rafmacalaba/datause-encoder-data huggingface dataset:
The above results using OLS and FE support that the main results found using GMM are in the credible range. Also, the results using typical LP are consistent with the GMM results, which supports the fact that the difference of coefficients between OLS, FE and GMM result from removing the simultaneous effects of inputs and output.

I also consider a translog production function, which is a flexible functional form of the production functions, to avoid the rigid assumptions of the Cobb-Douglas production function, such as a linear relationship between factor inputs and output, and perfect substitution between production factors (in other words, perfect competition on the production factors market). However, it is not included in the final results due to the high collinearity among regressors.<sup>18</sup> The collinearity among regressors in the translog specification is seen as “harmful” if the sign of at least one estimated parameter is contrary to the sign of the coefficient of correlation between the resultative variable and the analyzed explanatory variable (Pavelescu, 2010b). Using equation (3) in the translog function,<sup>19</sup> I find that, when other inputs are in the mean, the marginal effect of labor is 0.309, the marginal effect of capital is 0.158, the marginal effect of intermediate inputs is 0.694 and the marginal effect of innovation is -0.072. Likewise, equations (4) is measured with a translog function. When other inputs are in the mean, the marginal effect of labor is 0.296, the marginal effect of capital is 0.104, the marginal effect of intermediate inputs is 0.690, the marginal effect of internal innovation is -0.036, and the marginal effect of external innovation is -0.068. The negative coefficients for innovation variables seem to be caused by collinearity among explanatory variables, which is a major constraint to estimating the translog production function.

> 18 The translog production function can be used for the second order approximation of a linear-homogenous production function and allows to estimate not only the elasticity of input but also the elasticity of scale, which is equal to the marginal product (Ferguson, 1979; Klacek, et al., 2007).

> 19 For instance, equations (3) is measured as Y=f(K,L,I, T) = l k m t l2 lk lm lt k2 km kt m2 mt t2 lkm lkt lmt kmt lkmt y1997-y2009. The marginal product of innovation is potentially non-linear and a function of other inputs: dY/dN= _b[t]+_b[lt]*l+_b[kt]*k+2*_b[t2]*t+_b[mt]*m+_b[lkt]*lk+_b[lmt]*lm+_b[kmt]*km+_b[lkmt]*lkm.

47
There is unequivocal evidence that the global climate is changing (IPCC 2007). For southern Africa, numerous studies based on observed and projected changes highlight the risk of deteriorating conditions for an already vulnerable subsistence-farming sector, such as the occurrence of more extreme temperature events (New et al. 2006), later onset of the rainy season (Tadross, Hewitson and Usman 2005), and general decline in rainfall (Ngondongondo 2006). In a recent study based on observed daily rainfall, Tadross, Suarez and Lotsch (2007) found significant increases in mean rainfall intensity on wet days (which favors soil erosion) but a reduction of rain days at the end of the season, augmenting the chances of crop failure for many maize varieties currently used in the region.

There is consensus in the scientific community regarding an increase in the frequency and intensity of extreme events such as floods and droughts (IPCC 2001, 2007). Climate change is expected to affect southern Africa across sectors including health (Haines et al. 2006), agriculture and food security (Gregory, Ingram and Brklacich 2005), and water management (Arnell, Hudson and Jones 2003).

Malawi’s National Adaptation Programme of Action (Environmental Affairs Department 2006) identifies the most important impacts in key sectors such as agriculture, health, energy, water and fisheries. This document highlights response strategies for the country, with explicit responsibilities assigned to different Malawian institutions, both governmental and non-governmental. Lack of organizational capacity is often listed among the barriers to the implementation of this program.

# **4. Dreadful synergies: The complex relationship between climate and HIV/AIDS**

Climate change and HIV/AIDS together can lead to combined negative effects greater than would result from the sum of their separate effects. Figure 3 offers compelling anecdotal evidence of the complex interactions between droughts and the pandemic. Dreadfully synergistic mechanisms can create new, severe problems among vulnerable communities, therefore increasing the need for disaster-related institutions to provide more services among people at risk.

