rafmacalaba/datause-encoder-data huggingface dataset:
model’s assumption of lower (indirect) costs after the reform. Second, under the old law, we observe that firms filing for reorganization are indistinguishable from those filing for liquidation based on several measures of financial health. In contrast, relatively healthier (and hence more viable) firms are more likely to file for reorganization after the new law, producing a clear separation in the distribution of the two types of firms. Finally, we find that the level of recovery after the reorganization episode is significantly improved under the new law. While under the old law firms hovered at about the same low level of financial health for years after entering into reorganization, we observe a clear recovery under the new law. We conclude that the new law increased the efficiency of the bankruptcy system in Colombia.

Previous bankruptcy studies analyze the existing laws of a given country or make comparisons across different countries. Evidence from the US Chapter 11 reorganizations offers mixed conclusions on the magnitude of these costs. While Altman (1984) and Hotchkiss (1995) among others find high Chapter 11 costs, Alderson and Betker (1995) and Maksimovic and Phillips (1998) find them to be low. More recently, Bris et al. (2005) show that reorganization costs are heterogeneous across firms but comparable to US Chapter 7 liquidations costs, although reorganizations as compared to liquidations preserve the assets better. In contrast, Thorburn (2000) finds that liquidations in Sweden, which have a statutory deadline, are faster and cheaper than reorganizations in the US. Bris et al. (2005), however, question the validity of the comparison because the US and Sweden differ from each other in many other ways besides the bankruptcy codes.

In this paper, we look at the impact of a statutory deadline in the reorganization proceeding but avoid country comparisons by studying a bankruptcy reform in a country. We exploit the fact that the reform only affects the reorganization proceeding, allowing us to compare firms that file for reorganization to firms filing for liquidation before and after the law. However, since the law was introduced because of a major financial crisis, we need to make sure that we can attribute the effects to the new law and not the changing macroeconomic conditions.<sup>3</sup> Similarly, a gradual improvement of the judiciary system could also confound the results. We address these concerns by controlling for

> 3 See Uribe and Vargas (2002) or Urrutia and Zárate (2001) for details about the causes and magnitude of the crisis.

4
# **2.1 Measuring Plant-Level Automation**

Unlike some other studies in high-income countries (e.g. Acemoglu et al. (2020) and Stapleton and Webb (2020)), we do not directly observe plants’ use of robots. Instead, we match data on Indonesian imports of industrial robots by industry with plants’ observable characteristics to build various plausible measures of plant level exposure to automation.

The key assumption underlying our proxies of plant exposure is that routine taskintensive occupations are the most likely to be automated (Autor et al., 2003). As we do not observe the tasks performed by workers in each plant, our baseline measure of exposure proxies routine-intensive tasks with the educational level of each plant’s workforce, which we observe in 2006 when our manufacturing plants’ data becomes a census.

Specifically, we identify the production occupations most exposed to being automated using two alternative definitions of occupations’ “replaceability”. The first is the definition used in Graetz and Michaels (2018) (GM). GM exploit information on the applications for which robots are used. They look at 3-digit occupational classifications in the United States and assign a replaceability value of 1 to an occupation if its title corresponds to at least one of the IFR application categories and 0 otherwise. For instance, if robots perform the task “handling materials”, they deem an occupation to be replaceable if the occupational title includes the word “handling”. The second is the probability of computerization calculated by Frey and Osborne (2017) (FO henceforth), which update the probability of computerization by occupation originally computed by Autor et al. (2003) and David and Dorn (2013). Compared to these studies, FO has the advantage of accounting for the more recent technological progress.

Using Indonesian labor force survey data (described in Section 4.3), we compute the distribution of workers in occupations at high risk of automation by their educational attainments in 2007, the first year such information is available.<sup>5</sup> Using the GM definition, secondary education is the typical educational attainment of workers in occupations at risk of automation in Indonesia (column 1 of Table 1). The results in column (2) of

> 5As in FO, we deem an occupation as highly likely to be automated if the probability of computerization is larger than 0.7.

