rafmacalaba/datause-encoder-data huggingface dataset:
the Census ceased reporting the classi...cation of capital expenditures into new and used. Thus, after that year, proxies for the intensity of the resale market RES could not be constructed. However, even in 1992, it is not possible to construct RES for a large number of industries. To ensure that the three cross-sections are comparable in terms of their sample size, the variables used for the 1992 crosssection are averages of 1987 and 1992. Summary statistics for the key variables are provided in the appendix.

Under rental payments, RK; manufacturing establishments are requested to report payments made for the use of all items for which depreciation reserves would be maintained if they were owned by the establishment–e.g., structures and buildings, production equipment and transportation equipment. Establishments also report: the depreciation, DK; charged during the year against ...xed assets; their new expenditures for buildings and equipment NK; the value of all used plant and equipment UK purchased during the year; and their ...xed depreciable assets K: To facilitate estimation, we drop r since it is just a normalization.<sup>9</sup> We also restrict the length of the period � at the end of which the entrant exercises its disinvestment option, to being the same across industries and set it equal to one year. Then, we can compute UK the following proxies: REN =<sup>RKDEP=DKandRES=</sup> K<sup>;</sup> K<sup>;</sup> UK + NK<sup>:</sup> To estimate equation (15), we set m = 4 and thus employ the four-...rm concentration ratio C4. These concentration ratios were taken unadjusted from the Census listings. For each four-digit industry, the Census also reports the values of the truncated Her...ndahl index H, representing the sum of squared market shares of the largest 50 ...rms in the industry or the entire universe, whichever is lower. The census also reports the values of the 50-...rm concentration ratio C50. From the values of H and C50 we can derive V (s); the variance of market shares of the largest 50 ...rms or the entire universe, whichever is lower. This will permit us to estimate equation (18). It can be easily shown that<sup>10</sup>

> 9 It should be noted that dropping r leads to a change in units and could a¤ect the testing of H01 in (17) and H02 in (19) . Moreover, established ...rms are more likely to buy their own capital, so (1 �<sup>RK</sup> rK<sup>)islikelytooverstimatesunkenness.</sup>

...rms, then we compute the variance of the market shares of the largest 50 ...rms from the values of the truncated Her...ndahl index H50 and the 50-...rm concentration ratio C50 that are reported by 50 the census, i.e., V (s) = 50<sup>1(H50 �C</sup> 50<sup>2).If,ontheotherhand,anindustrycontainsN...rmswhere</sup> N < 50, then CN = 1 and V (s) =<sup>1�1</sup> N<sup>(HN</sup> N<sup>):</sup> 12
# **Table 3. Validity check using the pre-treatment elections of 2000**

||Dependent variable: Share of votes for<br>incumbent in 2000<br>(1)|
|---|---|
|Below Cutoff|-1.525<br>(1.236)|
|Rain Deviation|-0.036<br>(0.015)**|
|Constant|49.032<br>(9.619)***|
|Observations|1038|
|R-squared|0.75|
|State controls|Yes|
|Controls at electoral section level|Yes|
|Controls at municipal level|Yes|
|Mean of dependent variable|32.25|

