rafmacalaba/datause-encoder-data huggingface dataset:
temperature rise up to 3 °C. A study done on a high-altitude sub-basin of Satluj River Basin (tributory to Indus River Basin) revealed an increase in runoff up to 18% from snomelt and 38% from glacier melt for 2 °C warming (Singh and Kumar, 1997). Singh and Bengston (2004) report a three year simulation with 1-3 °C rise in temperature in the Satluj River Basin and show reduction in melt in snowfed basin but increase in galcierfed basin. On the contrary, in another tributary in the same basin, however, Rathore et al. (2009) report about 40% reduction in glacier extent, 5-19% reduction in snow extent, and 8-28% reduction in seasonal streamflow with 1 °C rise in temperature for 2004-2040. Temperature rise is expected to increase streamflow in the short term in some Chinese rivers due to increased glacier melt (Yao et al., 2007). Climate sensitivity analysis in a glacier-dominated region in the Niyang River Basin in the Tibetan Plateau indicates high sensitivity of streamflow to climate change, particularly temperature change (Zhang et al., 2011). Simulations with unchanged precipitation showed annual streamflow increase by an average of 65 mm per 0.5 °C temperature increment (Zhang et al., 2011).

Rees and Collins (2006) carried out a comparative study evaluating climate change impacts on two hypothetical conceputal catchments representing glaciological features similar to Batura Glacier in Karakoram and Langtang Glacier in the Nepalese Himalaya using a 0.06 °C year<sup>-1</sup> climate warming (for 150 years starting 1990) and time-varient glacial extent. Results for subcatchments with more than 50% glacial area showed an increase in streamflow, which peaked at 2050 and 2070 with 150% and 170% of intial flow and stabilized to lower values with the disappearance of glaciers in 2086 and 2109 respectively in the west (Batura) and east (Langtang) glaciers (Rees and Collins, 2006). In another basin scale study, the flow in upstream areas (≥2000 masl) was projected to decrease by 8.4% in Indus, 17.6% in Ganges, and 19.6% in Brahmaputra basins in 2046-2065 compared to 2001-2007, showing the relative importance of glacier melt contributions (Immerzeel et al., 2009). Immerzeel et al. (2009) used climate warming scenarios with the SRES A1B emission and a mass-balance based glacier evolution scenarios. Projected future precipitation increases in the region are expected to compensate for streamflow reduction due to declining glacial contributions (Immerzeel et al., 2009).

In a recent study of climate change impacts on the hydrology of the Langtang River catchment (360 km2), Immerzeel et al. (2011) applied five GCMs to a high-resolution combined cyrospheric-hydrologic model under SRES A1B and found that both downscaled precipitation 14
# Government and Enterprise

25. A third crucial layer of control is between government and the enterprise itself The government may impose financial targets, expenditure or cash limits, capital allocations, pricing and productivity targets, etc., on the enterprise. The imposition of these constraints will force the enterprise to operate inefficiently and employ a nonoptimal mix of inputs.43/ A more complicated, yet more realistic, way to approach the problem of control between the government and the enterprise is to view these financial targets and capital allocations not as genuine constraints on behavior, but as resulting from the bargaining process between the government and the enterprise.44/ The government's goal is assumed to be welfare-maximization, whereas the enterprise is assumed to act in the interests of those who operate it: the managers and the workers.45/ The enterprise, thus prefers to be constrained to earn a relatively low level of profit and to receive a relatively high capital allocation. The determination of the tinancial constraint and the capital allocation are the outcome of discussions and negotiations between the government and the enterprise. However, the enterprise chooses which operational information to reveal to the government. Thus the financial constraint and capital allocation depend on the enterprise's strategic selection of information. The larger the asymmetry of information between government and the enterprise, and about the enterprise's operations, the less able government is to control the enterprise. The bargaining and negotiation process itself can also play a role in determining what constraints are placed on the enterprise.

43/ R. Rees, "A Positive Theory of Public Enterprise," in The Performance of Public Enterprises, Marchand, Pestieau, and Tulkens (eds.), North Holland, 1984, p. 179-191.

