rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.9. E-commerce development and various categories of household expenditure per capita by household consumption level (Extended model 4)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|bottom 25%|25%-75%|top 25%|
|Total household|||||
|expenditure|0.226***|0.287***|0.131***|0.043|
||(0.064)|(0.100)|(0.048)|(0.051)|
|Observations|10,701|2,959|5,547|2,166|
|Adjusted R square|0.344|0.417|0.439|0.442|
|Cosmetics and beauty|0.353***|0.570**|0.194|0.141|
||(0.113)|(0.248)|(0.135)|(0.089)|
|Observations|10,597|2,931|5,500|2,137|
|Adjusted R square|0.343|0.330|0.367|0.425|
|Food|0.283***|0.442***|0.236***|0.098**|
||(0.063)|(0.129)|(0.058)|(0.048)|
|Observations|10,550|2,904|5,478|2,139|
|Adjusted R square|0.347|0.428|0.356|0.447|
|Of which: Food at home|0.304***|0.456***|0.264***|0.149**|
||(0.067)|(0.154)|(0.070)|(0.060)|
|Observations|10,547|2,904|5,475|2,139|
|Adjusted R square|0.390|0.465|0.401|0.392|
|Of which: Dining out|0.424***|0.191|0.172|0.143|
||(0.148)|(0.218)|(0.147)|(0.137)|
|Observations|10,707|2,962|5,550|2,166|
|Adjusted R square|0.331|0.453|0.341|0.379|
|Clothes|0.192**|0.204|0.036|-0.068|
||(0.094)|(0.254)|(0.086)|(0.089)|
|Observations|10,437|2,895|5,396|2,117|
|Adjusted R square|0.326|0.330|0.386|0.348|
|Utilities|0.171***|0.347**|0.145***|0.027|
||(0.041)|(0.134)|(0.047)|(0.033)|
|Observations|10,493|2,931|5,457|2,076|
|Adjusted R square|0.308|0.345|0.313|0.333|
|Communications|0.177***|0.049|0.116**|0.071**|
||(0.044)|(0.138)|(0.049)|(0.035)|
|Observations|10,383|2,888|5,404|2,064|
|Adjusted R square|0.337|0.302|0.384|0.432|
|Local transport|0.332***|0.045|0.139*|0.233*|
||(0.081)|(0.247)|(0.079)|(0.128)|
|Observations|10,327|2,886|5,362|2,050|
|Adjusted R square|0.306|0.340|0.327|0.309|
|Travel|0.650***|0.841**|0.358**|0.305*|
||(0.187)|(0.325)|(0.146)|(0.182)|

·· 45
Table 9: lnfluhI of Couitry-Specific Social **Class Mseures** on PUplI Achleveuent In Malawi

||ToT|AL TiiTSCE||CH|INCHE1A<br>STSAR|E||MATH<br>ITSTS|CaE|
|---|---|---|---|---|---|---|---|---|---|
||(1)",|(2)"|(3)0|(1)a|(2)"|(3)|(1)a|(2)||
|Jrban school<br>control)|-.33<br>(10.54)d|-.10<br>**(**0.03)e|-|-0.81<br>(-2.31)'|-.88<br>(-11.14)"|-|.49<br>(1.28)|.64<br>(3.38)|_|
|on's wealth|-. 005<br>(-0.15)|||.03<br>(1.52)|.04<br>**(**3.85)0||_.03<br>(-1.18)|||
|(girl.l)|-.28|||-.07|||-.22|||
||(-0.87)|||(-0.37)|||(-0.88)|||
|rade level|<br>-.05|||<br>-.02|||a.02|||
||(-0.48)|||(-0.37)|||(-0.39)|||
|other tongue|.51|||.21|||.30|||
|hichewa|( 1.56)|||( 1.07)|||(1.38)|||
|er's work|.17<br>(0.95)|||.15<br>( 1.3)|||.02<br>**(**0.18)|||
|er's<br>schoolI|ng.18<br>|||.41<br>|.35<br>||-. 23<br>|||
||( 0.46)|||( 1.72)|( 2.62)||(-0.88)|||
|er's readIng|-.15|||**-.10**<br>|||-. 03|||
|tices|(-0.82)|||(-1.05)|||(-0.11)|||
|**I's**<br>work<br>**tasks**<br>thatch**roof**|-.005<br>(-2.24)'|**-.005**<br>(-5.68)'|-.002<br>(-4.92)*|-.003<br>(-1.84)+|-.001<br> **(4.38)"**|-.001<br>(-5.71)'|-.003<br>(-1.69)|-.003<br>(-4.92)'|-.003<br>(-5.46)*|
|**Nuse**<br>w/electric|ity<br>1.01<br>|1.08<br>|1.13<br>|.59<br>|.42<br>|.78<br>|.43<br>|||
||( 2.16)*|( 8.07)**|( 9.39)"s|( 2.05)'|C 2.85)|(10.50)"|( 1.38)|||
|**idio**<br>In**house**|<br>.37|||<br>.26|||<br>.12|||
||( 0.89)|||( 1.02)|||( 0.42)|||
|s estimate<br>fathe|<br>rs.02|.02|.02|<br>.01||.01|<br>.02|.01|.01|
|Ing|(2.75)"|( 7.42)"0|(12.39)*'|( 1.52)||(11.46)08'|<br>( 2.78)"|( 7.09)"|(3.62|
|s estimate<br>homes|<br>.02|.02||.006|||.02|.02|.02|
|'thatch<br>roofs|(1.53)|(2.93)||(0.70)|||(1.67)|(5.17)'|4.50|
|cept|-2.13|-1.69|-1.93|-1.36|-.67|-1.25|-.77|-1.58|-.20|
||13,88|5,96|3,98|13,88|5,98|3,100|13,88|4,99|3,100|
|*square|.21|.16|.14|.27|.20|.14|.17|.10|.07|

