rafmacalaba/datause-encoder-data huggingface dataset:
table 6 shows that the DOT training had an impact on the psychology of its trainees. Although the magnitude of changes in the measures of psychology are small, we find that entrepreneurs in the DOT treatment group, one year after the training, have statistically significant higher index levels of personal initiative and selfefficacy, relative to those in the control group. Entrepreneurs who are in the treatment group are more confident in their abilities with the trained entrepreneurs more likely to report that they can overcome problems they encounter and are more likely to feel that they are competent in managing their business well. In addition, the trained entrepreneurs appear to take more initiative, as a result of the training. They report that they do more than they are asked to, and that they are good at realizing ideas. Overall, the picture that emerges is that entrepreneurs who participate in the training benefit from an improved sense of confidence in their abilities and are more motivated to improve their businesses. The psychological outcomes for the DOT treatment group, when measured two years after the training, showed no significant differences to those of the control group.<sup>9</sup> The time trend in the DOT study (not shown in the table) exhibits a fall over time in psychological outcomes for all business owners. We speculate that since the time frame of the study was between 2016 and 2017 this could be during a time of increased uncertainty in the business environment in Ethiopia, with the country in and out of a state of emergency during that period of time. The negative coefficient on entrepreneurial identity may be due to trainees being more critical about their own entrepreneurship two years after the training when impacts have faded perhaps making them question their own identity.

Panel B presents the results for the PI/BSED study where we find no evidence of an average impact on psychology among those who were offered the training. There is weak evidence of a boost in the error competence of BSED trainees. Without a more pronounced impact on psychological outcomes the channel for which profits can be influenced is missing.

> 9 As with profits, the personal initiative score at the two year follow up is not statistically different from the impacts at one year at conventional levels of significance. For self-efficacy, however, the 2-year coefficient is significantly lower than the one year impacts at a 10 percent level of significance.
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22

**Table 11 Econometric results for quality, 1990–2002**

|||**(1)**|**(2)**|
|---|---|---|---|
|||**Ln power T&D losses**<br>**(percent of output)**|**Ln phone faults**<br>**(reported faults/100 mainlines)**|
|**(1)**|IRA|0.121<br>[1.32]|0.767***<br>[3.67]|
|**(2)**|Privatization|–0.043<br>[0.43]|0.201<br>[1.14]|
|**(3)**|IRA * privatization|0.032|–0.072|
|||[0.53]|[0.76]|
|**(4)**|Corruption index<br>(0=low 1=high)|0.073<br>[1.57]|0.027<br>[0.12]|
|**(5)**|IRA * corruption|0.023<br>[0.14]|–1.188***<br>[3.43]|
|**(6)**|Privatization *<br>Corruption|0.196<br>[1.13]|–0.262<br>[0.92]|
|**(7)**|Ln GDP per capita<br>(2000 U.S.$)|–0.115**<br>[2.18]|0.605**<br>[2.54]|
|**(8)**|Ln urbanization|–0.284<br>[1.43]|–2.906***<br>[5.04]|
|**(9)**|Time trend|0.009**<br>[2.26]|–0.055***<br>[4.76]|
|**(10)**|Constant|–14.244*<br>[1.83]|119.837***<br>[5.59]|
|**(11)**|Observations|880|652|
|**(12)**|R–squared within|0.23|0.39|
|**(13)**|No. of countries in<br>|76|85|
||sample|||
|||**_Marginal effects_**||
|**(14)**|IRA|0.140***<br>[4.82]|0.136**<br>[2.02]|
|**(15)**|PRIV|0.070<br>[1.84]|0.037<br>[0.58]|
|**(16)**|CORRUPTION|0.125**<br>[2.42]|–0.550***<br>[3.04]|

Absolute value of t statistics in brackets. * significant at 10 percent, ** significant at 5 percent, *** significant at 1 percent.

The third marginal calculation suggests that corruption does not have the same impact in the two sectors, even though we find in both cases a statistically significant impact on quality. An increase in corruption was associated with an increase in power losses but with a decrease in telephone faults. Thus, corruption is on average harmful to quality in electricity but beneficial, at least in terms of quality, in telecoms.