Robust standard errors in parentheses State controls are dummy variables for each state. Controls at the electoral sections include altitude, distance from the weather station, distance to the nearest river and distance to the "cabecera". Controls at the municipal level include municipal income per capita for the year 2000. * significant at 10%; ** significant at 5%; *** significant at 1% Page **25** of **29**
||mu|rPR Wawnlk Paw sea<br>ADs|2|**_Coact_**<br>for_DIerw_|
|---|---|---|---|---|
|WPS31|PublicFinane andE<br>mk Deeopmet|BdaBalua|Augt 1968|N. Campbell<br>33769|
|WPS32|Municipal Development Funds and<br>intermediies|Keaneth Daey|July 1968|R. Blade-Charest<br>33754|
|WPS33|Fisa Poliy in Commodity-<br>ExpoingLDCa|Joint Cudd4igton|July 1968|R. Blade-Charmt<br>33754|
|WPS34|Fsal<br>**_1m_**in Macroeconomic<br>Stabilization|lAce Taylor|September 1968|R. Blade4Carest<br>33754|
|WPS35|Improvin the Aloation and Mana<br>ment of Pubic Spdin|Stepben L1ste|Augt 1988|R. Blade-Charmt<br>33754|
|VPS36|Meani and Implmtbn<br>of Social<br>Secuity Finance in Dc'eloping<br>Countres|Dougla<br>. Puffert|_Augut_198|R. Biade-Charat<br>33754|
|WPS37|Back Market Pmin. Echange Rate<br>Unifcation and bIflatin in<br>Sub-SaharnAfrica|Brian Pinto|July1968|R. Blae-Chaet<br>_337S4_|
|WPS38|Intergvernmental Grants in<br>Developing Countries|Lay Schdeder|September 1968|R. Blade-ChAet<br>_33754_|
|WPS39|Fiscal PoliCy in Law-lncome Afria|Stephen A O Connell|July 1988|R. Blade-Charest<br>_337S4_|
|WPS40|Fmandal Deregulation and the<br>Globaliation of Capital Markets|Eugene L Versluyen|August 198S|R. Blade-Chart<br>33754|
|WPS41|Urban Property Taxation in<br>Devdoping Countries|Wilam Dillinger|August 1988|R. Blade-Charest<br>337S4|
|WPS42|Government Pay and Employment Policies<br>and Government Performance in<br>Developing Economies|David L Undauer|August 1988|R. Blade-Charest<br>33754|
|WPS43|Tax Administration in Developing<br>Countries Strategies and Tools<br>of Implementation|Tax Administration<br>Divii|||
|WPS44|The Size and Growth of Government<br>Expenditures|David L Lindauer|September 1988|R. Blade-Charest<br>33754|
- Fajnzylber, Pablo & Ana M. Fernandes (2008). “International Economic Activities and Skilled Labor Demand: Evidence from Brazil and China”. _Applied Economics_ 40(1) (January): 1–15

- Filmer, Deon P. & Norbert R. Schady (2008). “Getting Girls into School: Evidence from a Scholarship Program in Cambodia”. _Economic Development and Cultural Change_ 56(3) (April): 581–617

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- Heckman, James J., Lance J. Lochner & Petra E. Todd (2006). “Earnings Functions, Rates of Return and Treatment Effects: The Mincer Equation and Beyond”. In Eric A. Hanushek & Finis Welch (editors), _Handbook of the Economics of Education_ , volume 1, chapter 7, pp. 307–458. Amsterdam, The Netherlands: Elsevier

- Jones, Garett B. & W. Joel Schneider (2006). “Intelligence, Human Capital, and Economic Growth, A Bayesian Averaging of Classical Estimates (BACE) Approach”. _Journal of Economic Growth_ 11(1) (March): 71–93

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28
Table 5: **Self and Social Image -** **_Armani_ Gift Cards** **<u>(mTurk</u> Experiment)**

|||Pref|er $ _Arm_|_ani_ to $50|0 _Old Nav_|_y_|
|---|---|---|---|---|---|---|
||Rosemberg<br>Self-Esteem Score|400|450|500|550|600|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||Panel i: without|controls||||
|Self-Afrmation|1.2214*|-0.0823***|-0.0719**|-0.0801*|-0.0336|-0.0718|
||[0.7023]|[0.0310]|[0.0349]|[0.0434]|[0.0488]|[0.0497]|
||(0.080)|(0.010)|(0.048)|(0.052)|(0.551)|(0.141)|
|Mean (neutral)|19.8333|0.1520|0.1814|0.2990|0.4167|0.5196|
||[0.5076]|[0.0252]|[0.0270]|[0.0321]|[0.0346]|[0.0351]|
|Sample size|405|405|405|405|405|405|
|||Panel ii: with c|ontrols||||
|Self-Afrmation|1.2318*|-0.0829***|-0.0728**|-0.0805*|-0.0319|-0.0680|
||[0.6890]|[0.0309]|[0.0349]|[0.0430]|[0.0469]|[0.0489]|
||(0.090)|(0.008)|(0.041)|(0.061)|(0.509)|(0.174)|
|Mean (neutral)|19.8333|0.1520|0.1814|0.2990|0.4167|0.5196|
||[0.5076]|[0.0252]|[0.0270]|[0.0321]|[0.0346]|[0.0351]|
|Sample size|405|405|405|405|405|405|