44/ H.S. E. Gravelle, 'Bargaining and Efficiency in Public and Private Sector Firms,' in The Performance of Public Enterprises, Marchand, Pestieau, and Tulkens (eds.), North Holland, 1984, p.193-220.

45/ This section is based on R. Rees, 'The Public Enterprise Game,' E:onomic Journal, 1984, p. 109-123.

19
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16
LxVI INhOn OfuON Table Al Sources Country, conjeJy or territory Total population, mid-1955 Total fertility, 1985-90 Life expectancy, 1985-90 Afghanistan Bank projection from 1979 census, U.N. 1990 revision nc. Afghan refuge*s abroad Albania UNPVSR 7/86 (official est.) **Based** on CBR, UNPVSR 10/89 Algeria lank projection from official 1/85 Bank sector report 6/88 est. American Samoa Samoa **U90C** 19e8 Based on CBR, UNPVSR 10/89 10/89 (official est.)est.) Bank **est.** U.N. 1990 rev'sion U.N. 1990 revision American Samoa Samoa **U90C** 19e8 Based on CBR, UNPVSR 10/89 10/89 USBOC 1988 (official est.)est.) Andorra US **OC 1963 Angola** U.N. 1990 rev!sion U.N. 1990 revision U.N. 1990 revision Anguilla UNPVSR 1/87 Antfu **and U.N.** 1990 **revision Based** on **official CBR Based on official CMR Barbuda** Argentina U.N. 1990 revision U.N. 1990 revision U.N. 1990 revision Aruba lased **on** UNPVSR 1987-89 - - Australia UNPVSR 4/87 (official est.) Based **on MR,** UNPVSR 10/88 U.N. 1990 revision (officiat est.) Austria UNPVSR 7/86 (official **est.)** Based on **onnier** 1988 Based on CDR (official **est.)** Bahamas, The UNDY 1986 Based on CBR, UNPVSP 4/87 Based **on** CDR, UNPVSR 10/89 (official **est.)** (official aet.) Bahrain Official est., Central Stat. Official **est.** (Central Stat. Based on official **est.,** Central Organization, 10/e9 Organization) Stat. Organization Bangladesh Bank projection from 1981 census Based on 1989 Fertii'zy Survey U.N. 1990 revision Barbados UNPVSR 7/86 (official est.) Based on CBR, UNPVSR 10/89 Based on CR, UNPVSR 10/89 (official est.) (official est.) Belgium Eurostat 1989 Based on CdR, rostat 1989 U.N. 1990 revision Belize UNPVSR 7/86 (official est.) Based on L 985 Based on USBOC 1985 lenin Bank projection from census Based or .1 survey and WFS Based on UFS Bermuda UNPVSR 1/87 (official est.) Bhutan Official est. (Statistical Hand- U.N. 1990 revision U.N. 1990 revision book of Bhutan 1985) Bolivia U.N. 1990 revision U.N. 1990 revision U.N. 1990 revision Botswana Bank projection from 1981 census Based on OHS 1988 Based on OHS 1988 Brazil U.N. 1990 revision U.N. 1990 revision U.N. 1990 revisior British Virgin UNPVSR 1/87 (official est.) Istands Brunei Bank est. based on 1981 census Based on CBR, UNPVSR 10/88 Based on official est, for 1971 and and 1986 official est. (official est.) 1981, Economic Planning Unit Bulgaria Bank projection from 1985 census U.N. 1990 revision U.N. 1990 revision Burkina Faso U.N. 1990 revision U.N. 1990 revision U.N. 1990 revision Burundi Bank projection from 1979 census DHS 1987 Bank est. Cameroon Bank projection frou 1976 and 1987 Bank est. Based on WFS censuses **and** official 1987 est. Canada UWPVSR 7/86 (official est.) Official est., Population Official est., Population Projections for Canada, 1984-2006 Projections for Canada, 1984-2006 Cape Verde UNPVSR 10/89 Based on official CBR Based on official CDR Cayman Islands Central African U.N. 1990 revision Bank est. Based on 1975 census chiLd Rep. survival data
**Table A3. Universes, Samples, and Response Rates, by country**

