rafmacalaba/datause-encoder-data huggingface dataset:
# **<mark>Table A2A – Parallel Trend Test of Fiscal Balance Components Across States of Varying Public Debt Before FD Law</mark>**

<mark>This table reports OLS estimates of the trends of fiscal balance components across states of varying public indebtedness prior to the introduction</mark> of the FD Law. On the public expenditure side, the variables _TotalExpendituress,y_ , _Infrastructures,y_ , _SocialServicess,y_ and _DebtServicings,y_ correspond to the ratios of total public expenditures and expenditures on infrastructure projects, social aid, and debt servicing over the GDP of state _s_ in year _y_ . On the public revenue side, the variables _Transferss,y_ and _TaxRates,y_ correspond to the amount of federal government transfers and tax income obtained by state _s_ in year _y_ and are calculated as ratios over the state yearly GDP. _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendy_ is a linear trend over time from the start of our sample in 2014 up to 2015. Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed <u>variable definitions are provided in Table A1 in the Appendix.</u>

||To<br>Expend<br>|tal<br>ituress,y<br>|Panel<br>Infrastruc<br>|A. Public<br>tures,y<br>|Expendit<br>So<br>Servi<br>|ures<br>cial<br>cess,y<br>|D<br>Servi<br>|ebt<br>cings,y<br>|Pan<br>Trans<br>|el B. Pu<br>ferss,y<br>|blic Reven<br>Tax<br>|ues<br>Rates,y<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Trendy|0.000||-0.001***||0.002*||0.000||0.001||0.002||
||(0.001)||(0.001)||(0.001)||(0.001)||(0.001)||(0.017)||
|Trendy*DebtStates,16Q1|-0.003|-0.003|-0.000|-0.000|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|0.018|0.018|
||(0.002)|(0.003)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|<br>(0.014)|(0.014)|
|Observations|90|90|90|90|90|90|90|90|90|90|90|90|
|R-squared|0.989|0.989|0.776|0.776|0.981|0.981|0.640|0.641|0.994|0.994|0.899|0.900|
|Macro Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

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# **The Impact of the 1999 Education Reform in Poland**<sup>**1**</sup>

Maciej Jakubowski Organisation for Economic Co-operation and Development (OECD) Harry Anthony Patrinos World Bank Emilio Ernesto Porta World Bank Jerzy Wiśniewski Center for Social and Economic Research (CASE), Poland JEL Classification Codes: I21, I28 Keywords: education, streaming, tracking, curriculum

>  We have benefited greatly from discussions with and comments from Mamta Murthi, Alberto Rodriguez, Joanna Sikora, Lars Sondergaard, and participants at seminars at the European Association of Labour Economists conference, PISA Research Conference and the World Bank.  The views expressed here are those of the authors and should not be attributed to the World Bank Group.  Address all correspondence to Harry Anthony Patrinos at <u>hpatrinos@worldbank.org.</u>
Giammateo’s study uses the Luxemburg Income Study (LIS) data for Poland, Hungary and Russia, and concludes that these changes led to an increase in inequality in these countries between 1990 and 2000. The study showed that Russia had the most unequal market and disposable income distribution, followed by Hungary and then Poland. The paper concludes that the redistribution policies in some countries played a key role during the transition period, allowing the government to contain inequality during the period of profound economic and social reforms. The inequality-decreasing effects of state transfers were robust and continued to be effective during the latter part of the 1990s, particularly in Poland and Hungary.<sup>3</sup>

> 3 Insignificant, and possibly perverse, effect of social transfers on inequality in Russia coincides with the earlier finding by Milanovic (1999, p. 316).

