rafmacalaba/datause-encoder-data huggingface dataset:
awareness of the program was not widespread during its first year.<sup>24</sup> For subsequent cohorts, program impacts are still large and statistically significant, ranging from 11 to 32 percent.<sup>25</sup> Impacts of comparable magnitude have been found in similar female-targeted CCTs in Bangladesh, Cambodia, and Malawi (Khandker and others 2003; Baird and others 2009; Filmer and Schady 2006).

An interesting result is that program impacts on female enrollment increase with school grades for the same cohort. Several factors may be behind this. First, the program might have incentivized girls who had left the education system to re-enter school. However, this does not seem to explain the changes for middle school grades since re-entry is not only very low (less than 0.5 percent) but also similar across stipend and non-stipend districts. Second, and more plausible, the program may have differential effects in retaining girls in schools, perhaps more in higher grades. Dropout rates in Punjab (as in most developing settings) increase with grades and age. In fact, a simple DD analysis between 2003-04 and 2007-08 using household survey data shows a decrease of 2-3 percentage points in drop-out rate in middle and high school grades in stipend districts, relative to non-stipend districts, while there are not changes in dropout rates for noneligible grades. Finally, the expansion of the FSSP in 2006-07 to cover grades 9 and 10 may provide additional incentives for girls to finish middle school and transition to high school.

The findings also show that the increases in enrollment attributed to the program remain as long as girls are enrolled in middle school. However, such effects are not continued to grades 9 and 10. Point estimates of the effects on enrollment in grade 9 only become positive and statistically significant for some econometric specifications in the academic year 2007-08 and 2009-10 when the expansion of the FSSP to high school was fully implemented. This may suggest that this second phase of the program has positive impacts on enrollment in high school.

The evidence on school progression based on household-level data seems to validate the findings obtained with school data as it also suggests that the program increases the chances that

> 24 Qualitative evidence based on interviews with officials in charge of implementing the program indicates that campaigns to raise the awareness of the existence of the program began only at the second year of the program.

> 25 The parameter of interest for the models discussed here is changes in the number of girls relative to number of girls enrolled at baseline. For instance, if the numbers of girls enrolled increased by five (from 16 to 21), this corresponds to a 32 percent change in enrollment. This, however, should not be confused with a change in enrollment rate in percentage points. Suppose that the universe of girls that should be enrolled in middle school is 100, the increase in enrollment rate in percentage points for the above scenario would be around five, from 16 percent to 21 percent.

18
|||Full Sample||Trimming Tail 5%|Bottom 25%<sup>∗</sup>|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Power Structure_||||||
|YRD|0.109**|0.116**|0.119**|0.087|-0.419**|
||(0.050)|(0.054)|(0.056)|(0.053)|(0.182)|
|_Initial Conditions_||||||
|Ln_GVOPC_52||0.033|0.028|0.031|-0.081|
|||(0.036)|(0.039)|(0.038)|(0.101)|
|Ln_pop_52||0.048**|0.041|0.032|-0.152*|
|||(0.022)|(0.026)|(0.024)|(0.076)|
|_Geography_||||||
|Share of Plain|||0.000|0.001|0.002|
|(%)|||(0.002)|(0.002)|(0.007)|
|Distance to Xiamen|||-0.000|-0.000|0.001|
|(Km)|||(0.000)|(0.000)|(0.001)|
|Obs.|58|57|57|53|58|
|_R_<sup>2</sup>|0.075|0.125|0.095|0.089|0.172|

Table 9: Effect of Factions on Famine Control during the Great Chinese Famine 1959-1961 in Fujian.

Notes: White standard errors are in parenthesis. Intercept not reported. In Column (5), the dependent variable is a dummy variable, which take the value of 1, if the county was in the bottom 25 percent of famine control (25 percent counties suffering the most severe famine during 1959-1961), and take the value of 0, otherwise.