The interaction dummies suggest that the reforms have no role in producing the effects of corruption in the electricity sector. In the case of telecoms, the advent of PPI has no effect on the impact of corruption on quality; by contrast, the creation of an IRA does have the expected effect. That is, corruption leads to a deterioration of quality, but the regulator mitigates that negative effect. The dummy for the interaction between IRA and PPI reveals no statistically significant effect.
affects both their own creative and managerial abilities, and their skill in appraising and adapting exogenous technologies. Patent data is used to account for private sector research and farm size is included as a cause of factor saving biases. The policy distortions of the apartheid period are also allowed for by including dummy variables for tax concessions, negative real interest rates for farm credit, and the operation of the Pass Laws.

# TIME SERIES ANALYSIS OF THE DATA

# **The Data**

The data are for the commercial agricultural sector of the Republic of South Africa, for the period 1947-92. The main source is the _Abstract of Agricultural Statistics_ (Republic of South Africa, 1993) supplemented by historical material and unpublished information from the Department of Agriculture. These data were used for total factor productivity measurement (Thirtle et al.,1993) and estimation of a dual profit function (Khatri et al., 1994).

The land measure is the total hectarage of agricultural land and the land price is the rental value. Labour is all hired labour and the price is the wage for hired labour. Fertilizer is a constant price series for an aggregate of all fertilizer and the price is for the average fertilizer mix. The machinery series is the service flow (interest, depreciation and running costs) from the capital stock of machinery, implements, motor vehicles and tractors, taken from Thirtle et al. (1993). This avoids the problem of mixing stock and flow variables. The series for R&D, extension, farmer education and patents are from Thirtle and van Zyl (1994). The patents are counts of chemical and mechanical patents, pertaining to agriculture, registered by all countries in the US. They are used both to account for private R&D activity and to try and capture some of the international R&D spillovers. The model was customised to suit the special problems of South Africa by including dummy variables to capture the effect of taxation policy, interest rates and the control of labour by means of the Pass Laws. The dummny variable for tax policy covers the period up to 1982, when concessions such as agricultural buildings subsidises and rapid tax write7
Two of the potential mechanisms described above are consistent with the combination of reduced demand for informal credit and lower interest rates: a decrease in the administrative costs associated with lending due to the use of SHG credit to service informal debt, and increased competitive pressure in the context of a market in which lenders exercise market power.<sup>14</sup> Household data on use of loans at endline indicate that only 2.8% of SHG loans in treatment areas, and 2.4% in control areas, were used for debt service, casting doubt that this mechanism plays a major role, and leaving a reduction in market power as the most convincing explanation.

# _4.5 Downstream impacts: Assets, consumption and women’s empowerment_

While Jeevika had immediate and dramatic effects on credit markets in targeted villages, we see more limited short-term impacts on household asset possession, material well-being or various measures of women’s economic and social empowerment (Tables 4 and 5). We do see significant positive effects of the program on asset holdings among landless households (Table 4). Consumption assets, productive assets, and housing quality are all significantly affected within this sub-group.

The program’s effect on women’s empowerment is less clear. We see a positive impact on an index of collective action, but a negative impact on women’s role in decision-making within the household (both p<0.1). These two effects cancel each other out in the aggregated index for this family of outcomes, resulting in an estimated family-wise treatment effect of zero.

# _4.6 Robustness tests_

In the presence of a baseline difference in outcomes, the ANCOVA estimator is generally less biased than either the simple difference or difference-in-difference estimators, which tend to under- or over- correct for the baseline difference respectively (Frison and Pocock, 1992). We present these two alternative estimators as upper and lower bounds of the true effect in Tables A2 through A5. Treatment effects are similar in magnitude and significance under these alternative

> 14 A third potential mechanism is that the cost of informal lending falls as demand shrinks due to increasing marginal costs. This however is at odds with evidence about the cost structure of informal lending, which is characterized by large fixed costs and not generally limited by availability of lending capital (Aleem, 1990). The fact that demand for informal credit falls most dramatically among landowners, but the decrease in interest rates is driven by landless households, provides additional evidence against this mechanism.
economic crisis: An empirical assessment of rural Indonesia,” Center for Global Development, Working Paper Number 115.