|||||**S**|**urvey status**<br>|||**Adjusted**|
|---|---|---|---|---|---|---|---|---|
|**Country**|**Universe**<br>**size**<br>**(1)**|**Sample**<br>**size**<br>**(2)**|**Effective**<br>**Surveys**<br>**(3)**|**Declined**<br>**to answer**<br>**(4)**|**Not effective**<br>**due to**<br>**COVID**<br>**(5)**|**Not effective**<br>**due to other**<br>**reasons**<br>**(6)**|**Closed**<br>**Programs**<br>**(7)**|<br>**response**<br>**rate**<br>**(8)**|
|Brazil|2,388|1,203|601|162|0|174|266|64%|
|Colombia|2,130|1,314|900|93|15|99|207|81%|
|Dominican<br>Republic|209|209|80|13|0|0|116|86%|
|Ecuador|543|543|294|66|0|124|59|61%|
|Peru|387|387|228|83|0|67|9|60%|
|Total|5,657|3,656|2,103|417|15|464|657|70%|

# _Source_ : Own calculations using WBSCPS.

_Notes_ : This table shows the universe, sample size, and effectiveness measures for the WBSCPS. For Dominican Republic and Ecuador, the universe of programs was included in the survey. For Peru, the universe of licensed programs was included. For Brazil and Colombia, representative samples were drawn from the States of Sao Paulo and Ceara and at the national level, respectively. Columns (1) and (2) show the number of programs in the universe and sample sizes for each country in the study, respectively. Columns (3) through (6) indicate the number of surveys by status for each country: column (3) indicates completed surveys; columns (4) and (5) indicate the number of surveys that SCP directors declined to answer or that did not answer because they were preparing for distance learning, respectively; and column (6) indicates surveys not answered for other reasons, such as lack of provost authorization. Column (7) shows the number of programs that were closed at the time of the survey but that were reported as open in the sampling frames. Columns (3) through (7) add up to total sample size (column 2). Adjusted response rate is the share of effective surveys (column 3) relative to all active programs in our sample (i.e., Sample size (2) – Closed programs (7)). For example, for Brazil, adjusted response rate = 601/ (1,203 – 266) = 64%.
of 1977 where it remained until Sept. 1980 whence it was again differentiated between agriculture and non-agriculture at TL 160 and 180/day, respectively. The following developments are shown in Table 3.5 which for the sake of comparison also shows SII averages for the private sector.

By the mid-1970s the legal mininrum for non-agricultural establishments was about half the average for insured workers in the private sector but lagged behind during the years of hyperinflation up to 1980. Since then the non-agricultural minimum wage has more than catched up with the SII average for private establishments. The gap has declined further after 1984. The discrepancy between agricultural and non-agricultural minimum wages has been some 5-20 percent, possibly indicating legislator's perceptions of the difference between agricultural and non-agricultural wages, a relatively small difference.

Table 3.6, col.s 1-4, compares census and survey averages for manufacturing for 1984, the three census years 1980, 1970, and 1963 (1950 included for the sake of comparison but is not used here) and 1960. It goes without saying that the most reliable source of wages in manufacturing is the censuses of 1980, 1970 and 1963. They cover in principle all establishments and give information about average number of employees and payments of wages and salaries during the year so that annual, average remuneration per employee may be computed. The annual surveys do not cover small establishments and since 1971 they do

- 50 -
where _Zk_ is the _k_ th z-score for a given outcome (out of 8), and _T_ 1, _T_ 2, and _T_ 3 are intent-to-treat indicators for households randomly assigned to the three treatment packages in the _Atención a Crisis_ program. Because of the standardization described above, all units are in standard deviations. Standard errors throughout the paper adjust for clustering at the community level.