32
# 4.2 Estimation results

Table 3: Weather uncertainty and fixed asset growth

|||Dep.|variable: Fi|xed assetgro|wth||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Uncertainty|−5.048<sup>∗∗∗</sup>|−0.831<sup>∗∗</sup>|−1.181<sup>∗∗∗</sup>|−0.768<sup>∗∗</sup>|−6.517<sup>∗∗∗</sup>|7.348<sup>∗∗</sup>|
||(0.268)|(0.357)|(0.370)|(0.360)|(0.342)|(3.337)|
|Mean|||−0.154<sup>∗∗∗</sup><br>(0.043)||||
|Maximum||||−0.064|0.236<sup>∗∗∗</sup>|−0.059|
|||||(0.048)|(0.021)|(0.048)|
|Capital Intensity|||||−2.029<sup>∗∗∗</sup><br>(0.393)||
|Income||||||−4.300<sup>∗∗∗</sup><br>(1.116)|
|Uncertainty:Capital Intensity|||||−1.038<sup>∗∗∗</sup><br>(0.179)||
|Uncertainty:Income||||||−0.903<sup>∗∗</sup><br>(0.366)|
|Constant|10.012<sup>∗∗∗</sup>|14.337<sup>∗∗∗</sup>|19.019<sup>∗∗∗</sup>|16.799<sup>∗∗∗</sup>|10.741<sup>∗∗∗</sup>|42.887<sup>∗∗∗</sup>|
||(0.604)|(2.210)|(2.559)|(2.873)|(1.224)|(9.652)|
|Year FE|No|Yes|Yes|Yes|Yes|Yes|
|Country FE|No|Yes|Yes|Yes|Yes|No|
|<br>Industry FE|No|No|No|No|No|Yes|
|Observations<br>|50,249|50,249|50,249|50,249|200,991|49,778|
|R<sup>2</sup><br>|0.007|0.138|0.138|0.138|0.063|0.140|
|Adjusted R<sup>2</sup>|0.007|0.136|0.137|0.136|0.063|0.138|

_Notes:_ This table presents the regression of fixed asset growth on weather uncertainty, measured by the standard deviation of deviations from expected temperatures. Models (1) through (4) progressively expand the baseline specification (Eq 10) by incorporating fixed effects (Model 2) and adding controls for mean and maximum temperatures (Models 3 and 4). Models 5 and 6 report the results for industry heterogeneity by capital intensity (Eq 11) and income-level heterogeneity (Eq 12), respectively. Standard errors are shown in parentheses. Significance level:<sup>∗</sup> p _<_ 0.1;<sup>∗∗</sup> p _<_ 0.05;<sup>∗∗∗</sup> p _<_ 0.01.

For the baseline regression results, we begin with the first four columns in Table 3, which summarizes the results from different specifications of the main model described in Equation 10. We consider four models, starting with the simplest specification that includes only temperature uncertainty as a predictor (Model 1), and gradually expanding it to test the robustness of the estimated coefficient as additional variables are included. Model 2 adds country and year fixed effects, while Models 3 and 4 incorporate maximum and mean temperatures, respectively. The results consistently show a negative relationship between location-specific temperature uncertainty and firm size growth across all 15
conditions, but these improvements did not actually impact workers’ day to day experiences (as captured in their answers to the labor force survey).<sup>11</sup> For instance, firms could instruct lowerlevel managers not to abuse workers, but the managers may not follow these directions. It is also – possible that working conditions may improve on some dimensions in particular, those that are – easier to scrutinize and get worse on others, leading to a net zero change in an aggregate index of working conditions. Thus, whether labor markets are perfectly competitive or monopsonistic, we anticipate that working conditions either increased, or did not change if the push toward better conditions was subverted by firms.

# **6.2 Contracts**

The international scrutiny that workers faced after Rana Plaza may have increased the uncertainty that firms face about the future. For instance, firms may perceive an increased likelihood that orders will be cancelled if audits find any issues in their factories. In this environment, firms would place increased value on flexibility, such as the ability to quickly scale up or down the size of their labor force, and they may be less likely to offer workers formal contracts. At the same time, if international scrutiny involved direct pressure related to contracts, firms might be more likely to offer formal contracts. Thus, the theoretical prediction on contracts is ambiguous, whether labor markets are perfectly competitive or monopsonistic.

# **6.3 Wages and labor supply in perfectly competitive labor markets**

If firms did respond to international scrutiny by improving working conditions, then this response could impact their wage in a world in which firms divide total compensation between wages – and non-wage benefits. In the context of a perfectly competitive labor market where total compensation was set at workers’ marginal product, pre Rana Plaza – this would mean that wages decreased a greater fraction of total compensation was shifted towards non-wage benefits (Summers, 1989, Mitchell, 1990, Almeida and Carneiro, 2012).