McKenzie, David and Christopher Woodruff. 2008. “Experimental Evidence on Returns to Capital and Access to Finance in Mexico”, _World Bank Economic Review,_ 22(3): 457-82.

McKenzie, D., and C. Woodruff. 2006. “Do Entry Costs Provide an Empirical Basis for Poverty Traps? Evidence from Mexican Microenterprises,” _Economic Development and Cultural Change_ 55 (1): 3-42.

McKenzie, David, and Christopher Woodruff. 2003. “Do entry costs provide an empirical basis for poverty traps? Evidence from Mexican microenterprises.” Working Paper 2003-20, Bureau for Research in Economic Analysis of Development.

McMillan, John, Christopher Woodruff. 2002. “The central role of entrepreneurs in transitional economies.” _Journal of Economic Perspectives_ 16 (3), 153–170.

Mukherjee, A., & Zhang, X. (2007). “Rural Industrialization in China and India: Role of Policies and − Institutions.” _World Development_ , 35(10), 1621 1634.

Mukharjee, A. and Zhang, X. 2005. _Rural Non-Farm Development in China and India: The Role of Policies and Institutions_ , DSGD Discussion Paper No. 24, IFPRI, Washington DC.

Mwabu1, Germano, Wilfred Mwangi and Hezron Nyangito. 2006. “Does Adoption of improved Maize Varieties Reduce Poverty? Evidence From Kenya,” Poster paper prepared for presentation at the International Association of Agricultural Economists Conference, Gold Coast, Australia.

Olley, G. Steven, and Ariel Pakes. 1996. “The dynamics of productivity in the telecommunications equipment industry.” _Econometrica_ 64(6), 1263–1297.

Onchan, Tongroj. 2001. “Non-Farm Employment Opportunities in Rural Areas in Asia” Report of 32
Indonesia provides a compelling case to examine. Gender wage gaps are very large and minimum wages are set at the provincial level, with local schedules for when they are reset, so they vary both across provinces and over time (see Rama, 2001; World Bank, 2010). Figure 1 shows the extensive variation of minimum wages across provinces and over time, which facilitates identification.

Previewing the main findings, in aggregate, minimum wage increases are associated with reductions in gender pay gaps, but not with changes in relative employment prospects by gender. However, these aggregate relationships mask important heterogeneity in the impact of minimum wages by education and across the distribution of firm-level average production worker wages. While gender pay gaps are largest among production workers with the lowest levels of educational attainment, minimum wage increases were least effective in reducing gender pay gaps among this group. The wages of the least educated women did not rise in response to minimum wage increases, while those of uneducated men did, exacerbating gender pay gaps at the lower end of the education distribution. By contrast, minimum wage increases appear to have had the largest impact on women who complete junior high and high school; they experienced significant wage increases both in absolute terms and relative to men with the same education.

Quantile regressions that examine how minimum wages relate to gender pay gaps across the firm-level average wage distribution attest both to wage compression and lighthouse effects. Allowing the impact of minimum wages to vary by gender (but not yet by education), the results show that at the lower end of the average earnings distribution, minimum wages are associated both with the largest increase in average wages and with the starkest reduction in gender pay gaps, suggesting minimum wage increases and wage compression go hand-in-hand. Consistent 3
# <u>Geography</u>

Certain riverine geographical configurations have been said to facilitate conflict while other have been said to be more conducive to cooperation. The literature has argued that the more asymmetric the river geography the harder it is to achieve cooperation (LeMarquand 1977; Haftendorn 2000). This is notoriously most common in upstream-downstream situations. In opposition, the more symmetric the river geography (i.e. the more retaliation is internalized to the river system), the less feasible conflict becomes. In other words, the more the river straddles the international boundary the more conducive such a typology may be for inter-state coordination over the river (Toset et. al 2000).

