rafmacalaba/datause-encoder-data huggingface dataset:
-2-

incentive structure operating in the system with emphasis-on the lack of private r- turns to effort. 'The incentive problem was widely recognized by planners and Table 1.1 Labour and Capital Productivity (average annual growth rates), 1966-85

||196|6-70|197|1-75|197|6-80|198|1-85|
|---|---|---|---|---|---|---|---|---|
||Lab|Cap|Lab|Cap|Lab|Cap|Lab|Cap|
|Bulgaria|8.1|n.a|6.2|-0.6|4.4|-3.6|3.5|-3.1|
|Czechoslovakia|5.5|n.a|5.9|1.1|3.7|-1.5|1.3|-3.4|
|GDR|5.0|n.a|6.2|0.1|4.4|-1.0|4.3|-0.4|
|Hungary|5.1|n.a|6.1|-1.5|4.5|-4.3|2.3|-3.1|
|Poland|4.0|n.a|7.3|1.0|4.3|-4.2|-0.1|-3.4|
|Romania|7.3|n.a|6.2|-0.3|5.8|-0.8|4.0|-4.2|
|USSR|6.3|n.a|5.8|-1.1|2.8|-2.9|3.1|-2.7|

Source: ECE-UN; cited in Nuti (1988), p<sup>367</sup> academics in socialist economies. While high material intensity, low quality capital goods and inflexible allocative mechanisms can explain part of this poor performance, the institutional framework, motivational structure and remuneration system are clearly central to explaining low productivity in the state sector. At the same time, the attentuation of private ownership and thet reward structure would exacerbate problems -- familiar also to a capitalist economy -- in monitoring performance and information asymmetry. Among other results, the absence of a conventional disciplinary mechanism -- su.:h as involuntary unemployment -- might be expected to facilitate shirking.

Despite widespread recognition that the lack of a conventional macroeconomic equilibrating mechanism in the system and the commitment to low wage dispersion has tended to dilute productivity growth, exacerbate wage pressures and been a component in the determination of the level of excess demand in the system, limited attention has been directed to the particular processes by which wages are determined in such economies. One assumption has been to view wages as largely exogenous. Yet -- as this paper attempts to show -- this is not appropriate once consideration of the incentive parameters is taken into account. We also aim explicitly to introduce incentive features particu.lar to a variety of socialist regimes where account is taken of the degree of ma-ketoriented reforms introduced into the system '.

This paper focusses on the issue of wage determination starting from the case of a classical centrally planned economy and then progressively introducing 4 It should, of course, be mentioned that there is an extensive literature on the Yugoslav experience with worker management/councils and more exotic ownership forms; see, inter alia, Ward (1958); Tyson (1979); Horvat (1986); Jones and Svejnar (1988). This paper explicitly does not concern itself wit) the Yugoslav case.
45 percent for the escapers, and 47 percent for the national average. The difference is more striking if we consider that the escapers started even below the chronic poor in 2009 (30 percent against 32 percent). In addition, the chronic poor are also concentrated in the low-productivity agricultural sector. In contrast, almost one out of every four adults among the escapers worked as an employee in 2015, against only 7 percent for the chronic poor. In fact wage employment increased quite dramatically among the escapers suggesting that they may have been able to take better advantage of the higher availability of jobs between 2009 and 2015.

Figure 5: Share of Adults Employed, by welfare status <!-- Start of picture text --> 60.0<br>51.8<br>50.0 47.4 2009 2015<br>44.1<br>40.0 35.1<br>30.5<br>30.0<br>23.4 22.7 22.6<br>18.8<br>20.0 40.2 36.3 14.5 15.7<br>32.3 29.8<br>10.0 7.5 22.3 15.4 20.5 14.6 14.3 3.9 3.3 4.4 4.3<br>4.1 8.9 10.6 4.8 3.0 4.4 4.1<br>0.0<br>Share Adults Employed Share Adults Employees Share Agrilcutural self- Share Non-agric. self-<br>employed employed<br><!-- End of picture text --> _Source:_ Own elaboration based on the 2009 and 2015 WMS.