Notes: Column 1 presents results of a regression of Rosenberg self-esteem Score on a dummy for self-affirmation treatment. Columns 2 to 6 present results of a regression of a dummy equal to one if the subject chose the _Armani_ rather than the _Old Navy_ gift card on a dummy for self-affirmation treatment for the corresponding offer. Panel i presents regressions without additional controls, while Panel ii presents results including race, gender, age, marital status, education and income as covariates.Robust standard errors in brackets. Permutation test p- values in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.
tuberculosis, and unknown cause (each at about 10%). The median length of the illness that preceded death was 3 months, but the mean is much higher, about 17 months. The consequence of this attrition is that we estimate the impact of prime-age deaths on elderly outcomes using the remaining sample of older individuals who are still alive in the fifth round of the panel (2004). Attrition is, as expected, strongly correlated with age. Of the 1,060 baseline respondents in our sample, 24% of those under 50 years in 1991 were deceased by 2004, compared to 46% of those over 50 years.

Using this selected sample may lead us to incorrectly identify the effect of a prime-age death on an elderly individual. For example, if the older adults who are still alive in 2004 are healthier or more able to do physical work than the individuals who died, then the labor supply response of these individuals to an adult death may be very different than an older adult on average.

In our case, in which attrition in the elderly sample is primarily because of the death of elderly individuals, we would expect attrition to be driven by both observable and unobservable determinants. To correct for attrition bias due to selection on observables, we use a standard inverse probability weighting procedure (Wooldridge 2001). The procedure involves weighting regressions using probability weights estimated from an attrition probit model. In addition, attrition itself, since it is largely a function of survival is in some sense a welfare outcome.

We model attrition in the canonical way, as follows. The dependent variable, Δyej, is observed only if the individual is alive in the last round of the survey (Ae = 1). Then, 15
value added in SSA comes in cereals grains, on which Japan currently imposes high tariffs for SSA, and even higher tariffs for the rest of the world. There is also an expansion in value added in categories of plant-based fibers and livestock, meat and dairy products.

What about the impact on the rest of the world? The impact on developing countries, who might fear the consequences of trade diversion, is surprisingly small, because SSA exports are not large enough to have a significant impact on prices in export markets (see discussion in Section 2). As can be seen from Figure 5, even in the QUAD liberalization scenario, the loss in export revenue for other developing countries is only around 650 million dollars or less than 0.04 percent of their initial exports. In all five scenarios, the gains for SSA in termns of export revenue outweigh the losses for other developing countries.

**FIGURE 5: EXPORT REVENUE CHANGES** IN **SSA AND OTHER DEVELOPING COUNTRIES**

<!-- Start of picture text --> 3000<br>2500-<br>2000-<br>, 1500-<br>.2 1000-<br>500-<br>0-<br>-500-<br>-1000-/IX. ,:} lli |<br>US-apparel US-AJI JPN-mnfcs EU-AIl QUAD-ALL<br>|0 SSA * Other developing countries<br><!-- End of picture text -->

<!-- Start of picture text --> Source: GTAP, authors' calculations.<br><!-- End of picture text --> 18 The relative imnportance of Japan is consistent with recent results obtained by Bradford (2000).