9
Adlung and Roy (2005) have assessed both the current coverage of specific commitments of WTO members in the GATS and the offers that were on the table as of 2006, when negotiations were suspended. For many countries the coverage of specific commitments is well below 50 percent of all services and modes of supply. Adlung and Roy conclude that not only do the requests and offers made in the 6 years following the launch of negotiations on services in 2000 imply little if any liberalization of policies; most countries were not even willing to use the GATS as a vehicle to “lock in” existing levels of openness.<sup>5</sup> The lack of progress on services matters for several reasons. First, as mentioned in the Introduction, the extant research concludes that the potential direct gains from reform of services trade for most WTO members are likely to be large. Second, services reform is needed to enable developing countries to take advantage of the new opportunities that arise from goods trade liberalization. For example, Sub-Saharan African exporters today pay transport costs that are at least five times greater than the tariffs they face in industrial country markets, but neither international maritime nor air transport services figure seriously on the WTO agenda. Third, the WTO negotiating process requires countries that seek better market access to offer improved access to their own markets. In particular, attaining global liberalization of agriculture – a key objective of many developing countries – will require them to offer a quid pro quo. Greater opening in services, an area of export interest to many of the countries that protect their agriculture sectors is one obvious quid pro quo. For example, services exports are of key importance for the EU and India, two WTO members that confront serious political resistance to reducing barriers to trade in agriculture.

A number of possible explanations for the lack of headway can be identified. As is well known, the WTO is driven by mercantilism: the desire by members to improve their access to the markets of other members. One explanation for the limited progress is that the standard mechanisms of reciprocity as developed over 50 years of GATT practice

> 5 A number of studies have shown that there is often a major gap between the actual level of openness of sectors and the level of commitments in the GATS. For example, Barth et al. (2006) compare data on specific GATS commitments for financial services with measures of actual policy in this sector for 123 countries. They conclude that in practice applied policy is much more liberal than what was committed to in the GATS.

7
**_Energy demand models for policy_** **_<u>formulation</u>_**

energy demand modeling tools and institutional capacities for developing countries. However, the challenge is quite significant.

# **References**

- Adams, F. G. and Y. Shachmurove, 2008, Modeling and forecasting energy consumption in China: Implications for Chinese Energy Demand and Imports in 2020, Energy Economics, 30, 1263-78.

- Adams, F. G., H. Graham and J. M. Griffin, 1974, Demand elasticities for gasoline: Another view, Discussion paper 279, Department of Economics, University of Pennsylvania.

- Adeyemi, O. I. and L. C. Hunt, 2007, Modelling OECD industrial energy demand: Asymmetric price responses and energy-saving technical change, Energy Economics, 29, pp. 693-709.

- Ailawadi, V. S. and S. C. Bhattacharyya, 2006, Access to energy services by the poor in India: current situation and need for alternative strategies, Natural Resources Forum, 30(1), pp. 2-14.

- Al-Azam, A. and D. Hawdon, 1997, Estimating the demand for energy in Jordan: A Stock-Watson Dynamic OLS (DOLS) approach, Working Paper, Surrey Energy

Economic Centre, Guildford, UK. (See http://www.econ.surrey.ac.uk/Research/WorkingPapers/seed97.pdf).

- Al-Faris, A. R., 2002, The demand for electricity in the GCC countries, Energy Policy, 30, pp. 117-24.