Looking into this a little bit deeper, it appears that males with advanced education benefited the most from the increase in employment opportunities between 2009 and 2015. Those who transitioned from no employment to waged employment are more likely to be males with higher education than those individuals who stayed out of employment. A regression analysis indicates that these are the main characteristics that distinguish the two groups at the national level (Annex 3). Age also plays a meaningful role, signaling that new employment opportunities did not appear only for young higher education recent graduates, but across all age groups. Being located in Tbilisi seems to play a role though this can be explained by the higher unemployment rates observed in urban areas—as opposed to rural areas where unemployment tends to be low, and large shares of the working age population are self-employed in their own land.

While the rest of the population has enjoyed higher returns to their human capital, the chronic poor have seen the returns to their labor decrease over this period. Salaries for those employed or earnings for the self-employed stagnated for the chronically poor (Figure 6). Earnings even 20
# 1 Introduction

In 2015, all United Nations members adopted the 2030 Agenda for Sustainable Development, which ushered in a new set of sustainable development benchmarks – known as the Sustainable Development Goals (SDGs) – across the world.<sup>1</sup> These SDGs supplanted the previous Millennium Development Goals (MDGs),<sup>2</sup> adding many new goals, including new goals related to the environment. However, one thing did not change with the adoption of the SDGs: Goal 1.1 was still the eradication of extreme monetary poverty.

Key to addressing progress towards the new poverty goals, however, is the accurate measurement of poverty (e.g., Ravallion, 2015). To this end, many household surveys in developing countries now include consumption and/or expenditure modules, designed to measure a specific type of monetary poverty. For example, Malawi’s Fifth Integrated Household Survey (IHS5) was a major input into the 2020 Malawi Poverty Report (NSO Malawi, 2021),<sup>3</sup> in large part due to its consumption module.

While household surveys like Malawi’s IHS remain key to measuring poverty, difficulties remain. First, large household surveys are expensive and, as such, generally are fielded only every few years, at most. Second, surveys are designed to be representative at certain levels of aggregation. For example, in Mexico, the Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH) is designed to give representative estimates for rural and urban areas across states, but not for most municipalities.<sup>4</sup> The inability to consistently estimate more disaggregated statistics of poverty can make targeting difficult, especially when poverty is found in pockets below the level of representativeness. Moreover, many SDGs disaggregate progress by groups (e.g., women and children), which can lead to additional difficulties with reliably estimating these statistics, since many of these subgroups may have relatively few observations. This is also true for the nonpoverty benchmarks, like access to health facilities. Therefore, improving the accuracy and precision of survey estimates in these contexts is a first-order question for many development economists and practitioners. In this paper, we employ a common statistical method – small area

> 1 https://sdgs.un.org/goals 2 https://www.un.org/millenniumgoals/ 3 https://microdata.worldbank.org/index.php/catalog/3818/related-materials 4 https://www.inegi.org.mx/programas/enigh/nc/2018/

2
**Table A14. Survey Methods’ Heterogeneous Effects by Respondent’s Sex and Age, Addressin the Substitution Protocol’s Effects** **<u>g</u>**

||(1)<br>D = F<br>|(2)<br>emale<br>|(3)<br>D = Y<br>|(4)<br>outh<br>|
|---|---|---|---|---|
||Employed|Working|Employed|Working|
|LOA|0.005|-0.000|0.033|0.044*|
||(0.030)|(0.027)|(0.027)|(0.025)|
||[0.866]|[0.993]|[0.215]|[0.059]|
|ESR|-0.023|-0.024|-0.019|-0.028|
||(0.033)|(0.028)|(0.028)|(0.025)|
||[0.492]|[0.375]|[0.481]|[0.262]|
|LOA x D|0.063|0.081*|0.016|-0.007|
||(0.044)|(0.042)|(0.052)|(0.052)|
||[0.174]|[0.059]|[0.753]|[0.879]|
|ESR x D|0.043|0.024|0.069|0.063|
||(0.047)|(0.044)|(0.052)|(0.051)|
||[0.339]|[0.568]|[0.195]|[0.241]|
|LOA + LOA x D|0.068**|0.081**|0.049|0.036|
||(0.033)|(0.033)|(0.045)|(0.045)|
||[0.051]|[0.014]|[0.256]|[0.405]|
|ESR + ESR x D|0.020|-0.000|0.050|0.036|
||(0.034)|(0.034)|(0.045)|(0.045)|
||[0.567]|[0.995]|[0.291]|[0.435]|
|Observations|2,480|2,480|2,480|2,480|
|Outcome(Control Group)Mean|0.571|0.670|0.571|0.670|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by sex and age for the reporting of employment or work status. We present the ITT estimates from Equation (2) and the total effect of each survey method. The indicator takes the value of 1 if the respondent is a woman (Columns [1] and [2]) and if the respondent is a youth between the ages iihss of 15 to 24 years (Columns [3] and [4]). The vector includes the indicator and the list of control DD variables selected using a double-LASSO procedure for each outcome (Table A3 in the Appendix shows iihss iihss XX DD the variables selected). All estimations include EA (stratification variable) fixed effects. Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 51
time measured in days over the period 2007-2012 while columns (8)-(14) provide estimates for clearance time measured in hours over the period 2010-2012. Note that some columns show coefficients from specifications with fewer fixed effects than are shown in Eqs. (2)-(3). The estimates from the first-stage regression show a positive and significant effect of the allocation to the red channel on customs clearance time, as expected. The F-statistics from the first-stage regressions are very large and indicate a strong correlation between the exogenous instrument and clearance time. The first-stage coefficient implies that a firm-product-country cell that moves from having the majority to having only a minority of its shipments physically inspected in a year has a decline in the expected median time to clear customs of 7% (=-1*0.07).