18
means and provides further support to the assumptions necessary to interpret both the difference-in-difference and treatment-on-treated results as causal.

# **8. COST-EFFECTIVENESS**

In order to assess the policy relevance of the estimated effects, we estimate the cost-effectiveness of Plan Nacer in terms of reducing low birth weight and neonatal mortality. One issue is that we have two outcomes that have different metrics. We follow the convention in the public health literature of converting the estimates of program impact into disability-adjusted life years (DALYs) saved (Jamison, 1993; Murray, C. J. L. 1994; Murray and Lopez 1996a-c). We then estimate cost-effectiveness by dividing DALYs averted due to Plan Nacer by the incremental costs of the program. As recommended by the World Health Organization based on the report of the Commission on Macroeconomics and Health (WHO 2001), we benchmark the cost per DALY against national per capita GDP to assess whether the intervention is cost-effective in the Argentine context. We begin by estimating incremental cost and then DALYs saved.

# **8.1. Costs**

<mark>In this section we describe how we estimate Plan Nacer costs for maternal services, that is, prenatal care, plus delivery, and hospital-based neonatal services in the seven provinces under study. We consider both fixed and variable (recurrent) costs. Table 8 presents our estimates of the costs computed using information provided by the Ministry of Health in local currency. We first converted them into US dollars using the official exchange rate and then deflated them to 2005 using the national GDP deflator.</mark>

**Table 8: Plan Nacer Costs for Maternal Health Services in Seven Provinces (Millions, 2005 US$)**

||**2005**|**2006**|**2007**|**2008**|**Total**|
|---|---|---|---|---|---|
|**Recurrent costs**||||||
|Payments for services|0.46|3.33|6.60|7.37|**17.77**|
|Other joint recurrent costs|0.18|0.50|1.16|0.69|**2.27**|
|**Fixed costs**||||||
|Use cost of durable goods|0.010|0.100|0.030|0.013|**0.150**|
|**Total costs (USD)**|**0.87**|**3.93**|**7.79**|**8.08**|**20.45**|

_Source:_ Unidad de Financiamiento Internacional de Salud (UFIS), Ministerio de Salud Nacional.

28
of the commercial banks was US$4.9 billion in December 1993, and declined to US$ 1.1 billion in June 1994. The same figure for end 1993 is noted in OECD Economic Survey on Turkey, 1995. Short term debt instruments were not issued by the Treasury in the last two months of 1993, and in the beginning of 1994, the stock market was in a steady decline, (see Figure 16) and the TL was losing its value sharply against foreign currencies. The banks rushed to the foreign exchange market to close their positions in foreign exchange, the Central Bank was defending the parity by selling foreign currency at rates below the market rate, as a result of which the Central Bank lost half of its reserves while the commercial banks closed their short positions. The fall in foreign liabilities of the depos:it banks from early 1994 on is seen in Figure 15. At the same time, there were large withdrawals of TL and foreign currency denominated deposits from the system. Three small commercial banks (with share in total deposits less than one percent) were placed in to receivership in April 1994. Following that, 100% of bank deposits were insured by the Central Bank at the end of April 1994. Although the amount of reserves were at an all time low, this seems to have helped to stop the withdrawals. This insurance on deposits has not been removed to date, signifying the fragility of the system.

In April 5, about a week after the local elections, the government announced a stabilization programme. This involved price increases of 70 to 100 percent on SEE goods, and a freeze thereafter up to 6 months. Public sector wages would be freezed, the planned consolidated government deficit was halved for 1994. This deficit reduction would be achieved mainly through one-time taxes on the net assets of firms, wealth and corporate taxes, also by the real decline in the wage bill, and a cutting down on public investment. Reserve and liquidity requirement rules were revised in favor of holding TL relative to foreign currency, and ratios increased in general.

