rafmacalaba/datause-encoder-data huggingface dataset:
sampling for the baseline survey; knowledge of the program design and setting can provide clues. Types of observation units with characteristics making them more likely to participate will often have to be over-sampled, to help assure adequate coverage of the population treatment group and to provide a sufficiently large pool of similar comparators to draw upon. Problems can arise later if one does not predict well-enough _ex ante_ who will participate. For example, Ravallion and Chen (2005) had designed their survey so that the comparison group would be drawn from randomly sampled villages in the same poor counties of rural China in which it was known that the treatment villages were to be found (for a poor-area development program). However, the authors subsequently discovered that there was sufficient heterogeneity within poor counties to mean that many of the selected comparison villages had to be dropped to assure common support. With the benefit of hindsight, greater effort should have been made to oversample relatively poor villages within poor countries.

The second source of concern is the _DD_ assumption of time-invariant selection bias. Infrastructure improvements may well be attracted to places with rising productivity, leading a geographic fixed-effects specification to overestimate the economic returns to new development projects. The opposite bias is also possible. Poor-area development programs are often targeted to places that lack infrastructure and other conditions conducive to economic growth. Again the endogeneity problem cannot be dealt with properly by positing a simple additive fixed effect. The selection bias is not constant over time and the _DD_ will then be a biased impact estimator. Figure 3 illustrates the point. Mean outcomes are plotted over time, before and after the intervention. The lightly-shaded circles represent the observed means for the treatment units, while the hatched circle is the counterfactual at date _t_ =1. Panel (a) shows the initial selection bias, arising from the fact that the program targeted poorer areas than the comparison units (darkshaded). This is not a problem as long as the bias is time invariant, as in panel (b). However, when the attributes on which targeting is based also influence subsequent growth prospects we get a downward bias in the _DD_ estimator, as in panel (c).

Two examples from actual evaluations illustrate the problem. Jalan and Ravallion (1998) show that poor-area development projects in rural China have been targeted to areas with poor infrastructure and that these same characteristics resulted in lower growth rates; presumably, areas with poor infrastructure were less able to participate in the opportunities created by China’s growing economy. Jalan and Ravallion show that there is a large bias in DD estimators in this 37
**As discussed, minimum wages target individual workers with low wages, rather than families with low incomes; in Kosovo, a large fraction of minimum wage workers are not in poor households.** Specifically, Figure 12 shows that the percentage of poor<sup>23</sup> in Kosovo was higher among workers who would be affected by the policy (16.2 percent) compared with workers who would not (8.3 percent), but about 83.9 percent of minimum wage workers were not in poor families.<sup>24</sup> As a result, a large share of the higher income from increases in the minimum wage would benefit households at the top of the distribution This suggests that the impact on poverty may be ambiguous, but likely small. Moreover, the percentage of poor is the highest among non-employed individuals who cannot be directly affected by the policy (19.9 percent).

**Figure 12. Decomposition of population by poverty level, aged 15-64**

<!-- Start of picture text --> Not affected not employed 19.91% 80.09%<br>Not affected workers 8.28% 91.72%<br>Affected 16.15% 83.85%<br>Poor Non-poor<br><!-- End of picture text --> _Source: Own estimates using 2017 Kosovo HBS._

# **Given this analysis, we conclude the following potential effects of the minimum wage policy:**

1. The increase in the minimum wage might be effective in reducing the gender pay gap, given that women are overrepresented in low-wage occupations. For the same reason, however, women are also more likely than men to lose their jobs. Because the policy directly affects the active working-age population only, and females have low labor force participation rates, few women are likely to be affected by the policy.

2. The majority of potentially affected workers are unskilled workers. Given the higher wage elasticity for unskilled workers, job losses could be substantial for this group.

3. Young, skilled workers might experience significant increases in earnings without losing their jobs, given that many young minimum-wage workers are skilled.

4. The impact on poverty of an increase in the minimum wage is expected to be small. Poor individuals are overrepresented among potentially affected workers. However, the net effect on labor earnings might be negative because job losses are higher among the unskilled. Leakage is high: about 83.9 percent of workers who could be impacted live in non-poor households. Quantitative evidence from Shapley decompositions indicates that the impact on poverty can be negative but small.

> 23 The poor are defined as those living below the national poverty line.

> 24 Leakage could be even higher considering there is underreporting of income in the HBS.

