rafmacalaba/datause-encoder-data huggingface dataset:
improve statistics, including by generating statistics that conform to the EU’s standards (for Excessive Deficit Procedure and ESA2010). But for the moment information on, for example, government assets and non-debt liabilities is usually inadequate, and accrualbased measures of the deficit are unavailable. Good information tends not to be routinely available on accounts payables, arrears (overdue accounts payable), nonfinancial assets, pensions liabilities, and obligations in concessions and other public-private partnerships.

- _Forecasting_ . In at least some countries, economic and fiscal forecasting has had an optimistic bias,<sup>15</sup> though this problem may be less acute than before. Albania, Serbia, and Republika Srpska have taken steps to solve this problem by using external forecasts or review by an independent fiscal council.

- _Medium-term planning._ Similarly, although medium-term spending plans are prepared, they do not always have the influence they are intended to have in the preparation of annual budgets. Possible reasons for this include optimistic revenue forecasts, which encourage spending plans that are themselves unrealistic; investment plans that cannot always be implemented; the major shocks created by the global financial crisis; and a widespread belief that medium-term spending plans will not in the end matter much and are therefore not worth preparing very carefully.

**Quantitative indicators paint a similarly mixed picture** . Scores on the Open Budget Index— which run from 0 to 100, where 100 is ideal—range from 35 in Bosnia and Herzegovina to 50 in Albania, with no scores available for Kosovo or Montenegro (Table 6). Scores for the comprehensiveness of government-finance statistics submitted to the IMF by 2013, which also run from 0 to 100, range from 5 in Montenegro to 21 in Serbia, with no score available for Kosovo. Recent Fiscal Transparency Evaluations for Albania and for North Macedonia do not result in numerical scores, but suggest moderate performance. PEFA scores for expenditure outturns, which indicate the extent to which aggregate on-budget spending is controlled, are good, but scores for medium-term fiscal planning, expenditure arrears, and the extent of centralgovernment operations outside financial reports are mostly poor, Kosovo’s scores being an exception.

**Table 6. Selected Indicators of the Quality of Supporting Aspects of Public Fiscal Management**

||**Open**<br>**Budget**<br>**Index**<br>**2017**|**GFS**<br>**submitted**<br>**to IMF**|**Medium-**<br>**Term Fiscal**<br>**Planning**<br>**(PEFA)**|**Expenditure**<br>**Outturn**<br>**(PEFA)**|**Expenditure**<br>**arrears**<br>**(PEFA)**|**Operations**<br>**outside**<br>**financial**<br>**reports**<br>**(PEFA)**|
|---|---|---|---|---|---|---|
|Albania|50|16|C+|A|C+|D|
|Bosnia and Herzegovina|35|13|||||
|Federation of B and H|||D+|A||D+|

15 For Albania, for example, see, for example, World Bank (2014, ch. 5) and IMF (2015). For Serbia, see World Bank (2015). For Montenegro, see World Bank (2019).

28
better management practices, and this occurs simultaneously with the decentralization of decisions.

Finally, recently a number of other field experiments in developing countries (for example Karlan and Valdivia 2010, Bruhn et al. 2010 and Drexler et al. 2010) have begun to estimate the impact of basic business training and advice in micro- and small enterprises. This research has found significant effects of some forms of training on performance in smaller firms, supporting our results on in larger firms.

# **II. MANAGEMENT IN THE INDIAN TEXTILE INDUSTRY**

## **_II.A. Why work with firms in the Indian textile industry?_**

Despite rapid growth over the past decade, India’s one billion people still have labor productivity that is only 15 percent of U.S. productivity (McKinsey Global Institute, 2001). While average productivity is low, most notable is the large variation in productivity, with a few highly productive firms and a lot of low-productivity firms (Hsieh and Klenow, 2009).

In common with other developing countries for which data is available, Indian firms are also typically poorly managed. Evidence from this is seen in Figure 1, which plots results from the Bloom and Van Reenen (2010) surveys of manufacturing firms in the US and India. The Bloom and Van Reenen (BVR) methodology scores firms from 1 (worst practices) to 5 (best practices) on specific management practices related to monitoring, targets and incentives. Aggregating yields a basic measure of the use of modern management practices that is strongly correlated with a wide range of firm performance measures, like productivity, profitability and growth. The top panel of Figure 1 plots these management practice scores for a sample of 751 randomly chosen US manufacturing firms with 100 to 5000 employees and the second panel for similarly sized Indian ones. The results reveal a thick tail of badly run Indian firms, leading to a lower average management score (2.69 for India versus 3.33 for US firms). Indian firms tend not to collect and analyze data systematically in their factories, they tend not to set and monitor clear targets for performance, and they do not explicitly link pay or promotion with performance. The scores for Brazil and China in the third panel, with an average of 2.67, are similar, suggesting that Indian firms are broadly representative of large firms in emerging economies.

