rafmacalaba/datause-encoder-data huggingface dataset:
Table A8: Psychometric score predictiveness for repayment of uncollateralized loans

|||**Treatmen**|**t group only**||
|---|---|---|---|---|
||(1)<br>WEDP-x loan amount<br>(Level)|(2)<br>WEDP-x loan amount<br>(Level)|(3)<br>Loan repayment overdue<br>by at least 90 days<br>(Yes 1; No 0)|(4)<br>Loan repayment overdue<br>by at least 90 days<br>(Yes 1; No 0)|
|Psychometric score|-586.6||-0.0051*||
||(413.2)||(0.0027)||
|Psychometric score above median (0/1)<br>34||-23151.4<br>(14332.5)||-0.15<br>(0.10)|
|Average monthly proft from main business (100 ETB)|166.9***|166.1***|-0.00011|-0.00011|
||(39.0)|(39.7)|(0.00021)|(0.00021)|
|Observations|58|58|58|58|
|_R_<sup>2</sup>|0.283|0.288|0.0686|0.0454|

Notes: Table only shows results for the IE treatment group. Outcomes 1-2 are the uncollateralized (WEDP-x) loan amounts disbursed according to Wasasa’s administrative data. Outcomes 3-4 indicate if the entrepreneur was over 90 days late on their scheduled (WEDP-x) loan repayment according to Wasasa (0/1). Columns 1 and 3 present specifications with the continuous psychometric score measure and columns 2 and 4 present specifications with the binary (above or below the median psychometric score for the IE sample) measure. Observations represent number of individuals. Loan amounts are in 100 Ethiopian birr (100 ETB = 3.4 USD at baseline and 2.2 USD at endline). Robust standard errors are in parentheses. * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001
- 17 -

variable-intercept models with panel data to deal widh this issue. By assuming that the effects of the numerous omitted country-specific variables are each individually unimportant but collectively significant and possess the property of a random variable that is uncorrelated with all other included and excluded variables, we can specify our model as having common slopes for all countries but the intercept varies over individual countries. This method is called the variable-intercept method.<sup>3</sup> We will also run simple OLS regressions based on M-R-W's assumption and compare the results from different methods, which would provide a test on their assumption.

3.2 The **Data and** Samples The data are assembled from the United Nations Statistical Office, the World Bank, Summers and Heston (1991), and some other sources. Definitions for all variables and data sources appear in Appendix 1. The data do not include OECD countries, since many development economists argue that the development process in developing countries is different from that of developed countries. We also exclude major oil producers fron our sample (as defined by World Bank in World Development Report). Countries with a population less than 1 million in early 1980s are excluded the sample because the determination of their real income may be dominated by idiosyncratic factors.

The data include annal variables and cover the period of 1965-1988. The panel data set albows us to conduct tests based on variable-intercept models, which can control for unobserved country-specific effects. We measure _Sf_ as the share of current foreign capital goods imports in current GDP. The data on foreign capital imports are obtained from the United Nation's 3See Hsiao (1986) for the details of the variable-intercept method in panel regression.
To examine these issues, we re-estimate equation (1) by re-defining the treatment group as the group of districts that was left out of the earlier analysis because this group held direct elections in 2006 or 2007. The set of comparison districts used is identical to that used in the earlier regressions, i.e. the districts that did not have direct elections until 2008 and later. The estimates in Table 4a reveal that the fiscal expenditure trajectories of the districts where direct elections are taking place (i.e. 2006) or are about to take place are very similar to the fiscal expenditure trajectories of the districts that already held had local elections in 2005 and have a locally elected district head in place (see Table 1a). Total expenditures as well as development and routine expenditures are higher than in the control districts. In accordance with the patterns observed in other countries such as India, the anticipation of elections in a district is associated with a significantly higher level of per capita development expenditures, the category of spending that is particularly ―targetable‖ and more discretionary. In addition, in the districts where elections are imminent the spending on public works programs appears to constitute the major component of the (higher) development expenditures, which implies that development spending on public works is the natural instrument for ―buying support‖ from the broader public. Contrary to expectations, revenues from own sources are also higher in these districts as is overall surplus, which suggests that the anticipation of district elections is not sufficient to deter current district heads from raising revenues, or at least intensifying efforts toward raising revenues from own sources (Table 4b).