The second-stage regression estimates show a clear negative and significant effect of customs clearance time on import value.<sup>37</sup> The IV estimates are substantially larger in magnitude than the OLS estimates. Our preferred specification in column (6) implies that a decrease of 1 day in the (instrumented) time to clear customs increases on average the value of imports in a firm-productcountry cell by 164%.<sup>38</sup> Column (7) provides estimates for the effects of a change in the log number of days instead of a change in days. Focusing on clearance time measured in hours in column (13) implies that a 1-hour decrease in the time to clear customs (induced by lower physical inspections) increases on average the import value of a firm-product-country cell by 2.3%.<sup>39</sup> Because there are so few estimates like these in the literature, and because our specification follows Volpe Martincus _et al._ (2014), an interesting exercise is to compare our estimates from Albanian imports to theirs from Uruguayan exports. Because our primary specification uses days as units, we compare with their secondary specification of the same form. Our first-stage regression estimates suggest much smaller impacts of changes in inspections on changes in median days in customs (0.07 in Albanian imports versus 2.726 in Uruguayan exports). The second-stage regression implies much larger effects of delays on trade in our sample (-0.97 versus -0.114). These are very different settings, of course, but it seems that in our settings

> 37 The magnitude of the OLS and the IV estimates increases in absolute value the richer is the set of included fixed effects. 38 The magnitude is calculated as (e(-0.97)*(-1)-1).

> 39 The magnitude is calculated as (e(-0.023)*(-1)-1).

23
**Figure 3.6. Urban poverty and GDP per capita growth**

<!-- Start of picture text --> 100<br>TJ 90<br>80<br>70<br>MD AZ<br>6 0<br>AM<br>KG 50<br>40<br>UZ 30<br>TM RO 2 0<br>B Y<br>10 AL<br>KZ<br>BG<br>0<br>-10 -8 -6 -4 -2 0 2 4 6 8 10<br>Annual GDP percapita growth rate between 1990 and 2000<br>Urban poverty rate<br><!-- End of picture text --> AL=Albania; AM=Armenia; AZ=Azerbaijan; BG=Bulgaria; BY=Belarus; KZ=Kazakhstan; KG=Kyrgyz; MD=Moldova; RO=Romania; TM=Turkmenistan; TJ=Tajikistan; UZ=Uzbekistan Source: WDI and staff calculations On the one hand, urban and national poverty rates are much higher than would be expected (notwithstanding their negative growth rates for the past ten years) in a few countries (Tajikistan, Azerbaijan, Armenia). On the other hand, urban and national poverty rates are lower than would be expected (given the rates of GDP change) in other countries (e.g. Albania, Romania, Bulgaria, Belarus, and Turkmenistan). These differences likely result from country-specific progress on structural reforms, wage policies and employment strategies.

Analysis of patterns and trends in poverty across the ECA countries reveals some commonalities, according to _Transition_ . The countries with the highest poverty incidence—Central Asia and the Caucasus, plus Moldova—have been those in which the progress of structural reform and liberalization has been very incomplete, and which have been least successful in switching from state enterprise to private sector-based output and employment. Employment has fallen even more sharply than output across the region, and in most countries the labor force participation has declined as well. Poverty outcomes have been worsened by policies that have contributed to sharply rising inequality in virtually all the transition economies. While part of this rise in inequality is a natural and necessary outgrowth of the shift to market-based wages and returns on assets and education, much of the increasing inequality reflects the gap between individuals stuck in nonproductive and publicly funded activities, and those able to exploit new opportunities.