98
**Table 6. RIF decomposition of the gender differential in agricultural productivity with region fixed effects (2008)**

|||**Log [Self-**|**reported Pr**|**oductivity (X**|**OF/ha)]**|||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|**overall**||||||||||
|Male headed households|9.842***|10.466***|10.856***|11.156***|11.396***|11.631***|11.901***|12.198***|12.643***|
||(0.068)|(0.050)|(0.044)|(0.038)|(0.035)|(0.036)|(0.035)|(0.036)|(0.048)|
|Female Headed households|9.642***|10.185***|10.568***|10.893***|11.190***|11.438***|11.708***|11.999***|12.398***|
||(0.095)|(0.083)|(0.076)|(0.073)|(0.072)|(0.066)|(0.065)|(0.065)|(0.074)|
|difference|0.200*|0.281***|0.288***|0.262***|0.205***|0.193***|0.193***|0.199***|0.245***|
||(0.105)|(0.084)|(0.074)|(0.069)|(0.067)|(0.062)|(0.066)|(0.068)|(0.084)|
|**% gap**|<br>**18%**|<br>**24%**|<br>**25%**|<br>**23%**|<br>**19%**|<br>**18%**|<br>**18%**|<br>**18%**|<br>**22%**|
|explained|-0.233*|-0.088|-0.129|-0.034|0.001|0.022|-0.003|0.065|-0.054|
||(0.124)|(0.090)|(0.080)|(0.066)|(0.064)|(0.065)|(0.065)|(0.066)|(0.092)|
|unexplained|0.433***|0.369***|0.417***|0.297***|0.204***|0.171**|0.196***|0.134|0.299***|
||(0.141)|(0.107)|(0.088)|(0.073)|(0.073)|(0.070)|(0.071)|(0.083)|(0.116)|
|**explained**||||||||||
|Log [Total land area cultivated in ha]|-0.576***|-0.409***|-0.347***|-0.294***|-0.276***|-0.272***|-0.277***|-0.311***|-0.493***|
||(0.077)|(0.048)|(0.039)|(0.032)|(0.029)|(0.029)|(0.030)|(0.034)|(0.059)|
|Household head age (years)|-0.019|-0.017|-0.017*|-0.021***|-0.015*|-0.015**|-0.022***|-0.020**|-0.025**|
||(0.015)|(0.011)|(0.010)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.012)|
|Married|0.011|0.031|0.007|0.011|-0.015|0.012|-0.010|-0.038|-0.021|
||(0.089)|(0.062)|(0.051)|(0.042)|(0.043)|(0.044)|(0.045)|(0.052)|(0.078)|
|Divorced/Separated|-0.007|0.001|-0.011|-0.001|-0.003|-0.020|-0.007|0.018|0.022|
||(0.031)|(0.021)|(0.016)|(0.014)|(0.014)|(0.015)|(0.015)|(0.017)|(0.024)|
|Widowed|0.009|0.019|-0.016|0.010|0.024|0.037|0.064|0.116**|0.070|
||(0.089)|(0.064)|(0.054)|(0.046)|(0.045)|(0.046)|(0.047)|(0.047)|(0.063)|
|Household head has Primary education|-0.006|-0.007|-0.006|-0.000|-0.001|0.005|0.004|-0.000|-0.009|
||(0.012)|(0.009)|(0.007)|(0.006)|(0.006)|(0.006)|(0.006)|(0.007)|(0.009)|
|Household head has Secondary|-0.011|-0.007|-0.005|0.004|-0.005|-0.000|-0.000|0.005|-0.008|
|<br>education|(0.013)|(0.008)|(0.007)|(0.006)|(0.005)|(0.005)|(0.005)|(0.007)|(0.009)|
|Household head has Tertiary education|<br>0.000|<br>0.000|<br>0.001|<br>0.002|<br>0.003*|<br>0.003|<br>0.003*|<br>0.005**|<br>-0.001|
||(0.003)|(0.002)|(0.002)|(0.001)|(0.001)|(0.002)|(0.002)|(0.002)|(0.002)|
|Household head disability|<br>-0.000|<br>-0.000|<br>0.000|<br>0.000|<br>0.001|<br>0.002|<br>0.003|<br>0.003|<br>0.011**|
||(0.004)|(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.005)|
|Dependency ratio|-0.015|-0.009|-0.009|-0.001|-0.004|-0.010|-0.006|-0.001|0.012|