:e: Lhnstandardlzed regression **coefficlients** reported. Full **model**

**Redjced** model with urban control **Reduced** model without urban control

T-values are reported for **full** models F-values repwrted for **reduced** models where backward elimination of non-slIgnlflcant exogenous variables was **run.** + pc.07 * pc.05 **p.01 "** p<.001 x.10 used as criterion for variable to remain In the equatlon.

- 32 -
- [26] Helpman, E., Melitz, M., Rubinstein, Y. (2008). “Estimating trade flows: trading partners and trading volumes.” _Quarterly Journal of Economics_ 123(2): 441-87.

- [27] Imbs, J., Mejean, I. (2017). “Trade Elasticities”, _Review of International Economics_ 25(2): 383-402.

- [28] Juarez, L. (2023). “Buyer Market Power and Exchange Rate Pass-through”. Job Market Paper, University of Michigan.

- [29] Kee, H.L., Nicita, A., Olarreaga, M. (2008). ‘Import demand elasticities and trade distortions’, _Review of Economics and Statistics_ 90(4): 666–82.

- [30] Kee, H.L., Nicita, A., Olarreaga, M. (2009). ‘Estimating Trade Restrictiveness Indices,” _Economic Journal_ 119: 172–99.

- [31] Kohli, U. (1991). _Technology, Duality, and Foreign Trade: The GNP Function Approach to Modeling Imports and Exports,_ The University of Michigan Press, Ann Arbor.

- [32] Krugman, P., Obstfeld, M., Melitz, M. (2022). _International Trade: Theory and Policy_ 12e. Pearson.

- [33] Krugman, P. (1981). Intraindustry Specialization and the Gains from Trade. _Journal of Political Economy_ 89(5): 959-73.

- [34] Melitz, M., Redding, S. (2015). “New Trade Models, New Welfare Implications.” _American Economic Review_ 105(3): 1105-46.

- [35] Novy, D. (2013). International Trade without CES: Estimating Translog Gravity, _Journal of International Economics_ 89(2): 271-282.

- [36] Lawless, M., Morgenroth, E. (2019) “The product and sector level impact of a hard Brexit across the EU,” _Contemporary Social Science_ 14(2): 189-207.

27
Van Ark (2003) of the previous ICT-intensity classification from Stiroh (2002). We interact this sectoral variable with the average elevation of the municipality and with the coefficient of variation of locality elevations. The rationale behind the use of average elevation and its coefficient of variation at the municipality level, is based on the difficulties associated with the implementation of Internet technologies at the geographical level. According to the Federal Institute of Telecommunications (IFETEL), in Mexico the main technology used to provide broadband is DSL cable, followed by Modem cable, optical fiber and microwave connections with still a very small share of satellite connections. Among these technologies, the only one that appears not to be restricted by terrain elevations is satellite, as in the case of cable-based technologies, in order to cover mountain regions, a greater length of cable is required, which represents higher costs. Furthermore, in the case of microwave technologies, an important requirement is that the towers need to have point-to-point visual, which is really difficult in the case of these types of regions. Therefore, we consider the average level of elevation of each municipality obtained from raster pictures of the country (INEGI). It is important to note that, even though in the last two decades highway construction has established infrastructure to increase the supply of optical fiber, the use of this technology is still very low in Mexico, which makes the highway networks a not very good instrument. The use of this instrument is broadly based on Akerman et al. (2015), who take advantage of broadband availability roll-out for Norway as a measure for ICT adoption with the purpose of overcoming the endogeneity problem, and on Duflo and Pande (2007) and Manacorda and Tesei (2016), who use geographical factors (rivers’ gradients and incidence of lightning) as instruments for dam construction and mobile technology infrastructure, respectively.