# **6. Are the Changes in Expenditures Based on Need?**

As acknowledged above, the estimated impacts of electoral reforms on district expenditures are only a necessary condition for the impacts of increased accountability on service delivery. One critical question is whether the shift to electoral accountability results in improvements in the measured outcomes of service delivery. In Annex B we investigate this issue and find no significant impacts on outcomes. One possible explanation for the absence of any measurable impacts on outcomes is the fact that we only have two years of data (2006 and 2007) on outcome indicators after the initiation of the electoral reforms. Two years is by all accounts too short of a time horizon in which to expect measurable impacts, even if electoral reforms are ultimately successful at improving outcomes. We can, however, examine whether expenditures in districts that had direct elections are more responsive to the ―needs‖ of districts. Expenditures being responsive to unmet needs for services would not necessarily imply that outcomes would improve, but make it _more likely_ that they would _,_ in comparison to a situation in which investments are uncorrelated with needs.

23
# **References**

<mark>Abdallah Ali, M., M. Mughal, and D. Chhorn. (2021). Empowering Women through Microfinance in Djibouti. Mimeo.</mark> <mark>Afridi, F., A. Mukhopadhyay, and S. Sahoo. (2016) “Female labor force participation and child education in India: Evidence from the National Rural Employment Guarantee Scheme.” IZA Journal of Labor & Development 5(7).</mark> <mark>Ajayi, K. F., A. Dao, and E. Koussoubé (2022). “The Effects of Childcare on Women and Children: Evidence from a Randomized Evaluation in Burkina Faso.” World Bank Policy Research Working Paper 10239.</mark> <mark>Alaref, J., A. Patil, T. Rahman, A.M. Munoz Boudet, and J. Rajbhandary. (2024). “Women’s Labor Force Participation in Nepal An Exploration of The Role of Social Norms.” World Bank Policy Research Working Paper 10810.</mark> <mark>Alik-Lagrange, A., N. Buehren, M. Goldstein, and J. Hoogeveen (2023). “Welfare impacts of public works in fragile and conflict affected economies: The Londö public works in the Central African Republic.”</mark> _<mark>Labour Economics</mark>_ <mark>81.</mark> <mark>Anderson, S. and M. Eswaran (2009). “What determines female autonomy? Evidence from Bangladesh in Bangladesh.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>90(2): 179-191.</mark> <mark>Bagga, A., M. Holmlund, N. Khan, S. Mani, E. Mvukiyehe, and P. Premand (2023). “Do Public Works Programs have Sustained Impacts? A Review of Experimental Studies from LMICs.” World Bank Policy Research Working Paper 10471.</mark> <mark>Beegle, K., E. Galasso, and J. Goldberg (2017). “Direct and Indirect Effects of Malawi’s Public Works Program on Food Security.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>128: 1-23.</mark> <mark>Belloni A., V. Chernozhukov and C. Hansen (2014). “Inference on Treatment Effects after Selection among High-Dimensional Controls.”</mark> _<mark>Review of Economic Studies</mark>_ <mark>81(2): 608-50.</mark> <mark>Bertrand, M., B. Crépon, A. Marguerie and P. Premand (2021). “Do Workfare Programs Live Up to Their Promises? Experimental Evidence from Côte d’Ivoire.” World Bank Policy Research Working Paper 9611.</mark> <mark>Boserup, E. (1970). Women’s Role in Economic Development. New York: St. Martin's Press.</mark> <mark>Brandily, P., I. Kiemtore, E. Mvukiyehe, and L. Smets (2020). “From Workfare to Welfare and Well-Being? Evidence From a Randomized Control Trial With the Urban Poor in Eastern DRC.” Mimeo.</mark> <mark>Bursztyn, L., A. L. González, and D. Yanagizawa-Drott (2020). “Misperceived Social Norms: Women Working Outside the Home in Saudi Arabia.”</mark> _<mark>American Economic Review</mark>_ <mark>110(10): 2992-3029.</mark> 28
2006). These “Ricardian” results provide a consistent welfare measure of the long run impacts of climate on agriculture. However, the Ricardian studies do not provide insight into how farmers are adapting to climate. By explicitly modeling adaptation, this paper seeks to explain the Ricardian results and also bridge the gap between the (MP) approach and the Ricardian approach.