> 11This possibility evokes a strand of the labor markets literature that highlights the potential for firms to evade labor market regulation, e.g. Ashenfelter and Smith (1979).

26
- Ahmad, F., Shah, G.M., Pradhan, B.B., Dixit, R., 2022. Towards an Environment-and-Worker Friendly Brick Kiln Sector in Punjab, Pakistan. Working Paper. International Centre for Integrated Mountain Development (ICIMOD), Kathmandu.

- Bhattarai, J., Ghale, D.B., Chapagain, Y.P., Bohara, N.B., Duwal, N., 2018. Study on the physical and mechanical properties of ancient clay brick samples of Kathmandu Valley, Nepal. Tribhuvan University Journal 32, 1-18.

BiofuelCircle, 2022. Can Coal be Replaced with Biomass Briquettes? BiofuelCircle, Pune.

- Budhathoki, P., Duwal, N., Bhattarai, J., 2018. Investigation on physico-sintering and mechanical properties of clay tiles available in Kathmandu valley of Nepal. Asian Journal of Chemistry 30, 2515-2520.

- Budhwar, R., Bihari, V., Mathur, N., Srivastava, A., Kumar, S., 2003. DNA-protein crosslinks as a biomarker of exposure to solar radiation: a preliminary study in brick-kiln workers. Biomarkers 8, 162-166.

- Build Better Nepal, 2018. Competition - Interlocking Bricks (CSEB) vs Fired Brick - Building 1m2 Wall. Build Up Nepal, Kathmandu, https://www.youtube.com/watch?v=wU2spFktjAA.

- CBS, 2014. National Population and Housing Census 2011. Volume 04, NHHC2011. Central Bureau of Statistics, Kathmandu.

- CBS, 2022a. National Industrial Survey 2019/20: National Report. Central Bureau of Statistics (CBS), Government of Nepal, Kathmandu.

CBS, 2022b. Statistical Yearbook 2021. Central Bureau of Statistics (CBS), Kathmandu.

CEN, 2009. Baseline Study of Brick Kilns in Nepal. Clean Energy Nepal (CEN), Kathmandu.

- Chapagain, Y.P., Sapkota, S., Ghale, D.B., Bohara, N.B., Duwal, N., Bhattarai, J., 2020. A case study on mineralogy and physico-mechanical properties of commercial bricks produced in Nepal. SN Appl. Sci. 2, 14.

- Deore, M., Maudgal, M., Maithel, S., Kumar, S., 2019. Market Transformation towards Energy Efficiency in Brick Sector: A Strategic Blueprint, From Vision to Mission. Bureau of Energy Efficiency (BEE), Indo-German Energy Programme (GIZ), Greentech Knowledge Solutions Private Limited (GKSPL), New Delhi.

- DOI, 2022. Micro, Cottage and Small Industry Statistics (in Nepali). Department of Industry (DOI), Government of Nepal (GON), Kathmandu.

- DOMG, 2022. Annual Report of Department of Mines and Geology. Annual Report No. 13 DMG. Department of Mines and Geology (DOMG), Kathmandu.

- DUDBC, 2017. Design Catalogue for Reconstruction of Earthquake Resistance Houses: Volume II. Catalog for Alternative Construction Materials and Technologies. Department of Urban Development and Building Construction (DUDBC), Ministry of Urban Development, Government of Nepal, Kathmandu.

36
From Equation (1) and Equation (4), we obtain: where ι<sup>GDP</sup> j,y<sup>is a target for GDP or expected economic growth, i.e.:</sup> where gdpj,0 is GDP of a country or region j in the base year. As an economy of a country grows, ι<sup>GDP</sup> j,y<sup>becomes greater than one.</sup> The understanding of the relationship between the absolute emission target and the intensity based target is very important. Equation (5) demonstrates that the relationship between these two targets depends on future expectations on economic (i.e., GDP) growths. For example, if a country expects higher economic growth, its emission intensity is required to be cut at a rate greater than that of its absolute emissions to achieve the same level of emission reductions. The reverse would be true if the country expects an economic downturn in the future.