As Figure 1 shows, the continuum of elevations taken from raster pictures of the country, was translated into polygons (municipalities and localities) to calculate the two indicators for the construction of these instruments.

An advantage of our instrument is that the sectoral ICT intensity is based on U.S. data, which are by definition exogenous to the decision of Mexican firms and a better measure of “technological characteristics” at the industry level.<sup>1</sup> Furthermore, as we are using a geographical indicator to account for ICT availability, it is clear that the instrument should be exogenous.

> 1We use different definitions of sectoral IT intensity based on this literature, which distinguishes IT producing and IT using industries. Therefore, we test whether the over-identifying restrictions are valid.

7
As with schooling, it is useful to begin by briefly reviewing the literature on the United States. A series of papers by Ruhm (2000, 2003, 2005, 2007) and Ruhm and Black (2002) argue that adult health status tends to improve during recessions in the United States. In part, this appears to be a result of increases in the fraction of adults who regularly exercise during recessions, and decreases in the corresponding fractions who smoke, drink excessively, and eat unhealthy foods.<sup>21</sup> Turning to child health, Chay and Greenstone (2003), and Dehejia and LlerasMuney (2004) both document counter-cyclical patterns in infant mortality, with more babies dying during economic expansions.

Chay and Greenstone (2003) show that pollution falls during recessions. Using variation over time and across counties in pollution levels, they show that lower pollution levels, in turn, results in fewer infant deaths. Dehejia and Lleras Muney (2004) use state-level data to show a decrease in the incidence of low and very low birth weight babies and in infant mortality during recessions. Part of the explanation for these patterns is that there are changes in the composition of women giving birth, with significant reductions in the fraction of black mothers who are highschool dropouts during recessions. This is consistent with high-school dropouts being more credit-constrained than more educated women, and therefore preferring to have children during economic expansions, when incomes are higher, despite a higher opportunity cost.

When they consider changes in behaviors that might explain the differences in infant mortality rates, Dehejia and Lleras-Muney (2004) find that (both black and white) mothers have more prenatal care visits during recessions. The incidence of smoking and drinking during pregnancy declines for black mothers, but increases for white mothers, during recessions. The authors conclude that child health improvements during recessions in the United States are driven both by changes in the compositions of the pool of mothers and by behavioral “improvements” for blacks, and by the dominance of a higher prenatal care effect over a “deteriorating” pool of mothers and higher drinking and smoking, in the case of white mothers.

In contrast with the literature from the United States, the evidence on developing countries—including middle-income countries, such as those in Latin America—suggests that child mortality is counter-cyclical, decreasing in booms and increasing in crises. Paxson and

> 21 More recent research suggests that improvements in health status during recessions do not affect all population groups, and are not apparent for all health outcomes. For example, Charles and DeCicca (2008) show that mental health deteriorates and obesity increases during recessions, especially for those whose (pre-recession) employment status is most marginal.