6
# B.1.2.2 Fertilizers

The production of fertilizers, _x_<sup>_n_</sup> _t_<sup>_,_isachemicalengineeringprocessthatcanbe</sup> described by a linear production function: where _θ_<sup>_n_</sup> is the rate of conversion of fossil fuels to fertilizers. There are three types of fertilizer used in agricultural production: nitrogen fertilizers, phosphate fertilizers, and potash fertilizers. In our model we focus on nitrogen fertilizers. These fertilizers are particularly important in the climate policy debate, because their production is the most energy- and GHG- intensive. They are also critical to boosting yields in response to scarcity of land.<sup>3</sup> We use the FAOSTAT database<sup>4</sup> to obtain the global production of nitrogen fertilizers in 2004. For fertilizers' production costs and conversion rates we consider anhydrous ammonia (NH 3 ), which is one of the most common nitrogen fertilizers. We use USDA ERS fertilizer use and price dataset<sup>5</sup> to obtain the fertilizers' price. We then subtract the fossil fuels' price from the fertilizers' price to obtain non-energy cost of fertilizers' production. This cost does not vary much across time because fossil fuels' and nitrogen fertilizers' prices are highly correlated and follow the same trend (USGAO 2003). To compute the normalized average cost of conversion of fossil fuels to fertilizers, _c_<sup>_o,n_</sup> _,_ we multiply the non-energy cost of fertilizers' production by its expenditure share in other primary resources.

# B.1.2.3 Crops

The production of crops, _x_<sup>_c_</sup> _,_ uses agricultural land and fertilizers inputs, which are imperfect substitutes in plant breeding. The output of crops is determined by the constant elasticity of substitution (CES) function:

> 3Note that by von Liebig's Law of the Minimum (yield is proportional to the amount of the most limiting nutrient, whichever nutrient it may be) the production of other two types of fertilizers will follow the path of nitrogen fertilizers.

> 4Thorough description of the FAOSTAT database is available from the following website: http://faostat.fao.org/.

> 5Thorough description of the dataset is available from the following website: http://www. ers.usda.gov/Data/FertilizerUse/.

13
_Note_ : Components sum to 1,000, according to COICOP methodology.

_Source_ : for Slovakia Eurostat; for Macedonia State Statistics Office (the Macedonia numbers are the 2002 published numbers. Given that weights were not changed between 1999 and 2005, the 2002 weights are assumed to be valid for 1999.

More broadly, higher output gap persistence (see the estimated IS curve in Table 1) also suggests that adjustments of the capital stock are more costly in Macedonia than in the Slovak Republic. The difficulties of Macedonian businesses to take advantage of changing business conditions are discussed under “output rigidity” in Section (v) below.

# _(ii) Why could the credit channel of monetary policy be weaker in Macedonia?_

The IS curve estimates suggest that Macedonia’s output gap is less responsive to interest rate changes than Slovakia’s output gap.<sup>11</sup> The direct interest rate channel works largely through its impact on domestic credit and hence aggregate demand. Its efficiency is thus crucially dependent on the transmission of changes in the monetary policy rate to deposit and lending rates. This transmission, see Figure 1, can be less effective for several reasons.

**Figure 1: Lending, Deposit and Monetary Policy Rates in Slovakia and Macedonia**

<!-- Start of picture text --> 25.0 25.0<br>20.0 20.0<br>15.0 15.0<br>10.0 10.0<br>5.0 5.0<br>0.0 0.0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Lending rate Deposit rate Lending rate Deposit rate<br>Monetary Policy rate Monetary Policy rate<br><!-- End of picture text --> _Source_ : IFS and NBRM; the monetary policy rate is approximated by the CB bill rate for Macedonia and the 3-month interbank rate for Slovakia, see the data description section for details.