In order to implement a common set of management practices across firms and measure a common set of outcomes, we focus on one industry. We chose textile production since it is the 4
estimated that to meet these needs, East Asia will need to increase its investment in infrastructure from about 5 percent of GDP currently to 7-8 percent over the next ten years. This translates into total infrastructure investment of some $1.5 trillion over 1995-2004, with annual investment rising from about $100 billion in 1995 to more than $200 billion by 2004 (at 1994 prices).<sup>8</sup> Whereas in the past these countries relied heavily on the public sector and foreign funding, they are increasingly switching their infrastructure investment toward the private sector and domestic financing. The development of local capital markets is one way in which these countries are encouraging the mobilization of domestic resources. Of the total private sector investment in East Asia over the next ten years, only about 4 percent is expected to rely on foreign financing.<sup>9</sup> _Financing the Balance._ The net claim developing countries make on industrial countries' savings equals their current account deficit-the excess of their gross domestic investment over their gross national saving-plus the net change in their external reserves.'<sup>0</sup> Experience shows that, in general, current account deficits in developing countries substantially in excess of about 2 percent of GDP on a continuing basis are unlikely, and inconsistent with sustainable external financial positions in the long run." Where persistent large imbalances are not corrected by domestic adjustment, international financial markets can be

> a World Bank (1995c); Kondury (1994).

> _9_ World Bank (1995c). This study of the development prospects of East Asia's domestic capital markets finds these markets to be playing a much larger role in coming years in financing the region's investment requirements. It estimates, for example, that the East Asian bond market will grow from about $340 billion in 1994 to more than $1 trillion by 2004. World Bank (1994b) documents the increasing role of the private sector in infratrcture investment in developing countries.

> 0 Abstracting from transactions included in net errors and omissions.

> As some recent studies of the differential experience of Latin American and East Asian countries with large capital inflows have found, external deficits used mainly to finance investment (as in much of East Asia) tend to be more sustainable in the longer-run than those used largely to finance consumption (as in several Latin American countries in recent years). See Corbo and Hernandez (1994) and World Bank (1995b).

13
||**Table**|**5: Bank con**|**centration**||||
|---|---|---|---|---|---|---|
|Dependent variable: Woman top|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|manager Y:1 N:0|**Pan**|**el A: Log od**|**ds ratios**||||
|Bank credit (ratio of GDP)*5-|0.061***|0.063***|0.078***|0.094***|0.067**|0.067**|
|bank asset concentration|(0.016)|(0.015)|(0.028)|(0.029)|(0.032)|(0.033)|
|Bank credit (ratio of GDP)|-2.541*|-2.457*|-2.561|-4.124*|-1.606|28.846**|
||(1.339)|(1.264)|(2.333)|(2.483)|(2.542)|(13.169)|
|5-bank asset concentration|-0.019**|-0.017**|-0.011|-0.022|0.002|0.007|
||(0.009)|(0.008)|(0.013)|(0.015)|(0.019)|(0.020)|
|GDP per capita (logs)||-0.855|-1.854*|-1.460|-1.690|-2.029*|
|||(0.792)|(1.064)|(1.112)|(1.145)|(1.187)|
|Number of workers (logs)||-0.205***|-0.230***|-0.198***|-0.195***|0.007|
|||(0.050)|(0.064)|(0.063)|(0.064)|(0.154)|
|Age of firm (logs)|||-0.066|-0.088|-0.088|-0.334|
||||(0.110)|(0.115)|(0.115)|(0.310)|
|Labor productivity (logs)|||-0.024|0.001|-0.023|0.089|
||||(0.047)|(0.049)|(0.046)|(0.097)|
|Women owners Y:1 N:0|||2.595***|2.692***|2.698***|2.714***|
||||(0.140)|(0.153)|(0.154)|(0.156)|
|Women workers (proportion)|||1.472***|1.474***|1.511***|1.523***|
||||(0.216)|(0.214)|(0.217)|(0.218)|
|Exports (direct, share of sales)|||-0.104|-0.118|-0.118|-0.089|
||||(0.252)|(0.249)|(0.248)|(0.247)|
|Foreign ownership|||0.097|0.132|0.151|0.145|
||||(0.257)|(0.255)|(0.255)|(0.253)|
|Quality certification Y:1 N:0|||0.055|0.085|0.084|0.095|
||||(0.139)|(0.139)|(0.138)|(0.138)|
|Overdraft Y:1 N:0||||-0.131|-0.133|-0.136|
|||||(0.127)|(0.127)|(0.126)|
|Multi establishment firm Y:1 N:0||||0.082|0.072|0.069|
|||||(0.181)|(0.183)|(0.181)|
|Obtaining license is a major||||<br>0.074|<br>0.068|<br>0.069|
|obstacle Y:1 N:0||||(0.195)|(0.197)|(0.197)|
|Losses from power outages||||1.453*|1.638**|1.789**|
|||||(0.822)|(0.796)|(0.812)|
|Firm audited Y:1 N:0||||<br>-0.091|<br>-0.094|<br>-0.085|
|||||(0.120)|(0.121)|(0.120)|
|Registered when started Y:1 N:0||||0.051|0.035|-0.005|
|||||(0.217)|(0.217)|(0.214)|
|Legal form fixed effects||||Yes|Yes|Yes|
|Visited by tax officials Y:1 N:0||||-0.043|-0.043|-0.050|