5
TABLE 7 Regression results for labor force participation rates, baseline (continued)

||**15-19 y**|**ears old**|**20-24 y**|**ears old**|**25-49 ye**|**ars old**|**50-64 ye**|**ars old**|**65+ ye**|**ars old**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|
|Completion of tertiary|||-0.127|0.153*|||||||
|education * EMDE|||(0.056)|(0.000)|||||||
|Life expectancy * EMDE|||||||||-0.143***<br>(0.000)|-0.608***<br>(0.000)|
|Cycle * EMDE|-17.90***|-24.21***||-11.72***|-1.456*|||||16.46|
||(0.000)|(0.000)||(0.000)|(0.038)|||||(0.526)|
|Cycle * life expectancy *<br>EMDE||||||||||0.039<br>(0.912)|
|Coefficient of fertility in<br>EMDEs|0.065***<br>(0.000)||-0.009<br>(0.234)||||||||
|Coefficient of secondary<br>enrollment in EMDEs|-0.133***<br>(0.000)||||||||||
|Coefficient of secondary|||-0.012|-0.058***|||||||
|education in EMDEs|||(0.570)|(0.000)|||||||
|Coefficient of tertiary<br>education in EMDEs|||0.031<br>(0.478)|-0.063<br>(0.189)|||||||
|Coefficient of cycle in|-0.145**|-2.78**||-0.18|0.048**||||||
|EMDE|(0.008)|(0.001)||(0.801)|(0.009)||||||
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Cohort fixed effects|No|No|No|No|No|No|Yes|Yes|Yes|Yes|
|County-cohort fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age fixed effects|No|No|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|4432|4484|3741|3789|21382|21654|12239|12261|5111|5111|
|Number of countries|163|165|151|154|158|160|145|145|168|168|
|Adjusted R-square|0.997|0.997|0.999|0.999|0.997|0.999|0.986|0.993|0.998|0.999|

Source: Barro and Lee 2013; Key Indicators of the Labor Market (KILM), International Labour Organization; Labour Force Statistics, Organisation for Economic Co-operation and Development (OECD); UN Population Prospects; World Development Indicators, World Bank; and World Bank staff estimations.

Note: Business cycles defined as deviation of real GDP from Hodrick-Prescott-filtered trend. Sample includes unbalanced panel of 35 advanced economies and 133 EMDEs for 1987-2020. p-statistics are shown in parentheses.

52
averages because we assume that world averages of governance are zero in each period, as a choice of units. While the aggregate indicators are of course informative about the relative performance of individual (or groups of) countries, in order to assess trends in global governance we need to return to our underlying individual data sources.

In Table 5 we summarize trends in world averages in a number of our individual data sources. Most of the sources in this table are polls of experts, with data extending over the whole period 1996-2005. Other than expert polls, only one of them, GCS, is a survey with sufficiently standard format to enable comparisons over this period of time. The first column reports the number of countries covered by the source in each of the periods shown, and the next five columns present the average across all countries of each of the sources in each of the indicated years. The underlying data have been rescaled to run from zero to one, and for each source and governance component, we report the score on the same question or average of questions that we use in the aggregate indicator. The next five columns report the standard deviation across countries for each source. The final columns reports t-statistics associated with a test of the null hypothesis that the world average score is the same in 1996 as in 2005, in 1998 as in 2005, and in 2002 as in 2005.

The picture that emerges from Table 5 is sobering. There is very little evidence of statistically significant improvements in governance worldwide. Over the longest period 1996-2005 the 18 changes reported here are divided exactly in half into 9 improvements and 9 declines in global averages, many of them quite small. There are just six cases of statistically significant changes at the 10 percent level or better (tstatistics greater than 1.64 in absolute value), and these are split between two improvements and four declines. It is not clear how much importance ought to be ascribed to these trends in world averages based on individual indicators. On the one hand, these statistics represent the only information we have on trends over time, and so they should be taken seriously. On the other hand, it is also clear that there is substantial disagreement among sources about even the direction of changes in global averages of governance. For now we cautiously conclude that we certainly do not have any evidence of any significant improvement in governance worldwide until end-2005, and if anything the evidence is suggestive of a possible deterioration - at the very least in dimensions such as regulatory quality, rule of law, and control of corruption.

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resistant service sectors and the quality adjustments made in the calculation of PPPs in a number of selected areas.

b) Based on the impact of inflation in the United States on the purchasing power parity and the growth rates computed from national currency GDP data in constant price for China, China's per capita GDP in 1991 turns out to be between 1S1227 and IS1,663.

3. These estimates are compared with the figures of the U.S., Germany, India and Japan, as shown in Table 1. These figures are comparable since all figures are estimated in the same framework (ICP). But there are two points which should be considered when one wants to use these figures to discuss the difbrent issues. One is that this study is a bilateral comparison, and all other figures are from the ICP bench k studies which are multlateral comparisons, The other is that some adjustments (oth quality adjusmnt and institutional adjustment) have been made in this study which are not the nonnal procedure in ICP comparison.