11 A recent NBRM report on the effect of the interest rate transmission mechanism largely supports the view that this channel is weak. Available in Macedonian at: http://www.nbrm.gov.mk/defaultMK.asp?ItemID=98BEA8015487194DB5825340B5C5ECCF 15
# **Figure 11. ECA: Investment gaps and projects**

_Amid sizable investment gaps across the region, large-scale infrastructure investment projects are underway._ <!-- Start of picture text --> A. Investment gaps B. Projects in Central Asia<br>Percent of GDP $,billions Transport Energy<br>10 2<br>5<br>1<br>0<br>0<br>2013 2014 2015<br>EBRD Turkey Russia<br>countries<br>Europe<br>Central Europe and the Baltic South-eastern Central Asia<br>Eastern Europe<br>and the Caucasus<br><!-- End of picture text --> Sources: Central Asia Regional Economic Cooperation (CAREC), European Investment Bank. A. Range of different investment gap estimates for each region from EBRD (2015a). EBRD countries include Estonia, Latvia, Lithuania, the Slovak Republic, Slovenia Mongolia, Egypt, Jordan, Morocco, and Tunisia in addition to 24 countries in ECA. Financing gap for Central Asia and the Caucasus includes all infrastructure financing requirements that are not covered by national governments. For Central Asia, the range is GDP weighted average for Azerbaijan, Kazakhstan, Kyrgyz Republic, Mongolia, Tajikistan, and Uzbekistan.

Initiatives are already underway to improve infrastructure in the region:

- In _Russia,_ several hundred infrastructure projects were announced in the past five years, with more than half scheduled for completion by 2020. These projects are mostly in more densely populated western Russia. The largest allocations are for transport infrastructure (especially high-speed rail, and road and bridge construction). But there are also a large number of projects underway to improve the supply of utilities (electric power, gas, and water).

- _Turkey_ has initiated several public-private partnership projects, including the Caspian and Middle Eastern oil and gas pipeline and the $10.2 billion Istanbul Grand Airport. _Countries in Central Asia_ —aspiring to become an overland transit and energy hub linking Chinese and European markets—has initiated investment projects in energy and transport sectors. In the energy sector, major projects include a pipeline from Turkmenistan to India, gas sector development in Uzbekistan, and hydroelectric power in Tajikistan. In the transport sector, key projects include highways in Kazakhstan, railroads linking Tajikistan and Kyrgyz Republic to China and the Islamic Republic of Iran, ports in Turkmenistan and Kazakhstan, and an airport in Kyrgyz Republic.

- In _Central and South Eastern Europe,_ the investment pipeline largely reflects EU funding to further integrate the EU member states of the region with Western European countries.

23
- 12 -

# **Table 7:** Central Government Capital Transfers to **the Rest** of **the** Public **Sector.** 1984-86

(mililons of t)

||1984|**1986**|**1986**|
|---|---|---|---|
|Public<br>Sector<br>Enterprises|197.4|118.8|59.4|
|ENEE|(142.8)|<br>(80.9)|<br>(18.2)|
|INA|(18.2)|<br>(4.0)|(12.6)|
|SANAA|(14.0)|<br>(16.8)|<br>(20.8)|
|HONDUTEL|(4.2)|(-)|(-)|
|INVA|(1.7)|(2.2)|(2.8)|
|**INMA**|(9.8)|(6.6)|(-)|
|COHDEFOR|(-)|(2.0)|**(8.6)**|
|INCAFE|(8.8)|(-)|(1.2)|
|Others|(8.9)|(1.9)|**(0.8)**|
|Municipalities|**9.8**|12.8|10.0|
|Others|_|2.0|_3.6_|
|Total<br>Public<br>Sector|206.7|128.1|78.0|
||=|==|3|
|Memorandum Item||||
|Total**capital**<br>transfers||||
|***a**X**of**CDP|3.3|2.0|1.0|

Source: Central Dank **of** Honduras.

## **3.2.** Education ExDenditures

46. Education expenditures grew rapidly during 1981-86. About twothirds of expenditures were spent on wages and salaries. While there has been a freeze on public sector wages over the last couple of years, teachers have consistently received salary increases well above domestic inflation each year. Investment expenditures for the sector, however, fell during this period.

47. Central Government transfers covered over 90Z of the financial needs of the sector in the recent past, while user charges financed the rest. As financial resources devoted to investment contracted, the quality of education also suffered.