# **4. Data and Variables**

Based on the literature reviewed earlier, we divide our data construction efforts into two parts. We focus first on data and variables that represent water supply variability in a basin. At a second stage we discuss the data and variables that represent the international relations, economic, political and institutional situation in the basin countries, and the basin geography.

# <u>Data on climate and water variability</u>

# **_Basin maps_**

A list of 224 bilateral basins is adopted from Dinar, S. (2008) (See Map 1). The Transboundary Freshwater Dispute Database (TFDD) provided geo-referenced basins for almost all the international river basins (http://www.transboundarywaters.orst.edu/database/). Since some of the bilateral basins are sub-basins of TFDD basins, or are not included in the TFDD, it was necessary to delineate the catchments for the unit of analysis—the treaty basin. When both datasets matched, we selected the TFDD basin delineation. Otherwise the remaining basins were identified using ancillary data sources (See Appendix 3). For these remaining basins, hydrologically conditioned elevation datasets (HydroSHEDS) are used to determine the flow paths and watershed boundaries.<sup>ii</sup> Ancillary data sources provide location information to identify the mouth of the given river. With this geo-referenced point and HydroSHEDS data, we used Environmental Systems Research Institute ArcGIS software to delineate the catchment via a twostep process: first, adjusting the mouth location to the nearest center point of the 30 arc-second flow accumulation grid in HydroSHEDS and, second, employing the watershed function in 11
reasons to plausibly expect that oil prices will remain roughly where they stand today for the foreseeable future, but there always remains some chance that prices could return to earlier high levels.

The decline in oil and gas prices has also had an impact on Egypt’s domestic energy sector. Egypt is the largest non-OPEC oil producer in Africa and the second largest dry natural gas producer.<sup>7</sup> Oil production in 2013 averaged slightly less than 700,000 bbl/d, down from a peak of around 900,000 in the 1990s.<sup>8</sup> Output of natural gas output increased until 2009, which partially offset declines in crude oil production. Egypt produced almost 2.0 Tcf of natural gas in 2013, of which almost 1.9 Tcf was domestically consumed. Much of the natural gas consumed in Egypt is used to fuel electric power plants. The government encourages households, businesses, and the industrial sector to consider natural gas as a substitute for petroleum products and coal.

Every percentage fall in international energy (oil) prices would result in lower budgetary costs for Egypt’s energy subsidies. With the July 2014 reforms in place, lower energy prices reinforce the steps taken by the Egyptian government, in effect providing an ideal environment in which to reduce and eventually remove subsidies.

Our analysis examined the combined effects for Egypt of the July 2014 reforms and a sustained 50% reduction in energy prices as well as the impacts of each separately. The _combined_ impact of these changes can be summarized as follows:<sup>9</sup>

- There would be a dramatic reduction (from 2013/14) of energy subsidies as a share of GDP from 9.2% to 2.2%;

- Government revenues would decline by 4.7% of GDP, mainly due to declines in oil and gas profit shares (3.9% of GDP);<sup>10</sup>

- Total government expenditures would decline by 7.2% of GDP;

- The government operational deficit would decline from 8.2% to 5.5% of GDP;

> 7   Source: U.S. Energy Information Agency (EIA), 2015.

> 8   Source: EIA, Egypt 2015.

> 9 There is no employment or real GDP impact – this is a static analysis with market clearing for all factors at the initial level (GDP impacts are shown in the dynamic analysis below).

> 10   Part of this decline (0.7% of GDP) is in the imputed revenue for the zero priced crude oil supplied to the refining sector, which is offset by declines in the imputed crude oil subsidies.