Table 1: PER CAPITA AND AGGREGATE GDP OF CHINA, U.S., GERMANY, INDIA AND JAPAN IN 1991

||Per Capit|a GDP|Aggrege|GDP|Average Growth Rate of<br>AggMgae GDP in|
|---|---|---|---|---|---|
||L___|SUS=100|BillionS|US=100|1980-1991 (%/0)|
|U.S.|22,130|100.00|5,592.0|100.00|2.5|
|Jaan|19,390|87.62|2,402.8|42.97|4.3|
|(Germany|19,770|89.34|1,584.2|28.33|2.4|
|China (ih)|1,663|7.51|1,909.1|34.14|9.4|
|(low)|1,227|5.54|1,408.6|25.19||
|India|1,150|5.20|996.5|17.82|5.4|

Note: The growth rate figures which use 1987 as base year are taken firm the World Bank data bae. China's figures are firm this study. Other counties' figures are ICP esumates which are m intenational dollars. Except Iniabs figures, which are extrapolated from 1985 bencbiark estimates, all figures are extrapolated from 1990 benchmark estimates. Germany's figures refer to the Federal Republic of Germany before unification.

2
list typically includes 8 to 20 countries which can be thought of as belonging to three groups: (i) most popular FDI destinations in the world, (ii) countries close to the existing operations of the investor, and (iii) emerging FDI destinations (that is, countries that the investor may not be initially very serious about but which represent “out of the box” thinking). The inclusion of the third category presents an opportunity for IPAs. The potential investor or the consulting firm working on its behalf is likely to include in the third group countries whose advertisements they have recently seen in international media, countries whose IPAs have recently approached them or their colleagues, or countries whose IPA representatives they have met at conferences and industry fairs.<sup>6</sup> Based on the trade-off between costs and the quality of business environment, the long list is narrowed down to a short list of up to 5 potential host countries. This is usually done without visiting the potential host countries, so the accessibility of the information about the business conditions in a host country plays a crucial role. IPAs that provide up-to-date, detailed and accurate data on their websites and IPAs that are willing to spend time preparing detailed answers to investors’ inquiries and customize these answers to the needs of an individual investor can increase the chances of their countries being included in the short list.

The next step in the decision-making process involves visiting the countries included in the short list. This can be done by the potential investor, consultants or both. Multiple sites in each country may be visited. A visit often involves interactions with an IPA which has the opportunity to emphasize the advantages of locating in its country, answer questions, show executives potential investment sites or introduce them to potential local business partners.

In the final stage of the process, the foreign investor chooses an investment location based on the availability of potential sites, costs, the overall quality of business climate and availability of incentives. An IPA can assist in providing information on incentives and offering help with the registration process.

> 6 For instance, the Polish IPA believes that TV advertising spots increased the number of visitors to its website by 43 percent in 2006 (source: _Dziennik online_ . “Wielka promocja rozpoczeta. Polska jak proszek do prania” December 29, 2006.  http://www.dziennik.pl/Default.aspx?TabId=97&ShowArticleId=26406).

8
In addition to the lack of interest payments on small saving deposits in Kwacha, most commercial banks in Zambia have adopted high balances to open or maintain an account, making it impossible for most people to have a savings or deposit account. As illustrated in the following table, five banks require customers a minimum balance of $16 to $78 dollars to open a savings accounts. In a group of five other banks, the minimum balance to open an account ranges from $156 to $313 dollars. In the context of Zambia, where 71% of the population lives on less than one dollar a day, the minimum balances currently required by banks are high and prevent most people from having access to basic banking services.