# **3.3. Migration and urban change**

During the past decade, transition countries have experienced large international and domestic migration flows, which have resulted in unique pressures on urban areas. During the 1990s, three main groups comprised most international migrants in transition countries: (1) Russians and other Slavs who were returning to their historic homelands; (2) people seeking jobs in other countries; and (3) people displaced by conflict. Domestic migration consisted of two main groups, people seeking economic opportunities and internally displaced people.

The first group of international migrants resulted from Soviet policies to promote industrialization across the country. In order to build and run the new factories built in the republics, Russian and other Slavic (Ukrainian and Belarusans) managers and technical specialists were moved to the republics. As a result, at 16
- dreas Beyer and Lucrezia Reichlin, editors, _The Role of Money: Money and Monetary Policy in the Twenty-First Century_ . The Fourth European Central Banking Conference, 2006.

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- Anusha Chari, Peter Blair Henry, and Diego Sasson. Capital market integration and wages. _American Economic Journal: Macroeconomics_ , 4(2): 102–132, April 2012.

- Benjamin Eden. Liquidity premium and international seigniorage payments. Working Papers 0901, Department of Economics, Vanderbilt University., 2009.

- Maya Eden. Should wall-street be occupied? an overlooked price externality of financial intermediation. 2012.

- Emmanuel Farhi and Jean Tirole. Bubbly liquidity. _Review of Economic Studies_ , 2011. Forthcoming.

- Milton Friedman. The optimum quantity of money. In _The Optimum Quantity of Money and Other Essays_ , pages 1–50. Chicago: Aldine, 1969.

- Gary Gorton, Stefan Lewellen, and Andrew Metrick. The safe-asset share. _American Economic Review_ , 102(3):101–106, May 2012.

- Pierre-Olivier Gourinchas and Olivier Jeanne. The elusive gains from international financial integration. _Review of Economic Studies_ , 73(3):715–741, July 2006.

38
To motivate the use of average bribery statistics as an instrumental variable, consider the simplified linear regression model where sales growth is a function of bribery and a firm-specific component ( ) (1).

The dependent variable, , is the sales growth of firm who is in a location-sector cluster . The variable is a firm’s individual bribery statistic. Corruption distorts firm growth and the coefficient is expected to be negative. The error term, , is a mean-zero idiosyncratic error term. For simplicity, assume the firm-specific component is correlated with growth and factors influencing a firm’s exposure to bribery. We can also interpret the firm-specific component to capture the likelihood of a firm conducting a business transaction, such as intentions to expand their business. Omission of this variable will lead to biased coefficient estimates. When is omitted, the error term is no longer orthogonal to the regressors. Assuming corr( and , the bias in the coefficient estimate of will be positive, and lead to a positively-biased OLS coefficient<sup>4</sup> .

Identification relies on the assumption that bribery has an additive structure where a first component is firm-specific, and a second component is location and sector specific (2).

The variable is the average bribery levels in a country-location-sector group , while is the firm-specific contribution to the bribe statistic. The necessary assumption is that across locations and sectors, bribery exposure is partly explained by local external effects that are exogenous to the characteristics of an individual firm.

Certain sector specific factors like international engagement or dependence on publicly provided infrastructure services can affect exposure to bribery. Since the sample size is small for many countries, we define two sector groups manufacturing (ISIC 15-37) and services including

> 4 We verified this hypothesis. Estimation result with OLS method, which is available upon request, yielded positive coefficient on bribery measure. Fisman and Svensson (2007) also find positive OLS estimates of bribery coefficients.

7
**Figure 5. Medium-Term and Long-Term Income Growth Rate for Immobile Individuals, Russia 1994-2015 (percentage)**

<!-- Start of picture text --> Panel A: 1994-2004 Panel B: 2004-2015 Panel C: 1994-2015<br>350<br>317.2<br>300<br>250<br>200 176.2 180.9<br>150<br>125.4<br>110.5<br>100<br>76.0<br>50 38.6<br>29.8 26.3<br>0<br>Poorest tercileMiddle tercileRichest tercile Poorest tercile Middle tercile Richest tercile Poorest tercile Middle tercile Richest tercile<br>Growth rate(%)<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita. The distribution by terciles is based on the cross-sectional sample for each year, then the tercile thresholds were assigned to panel sample. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. The exact growth rates for each income group are shown on top of the bars.