11
**<u>Figure 5. Aggregate Saving Increases When Government Invests Oil Revenue</u>**

<!-- Start of picture text --> 0.0658<br>0.0656<br>0.0654<br>0.0652<br>0.0650<br>0.0648<br>0.0646<br>0.0644<br>0.0642<br>Investment Share of Oil Revenue equals 0 percent Investment Share of Oil Revenue equals 10 percent<br>Private Saving (index) Economy's saving (index)<br><!-- End of picture text --> Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

In the baseline scenario it is assumed (but can be changed by varying kappa) that revenue is distributed equally across generations; i.e. equally many dollars per person for workers and retirees alike. Consequently, retirees experience a direct injection of cash into their disposable income since they do not save. But workers, on the other hand, save a fraction of their gross income which includes the transfers from oil revenue. As a result, when they can look forward to receiving a free transfer in old age, they feel no need to save as much today — as a result, saving falls.

**Figure 6. When Public Saving is Always 10 Percent of Oil Revenue, Then Public —** **<u>Saving Crowds Out Private Saving But Increases Aggregate Saving</u>**

<!-- Start of picture text --> 0.0700<br>0.0680<br>0.0660<br>0.0640<br>0.0620<br>0.0600<br>Oil revenue as a share of output<br>0.0580<br>1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%<br>Private Saving Aggregate Saving<br>Saving<br><!-- End of picture text --> Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

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- 12 -

# **The Ixteus ion Service**

5.09 The implications for extension activities are equally strong. In very few Asian settings can extension workers establish a one-to-one relationship with individual farmers. There are simply too many farmers and too few extension workers. This is not a serious constraint if the new technology and cropping practices being extended are relatively homogeneous for extensive areas, as in an irrigated river basin devoted to rice. Messages transmitted directly to village leaders or respected farmers can be spread fairly accurately and effectively by word-of-mouth and demonstration. But when each farmer might be trying a unique combination of crops, timing, and cultivation techniques, the problems that arise will also be unique. Extension agents who cannot help when problems come up, or who do not know how to grow the crops the farmers want to try, quickly lose their credibility and usefulness. Close interaction between local research stations and the extension agents is the obvious solution, but not one that can be established quickly except on a pilot basis.

# O°portunities for Contract Farming

5.10 One mechanism for providing appropriate technology, extension advice, and stable markets for the output is through contract farming for a central processing unit or marketing firm. Many countries are now trying to find the right set of incentives and local institutional arrangements to attract foreign and domestic investors to participate in schemes. For certain products in some locations--for example, pineapple in Thailand and the Philippines and oil palm in Malaysia--contract farming has been mutually profitable for farmers and processors. But for a variety of reasons, integrating farmers into agro-processing activities has been difficult. The recent World Bank Agricultural Assessment for Indonesia points out that licensing, restrictions on foreign investors, short-term land leases, quality control, and other problems in marketing and transportation have inhibited the development of secondary food crops and horticultural products.41

- 41/ World Bank, 'Indonesia: Agriculture Assessment" (Washington, D.C., July 1986; typescript), pp. 67-68).
**Table 8. Impact of EPAG on Household Characteristics**

||**Pa**<br>|**nel A: Household Size**<br>|||
|---|---|---|---|---|
||Baseline mean|OLS|Controls|Observations|
|Number of household|4.738|-0.049|||
|members|[2.603]|(0.088)|Yes|3182|
||**Panel B**<br>|**: Household Food Security **<br>|||
|Egg included at least once in|0.314|0.034|Yes|3154|
|household meal each week|[0.464]|(0.020)|||
|Fish included at least once in|0.900|0.038**|Yes|3165|
|household meal each week|[0.301]|(0.015)|||
|Meat/chicken included at|0840|0039***|||
|least once in household meal<br>each week|.<br>[0.367]|.<br>(0.013)|Yes|3161|
|Dairy products included at|||||
|least once in household meal|0.546|0.021|Yes|3141|
|each week|[0.498]|(0.026)|||
|Worry that household|||||
|member would not have|0.619|-0.126***|Yes|3171|
|enough food|[0.486]|(0.021)|||
|A household member went to|0.306|-0.103***|Y|3170|
|bed hungryinpast 2 weeks|[0.461]|(0.021)|es||

Notes: Controls include household size, sex of household head, asset-based quintiles all at baseline, and month of interview at midline. Standard errors clustered by classroom.

43