18
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<!-- Start of picture text --> French test scores<br>by age, treatment and wave<br>Baseline Endline<br>S Sa<br>(a_i —-—, Y .<br>©44 2~~ —<br>B%<br>0o4 4 =® 0 4 F<br>2 / en 4<br>s / o* {<br>us / ira /<br>es ! fo /<br>2 / o- /<br>om / 6 /<br>3 / 3 wo I<br>B / % j<br>BY) / Be};<br>ze / z5 /<br>"= / a)<br>. ¥<br>° nu<br>5 6 7 8 9 10 11 12 13 14 15 5 6 7 8 9 10 11 12 13 14 15<br>Age Age<br>——— Treated villages —---- Control villages<br>1 point for correct response, 0 for wrong.<br>French test scores<br>by age, treatment, wave and school enrollment<br>st Baseline nN Endline<br>aaOr aOr<br>& e<br>So =<br>2 20<br>oO oO<br>IL ¢ i<br>a £<br>oO oO @<br>[ome] ro]<br>3 a a<br>(0) ot Se SS ee<br>@ © weca<br>(a) ) ar<br>z nN Z N _ ao<br>pe 17<br>° TO ro<br>5 6 7 8 9 10 11 12 13 14 15 5 6 7 8 9 10 11 12 13 14 15<br>Age Age<br>— Treated, schooled —-—-—-- Treated, not schooled<br>— Control, schooled —-—--- Control, not schooled<br>1 point for correct response, O for wrong.<br><!-- End of picture text -->
_Low-Income Countries’ Access to Private Debt Markets_ borrowing by corporations.<sup>12</sup> The limited data available indicate that the cross-country correlation between treasury bill yields and GDP per capita is quite low (figure 12). The wide dispersion in domestic interest rates across countries reflects several factors, including inflation differentials as well as cases where governments oblige commercial banks to hold treasury bills with yields well below market rates.

# **Figure 12 Treasury bill yields and GNI per capita, 2007**

<!-- Start of picture text --> Percent Low-income cut-off<br>20<br>15<br>10<br>5 R 2 = 0.14<br>0<br>5 6 6 7 7 8 8 9 9<br>Log(GNI per capita)<br>Treasury bill yield<br><!-- End of picture text --> _Source:_ IMF International Financial Statistics.

Debt management considerations also play a prominent role in assessing the relative cost of domestic versus external financing. Although domestic debt is typically denominated in domestic currency, a significant portion is indexed to the exchange rate or inflation in many countries. In addition, domestic debt has tended to have a much shorter maturity than external debt. Thus, the decision to borrow in domestic versus external markets needs to be addressed in a debt management framework that takes into account exchange rate risk and maturity structure considerations. Unfortunately, there is scant data on indexation and maturity structure of domestic debt in developing countries, making it difficult to compare the relative cost of external versus domestic financing.<sup>13</sup> Governments and corporations in low-income countries are generally believed to have significant financing gaps. According to this view, governments are often unable to finance key investments in infrastructure, health and education, while corporations often unable to finance their operations and take advantage of profitable investment opportunities. From this perspective, one would expect that the overall financing needs of low-income countries would be higher than that of middle-income countries, on

> 12 Only about one-half of the 135 developing countries examined in this paper report data on treasury bill yields or money market interest rates in the IMF International Financial Statistics database; very few countries report long-term government bond yields.

> 13 See the discussion in Jeanne and Guscina (2006) and Abbas and Christensen  (2007) and the references therein.

15
# **Table 17.  Impact of WTO Accession on Unskilled Employment by Sector and Regional Market** (percentage change)