48. Regarding the allocation of expenditures by branches, almost half of the total goes to primary education, while 20Z of the total is allocated to secondary education and the rest is spent on universities. Given the difficulties in raising user charges for primary and secondary education--which comprise about 901 of the expenditures spent in the sector--the scope for increasing savings through adjustments in user charges is limited.
7

# **III. EMPIRICAL ANALYSIS**

## **A- Sample**

Our empirical objective is to examine how the poverty reduction effect of openness may depend on a variety of country characteristics, including some that are subject to reform. For this purpose, we work with pooled cross-country and time-series data for 30 African countries averaged over five-year periods from 1981 to 2010. Summary statistics and the correlation matrix for the variables used in our estimation exercises are provided in Appendix 1. Following the same approach as Chang (2009), we start with a linear regression specification and then extend it to account for interaction terms between an openness measure and proxies for various county characteristics (financial depth, education and governance).

These three dimensions (finance, education, and governance) should characterize an economy’s ability to reallocate resources away from the less productive sectors to the more productive ones and, hence, take advantage of the opportunities offered by greater trade openness. A more developed financial sector, as measured by the private sector credit-toGDP ratio, would allow a faster identification of new and promising sectors and a redirection of credit. A more educated population, as measured by primary completion rates, would be more able to acquire the new skills sought by growing sectors and adjust more rapidly to the new conditions of the labor market. Finally, better governance, as measured by the rule of law, would allow contracts to be made and conflicts to be resolved more easily.

## **B- Model and Definition of Variables**

## _Model_

<mark>We use the basic growth–poverty model suggested by Ravallion (1997) and Ravallion and Chen (1997) as well as the frameworks posited by Dollar and Kraay (2001) and Berg and Krueger (2003). Our point of departure can be written as:</mark> <mark>Poverty</mark> i,t <mark>= β</mark> 1 <mark>TO</mark> i,t <mark>+ β</mark> 2 <mark>X</mark> i,t <mark>+ φ</mark> t <mark>+ μ</mark> i <mark>+ ε</mark> i,t <mark>(1)</mark> where the subscripts i and t represent country and time period, respectively, Poverty is the log of a poverty indicator, _X_ is the matrix of control variables, TO is a measure of trade openness, φt corresponds to time effects, μi denotes unobserved country-specific effects, ε the error term. and i,t
- 9 -

**<u>Table 1. Selected project roads under ADSP in Chipata and Katete</u>**

||Chipata<br>Road wor|ks (km)||Katete<br>Road wor|ks (km)|
|---|---|---|---|---|---|
|Link name|Planned|Actual|Link name|Planned|Actual|
|D128|23.4|23.4|RD410|4.2|4.2|
|RD595-R280|11.0|11.0|RD411|38.1|38.1|
|R348-RD596|27.7|26.7|RD412|18.8|18.8|
|RD116|48.4|48.4|U20|23.2|23.2|
|RD117|19.9|19.9|U22|1.5|4.5|
|R266|13.3|13.3|RD687|19.9|18.0|
|RD694|34.2|30.5|RD592-1|6.0|6.0|
|RD697|16.6|16.6|RD592-2|12.1|12.1|
||||R300|11.2|11.2|
||||R306|15.3|15.1|
||||U29|17.8|17.8|
||||R291-R292|26.6|26.6|
|Total|194.6|189.9||194.6|195.5|

Source: RDA (2014).

To assess the impacts of road works under OPRC on agricultural production, the following agricultural production function is considered: where _V_ is the total value of crops produced by household _i_ at time _t_ . The analysis only takes into account 7 major crops that are grown in the survey area: _j=_ { _maize, cotton, cassava, vegetable, soybean, tobacco, groundnuts_ }. The vast majority of farmers in the area grow maize, and the second most popular crop is groundnuts. Some farmers also produce other crops, such as cotton. To aggregate different types of crops, regional producer prices ( _pj_ ) are used from FAOSTAT.<sup>4</sup>

> 4 Average producer prices are used from three countries: Kenya, Rwanda and South Africa, where comprehensive data are available. The prices of maize, cotton, cassava, tobacco, vegetable, soybean and groundnuts are assumed to be $0.22 per kg, $1.34, $0.25, $2.66, $0.16, $0.47 and $0.97, respectively.
# **Table 11. Credit Default Swaps Premia and bank capital over the financial cycle**