20
**Table 6: Ten most active markets, scaled by population, 2018-2020.**

|**2018**|**2019**|**2020**|
|---|---|---|
|Russian Ruble|Venezuelan Bolivar|Venezuelan Bolivar|
|Venezuelan Bolivar|Russian Ruble|Russian Ruble|
|Belarusian Ruble|Belarusian Ruble|Belarusian Ruble|
|Swedish Krona|Panamanian Balboa|Colombian Peso|
|Panamanian Balboa|Peruvian Sol|Chilean Peso|
|British Pound|Swedish Krona|Peruvian Sol|
|Peruvian Sol|Colombian Peso|Swedish Krona|
|New Zealand Dollar|Chilean Peso|Panamanian Balboa|
|Colombian Peso|British Pound|British Pound|
|Nigerian Naira|Kazakhstani Tenge|Argentine Peso|

Source: LocalBitcoins.com API, Authors’ Calculations The question of the determinants of crypto use in different markets merits further investigation, which will be facilitated by expanding the number of exchanges incorporated, as comparable data becomes available. Yet, the notion that the use of crypto currencies is changing the economics of capital control evasion, and that, in turn, capital control evasion is an important driver for the expansion of crypto markets is broadly in line by the list of countries in Table 6. Though many factors render a more systematic analysis impossible,<sup>30</sup> Figure 6 presents the results of an exemplary case study, further illustrating the link between capital controls and crypto market

> 30 The most important factor being the small sample size of countries introducing capital controls over the period of our sample, Additionally, when demand for crypto currencies rises, as the need to evade capital controls becomes imminent, often, alternative and more cost-efficient exchanges open, which then reduces trade volumes on LocalBitcoins.com. Finally, one of the authors’ field research suggests that the recent rise of stable coins has at least in some cases, such as Lebanon, led to the former taking over the role of Bitcoin as the major crypto vehicle to evade capital..

25
**Table 1. Countries of the Western Balkans: Some Basic Indicators**

||GDP per capita<br>in $1000 at<br>purchasing-<br>power parity,<br>2018|Public<br>spending,<br>% of GDP,<br>2018|Government and<br>government-<br>guaranteed debt,<br>% of GDP,2018|Fiscal<br>balance, %<br>of GDP,<br>average<br>2015–18|Monetary-<br>and-<br>exchange-<br>rate regime|EU-<br>accession<br>status|
|---|---|---|---|---|---|---|
|Albania|13.3|29.0|68.6|-2.6|Inflation<br>targeting|Candidate|
|Bosnia and<br>Herzegovina|13.5|44.3|36.7|1.0|Euro<br>currency<br>board|Potential<br>candidate|
|Kosovo|11.3|29.6|16.8|-1.8|Euro|Potential<br>candidate|
|North<br>Macedonia|15.5|31.5|48.5|-2.5|Inflation<br>targeting|Candidate|
|Montenegro|18.9|46.2|75.7|-4.9|Euro|Candidate|
|Serbia|16.0|41.0|54.3|-0.8|Inflation<br>targeting|Candidate|

Sources: World Bank Staff; European Commission (status of EU accession).

**This paper aims to help the Western Balkans countries refine their approach to fiscal rules.** Section II provides background on fiscal rules—what they are, how common they are around the world, and how they work (when they do). Section III describes the fiscal rules that are in place in the Western Balkans. Section IV assesses the rules, considering whether they have been followed; whether there is enough public awareness of their content; whether they appear to be well-designed, from both a qualitative and a quantitative perspective; and whether they are well supported by public financial management. Section V concludes by suggesting five principles to govern policy toward fiscal rules in the Western Balkans in the period before the countries join the EU.

# **II. OVERVIEW OF FISCAL RULES**

**Fiscal rules are long-term numerical constraints on fiscal aggregates** (Kopits and Symanksy 1998). The most common rules limit the government’s debt or deficit or the growth of its spending. Fiscal rules can be distinguished from other kinds of fiscal norms, including:

- Shorter-term fiscal constraints, such as the annual spending limits in the budgets of all the countries in the region, and most medium-term fiscal plans

- Nonbinding fiscal targets, like North Macedonia’s plan to keep debt below 60 percent of GDP