44
<!-- Start of picture text --> a<br>8<br>?<br>6<br>5<br>4<br>2<br>a | | | [ | | | [ |<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>mEnactment m Failure<br>70%<br>Ox<br>50%<br>40%<br>30%<br>20%<br>- | | [<br>0% i<br>ito3 4to6 7tod 10 to 12 itis<br>Allregions Europe &CentralAsia @LatinAmerica& Caribbean wm SouthAsia wm Sub-Saharan Africa<br><!-- End of picture text -->
_5_ that current growth rates are much higher than growth rates in the far distant past<sup>5</sup> . Kremer (1993) provides empirical support for the proposition that, at least until the 1950s, more rapid world population growth has been associated with more, not less, rapid growth in world per capita output.

Third, emining the post-war experience of the developed and developing counties, numerous studies have found no consistent, statistically significant, correlation (or pardtal correlation) between GDP per capita growth and population growth. Literature surveys by Kelley (1988), Birdsall (1988) or the National Academy of Science (1986) typically concludc on an ambivalent note that the empirical evidence is not clear<sup>7</sup> . Kelley's (1988) statement, which fails to exclude any possibility for the impact of population growth (negative, zero, or positive), **_.D._** is typical: . a bottom line assessment can be offered. Economic growth (as measured by per capita output) in many developing countries would have been more rapid in an 5 Although there is some debate about establishing a "subsistence" level of income, some level of per capita output is necessary for demographic sustainability- The power of compound interest is such that, if poor countries had been growmg over the "long term" at anyding like the rates of the past fity years they would be immensely richer than they actually are. That is, if output had grown by vea 1 percent per capita for 200 years output would have increased seven-fold. But if the calculations of the "poverty level" _for_ the 1990 World Development report are anywhere near accurate epresetation of a bare miTnm level, most of the low income counties were more like 2 or 3 time the bare minimum in 1960. This implies **that** past growth, on average, must have been considerably slower than 1 percent per annum. Since growth in the post-war period in developing countries has been roughly 2 percent per annum, current growth is faster than historical growth by a multiple of at least three or four.

6 Kremer, in the whimsically tided "Population growth and technological change: One milhon B.C. to the present" extends an argument of Kuzaets (1960) about the essentally "public good" (e.g. non-nvalrous) nature of knowledge with the assumption that the producdon function of knowledge is cnstant returns with respect to population (more particularly, the educated population) to argue that worldwide technical ciange will increase with increased populaLtion 7 Even in the United Nations Population Fund's annual report State of the World Powulation.-1992 (an inelly source for ambivalence on the negative inpact of population growth) one finds the assertion 'there was no correlation, negative or positive betwee economic and population growth" in the period up to 1980 (pg 8) (although coupled with a statement that this wasn't tme over the 19809 period).
**Figure 4. Housing Expenditures by Country and Quintile, in Ascending Order of GDP per Capita—Urban Areas**

<!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV<br>NER ETH MWI LBR RWA UGA MOZ SLE TZA BFA KEN COM GHA ZMB SEN STP CIV CMR NGA AGO<br>housing expenditure as % of total<br><!-- End of picture text -->

**Figure 5. Transportation Expenditures by Country and Quintile, in Ascending Order of GDP per Capita—Urban Areas**

<!-- Start of picture text --> 20%<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV I III V II IV<br>NER ETH MWI LBR RWA UGA MOZ SLE TZA BFA KEN COM GHA ZMB SEN STP CIV CMR NGA AGO<br>transportation expenditures as % of total<br><!-- End of picture text -->