The dependent variable is the arithmetic quarterly change in bank (5-year senior tranche MR) CDS premium. The sample covers 33 banks operating in 15 different countries. See Table A1 for a list of the countries covered and distribution of sample banks across countries. The sample period is Q12006 to Q12009. Tier1 (Tier2) is Tier1 (Tier2) capital scaled either by risk-weighted assets (RWR) or total un-weighted assets (LR). Tier1 capital includes shareholder funds plus perpetual, non-cumulative preference shares, plus retained earnings. Tier2 capital includes subordinated debt, hybrid capital, loan loss reserves, and valuation reserves. D.Equity volatility is the change in the quarterly standard deviation of the stock return, D.Capital structure the quarterly change in the ratio [Book value of Debt/(Book value of Debt+Market value of equity)], and D.Yield the quarerly change in the 10-year government bond yield; for the Euro-Area countries (Germany, Italy, France, Spain, Portugal) we use the German yield. Given the small number of banks per country we control for regions*year fixed effects rather than country*year fixed effects. The regions are: Europe, USA, and Asia. See Table A3 for a detailed definition of the control variables and sources. All explanatory variables are lagged one period (4 quarters) except the Yield which is measured at country level. We report standard errors clustered by bank in brackets and the p-value for the test of significant difference between the pre-crisis and crisis coefficients in parentheses.

||(1)|(2)|(3)|
|---|---|---|---|
|||LR|RWR|
|Tier1*PreCrisis||0.003|0.017|
|||[0.023]|[0.026]|
|Tier2*PreCrisis||0.001|0.009|
|||[0.013]|[0.014]|
|Tier1*Crisis||‐0.064|‐0.016|
|||[0.042]|[0.022]|
|||(0.049)|(0.059)|
|Tier2*Crisis||0.164*|0.087|
|||[0.083]|[0.055]|
|||(0.040)|(0.085)|
|D. Equity volatility|0.030|0.021|0.028|
||[0.036]|[0.034]|[0.039]|
|D.Capital Structure|5.273*|4.670*|5.053*|
||[2.762]|[2.668]|[0.106]|
|D.Yield|‐0.254***|‐0.253**|‐0.255**|
||[0.091]|[0.104]|[2.764]|
|Constant|‐0.028|0.051|‐0.168|
||[0.227]|[0.243]|[0.287]|
|Region*Year|x|x|x|
|Year|x|x|x|
|Observations|248|248|248|
|R‐squared|0.16|0.17|0.16|

Robust standard errors in brackets

* significant at 10%; ** significant at 5%; *** significant at 1%

28
# **Getting Incentives Right: An Impact Evaluation of District Hospital**

# **Capitation Payment in Vietnam**

Ha Thi Hong Nguyen<sup>a</sup> , Sarah Bales<sup>b</sup> , Adam Wagstaff<sup>c</sup> , and Huyen Dao<sup>d</sup> a Human Development Network, The World Bank, Washington DC, USA b National University of Singapore, Singapore c Development Research Group, The World Bank, Washington DC, USA d Research and Development Center for Community Health Strategy, Hanoi, Vietnam JEL code: I1 Keywords: Hospitals; provider payment reform; purchasing; capitation; health insurance; Vietnam

# **Acknowledgements**

The research reported in the paper was supported by a grant from the European Union’s FP7 program to the “Health Equity and Financial Protection in Asia” project (HEALTH-F2-2009-223166). Our thanks to Ha Thai Son for advice on the hospital inventory data and Pham Thi Lan for extensive data-cleaning.. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent, including that of Vietnam.
# **<mark>Table 5: Differences in Northern Nigeria (excluding the west) Dependent variable: Log</mark>** **<u><mark>[Value of harvest per hectare]</mark></u>**