6
Table 13: Government Closeness Index: Adjusting decentralization index for population and heterogeneity

|N|no adj.|Sensitive<br>(A =0.01)|moderate<br>(A=0.1)|conservative<br>(A=1)|ard].<br>area<br>(4 =0.1)|heterogeneity<br>(4 =0.1)|
|---|---|---|---|---|---|---|
|1<br>2|Denmark<br>(34)|Switzerland<br>(40)|Switzerland<br>(31)|Denmark<br>(31)|Switzerland<br>(31)|Denmark<br>(32)|
||Sweden (21)|Denmark<br>(29)|Denmark<br>(31)|Switzerland<br>(27)|Denmark<br>(31)|Switzerland<br>(29)|
|3|Switzerland<br>(20)|USA (21)|Sweden (20)|Sweden (20)|Sweden (20)|Sweden (20)|
|4|Hong<br>Kong<br>(17)|Finland (21)|Finland (19)|Finland (18)|Finland (19)|‘Finland (19)|
|Hi|Singapore<br>(17)|Sweden (20)|USA (19)|USA (17)|USA (19)|USA (18)|
|6|Finland (16)|Iceland (19)|Norway (17)|Norway (16)|Norway (17)|Norway [17)|
|T|Japan(15)|Norway(18)|Iceland(16)|Iceland(14)<br>|Iceland(15)<br>~|—Iceland(16)|
|8<br>|<br>Norway (15)<br>|<br>Japan (13)<br>|<br>Japan (13)<br><br>|<br><br>Japan (14)<br><br>|<br><br>Japan (13)<br><br>|<br>Japan (13)<br><br>|
|La)|USA (14)|Austria (12)|Hong<br>Kong<br>(13)|Hong<br>Kong<br>(13)|Hong<br>Kong<br>(13)|Hong<br>Kong<br>(13)|
|10|Korea (12)|Hong<br>Kong<br>(11)|Singapore<br>(11)|Singapore<br>(12)|Singapore<br>(11)|Singapore<br>(11)|
|Source: <br>Note In<br>for: co<br>populat<br>corresp<br>benchm<br>N = 43<br>1.|Authors’ calcu<br> each column t<br>lumns 2-4 - only <br>ion and area, 6 <br>onding sensitivit<br>ark country is a<br>2953, area — 0.|lations based u<br>op ten most de<br> for average LG<br> - for average L<br>ies (defined by<br> hypothetical <br>076, — 0.359.|pon data sources<br>centralized countr<br> unit population<br>G unit populatio<br> parameter A) a<br>country with med<br> The original dece|reported in An<br>ies are presente<br>, column 5 - for<br>n and heterogen<br>re in the bracke<br>ian parameters <br>ntralization ind|nex Table Al.<br>d. Indexes are <br> average LG uni<br>eity index. The<br>ts of column titl<br> from thesample<br>ex is presented|adjusted<br>t<br><br>es. The<br>:<br>in column|
- <u>International Labour Organization. (2021).</u> _ILOSTAT database_ [database]. Available from https://ilostat.ilo.org/data/.

- <u>IMF (2019).</u> _World Economic Outlook October 2019: Global Manufacturing_ Downturn _, Rising Trade Barriers_ . Washington, DC: IMF

- <u>IMF (2021).</u> _World Economic Outlook October 2021: Recovery During a Pandemic_ . Washington, DC: IMF

- <u>Jäger, S., Schoefer, B. and J. Zweimüller (2019). “Marginal Jobs and Job Surplus: A Test of the</u> Efficiency of Separations,” _NBER Working Paper_ 25492, National Bureau of Economic Research.

- <u>Karabay, B. and J. McLaren (2011). “Pareto-Improving Firing Costs?” European</u> _Economic Review_ 55: 1083–1093.

<u>Knotek, E. (2007).</u> _How useful is Okun’s law?_ Federal Reserve Bank of Kansas City, Kansas.

- <u>Lakner, C., Mahler, D. G., Negre, M., and E. Beer Prydz (2020)</u> “How Much Does Reducing Inequality Matter for Global Poverty?” _Global Poverty Monitoring Technical Note 13_ , World Bank.

- <u>Landais, C., Michaillat, P., and E. Saez (2010). “Optimal Unemployment Insurance over the</u> Business Cycle.” _NBER Working Paper_ No. 16526.

- <u>Lazear, E. (1990). “Job Security Provisions and Employment.”</u> _Quarterly_ Journal _of Economics_ 105: 699–726.

- <u>Ljungqvist, L. (2002). “How Do Layoff Costs Affect Employment?”</u> _The_ Economic _Journal_ 112(482): 829 – 853.

- <u>Lommerud, K., Straume, O. and S. Vagstad (2018). “Employment Protection and Unemployment</u> Benefits: On Technology Adoption and Job Creation in a Matching Model.” _Scandinavian Journal of Economics_ 120(3): 763–793.