The theoretical model of the farm allows a farmer to choose among crops, livestock, and irrigation to maximize profit. Although many farmers in developed countries either specialize in crops or livestock, many farmers in developing countries choose to do both activities. We first explore whether farmers who face different climates tend to choose different types of farming. Following Kurukulasuriya and Mendelsohn 2006b, the model is extended to include the choice of irrigation. We then explore the conditional net revenue the farmer should expect given the choice of farm type and irrigation.

The paper is divided into five parts. The next section develops the theory. The third section describes the survey of over 2000 subsistence and commercial farmers across 7 Latin American countries and other data sources. The fourth section discusses the cross sectional results. The fifth section presents forecasts of impacts from a set of future climate scenarios. We compare the forecasts one would make assuming these choices are endogenous with the results if one assumed the choices were fixed. We conclude the paper with the policy implications and the limitations of the paper.

# **2. Theory**

We assume that farmers choose amongst three types of farms: crops only, livestock only, and a combination of crops-livestock. For each of the farm types that have crops, the farmer can also choose to do dryland farming or use irrigation. Given these choices, the 3
and evaluations of education and training programs. Labor market information in smaller, low income cour,tries may be limited to a decennial census. In other cases, an infrastructure of periodic household and establishment surveys may be in place to collect this information, but poorly funded, affecting the quality and timeliness of the information gathered. Labor market signals can be produced, however, even without a well-developed infrastructure of labor market information. Regular tracer studies of the employment and wages of graduates of education and training programs can provide effective signals of the balance of skills demand in local markets. Periodic surveys of employers in these markets can yield valuable information on expected changes in skill needs, assessments of pre-employmen **t** raining, and barriers to the productive utilization of skilled labor. Laos provides an example of this rudimentary approach to the use of labor market signalling in Box 4.

# E. Conclusions

Manpower planning is important to improving training efficiency. By monitoring the operation of labor markets and training incentives and providing information for guiding training decisions and managing training systems, this planning can improve the efficiency of training and also help improve the performance of labor markets. Achieving these objectives will require the use of planning techniques that balance the social benefits and costs of training and are responsive to changing economic conditions. Except in planning the supply of strategic skills with long lead times for skills acquisition, much of manpower planning can be focused on providing information about the demand for skills, which can be used by individuals, enterprises, and managers of training institutions in training decisions.

25
7 taken. The same observation applied to bilateral export credit agencies, which had provided considerable financing before the debt crisis but cut back much like the private banks after its onset.

There was an awkwardness in the seeming interventionism of the industrial country governments into what could be considered the internal affairs of the debtor countries. While liberalized treatment of imports and foreign investment and slimming of the state sector were in practice desirable in many o- the debtor countries, in principle adjustment and growth could have been carried out through other means of fiscal correction and improved efficiency at the choice of each country according to its political tradition. There were de'lays in the mobilization of new lending efforts in early 1986 as each major debtor waited for another to bear any taint that might be associated with being the first to sign up in the program, thus appearing to accept foreign conditions in areas more fundamental than the macroeconomic guidelines familiar in IMF programs.