**Table 2. Minimum Balances to Open and Maintain a Bank Account in March 2006**

|**BANK**|**Account Openi**|**ng Balance**|**Minimum balance**<br>**penalty**|**to avoid**|
|---|---|---|---|---|
||Kwacha|US$|Kwacha|US$|
|African BankingCorporation Zambia|500,000|$ 156|500,000|$156|
|Bank of China|1,000,000|$313|1,000,000|$313|
|Barclays Bank|N/A|N/A|N/A|N/A|
|Cavmont Capital|1,000,000|$ 313|N/A|N/A|
|Citibank|N/A|N/A|N/A|N/A|
|Finance Bank|100,000|$31|50,000|$16|
|First Alliance Bank|250,000|$78|250,000|$78|
|Indo Zambia Bank|150,000|$ 47|250,000|$78|
|Intermarket BankingCorporation|1,000,000|$ 313|250,000|$78|
|Investrust|50,000|$ 16|50,000|$16|
|Stanbic Bank|50,000|$ 16|N/A|N/A|
|Standard Chartered|750,000|$ 234|750,000|$234|
|Zambia National Commercial Bank|N/A|N/A|N/A|N/A|

Source: Bank of Zambia (2006) Interviews with bankers revealed that at this time most banks in Zambia do not seem to be interested in serving the low-income part of the population. Most banks prefer to target only a small universe of the population, approximately 500,000 people with medium to high level incomes, which is defined as the “bankable” group by the bankers themselves. Besides commercial banks, there are no other types of financial institutions offering savings and deposits in Zambia, which could serve the needs of low-income households not served by commercial banks. By law, the approximately 50 microfinance institutions that operated in Zambia in 2005 were not allowed to take deposits from the public.

# **1.2 Bank branches in rural areas**

The limited access to banking services in Zambia is reflected in the low number of bank branches serving both urban and rural areas. While in 1990 there were 120 bank branches, in 2005 there were only 152, giving a ratio of one bank branch per 70,000 people in 2005, one of the lowest bank penetration ratios in the world.

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_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_ But wage setting might not solely depend on worker’s ability. Indeed, the empirical literature has found ample evidence of a strong link between firm characteristics and worker wages (e.g., Krueger and Summers, 1988; Blanchflower _et al._ , 1996, Abowd _et al._ , 1999, Söderbom et al., 2004), suggesting that observable proxies of worker ability do not explain the entire wage setting process. Several recent studies use 1990s manufacturing data from the Regional Program for Enterprise Development (RPED) to analyze wages in African manufacturing, finding evidence of rent-sharing (e.g., Teal, 1996; Azam and Ris, 2001; Alby, 2003, 2007; Söderbom and Teal, 2001; Söderbom et al., 2004), efficiency wages (Azam and Lesueur, 1997; Söderbom et al., 2004; Mazumdar and Mazaheri, 2002), inefficient labor management (Fafchamps and Söderbom, 2006), job sorting (Benhassine _et al_ , 2006), as well as a role of unions in wage setting (Manda, _et al.,_ 2001; Alby, 2007), which is another form of rentsharing.<sup>6</sup> Interestingly, the evidence also suggests that the unexplained gap between earnings and productivity tends to narrow in relatively more advanced countries (Van Biesebroeck, 2007).

<!-- Start of picture text --> Figure 2<br><!-- End of picture text --> <!-- Start of picture text --> Doing Business Rankings<br>103.7<br>General rank 158.8<br>116.2<br>Entry regulation rank 128.5<br>111.4<br>Licensing regulation rank 140.0<br>Labor regulation rank 83.4 131.3<br>123.5<br>Land registration rank 134.0<br>109.7<br>Access to credit rank 139.4<br>Investor Protection rank 87.2 122.1<br>71.0<br>Taxation regulation rank 133.2<br>Trade regulation rank 114.8 133.2<br>74.2<br>Contract enforcement rank 141.8<br>73.5<br>Bankruptcy regulation rank 140.3<br>Common Law (Anglophone) Civil Law (Francophone)<br>Source: Doing Business. Higher ranks denote more stringent regulations. The worst score is 178.<br><!-- End of picture text --> Labor regulations, in particular hiring and firing costs might also play a role in the process of upgrading the labor force to add more skilled labor. Increasing evidence in the literature suggests that the legal origin of a country has a significant impact on several measures of institutional quality,

> 6 For a complete review of papers that use this data, see Bigsten and Söderbom (2006).

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- 13 -

**Figure 8. Average monthly precipitation for the past 10 years**

<!-- Start of picture text --> 1200<br>Siraha<br>Makwanpur<br>1000 Banke<br>800<br>600<br>400<br>200<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>Monthly precipitation (mm)<br><!-- End of picture text --> Source: Authors’ calculation based on data from the Ministry of Environment, Nepal.