As we show, the coefficients on _T_ 1, _T_ 2, and _T_ 3 are very close in magnitude, and we can never reject the null hypothesis that they are equal. We therefore next focus on a specification that treats the three packages as if they were a single _Atención a Crisis_ program: Results for these specifications are presented in Table 3, for the three separate treatment packages (upper panel) and for the specification with a single treatment (lower panel). The table shows there are significant program effects for the social-personal and language measures of the Denver test, corresponding to impacts of 0.13 and 0.17 standard deviations, respectively, as well as for the TVIP test of receptive vocabulary, corresponding to an impact of 0.22 standard deviations. Treatment effects for the three packages are very similar throughout. Although this may seem surprising, we note that the observed increase in overall consumption levels at the time of the follow-up survey was similar for households assigned to the three treatment packages (Macours and Vakis 2008). At this point, the vocational training courses had not yet started, and the beneficiaries of the productive investment package had received the investment grant only 2-3 months before. Also, all three groups were exposed to the same information about the importance of better and more varied diets, and all households were subject to the same requirements in terms of school enrollment of school-aged children.

Table 3 makes obvious that program effects are concentrated in social-personal skills and, especially, in language development. Note that these are domains where children had particularly large delays, and where socioeconomic gradients were steeper. Program effects for all other outcomes are also positive, but not significant, with particularly small coefficients for the measures of gross motor skills and leg motor skills. These are precisely the domains in which children in our sample were least likely to be delayed, and where socioeconomic gradients were generally less steep. The results in Table 3 are thus consistent with the 11
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UNICEF, 1992. The State of the World's Children: 1992 New York: Oxford University Press.

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- Wolfe, Barbara, 1986, "Health Status and Medical Expenditures: Is there a Link?" Social Science and Medicine 22(10), pp 993-999.

- World Bank, 1993. World Development Report 1993: Investing in Health. New York: Oxford University Press.

- World Bank, 1997a. Sector Strategy: Health. Nutrition. and Population. Human Development Network, The World Bank, Washington, DC.

- World Bank, 1997b. Social Indicators of Development. New York: Oxford University Press.

41
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28
We also want to use two additional firm-level variables that represent the strength of forward links of a firm as an exporter (exporting intensity); and the strength of backward-level links abroad (import share).<sup>8</sup> Table 12 further decomposes the GVC participation into import share and exporting intensity. We find that the mitigation of the FDI spillovers is mainly due to higher exporting intensity rather than import share. In column (2), the coefficient of the interaction term is negative and statistically significant, while in column (3) the statistical significance disappears.

**Table 12: Sectoral FDI, export, import, and firm productivity**

|Dependent Variable: ln labor<br>productivity at the firm level|(1)|(2)|(3)|
|---|---|---|---|
|_ln(FDI)_|0.057***|0.041***|0.051***|
||(0.01)|(0.01)|(0.01)|
|_ln(FDI) × GVC_|-0.061***|||
||(0.02)|||
|_ln(FDI) × Exporting intensity_||-0.035**||
|||(0.02)||
|_ln (FDI) × Import share_|||-0.024|
||||(0.02)|
|_GVC_|0.728***|||
||(0.12)|||
|_Exporting intensity_||0.460***||
|||(0.08)||
|_Import share_|||0.397***|
||||(0.06)|
|_Foreign ownership share_|0.418***|0.482***|0.472***|
||(0.04)|(0.04)|(0.04)|
|_ln (# skilled workers at t-1)_|-0.130***|-0.142***|-0.127***|
||(0.02)|(0.01)|(0.02)|
|_ln (years of top mgmt)_|0.139***|0.135***|0.139***|
||(0.02)|(0.01)|(0.02)|
|_ln (firm size)_|0.241***|0.254***|0.258***|
||(0.02)|(0.02)|(0.02)|
|_Services_|3.167***|0.942***|3.071***|
||(0.12)|(0.10)|(0.12)|
|Year FE|Yes|Yes|Yes|
|City-year FE|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|
|Observations|30,415|34,860|30,451|
|R-squared|0.196|0.181|0.192|

8 These two variables are not quite the same as the GVC participation of the firm, but very much capture the same concept. We are limited by the variables in the WBES.