|Good|Central|Siberia|South|North|Urals|Far East|Moscow|St.<br>Peters.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|FME|35.2|14.0|45.3|24.3|17.0|20.1|44.1|359.0||12.5|
|CHM|9.9|22.5|10.9|-1.3|14.4|0.5|3.5|6.4|45.5|30.9|
|NFM|12.9|5.4|34.6|11.1|24.8|6.6|36.3|1.7||-9.7|
|TMS|10.7|5.8|9.4|17.3|-0.9|17.3|-3.9|17.4|11.5|18.7|
|TRK|8.6|5.5|6.8|9.6|3.9|11.2|2.6|11.5|6.6|12.8|
|COL|9.5|5.5|8.3|8.2|8.0|7.5|||||
|TRD|6.6|4.1|3.7|3.4|5.1|4.1|4.4|6.8|1.7|8.8|
|OIL|6.4|-1.2|4.6|3.6|0.4|4.1|0.3|-0.4|6.9||
|HOU|2.7|2.2|2.6|2.7|2.2|2.6|2.2|3.2|2.6|3.8|
|ELE|3.5|2.4|2.3|1.6|3.0|1.1|0.8|3.4|0.4|5.9|
|RLW|4.6|0.6|1.3|1.0|2.0|1.3|-0.9|5.9|0.8|4.2|
|SCI|0.0|1.5|-0.1|3.0|-0.5|3.7|0.0|5.4|-0.7|5.3|
|HEA|1.8|1.4|1.6|1.4|1.7|1.5|1.5|2.2|1.0|2.4|
|CON|1.7|-1.1|0.8|-1.1|1.0|-1.2|1.7|-1.6|2.2|-3.5|
|CRU||-1.2|0.5|-0.1|0.5|-0.3|||1.3|-1.9|
|PST|0.5|-0.5|-0.1|-0.8|0.3|-0.6|-0.1|-0.5|-0.8|0.3|
|TPP|-6.0|-4.2|-10.0|-0.4|-1.3|30.1|-13.0|-8.0|15.1|-9.5|
|AIR|-1.5|-5.5|-2.2|0.1|-3.4|-0.4|-3.3|-3.2|3.8|-3.0|
|CLI|-1.0|-4.1|-1.8|-2.8|-2.4|-2.6|-3.4|-5.4|1.5|-6.2|
|AGR|-1.3|-2.9|-1.4|-3.5|0.1|-4.6|-2.7|-7.8|3.3|-9.2|
|OTI|-3.2|-5.9|-2.5|-4.4|-3.6|-5.8|-0.1|-3.0|-1.5|-5.3|
|PIP|-1.8|-4.8|-3.8|-5.4|-1.6|-5.7|-5.9|-5.7|-1.3|-0.6|
|MWO|-9.3|0.0|-2.4|-7.0|-8.9|-5.0|8.2|-2.8|-2.6|-11.3|
|MAR|-3.9|-11.9|-7.4|-5.7|-5.5|-4.6|-9.7|-6.2|-6.6|-8.0|
|TRO|-5.3|-10.8|-7.7|-2.5|-14.7|-2.1|-19.5|-1.5|-10.4|-0.1|
|CNM|-6.2|-10.8|-5.2|-7.4|-5.8|-8.6|-12.3|-13.0|1.8|-14.3|
|FIN|-7.5|-8.5|-8.3|-9.5|-7.6|-9.3|-8.3|-8.4|-9.7|-7.5|
|FOO|-11.9|-11.0|-10.0|-7.9|-9.7|-11.8|-16.4|-12.3|0.8|-15.2|
|OTH|-6.8|-14.1|-9.1|-13.7|-6.7|-15.1|-7.4|-17.3|-5.2|-21.1|
|GAS||-15.8|-9.4|-13.8|-12.1|-14.5|||-1.3|-21.9|

See Tables 1 and 2 for sector and regional market definitions.

Source: Authors' calculations.

49
11

**Table** 4: **Federal Education Spending per Student by Level**

||F<br>**primary**|ederal Spen<br>(constant<br>**secondary**|ding per Stu<br>1994 Pesos)<br>**preparator**|dent<br> <br>**y university**|(as sh<br>**prim.**|are spent<br>**second.**|on Pri<br>**prep.**|mary)<br>**univ.**|
|---|---|---|---|---|---|---|---|---|
|1980|1166.1|2571.8|4823.7|8667.3|1|2.2|4.1|7.4|
|1981|1447.6|2790.6|6156.9|11544.0|1|1.9|4.3|8.0|
|1982|935.8|2027.3|4586.4|10061.0|1|2.2|4.9|10.8|
|1983|710.5|1562.0|2714.7|6451.4|1|2.2|3.8|9.1|
|1984|691.1|1644.3|3108.4|6544.2|1|2.4|4.5|9.5|
|1985|715.0|1473.3|3582.2|7174.8|1|2.1|5.0|10.0|
|1986|961.5|2094.6|3329.3|11686.2|'|2.2|3.5|12.2|
|1987|683.7|1308.3|3364.0|6334.3|1|1.9|4.9|9.3|
|1988|665.1|1303.0|3423.0|6520.2|1|2.0|5.1|9.8|
|1989|734.0|1438.9|3756.0|6176.3|1|2.0|_5.1_|8.4|
|1990|808.4|1685.8|3502.8|6473.5|1|2.1|4.3|8.0|
|1991|1011.4|1942.6|3772.9|6898.3|1|1.9|3.7|6.8|
|1992|1285.9|2318.7|3384.3|9313.2|1|1.8|2.6|7.2|
|1993|1584.6|2568.4|4304.6|11637.2|1|1.6|2.7|7.3|
|1994|1731.3|3139.4|5215.1|13253.0|1|1.8|3.1|7.7|
|1995|1679.0|2511.0|4508.1|11202.3|1|1.5|2.7|6.7|
|1996|1647.7|2464.2|4442.9|11328.5|1|_1.5_|2.7|6.9|
|1997|1811.9|2709.7|4882.1|12692.2|1|1.5|2.7|7.0|

Source: Presidencia de la Republica, Primer Informe de Gobiemo, 1989, and Tercer Informe de Gobiemo, 1997.