The study uses data, particularly CO2 emissions and GDP from the Energy Information Administration (EIA) of the United States Department of Energy (EIA 2007a and EIA 2007b). Besides, EIA, CO2 emission inventories are also produced by various

-7-
rivers (see RAIN (2007) and Lasage et al. (2008) In contrast, such rivers are generally unsuitable for large dams, which, in South Africa, are almost exclusively buttress, arch, or gravity dams.<sup>5</sup> We thus use the interaction term of our policy variable with perennial (year-round) river gradients as instruments for both large and small dams, but we also use ephemeral length within a basin (interacted with the policy variable) to separately identify the potentially different effect of small dams on agricultural productivity.<sup>6</sup> Our specification for exogenously predicting dam location relies on the use of differences in dam construction because of the change in water infrastructure policy after the end of apartheid as well as differences in the geographic suitability of river basins to predict the number of dams:

> where _D s it_ is the number of dams in river basin _i_ at time _t_ and of size , _νi_ are river basin fixed effects, _ωit_ is a vector former homeland and former nonhomeland year interaction terms, and _μit_ is the error term.<sup>_P_</sup> _it_<sup>is the policy</sup> proxy described above. The river gradient variable, _RGr(k)i_ ’s, , represents the fraction of perennial rivers in basin _i_ with a 1.5–3 percent (moderate), 3–6 percent (steep), and over 6 percent (very steep) river gradient. _ERLEGNTHi_ is the length of ephemeral rivers within a basin and is not used when _sz=LARGE_ . Our source for all geographical information on rivers is the Food and Agriculture Organization’s Rivers of Africa geospatial database (FAO 2000). To calculate all other geographical features, we

13
WPS4005

# **What Is Effective Aid? How Would Donors Allocate It?**

## Charles Kenny<sup>i</sup>

## **Abstract**

There are significant weaknesses in some of the traditional justifications for assuming that aid will foster development. This paper looks at what the cross-country aid effectiveness literature and World Bank Operations Evaluation Department (OED) reviews have suggested about effective aid, first in terms of promoting income growth and then for promoting other goals. This review forms the basis for a discussion of recommendations to improve aid effectiveness and a discussion of effective aid allocation. Given the multiple potential objectives for aid, there is no one right answer. However, it appears that there are a number of reforms to aid practices and distribution that might help to deliver a more significant return to aid resources. We should provide aid where institutions are already strong, where they can be strengthened with the help of donor resources, or where they can be bypassed with limited damage to existing institutional capacity. The importance of institutions to aid outcomes, as well as the fungibility of aid flows, suggests that programmatic aid should be expanded in countries with strong institutions, while project aid should be supported based on its ability to transfer knowledge and test new practices and/or support global public good provision rather than (merely) as a tool of financial resource transfer. The importance of institutions also suggests that we should be cautious in our expectations regarding the results of increased aid flows.

## World Bank Policy Research Working Paper 4005, September 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> `i` Senior Economist, Infrastructure Economics and Finance, the World Bank (ckenny@worldbank.org).

1
<!-- Start of picture text --> : : : Py<br><!-- End of picture text --> <!-- Start of picture text --> Percent deviation from trend<br>6 Median --Upper and lower quartile<br>4<br>2<br>. a<br>0 baal i sot<br>-? + . o + a+ :<br>4 as<br>-6<br>-3 -2 -1 0 4 2 3<br>Year<br><!-- End of picture text -->
Burbidge, J.B., Magee, L., and Robb, A.L. (1988) “Alternative transformations to handle extreme values of the dependent variables” _Journal of the American Statistical Association_ , 83: 123-127.

- Burger, M.J. and Ianchovichina, E. (2013) “FDI surges and stops in developing countries: differences between M&A and greenfield investments” Mimeo.

- Burger, M.J., Van der Knaap, G.A., and Wall, R.S. (2013) “Revealed competition for greenfield investments between European regions” _Journal of Economic Geography_ , 13, 619-648.

Busse, M. (2004) “Transnational corporations and repression of political rights and civil liberties: an empirical analysis” _Kyklos_ , 57, 45-65.

- Busse, M. and Hefeker, C. (2007) “Political Risk, institutions and foreign direct investment” _European Journal of Political Economy_ , 23, 397-416.

- Chan, K.K and Gemayel, E.R. (2004) “Risk instability and the pattern of foreign direct investment in the Middle East and North Africa” International Monetary Fund Working Paper. WP/04/139.

- Collier, P. and Hoeffler, A. (2009) “Testing the neocon agenda: democracy in resource-rich countries” _European Economic Review_ , 53, 293-308.