# **2.3 Housing expenditures in SSA countries are indicative of their stage of development**

An examination of benchmarks on expenditures reveals that the explanation for the lack of consistent variation in housing expenditures across quintiles may lie in the level of development of the Sub-Saharan African countries examined. In a series of reports by the United States’ Department of Agriculture (USDA), cross-country analysis of food expenditures—one of the best documented shares of expenditures across countries—reveals that lower income countries tend to spend more on food as a percentage of their 7
**Table** **<u>5: Impact of reform on land transfers</u>**

||**Construction**|**For**|**mal**|**Agricu**|**ltural**<br>**Inf**<br>|**ormal**<br>||
|---|---|---|---|---|---|---|---|
||**any rent**|**any rent**|**any rent**|**any rent**|**area %**|**rent out %**|**rent in%**|
|**Panel A**||||||||
|Chengdu*After|0.018**|0.071***|0.049***|0.068***|0.007|0.010**|0.008**|
||(0.009)|(0.019)|(0.015)|(0.022)|(0.005)|(0.005)|(0.003)|
|Observations|4,186|4,186|4,186|2,094|2,040|2,080|1,944|
|R-squared|0.004|0.010|0.010|0.020|0.016|0.026|0.022|
|**Panel B**||||||||
|Chengdu*After|-0.006|0.035***|0.026**|0.006|-0.000|-0.003|0.001|
||(0.007)|(0.010)|(0.010)|(0.015)|(0.003)|(0.003)|(0.001)|
|Observations|2,095|2,095|2,095|1,049|1,031|1,042|991|
|R-squared|0.006|0.015|0.023|0.020|0.015|0.051|0.049|
|**Panel C**||||||||
|Chengdu*After|0.013|0.067***|0.052***|0.061***|0.008|0.010*|0.007**|
||(0.010)|(0.021)|(0.017)|(0.023)|(0.006)|(0.005)|(0.003)|
|Chengdu*After|-0.000|0.004|-0.001|-0.011|0.006|0.005|0.005**|
|*Time|(0.009)|(0.018)|(0.015)|(0.017)|(0.005)|(0.004)|(0.002)|
|Observations|4,186|4,186|4,186|2,094|2,040|2,080|1,944|
|R-squared|0.004|0.010|0.010|0.020|0.017|0.026|0.024|
|Spatial fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Spatial fixed effects and village fixed effects are included throughout. Village time-variant characteristics are controlled for throughout but not reported. These include total area and population, share of the population with access to pension and health schemes as well as agricultural subsidy, distance to the nearest county road, surface of the village road, and indicator variables for primary schools and medical stations within the village. Time is z score of google travelling time from a village to the center of Chengdu ( _μ_ =1.43h, _σ_ =0.40h). Standard errors adjusted for spatial correlation and serial correlation are in parentheses. If spatial fixed effects are included, otherwise they are adjusted for serial correlation within the village. *** p<0.01, ** p<0.05, * p<0.1.

21
resources for their states, however, depends at least in part on their ties to the president. The emphasis is on membership in either the President’s party or coalition on the part of Senate delegations from each state. For each state’s senate delegation we incorporate a measure of the proportion of the federal senators from each state that are members of the PSDB, and similarly the proportion that are members of parties included in the president’s coalition. The relationship between these measures of political closeness to the Cardoso government and PROES decision, however, is not unambiguous. On the one hand, one could expect states whose senators are politically closer to the federal government are better able to negotiate to keep their banks or at least privatize themselves. On the other hand, one could expect states whose leaders were more closely tied to the ruling coalition in the federal government to more easily agree to liquidation or federalization in return for other political or fiscal benefits.

We assess the explanatory power of political economy and developmental theories of government-owned banks by regressing _Transformation_ on an array of economic, social and bank-level financial indicators.<sup>30</sup> Given the ordinal character of _Transformation_ , we employ an ordered probit model.<sup>31</sup> We use data bank-level data for 1996 and state-level averaged over 1995 to 2000, assembled from different Brazilian data sources.<sup>32</sup>

> 30 We do not include the two states without state banks and the Federal District, which opted out of the PROES, and did not privatize its bank. We do, however, include Paraiba, since it privatized its state bank, though outside the PROES; our results, however, are robust to its exclusion. We also include São Paulo as a case where bank privatization happened under the auspices of the federal government.  For the regressions including bank-level performance indicators as explanatory variables, we lose observations since we do not have observations for most of the liquidated banks.  In the cases where we have bank-level data for more than one state bank, we average these data for use in the ordered probit analysis.