The great disparity in per-student spending at different education levels in Mexico is also reflected in Table 4, which only refers to federal expenditures. This Table also shows, however, that the evolution of public spending on education in Mexico has become more egalitarian across different schooling categories over time. In the early 1980s, the amount of federal spending per university student averaged 10 times the amount spent per primary student. This ratio fell to around 7 times in the early 1 990s. Federal spending on the other levels relative to the primary level indicate a similar decline, even though the absolute amounts increased at all levels. In other words, even though the recent pattern of spending on education in Mexico exhibited a highly uneven bias in favor of tertiary education relative to other categories, the pattern of spending in earlier periods exhibited and even greater bias.

# D. **The Rates of Return on Investments** in **Education**

One way to assess the economic merits of the preceding spending patterns, considered as investments in education, is by comparing their relative rates of return. This section compares the rates of return to education as derived from several human capital earnings functions. These were estimated with data from two income-expenditure surveys (ENIGH) carried out by the National Statistical Institute (INEGI) in the third quarters of 1984 and 1994. These two survey years provide an excellent setting for comparisons: first, both years can be considered macroeconomically comparable in that the economy is neither in recession nor booming. Secondly, both years are almost equidistant in time from the major stabilization and structural reform measures implemented
Himalayas (Lucas-Picher et al., 2011; Rupa Kumar et al., 2006). Regional Climate Models (RCMs) refine the spatial scale (e.g. 50 km) and are thus better suited for more realistic projections in daily climatic extremes (Christensen et al., 2007). With the climate large-scale information from GCM, RCM can be applied over a limited regional area for high resolution climate model output (Rummukainen, 2010). Despite their usefulness, the RCMs also have their own set of limitations (Rummukainen, 2010). One important limitation is the high computational cost. For this reason, the region lacks dedicated program like North American Regional Climate Change Assessment Program for climate modeling in North America (Mearns et al., 2009) using multiple GCMs and RCMs. The reliance on few climate models grossly underestimates modeling uncertainties thus limiting their use in climate change adaptation planning.

The most widely used RCM in the region is _Providing Regional Climates_ _for Impacts Studies_ (PRECIS), which is based on the Hadley Centre's regional climate modeling system with the HadCM3/HadAM3 as a driving GCM. The PRECIS simulation for 2071-2100 (Rupa Kumar et al., 2006) with increased greenhouse gas concentrations and sulfate aerosols generally appears to agree with IPCC AR4 results. The simulation shows an all-around warming over the Himalaya including Tibetan Plateau and the Indian subcontinent. The warming seems to be more pronounced over high altitude areas in the northern parts of India, Nepal and Bhutan. The annual rise in mean surface air temperature for the region ranges from 3-5 oC in A2 scenario, and 2.5-4 oC in the B2 scenario. Further analysis of climate change projection maps in Rupa Kumar et al. (2006) reveals that temperatures increase more during pre-monsoon and winter months compared to monsoon and post-monsoon months over the Himalayan region, which is in agreement with IPCC AR4 GCM ensembles. Extremes in maximum and minimum temperatures were also projected to increase in the future.

In a recent study, Shi et al. (2011) report results of applying a high-resolution regional climate model (RegCM3) using the SRES A1B scenario over the Yarlung Zangbo-Bramhaputra River Basin for 1948-2000 and 2001-2100. They estimate temperature increases of 2.8 oC annually, 3.3 oC in winter, and 2.3 oC in summer for 2041-2060 over 1981-2000 temperatures.

# **3.2 Precipitation**

Unlike temperature, precipitation projections by GCMs are less consistent, reflecting the greater uncertainty associated with precipitation. Precipitation projections for South Asia show a 11
||||<sup>3</sup>||56<br><sup>3</sup>||<sup>3</sup>|||<sup>2</sup>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||journal|% not|_I_ =0|Rank|_I_ =0.5|Rank|_I_ =1|Rank|_h_-index<br>|_I_|Rank|Normalized|Rank|
|||found in|||||||(_I_<sup>1</sup>)|||_h_-index||
|||Google|Publication||||Total|||QIF||||
|||Scholar|count||||citations|||||||
|93|Journal of Urban Economics|5%|21|188|94|108|636|107|12|0.289|107|35%|167|
|93|Journal of the European Economic Association|0%|16|277|90|111|598|111|12|0.273|112|38%|154|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. An _h_ -index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is defined in eqn (2). A normalized h-index of 20% means that 20% of articles in the journal have been cited at least 20 times.