- <u>McKay, A. and R. Reis (2016). “The Role of Automatic Stabilizers in the US Business Cycle,”</u> _Econometrica_ , 84 (1): 141–194.

- <u>McKinsey (2020).</u> _The Next Normal: The recovery will be digital_ . McKinsey Global Publishing.

- <u>Michaillat, P. and E. Saez (2019). “Optimal Public Expenditure with Inefficient Unemployment,”</u> _The Review of Economic Studies_ , 86 (3): 1301–1331.

- <u>O’Campo, P., Molnar, A., Ng, E., Renahy, E., Mitchell, C., Shankardass, K., St. John, A., Bambra, C. and C. Muntaner (2015). “Social welfare matters: A realist review of when, how, and</u> why unemployment insurance impacts poverty and health.” _Social Science & Medicine_ , 132: 88-94

- <u>OECD (2020). “Corporate sector vulnerabilities during the Covid-19 outbreak: assessment and</u> policy responses.” OECD Policy Responses to Coronavirus (COVID-19). Available at http://www.oecd.org/coronavirus/policy-responses/corporate-sector-vulnerabilitiesduring-the-covid-19-outbreak-a6e670ea/.

- <u>OECD (2021a). “Job retention schemes during the COVID-19 lockdown and beyond” OECD</u> Policy Responses to Coronavirus (COVID-19).

- <u>OECD (2021b),</u> _Social spending (database)_ . doi: 10.1787/7497563b-e

24
Table 3.2: Numbers of Antidumping, Article XIX Safeguard and Countervailing Duty Measures Notified by WTO Members; - <u>1995 2007</u>

||1995|1996|1997|1998|1999|2000|2001|2002|2003|2004|2005|2006|2007|1995-<br>2007|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|All Members*<br>|||||||||||||||
|AntidumpingMeasures|119|92|125|170|185|227|167|214|220|152|132|137|109|2049|
|Safeguard Measures|0|1|3|5|5|7|9|14|15|6|6|7|5|83|
|Countervailing duty<br>Measures|19|5|3|6|14|19|14|14|6|8|4|3|4|119|
|Developing Members|||||||||||||||
|AntidumpingMeasures|59|52|74|101|127|129|83|151|189|114|82|114|83|1358|
|Safeguard Measures|0|0|3|4|2|5|7|8|4|4|5|7|5|54|
|Countervailing duty<br>Measures|14|2|0|2|0|7|4|2|1|4|3|0|1|40|

Source: WTO website Note 1: * Over 1995-2007 only four percent of total measures involved agricultural products, HS Categories I-II. Note 2: WTO Balance of Payments Committee Reports (WT/BOP/R/10 and WT/BOP/R/19) for 1995 and 1996 report balance of payments measures in place in 18 Members. Subsequent Reports indicate that all such measures have been removed.

17
_S_ _SDI = ------------------_

# _LP * i_

where _SDI_ = Index of subsidy dependence of DFI

- _S_ = Annual subsidy received by the DFI (see above)

- _LP_ = Average annual outstanding loan portfolio of the DFI

_I_ = Average yield earned on the loan portfolio of the DFI.

Conventional accounting measures the cost of funds that are priced at their accounting value. The ROE and ROA reflect the volume of subsidies that were injected into the DFI through concessionary borrowed funds. Thus, they do not necessary present the DFI’s true financial performance. The opportunity cost of a DFI’s borrowed funds (that is, the cost that the DFI would have to pay for its funds if access to concessionary funds were eliminated) is not taken into account. The SDI calculation assumes that the volume of the DFI’s OLP remains unchanged. Hence, the change is caused by substituting voluntary savings for concessionary borrowed funds (or other accessible borrowing sources) at a market-related interest rate.

The SDI illustrates the percentage of increase that is needed in the annual yield on the OLP as a percentage of the current yield achieved, if full subsidy independence were to be reached instantly.<sup>13</sup> This illustration assumes, for simplicity, that an increase in the lending interest rate would generate the subsidy elimination. However, it is clear that any type of cost saving would automatically be reflected as an increase in annual profit (or as a reduction in annual loss). Therefore, that cost saving would inevitably lower the SDI. Figure 2 summarizes the effect that each of the parameters may have on the SDI.

> 13 This complete SDI ratio also provides the notion of a matching grant, when the numerator is the grant received from society and when the denominator is the value of interest and fees paid by the ultimate clients for the financial services they benefit from.

