turkey's debt-output ratio increased by an amount roughly similar to the increase in the debt-output ratio of the high-debt countries, from 28% at the end of 1980 to 56% at the end of 1986 (see Table 1).

in fact, it is surprising that Turkey's debt-output ratio did not increase a great deal more than it did in the higt.-debt countries. As a percentage of GNP, Turkey ran a much lower non-interest current account surplus than the high-debt countries did on average after their debt-crisis: -0.25 percent of GNP for Turkey over the period 1980-1986 versus +2.6 percent over 1982-1986 for the high-debt countries. This apparent inconsistency is explained by th. higher growth rate that Turkey managed to sustain. Turkey's debt-output ratio followed a path similar to that of the high-debt countries, not so much because of large trade surpluses, but because of its high output growth coupled with continued access to foreign financing.

This paper discusses two issues this experience raises. One, how did Turkey translate the extra breathing space continued access to foreign financing gave it into sustained high real growth? In particular, what was the

> 1/ Turkey's rescheduling exercise took place over the period 1978-80, before other debtor countries rescheduled their debt. Hence the shift if comparison period. The data for the high debt countries are taken from the IMF World Economic Outlook, October 1987. The WEO refers to this group as "Countries that experienced recent debt servicing problems". For brevity's sake, we refer to the same group as "high-debt" countries in this paper.

ChhibberDisk/chh4hbPr-"
panels each enabling us to observe the situation of individuals over two consecutive years. 5 such data sets have been created for years 2003/2004 to 2007/2008.

At first, I tried to analyze whether working in shadow employment increases the probability of losing or quitting a job and whether this interdependence changed over the analyzed period. This has been performed by estimating the series of probit models in which the probability of transition out of employment between ―t‖ and ―t+1‖ has been set as the dependent variable and estimated probability of working in shadow employment<sup>2</sup> (―shadow‖) in period ―t‖ has been one of explanatory variables. Apart from that, I used the set of standard control variables, all for period ―t‖ such as: region, age, gender, education level, and characteristics of employing company.

Three versions of the model have been estimated for each year. One in which both the ―contract‖ variable and the ―shadow‖ variable have been added to model specification, and two with only one of those variables used.

The estimated coefficients on control variables have been as expected - a negative relationship between education level and transition probability, and the lowest transition probability for those in prime age and higher probabilities for females.

The contract variables are significant and similar for both specifications for all years (see Table 2). All employment contracts increase the probability of transition out of employment as compared to full time and open term contracts. It also seems that between years 2003-2007, when the labor market situation was clearly improving, the influence of the fixed term employment on the probability of losing a job was weakening, and then it started to gain importance. What makes this result unexpected is that this tendency started to change already in 2007 when the labor market was still blossoming.

Being a self-employed employee negatively influenced the probability of losing a job until 2006, but afterwards it lost its significance. This may mean that the booming
Lubotsky, Darren. 2007. “Chutes or Ladders? A Longitudinal Analysis of Immigrant Earnings,” _Journal of Political Economy_ , 115:5, 820-867.

Martin, Philip L. 1993. “The Missing Bridge: How Immigrant Networks Keep Americans Out of Dirty Jobs.” Population and Environment 14 (July): 539-565.

Marvasti, Akbar. 2010. “Occupational Safety and English Language Proficiency.” _Journal of Labor Research_ 31: 332-347.

Mongey, Simon, Laura Pilossoph, and Alex Weinberg. 2020. “Which workers bear the burden of social distancing policies?’, _National Bureau of Economic Research_ , Working Paper No 27085. doi:10.3386/w27085.

Montgomery, James D. 1991. “Social Networks and Labor-Market Outcomes: Toward an Economic Analysis,” _American Economic Review_ , 81 (5): 1408-1418.

Munshi, Kaivan. 2003. “Networks in the Modern Economy: Mexican Migrants in the U.S. Labor Market”, _Quarterly Journal of Economics_ , 118: 549–597.

Munshi, Kaivan and Mark Rosenzweig. 2006. “Traditional Institutions Meet the Modern World: Caste, Gender, and Schooling Choice in a Globalizing Economy,” _American Economic Review_ , 96(4): 1225-1252.

OECD. 2021. “Main Economic Indicators - complete database”, Main Economic Indicators (database), https://doi.org/10.1787/data-00052-en (accessed on 02 November 2021).

Oreopoulos, Philip. 2011. “Why Do Skilled Immigrants Struggle in the Labor Market? A Field Experiment with Thirteen Thousand Resumes.” _American Economic Journal: Economic Policy_ 3(4): 148-171.

Orrenius, Pia M, and Madeline Zavodny. 2009. “Do Immigrants Work In Riskier Jobs?” _Demography_ . 46(3): 535–551.

Orrenius, Pia M. and Madeline Zavodny, M. 2010. “Mexican immigrant employment outcomes over the business cycle.” _American Economic Review_ 100(2): 316–
# **Appendix 1.**

**Table A1. Determinants of feeding posts placement. Dependent variable: presence of Partage / feeding post in the village**

|**Explanatory variables**|**coefficient**|
|---|---|
|Mean of (log) per capita consumption in the village|0.101<br>(1.48)|
|Daily Market in Community|0.121<br>(2.61)***|
|Crop disaster in past 10 years|0.273<br>(2.76)***|
|Drought disaster in past 10 years|-0.054<br>(1.11)|
|AIDS main adults’ death cause|0.045<br>(0.97)|
|Mean male height of adults aged 25-32|-0.003<br>(0.60)|
|Mean female height of adults aged 25-32|<br>0.002<br>(0.31)|
|Urban|-0.017|
||(0.27)|
|Health worker in village|-0.120<br>(2.08)**|
|Distance to the closest health center|-0.013<br>(1.96)*|
|Is Motorable Road Near|-0.041|
||(0.35)|
|Road impassable|0.115<br>(2.46)**|
|Ratio of children aged 0-3 that are vaccinated|<br>-0.198<br>(1.75)*|
|Constant|<br>-0.612|
||(0.37)|
|Observations|<br>197|
|R-squared|0.21|

**Note:** Regression based on observations at village level and not
as an agricultural supply chain project that has boosted the local fruit and vegetable supply, a project to train informal sector operators (juicers, crafters, guides) and hotels to upgrade the quality of services and sales levels.