<!-- Start of picture text --> Figure 9. Average monthly precipitation<br>Average monthly precipitation<br>in 2000-2009 (mm)<br>73<br>74 - 136<br>137 - 173<br>174 - 208<br>209 - 329<br>330 - 426<br><!-- End of picture text --> <!-- Start of picture text --> .<br><!-- End of picture text --> Given these backgrounds, road procurement costs vary significantly across districts, though the absolute levels remain low by global standards. The average unit cost of road upgrading is about NRs1.6 million or $23,000 per km (Table 1), which is far below the range of unit costs supported by the literature.<sup>6</sup> This is mainly because of the project specifications. Our sampled roads are mostly unpaved, gravel roads with simple surface treatment, if any. If an additional surface material, such as otta seals, is added, the project cost increases by $3,0004,000 per km. Still, it is relatively cheap.

Normalized bids, which are bid prices relative to engineering cost estimates, vary from 0.55 in Dhanusa to 1.05 in Syangja (Figure 10). Normalized bids are a good indicator to incorporate unobserved heterogeneity in road specifications. The average normalized bid is

> 6 An estimate of unit cost is about $410,000 per km (Fay and Yepes, 2003). Another is $390,000 for rehabilitation and $360,000 for upgrading in Africa (Alexeeva, Padam and Queiroz, 2008). Foster and BricenoGarmendia (2010) also estimate the road rehabilitation cost at $200,000 to $500,000 per km, depending on country and the scale of roads.
# **E.9 Extending Panel to August 2014**

**Table E.9:** Effect on Reported Cases in Extended Panel (August 2014–April 2015)

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Total|0.257|0.163|0.189|0.142|0.048|6,079|
||(0.706)|(0.077)**|(0.106)*|(0.091)|(0.122)||
|Confrmed|0.014|0.058|0.079|0.041|0.038|6,079|
||(0.146)|(0.022)**|(0.034)**|(0.025)*|(0.038)||
|Negative|0.216<br>(0.621)|0.092<br>(0.055)*|0.074<br>(0.07)|0.105<br>(0.068)|-0.031<br>(0.084)|6,079|
|IHS(Ebola Cases)|||||||
|Total|0.189|0.078|0.088|0.07|0.018|6,079|
||(0.454)|(0.039)**|(0.052)*|(0.046)|(0.059)||
|Confrmed|0.011|0.028|0.032|0.025|0.007|6,079|
||(0.109)|(0.01)***|(0.014)**|(0.011)**|(0.016)||
|Negative|0.163|0.054|0.048|0.058|-0.01|6,079|
||(0.415)|(0.032)*|(0.04)|(0.039)|(0.047)||

_Notes_ : Standard errors clustered on section. We drop a single outlying observation from Konjo Njeigor for the week of August 24, 2014, which is 25 times larger than any other weekly total from that section and 5 times larger than any other observation in the full time-series. Konjo Njeigor is a CM section, so removing this observation only depresses the pooled and CM treatment effects. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

A41
assume that households allocate their available labour-time to production as much as possible. The representative household is assumed to supply one unit of labour inelastically.

Both Ricardian and liquidity-constrained households have identical preferences defined over a single commodity, where _Cti_ denotes total consumption of the household of type _i_ . Ricardian households are indexed as _R_ and liquidity-constrained households as _L_ . A Ricardian household maximises discounted stream of utility, subject to the following budget constraint, where β ∈ (0,1) denotes the subjective discount factor. Here _CtR_ is total consumption of the Ricardian household in period _t_ . The variables<sup>_I_</sup> _tR_<sup>and</sup> _KtR_ denote investment and capital stock of the household, respectively. The economywide return to capital and wage rate are given by<sup>_R_</sup> _t_<sup>and</sup> _Wt_ . In each period, the Ricardian household divides her disposable income, comprised of wage and rental income, into consumption and savings.

The stock of capital of the representative Ricardian household evolves via the following law of motion,

> informal sectors, only 28 million people (6% of the total employment) are employed in the formal sector, while 437 million workers (94% of the total employment) are in the informal sector (National Sample Survey Organisation 2009–10), (http://labour.gov.in/content/aboutus/about-ministry.php). Data on hours worked are not officially published in India. The officially published employment data captures the employment scenario in the formal sector, which constitutes only 6% of the total employment.

12