10
less than their male colleagues, they have low occupational safety and health standards as well.<sup>12</sup>

# 2.2.2 India

Despite significant progress in female labor force participation over the past two decades, the formal labor force participation rate of women is still very low, and women in many sectors of the economy receive lower wages than men. According to the Global Wage Report (ILO, 2017), India suffers from a huge gender pay gap. The report finds that India has among the worst levels of gender wage disparity (men earning more than women in similar jobs) with the gap exceeding 30 percent. There remains a pervasive and persistent gender gap in earnings for women across different sectors and jobs, and the extent of the gender wage gap is measured on various parameters such as age, educational qualifications, industry, work experience, type of contract, marital status, level of skill, etc.<sup>13</sup> As can be seen in Table 3, only 22 percent of women were employed as compared to 78 percent of men employed in the same economic activities in India in 2012. Moreover, women appear to be concentrated mainly in low-productivity jobs and in the informal sector. The distribution of female workers in different sectors reveals that the highest share of total female employment is in agricultural, forestry, fishing and hunting (76.8 percent) and manufacturing of textiles and wearing apparel industry (7.8 percent), followed by retail and health services (7.2 percent). These are the industries with the highest concentration of women, even though, the percentage share of females in these industries is not more than 36.2 percent which is highest share of female workers in human health services followed by manufacture of wearing apparel industry with 35 percent share. Overall, among the female workers, about 77 percent were engaged in the agricultural sector and related activities and nearly 13 percent of the female workers were engaged in secondary sector while only 10 percent were engaged in the tertiary sector in 2012.

**Table 3:  Women Labor Force Participation by Economic Activity in India (in 2012)**

|**S.No.**|**Economic activity**|**No. of**<br>**Men**|**No. of**<br>**Women**|**Share of**<br>**women in**<br>**sectoral**<br>**employment**<br>**(%)**|**Share of the**<br>**sector in**<br>**total women**<br>**employment**<br>**(%)**|
|---|---|---|---|---|---|
|1|Crop and animal production, hunting and related<br>service activities|127841|50409|28.3|76.8|
|2|Retail trade, except of motor vehicles and<br>motorcycles|26969|3565|11.7|5.4|
|3|Manufacture of wearing apparel|4783|2575|35.0|3.9|

> 12 The Guardian, Urbanization in Bangladesh proves a double-edged sword for women, 5 November 2012 assessed online at <u>https://www.theguardian.com/globaldevelopment/2012/nov/05/urbanisation-bangladesh-women.</u> 13 Gender Gap in India: An Analysis, Sep 2017 accessed online at <u>https://www.2thepoint.in/gender-gap-india-analysis/.</u>

14
taxpayer registry in 1993, which is indeed impressive. At the same time only 35% of taxpayers have filed on average over the period, and SUNAT has not been able to increase the relative amount filing (as a percentage of those registered).

A more detailed analysis shows that SUNAT has been using its limited resources judiciously. Data from 1997 show that SUNAT has focused its efforts on large and medium taxpayers, and has been extremely successful in the process. The 33,300 large and medium taxpayers are well-controlled by SUNAT: the ratios of those declaring and paying are 100%. The same ratios for the general and other simplified regimes, however, were quite low.

The data available for Venezuela allow for a limited comparison, revealing that SENIAT’s control over its large taxpayers was weaker when compared with SUNAT’s. The absolute number of non-filers in SENIAT’s large taxpayers unit increased steadily since 1995, nearly tripling by 1998. The average percentage of non-filers to the number of large taxpayers registered was 32% over the period, suggesting a large hole in the administration’s effort to increase compliance.

Another useful indicator of administrative performance is late payments by taxpayers (total late payments by taxpayers during the year). Data also showed that the number of large taxpayers in Venezuela making late payments increased since 1995, more than tripling by 1998, though the amount of taxes paid late as a percentage of total collections was quite small (about 1%).

In South Africa SARS steadily broadened the tax net with impressive year on year growth. From March 1996 to March 2001 the number of taxpayers with active status grew from 518,649 to 976,720 for corporate income taxpayers; 1,929,274 to 3,187,072 for individual income taxpayers; 388,454 to 450,630 for VAT taxpayers; and 188,841 to 211,425 for PAYE taxpayers. From 2000-2001 the percentage of active individual taxpayers grew by 13.9% while the growth of active corporate taxpayers reached 16.3%. Note that the growth of corporate taxpayers was due principally to compliance activities by SARS as company registrations did not grow much over the same period.<sup>46</sup>

> 46 Some component of the slow growth in the VAT registry is due to change in the threshold for registration.

31