Tourism development can become a particularly important source of off-farm income in rural and peripheral areas in Panama, and for some population groups it may exceeds the importance of agricultural activities. In Panama, income from agricultural production contributes only between around 10 and 20 percent to total household income in rural areas, with the exception of indigenous areas where it is close to a third of total income; and nearly 40 to 50 percent of total income is from skilled and unskilled labor (Appendix Table 3).<sup>10</sup> By creating new employment opportunities and through the effect on wage levels, the tourism sector is likely to have a significant impact on the local economy, particularly high for the poor and in the indigenous communities. In terms of employment in rural areas, dependence on agriculture is highest among the poor and for indigenous households; employment in the services sector (large and small-scale commerce and repairs) tends to be higher among the non-poor (Appendix Table 4). Since these types of services will be affected by tourism growth, tourism can have significant indirect benefits for the poor.

# **5. Methodology and Data Sources**

The true size of the tourism sector, more accurate than a partial picture provided by national accounts, can be measured using Input-Output (I-O) and Tourism Satellite

> 10 Some of this is agricultural labor.

7
simulation, the Southwest region will be impacted most severely by the transition, followed by the South (Figure 10). In the South and Southwest regions, declines in household expenditure per capita relative to their 2013 peak will reach 10.8 and 12.4 percent, respectively.

Although the simulation results reported so far refer to average household expenditures at the regional level, the chosen approach also make it possible to compute statistics for subsets of the population in each region. In particular, by simulating the impact of the triple shock on each individual household in the NRVA sample, it is possible to assess which share of the population in each region will have expenditures per capita below any given expenditure threshold. When the chosen threshold is the poverty line, this procedure yields the estimated poverty rates over the simulation period (Figure 11).

Using this approach, poverty incidence in Afghanistan can be expected to increase from 35.8 percent in 2011 to 36.7 percent in 2016. Although this change may seem minor, the increase is much more substantial when comparing the predicted poverty rate for 2016 to that observed around 2013, when foreign aid levels and the presence of troops on the ground were at their peak. The increase will be more marked in the East, South, and Southwest regions.

# **8. Out-of-sample validation**

The results in this paper help understand the paradox of lower poverty in regions more severely affected by conflict, but the relevance of the forecasts on the dynamics triggered by the transition could be questioned. The estimated models ensure a good fit with the data during the estimation period, but they could be less relevant beyond it, when political processes and exogenous shocks may put the country, and conflict intensity, on a different trajectory relative to the forecasts. This is a valid concern. One way to address it is to assess how well the forecasts in this paper predict actuals for the year 2015.

Data recently made available on conflict intensity in recent months reveals a strong similarity between
as the difference between the B40 and the studied percentiles, , 60 70 80 YYcc,tt , also increases. Note that the shared prosperity gaps in the MOGC simulations pp always have a positive sign. The second major outcome indicates that in terms of the median YYcc,tt YYcc,tt YYcc,tt pp , pp , pp values, the absolute value of the shared prosperity gaps in LOGCs and HOGCs increases as the difference between the B40 and the studied percentiles expands; this pattern means that depending

**_ROCs and NROCs._** For this country typology, we distinguish two main patterns in the set of simulations of shared prosperity gaps. The first pattern  based on median outcomes  indicates that the B40 is expected to grow at faster rates, on average, than higher percentiles of the income distribution across both country classifications (ROCs and NROCs) by 2030 and throughout the period 2015–2030 (Figure 5). However, a second pattern shows that the outcomes of shared prosperity measures in both types of economies (ROCs and NROCs) present a more positive standard deviation, or uncertainty, as the difference between percentiles increases.<sup>36</sup>

> 50 60 70 80 35 Note that the studied shared prosperity gaps are: , , , , and . Additional statistics of these simulated shared prosperity gaps are available upon request. BB40−m ggnn BB40−ppYYcc,tt BB40−ppYYcc,tt BB40−ppYYcc,tt BB40−ppYYcc,tt 36 Based on our selected shared prosperity gaps, this second pattern means that the gap in the growth rate between the DDh,tt DDh,tt DDh,tt DDh,tt DDh,tt B40 and the 80<sup>th</sup> percentile, 80 , has the most uncertainty, whereas the gap that compares the B40 with the median, 50 , has the least amount of uncertainty.  YYcc,tt pp YYcc,tt pp

Page **25** of **45**
.083<br>(41.13)|
|_K_|6|5|7|
|_R_-squared|0.620|0.226|0.713|
|Adjusted_R_-squared|0.620|0.225|0.713|
|Observations|5149|5149|5149|
|_F_-statistic|1710.0|385.0|2317.0|

Note: Dependent variable for first and third stages is total bilateral exports (in logs) in 2003 or latest year available. Dependent variable for second stage is first-stage residuals. Corruption Perception Index is for 2004, from Transparency International. Figures in parentheses are _t_ -statistics (based on robust standard errors). Constant terms are not shown. Source: Authors' calculations.

21