46
-23-

# Improving SAL design

39. Appropriately designed adjustment programs can create a policy framework that fosters pro-poor growth in the long run and protects the poor from adverse short run effects.<sup>13</sup> 40. Modifying the Policy-mix. A number of policy options, each with differing implications for the poor, are generally available to effect a specific economic change. For example, an external current account deficit can be reduced by fiscal contraction or expenditure switching. The choice of instrument and the specific policy-combination will have differing long and short run consequences for employment patterns and income distribution between different groups in poverty. A study for the Philippines (referred to earlier) concluded that greater reliance on expenditure switching policies rather than fiscal contraction would have been preferable in the Philippines context (see Box 4). The Philippines study was completed in five staff weeks at a cost of roughly US $9000.14 Similarly, phasing of price adjustments may be an option in cases where supply elasticities are

- 13/ For further discussion **see** "How Adjustment Programs Can Help The Poor", Ibid.

- 14/ The figure refers to the amount spent on the "adjustment and poverty' part of a larger study on poverty in **the** Philippines (referenced in footnote 10). The cost of much studies will vary from country to country depending on how much **data and** analysis **are already** available **at the** time of the study. The Bank may already have Invested in such data and analyses In **the** context of its CEVs, CSPs or other similar work. If **quarterly** data on income shares **are** not available, **as** may often **be the** case, yearly Income **and** expenditur, data for some years may **be used.** Where this **data** is **also** lacking, **a** qualitative assessment may still **bt** possible.
# **7. Annex**

**<u>Table A.1:  Shares of different income components in total income across years, %</u>**

||2003|2007|2009|
|---|---|---|---|
|Labor earnings|65.1|74.6|61.2|
|Pensions|12.7|11.8|8.1|
|Social assistance|3.1|1.0|2.5|
|Remittances|11.0|8.7|17.0|
|Other income|8.0|3.9|11.1|
|Source: Authors’ calculations based on ECAPOV data.||||

**Table A.2:  Growth rate of different income components, share of adults and share of employed -** **<u>across quartiles during 2003 2009, %</u>**

|Quart<br>iles|Share of<br>adults|Share of employed (non-zero<br>labor earnings)|Labor<br>earnings|Pensi<br>ons|Social<br>assistance|Remitta<br>nces|Other<br>income|
|---|---|---|---|---|---|---|---|
|botto||||||||
|m|11|90|1277|381|857|1244|1463|
|II|10|3|522|451|491|898|906|
|III|12|-1|439|428|322|848|715|
|top|9|0|332|221|170|951|415|

Source: Authors’ calculations based on ECAPOV data. Notes: Quartiles are constructed based on income per capita. Income components are USD/PPP 2005 prices.

**<u>Table A.3:  Profile of population in the bottom 40 percent for selected years</u>**

|||Bottom 40||Top60||
|---|---|---|---|---|---|
||2003|2007<br>2009|2003|2007|2009|
|||**Residence**||||
|urban|26.5|27.8<br>23.6|31.6|25.3|28.2|
|rural|73.5|72.2<br>76.5|68.4|74.7|71.8|
|||**Oblast**||||
|Dushanbe|5.2|10.4<br>6.5|12.3|8.7|11.4|
|Sogd|32.4|44.4<br>26.7|31.4|19.9|31.3|
|Khatlon|44.8|21.9<br>44.3|24.4|44.9|30.2|
|RRP|13.7|21.3<br>19.6|29.7|22.8|24.2|
|GBAO|4.0|2.0<br>2.9|2.9|0.0|0.0|
|||**Education (16+)**||||
|none/<1-4|6.6|6.7<br>7.8|6.0|6.6|5.3|
|incomplete 5-12|18.6|23.0<br>21.3|16.5|19.9|17.6|
|gen sec|55.0|50.6<br>52.3|49.3|48.3|52.0|
|spec sec|6.9|9.1<br>8.6|11.2|12.3|11.2|
|tertiary|4.5|6.4<br>6.1|10.5|10.4|11.6|
|missing|8.4|4.2<br>3.8|6.4|2.5|2.4|
|||**Labor force status (16+)**||||
|employee|31.8|19.6<br>17.2|30.4|25.0|18.3|

28