14
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Lerma-Chapala Basin in Mexico (Reynoso, 2000; Merrey, 2000), the São Francisco Basin in Brazil (Ioris, 2001), the Bicol Basin in the Philippines (Koppel, 1987), the Olifants Basin in South Africa (Merrey, 2000), and the Cross Basin in Nigeria (Udofia, 1988). For a summary of major components of the decentralization reform in these basins, see Mody (2004). To address this issue in a more comprehensive manner, a research study was developed that would (i) carry out eight additional case study analyses and (ii) undertake a global survey of river basin organizations. The eight case studies were completed recently, employing a single analytical framework, as part of the study “Integrated River Basin Management and the Principle of Managing Water Resources at the Lowest Appropriate Level – When and Why Does It (Not) Work in Practice?” that was funded by the World Bank. The eight case studies are: the Guadalquivir Basin in Spain (Blomquist et al., 2004), the Warta Basin in Poland (Blomquist, et al., 2004), the Brantas Basin in Indonesia (Bhat ey al., 2004), the Tárcoles Basin in Costa Rica (Blomquist et al., 2004), the Murray Darling Basin in Australia (Blomquist et al., 2004), the Fraser Basin in Canada (Blomquist et al., 2004), and the Alto Tietê and Jaguaribe Basins in Brazil (Formiga and Kemper, 2005; and Formiga and Kemper, 2005).

While the case study analyses cited in Mody (2004) and the recent eight case studies mentioned above shed light on the direction of development in river basin decentralization, these do not yet permit the identification of generic reasons and forces behind decentralization, of process characterization and of success levels, just because the focus was on individual case study description. A more analytical approach would allow finding general relationships and patterns. For that one needs to have a framework that incorporates the political, institutional and economic variables and the paths by which they may influence decentralization outcome. Such framework also needs to take into account the initial and contextual conditions. An important consideration in this context is that “the lowest appropriate level” for integrated river basin management varies between basins, i.e. while full decentralization of decision-making can be optimal in one case, it can be destructive in another one. For this reason, the methodology necessarily needs to take into account hydrological, socio-economic, cultural and historical conditions in each basin (see also Saleth and Dinar, 2004).

In this paper we use the theory that has been developed within the research project and expand it to explain levels of success of decentralization processes in river basins and apply it to the global data collected from river basins around the world. The next section presents the theory and the hypotheses we plan to test. The third section presents the process of data collection and the 5
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# **6  Potential Impact of Policy Changes**

We start by looking at the marginal impact of each of the programs on retirement ages and contribution densities “across” the joint distribution of parameters. We basically ask the question what would be the impact on the output variables of interest of removing, one at the time, the pension system, the unemployment insurance system, and the unemployment FGTS savings accounts. We then look at the effect of policy interventions that aim to separate the insurance and redistributive functions in the pensions system. This is done by having one single formula for pensions that is “incentives neutral” and then using various forms of explicit subsidies to finance transfers for targeted individuals.

A general, and important, message from the analysis is that, like in the case of the baseline, the effects of any policy intervention on behaviors are very sensitive to individual preferences. One could compute an average effect for each intervention, for instance, an average increase or reduction in contribution densities and retirement ages. But this average effect would hide considerable variation resulting from unobserved heterogeneity in preferences. Thus, in what follows we look at the impact of policy changes on a sub-region of the parameters space, basically the 35 points with the highest likelihood or the “center” of the joint distribution. We limited ourselves to 35 points mainly given constraints in terms of computing time.

We first remove the pension system. The effects on contribution densities<sup>12</sup> and retirement ages are quite different for high and low income workers. For high income workers we observe, in

> 12 Although there is no more pension system and, therefore, no more individual contributions, we still consider formal work to be contributory, because of the taxes paid to finance UI and the FGTS contribution paid by the employer.

24
28

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20