15
**Table A3.2  Economic classification of government expenditures—Capital expenditures**

|**Category/Subcategory**|**Definitions (from IMF)**|
|---|---|
|**Buildings, structures,**<br>**machinery, and equipment**|Explicit spending flows allocated to capital investment during the period recorded. Includes flows<br>into new assets and rehabilitation of existing ones.<br>Buildings and structures consist of dwellings, nonresidential buildings, and other structures.<br>Other structures consist of all structures other than buildings. Included are the following:<br>highways, streets, roads, bridges, tunnels, railways, subways, runways, sewers, waterways,<br>harbors, dams, other waterworks, communication lines, power lines, and pipelines.<br>Machinery and equipment include transport equipment and other machinery and equipment.<br>Transport equipment includes motor vehicles, trailers, ships, railway locomotives and rolling<br>stock, aircraft, motorcycles, and bicycles.<br>Other machinery and equipment consists of machinery and equipment other than transport<br>equipment.|
|**_Of which rehabilitation_**|If the government expenditures report rehabilitation expenditures separately, the proportion of<br>total capital expenditures which are rehabilitation should be reported here. If the government do<br>not classify rehabilitation expenditures, it will be useful to state the approximate amount used for<br>rehabilitation. It could be done by estimating capital expenditures on new fixed assets and then<br>deducting that from total capital expenditures. Alternatively the split between new and<br>rehabilitation expenditures can be allocated on a project-by-project basis by allocating a specific<br>project to either new investments or rehabilitation based on where the bulk of the expenditure<br>lies.|
|**Other fixed assets**|Other fixed assets consist of cultivated assets and intangible fixed assets.<br>Intangible fixed assets include mineral exploration, which is undertaken to discover new deposits<br>of petroleum, natural gas, and other subsoil assets that may be exploited commercially.|
|**Other capital expenditure**|Includes capital expenditures not elsewhere classified, for example, capital tax and<br>compensation for damages caused by natural disasters.|
|**Transfers of capital**<br>**expenditures to lower levels of**<br>**governments**<br>**Bd**|This category captures transfers of conditional grants to lower levels of local government.|
|**_Of which_**<br>**_tl_**<br>**uget**<br>**support**|External funding that enters the budget with no earmarking.|
|**_exerna_**<br>**_funding_**<br>**Earmarked for**<br>**projects**|External funding earmarked for specific projects.|

_Source:_ Authors’’ elaboration.

62
according to some rule. Average tax and transfer rates are used in order to compute the disposable income for each socioeconomic group. It is the percentage change in this disposable income that is transmitted to the poverty simulator in order to simulate the impact of shocks and policies on poverty and between-group inequality.

# **_The Poverty Simulator_**

The poverty simulator in this version of PAMS (PAMS I) is analogous to PovStat. Intra-group distributions are represented by unit records pertaining to each socioeconomic group. In order to compute the impact of a macroeconomic shock or a policy on the welfare of a given household, the per capita income or expenditure of the household is multiplied by the induced growth rate of the disposable income of the representative group to which the household belongs. This growth rate is jointly determined by the macro- and the meso-layers.

As with any of the frameworks examined here, there are structural and, possibly, data constraints that can limit the reliability of the framework in predicting the poverty and distributional impact of macroeconomic shocks and policies. In the particular case of PAMS I, there is a data constraint stemming from the fact that most household surveys provide some information at the household level, such as expenditure and selfemployment income, while other information, such as labor income and employment status, is given at the individual level. Choosing the household as the unit of analysis means that individual-level data must be aggregated up to the household level. Socioeconomic groups are thus created on the basis of the characteristics of heads of household. This obviously leads to results that are coarser than results based on relevant individual-level data.

In addition, the poverty simulator is bound to inherit the strengths and weaknesses of the top layers. It is well known, for instance, that the basic RMSM-X is a simple macroeconomic accounting framework and shows no behavioral relationships in the economic sense. Also, the use of a labor demand function that is defined by sector and skill level is equivalent to assuming a homogenous labor factor with different, sectorspecific remuneration.

46