> 31 While the observed variable _Transformation_ has only four outcomes, there is a continuous underlying unobserved variable, the political preference of state governments.  Each outcome of _Transformation_ corresponds to a specific range of political preferences.  We use standard maximum likelihood estimation with heteroskedasticity-robust standard errors.  In robustness tests, we also use the multinominal logit model, which yields the same results.

> 32 The political, economic, and financial variables used here, and in subsequent sections, are elaborated in the paper’s appendices.

14
Table 4: Summary of Policy Interventions

|**REFORMS**|**DESCRIPTION**|
|---|---|
|Reform 1|Pensions benefit formulas are unified. Eligibility age for<br>pension is fixed at age 55. There is no vesting period and<br>no minimum pension guarantee. No changes in UI and<br>FGTS.|
|Reform 2|Like Reform 1 but a minimum pension guarantee equal to<br>42% of economy wide average earnings is offered at age 55<br>as a top-up (100%marginaltax).|
|Reform 3|Like Reform 2 but the minimum pension guarantee is only<br>offered at age 65.|
|Reform 4|Like Reform 3 but the minimum pension is offered as a flat<br>rate (0%marginaltax).|
|Reform 5|Like Reform 1 but a matching contribution equivalent to 75%<br>of the total contribution is offered and financed by the<br>government. Two retirement ages are explored: 55 and 65.|
|Reform 6|Like Reform 5 but the matching contribution is equivalent to<br>2.25 times the total contribution. Two retirement ages are<br>also explored:55 and 65.|

_Source:_ Authors.

We first analyze the case where the pension formula is unified, the eligibility age becomes 55, the vesting period is eliminated, and there is no minimum pension (see **Figure 11** ). This scenario tells us what would happen in a pension system that is actuarially fair (and financially sustainable) and where there are no restrictions on retirement.

We observe that for the average earner the results are similar to the case where the pension system is eliminated. In essence, relative to the status-quo, these individuals lose subsidies to retire late. Their optimal reaction is therefore to reduce retirement ages and increase contribution densities (and savings). Relative to the case with no pension system ( **Figure 8** ) there is a small increase in contribution densities and retirement ages. Basically, for average earners, adding an actuarially fair pension system where the employer matches the contribution rate can increase marginally the time individuals spend in formal sector work. And, in most cases, there are no major impacts on the retirement age, although one can observe a few cases where the retirement age increases (waiting to retire increases the value of the pension) and others where it decreases (pensions allow individuals to retire early).<sup>14</sup> For low income workers the results relative to the status-quo are also similar to the case with no pension system: retirement ages and contribution densities can increase or decrease depending on individual preferences. When we compare the results to the case without pension system we also see that retirement ages and contribution densities can go up or down. The

> 14 Although not shown, in most cases, the actuarially fair pension system with a matching contribution from employers would decrease savings.

29
_Page 5_ Figure 3: Total Fertility Rate <!-- Start of picture text --> 5 ~_<br>4 -<br>2<br>0<br>Brazil Mex Venez China Korea Taiw Greece Italy Sweden Lat Amer Asia Europe<br>Source: World Bank/UNESCO<br><!-- End of picture text --> Figure 4: Female Labor Force Participation <!-- Start of picture text --> 50<br>40<br>20 -<br>10<br>0-<br>Brazil Col Peru China Talw Thall Fin Nor Port Lat Amer Asia Europe<br><!-- End of picture text --> Source: World Bank/UNESCO
**Figure 1  Net Inter-provincial Migration by Regions/Major Cities** (1,000 individuals)

<!-- Start of picture text --> 1000<br>800<br>600<br>400<br>200<br>1994-1999<br>2004-2009<br>0<br>Hanoi Red River Northeast North and Central HCMC Southeast Mekong<br>Delta and South Highlands River Delta<br>-200 Northwest Central<br>Coasts<br>-400<br>-600<br>-800<br><!-- End of picture text --> Sources: GSO, Table A-2.12, 2011 for the period 2004-2009; GSO/UNDP, 2001 for the period 1994-1999. Notes: The Red River Delta excludes Hanoi.

The Southeast region excludes HCMC.

**Figure 2  Inter-provincial Migration by Gender** (1,000 individuals)

<!-- Start of picture text --> 2000<br>1800<br>1600<br>1400<br>1200<br>1000 Male<br>Female<br>800<br>600<br>400<br>200<br>0<br>1984-1989 1994-1999 2004-2009<br><!-- End of picture text --> Source: GSO, p.21, 2011.