||**De**|**pendent va**|**riable: Log [V**|**alue of harves**<br>_Pool_|**tper hectare]**<br>_ed sample_|||_Female_|_Male_<br>|
|---|---|---|---|---|---|---|---|---|---|
|_VARIABLES_||||||||_manager_|_Manager_|
||_(0)_|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|
|Female manager=1|-0.0389|-0.103|-0.0272|-0.379***|-0.372***|-0.297***|-0.274***|||
||(0.121)|(0.121)|(0.125)|(0.101)|(0.100)|(0.0999)|(0.103)|||
|Age|||-0.00481|0.00370|-0.00184|-0.00461|-0.00647|0.0728**|-0.0148|
||||(0.0150)|(0.0131)|(0.0129)|(0.0130)|(0.0130)|(0.0357)|(0.0142)|
|Age Squared|||4.73e-05|-4.48e-05|1.06e-05|2.97e-05|3.90e-05|-0.000879**|0.000124|
||||(0.000146)|(0.000127)|(0.000125)|(0.000127)|(0.000128)|(0.000381)|(0.000137)|
|Years of schooling|||0.00590|-0.00344|-0.00203|-0.00622|-0.00859|-0.0122|-0.00735|
||||(0.00687)|(0.00593)|(0.00587)|(0.00593)|(0.00586)|(0.0291)|(0.00601)|
|Manager is nonmuslim†|||-0.0222|-0.0811|-0.0723|-0.109|-0.0320|-0.487*|-0.00958|
||||(0.108)|(0.0915)|(0.0906)|(0.0901)|(0.0903)|(0.278)|(0.0955)|
|Manager has nonfarm activity†|||0.00866|-0.00902|-0.00129|-0.0244|-0.0570|0.343|-0.0848|
||||(0.0706)|(0.0583)|(0.0579)|(0.0581)|(0.0578)|(0.262)|(0.0604)|
|Household female adult size(age 12-60)|||0.0536**|0.0830***|0.0828***|0.0447**|0.0367*|-0.178|0.0384*|
||||(0.0251)|(0.0218)|(0.0211)|(0.0219)|(0.0221)|(0.108)|(0.0227)|
|Household male adult size (age 12-60)|||0.0601**|0.0675***|0.0740***|0.0556**|0.0677***|0.0463|0.0679***|
||||(0.0288)|(0.0244)|(0.0236)|(0.0229)|(0.0227)|(0.0933)|(0.0245)|
|Child dependencyratio|||0.00211|0.0164|0.0192|0.00898|-0.00260|0.0187|-0.00448|
||||(0.0491)|(0.0426)|(0.0433)|(0.0410)|(0.0393)|(0.130)|(0.0421)|
|Log [GPS-Land Area (HA)]||||-0.788***|-0.803***|-0.725***|-0.711***|-0.723***|-0.704***|
|||||(0.0395)|(0.0398)|(0.0408)|(0.0414)|(0.185)|(0.0425)|
|Log [GPS-Land Area(HA) Squared]||||-0.0432***|-0.0439***|-0.0414***|-0.0452***|-0.0251|-0.0517***|
|||||(0.0146)|(0.0149)|(0.0147)|(0.0149)|(0.0565)|(0.0160)|
|Plot distance to household||||0.000271|0.000224|0.000233|0.000268|0.00996|0.000304|
|||||(0.000279)|(0.000244)|(0.000193)|(0.000229)|(0.00862)|(0.000227)|
|Access to extension†||||<br>0.142|<br>0.180*|<br>0.154*|<br>0.124|<br>0.178|<br>0.131|
|||||(0.0958)|(0.0957)|(0.0913)|(0.0891)|(0.320)|(0.0950)|
|Grows cash crop†|||||0.0449|0.0980|0.0944|0.574**|0.0559|
||||||(0.0691)|(0.0691)|(0.0696)|(0.280)|(0.0728)|
|Only one crop onplot †|||||-0.424***|-0.456***|-0.454***|-0.161|-0.472***|
||||||(0.0624)|(0.0622)|(0.0615)|(0.198)|(0.0647)|

32
<u>OHKqg</u> | <u>OHKq e</u> < <u>l</u> ane ani,

Yo Wa Pa 5° ~~OU OU~~ <u>alg</u> *% + <u>anl,</u> ~~(se~~ a + <u>at)</u> We he =0 <u>OHKg au aU;</u> aq ~~—*~~ <u>ant,</u> B ~~-—~~ <u>an</u> = 6* ~~,—~~ <u>ACq (p</u> —wW,a) Wa > Pa Yo Yo + ha(Wal — d p*) Cy = ~~OU~~ d ~~OHKg~~ 6 ~~50,~~ a P — Wa) ~~<r~~ a hg SH-hag = CaS waha + Yo — palhé + ha)