Corden, W.M. and Neary, J.P. (1982) “Booming sector and de-industrialisation in a small open Economy” _The Economic Journal_ , 82, 825-848.

- Cuervo-Cazurra, A. (2006) “Who cares about corruption?” _Journal of International Business Studies_ , 37, 807-822.

Darvas, Z. (2012) “Real effective exchange rates for 178 countries: a new database”. Working Paper, Brussels: Bruegel.

Daude, C. and Stein E. (2007) “The quality of institutions and foreign direct investment” _Economics and Politics_ , 19, 317-344.

- Dixit, A. and Pindyck, R. (1994) _Investment Under Uncertainty,_ Princeton, NJ: Princeton University

Press.

22
supported by the World Bank Africa Region Social Protection Sector, including the Tanzania Productive Social Safety Net Program, could further offer useful insights in terms of the way in which CBT and PMT can be combined at the local level.<sup>26</sup> More broadly and beyond the MoAFS statements regarding the main objective of the FISP, which implies overcoming food insecurity and poverty at the household level, the program has been associated with the goal of increasing production/attaining national food self-sufficiency. Given the scale of the program and its dwarfing effects on the financing of social safety nets in the country, the FISP continues to be framed as a social protection program as well. The targeting criteria for a program aimed at overcoming food insecurity at the national versus household level or providing social protection for the most vulnerable, however, need not to be identical and need to be more carefully articulated (Dorward and Chirwa, 2013).

If the FISP is designed to address poverty directly by targeting poor households that cannot otherwise afford unsubsidized agricultural inputs, it is failing to achieve this objective. The stagnant poverty trend in the country during the period that the FISP has been operational supports this insight. Alternatively, if the FISP is designed to indirectly address poverty—as well as food security—through expanding national agricultural production, it might be argued that favoring those with greater agricultural potential makes sense. To simultaneously achieve these objectives, the program should be able to identify households with high input use efficiency who also cannot otherwise buy subsidized inputs at prevailing market prices. Empirically, the identification of this segment of the population is difficult as one cannot simply work with the distribution of consumption and input marginal productivity and their overlap as observed in the data. The sound research design needs to simulate these outcomes in the absence of the program (see Pan and Christiaensen, 2012) such that the size of each alternative beneficiary universe, including the nexus between poverty and high input marginal productivity, could be estimated and the correlates of being in these universes are unpacked as possible inputs into redesigning and operationalizing the FISP eligibility criteria. Our future research on the FISP will explore these ideas based on the IHPS data.

> 26 For more information on the approach to targeting as part of the Tanzania Productive Social Safety Net Program, see World Bank (2012).

24
level of carbon tax would reduce developed countries’ GDP by 2.4 percent and global trade volume by 2 percent. The economic costs vary significantly across countries, with heavy burden on fossil fuel intensive economies, such as Russia, Australia, United Kingdom and the United states. On the other hand, global emissions in 2030 would be still more than 15 percent higher from the current level.

The paper is organized as follows. Section 2 briefly describes the model following by discussions on the impacts on CO2 emissions and emission intensities of various countries and region due to the policy instrument implemented to reduce the intensity gaps in Section 3. Section 4 presents the impacts of the policy instrument on economic outputs and international trade. Finally, key conclusions are drawn in Section 5.

# **2. Methodology and Data**

We used a multi-regional, multi-sector, recursive dynamic CGE model for the purpose of this study<sup>1</sup> . The basic data needed for the calibration of the model is derived from the GTAP 7.0 database. The main reason for using the GTAP database is that no other comprehensive global database required by this study exists. Moreover, most CGE models simulating climate change policies use the GTAP database, and the use of this database in our study helps compare our results with those of others. The original GTAP database provides information for 113 countries and 57 commodities and production sectors. We further worked on the database to have only 28 sectors and 25 countries/regions as needed by our model.

Each of the 28 sectors is depicted by a set of nested constant elasticity of substitution (CES) production functions (see Figure 2). At the top tier of the production structure, firms in each country/region minimize their production costs by choosing an optimal combination of the aggregate non-energy intermediate input (ND) and the composite of value added and the aggregate energy input (VAE). On the left hand side of the second tier of the nested production structure, a non-energy commodity in a country/region is formed through a CES combination of that commodity produced in the country/region and that imported from various countries/regions.

> 1 For a more elaborated discussion on the model please refer to Timilsina et al. (2010 and 2011).

4