There was also considerable doubt about the adequacy of the capital flow targets of the Baker Plan. From 1981 to 1985 the net transfer of resources (capital inflow less net payments of interest and profits abroad) to the highly indebted countries identified in the Baker Plan fell from $18.3 billion to -$26.5 billion,l a decline of almost $45 billion. The 1981 flows had been seriously I World Bank, World Debt Tables: External Debt of Developing Countries. 1988-89 Edition, Vol. I (Washington: World Bank, 1988), p. xvii. Hereafter referred to as World Bank Tables 1988.
**Table 3.A. Bias of commodity price forecasts**

(U.S. dollars per metric tonne unless otherwise specified)

|**Horizon**<br>**Consensus**||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|
|**Commodity**<br>**(months)**<br>**forecastsF**|**utures**<br>**c**|**orrelations**|**BVAR**|**OEM**|**learning**|
|<br><br> <br>Oil<br>3<br>-3.1|-1.5|<br>1.7|2.2|3.6|2.2|
|(U.S. dollars per barrel)<br>6<br>-2|-1.6|4.6|3.8|2.5|3.8|
|<br>9<br>-1|-2|5.8|5.4|1.5|3.6|
|12<br>-0.1|-2.3|7.6|8|1.3|3.2|
|15<br>0.7<br>18<br>13|...|...|10.7<br>146|1.3<br>13|2.6<br>21|
|.<br>21<br>1.8|...<br>...|...<br>...|.<br>17.3|.<br>1.5|.<br>2.3|
|24<br>2.3|...|...|25.4|0|2.4|
|Aluminum<br>3<br>-6|354|6|-23|16|14|
|6<br>-3|355|10|-28|0|22|
|9<br>-3|363|114|-28|-9|22|
|12<br>-2|371|235|-2|-7|14|
|15<br>4|...|...|-49|-5|7|
|18<br>12|...|...|-17|7|4|
|21<br>19|...|...|11|17|-6|
|24<br>30|...|...|23|-7|-16|
|Copper<br>3<br>-121|8|133|-69|-73|-49|
|6<br>-112|29|527|-92|-92|-31|
|9<br>-107|43|875|-138|-103|-28|
|12<br>-93|52|1105|-95|-88|-30|
|15<br>-71|...|...|-298|-64|3|
|18<br>-36|...|...|-194|-12|43|
|21<br>4|...|...|-70|43|78|
|24<br>52|...|...|-119|117|104|
|Lead<br>3<br>-71|-39|-3|-49|-77|-115|
|6<br>-75|-36|138|-68|-85|-124|
|9<br>-76|-34|211|-82|-89|-125|
|12<br>-76|-32|222|-50|-81|-119|
|15<br>-78|...|...|-68|-88|-100|
|18<br>-81|...|...|-35|-76|-78|
|21<br>-79|...|...|69|-68|-88|
|24<br>-78|...|...|103|-21|-87|
|Nickel<br>3<br>-546|-221|-125|-309|-222|-1279|
|6<br>-344|-193|1123|-267|-127|-1090|
|9<br>-167|-162|1057|-333|-128|-1143|
|12<br>22|-132|1950|-155|23|-1276|
|15<br>181|...|...|-393|253|-1371|
|18<br>296|||-371|442|-1407|
|21<br>425|...<br>...|...<br>...|624|685|-1387|
|24<br>598|...|...|1136|477|-1315|
|Tin<br>3<br>-910|-479|197|-1092|21|-38|
|6<br>-1168|-675|691|-1636|-245|151|
|9<br>-1357<br>12<br>-1402|-814<br>-939|1542<br>2622|-2614<br>-3149|-497<br>-661|290<br>436|
|15<br>1422|||4092|831|683|
|-|...|...|-|-||
|18<br>-1430|...|...|-4287|-839|890|
|21<br>1381|||4028|17103|1102|
|-<br>24<br>-1380|...<br>...|...<br>...|-<br>-4202|-<br>-901|1347|
|Zinc<br>3<br>-98|-45|-20|-102|-55|-56|
|6<br>-103|-52|130|-130|-64|-34|
|9<br>-125<br>12<br>-131|-62<br>-75|227<br>331|-127<br>-106|-73<br>-84|-16<br>2|
|15<br>-135|...|...|-177|-86|27|
|18<br>-141|...|...|-161|-75|57|
|21<br>-147<br><br>|...|...|-105<br>|-69<br>|68<br>|
|24<br>-157|...|...|-110|12|70|

_Source_ : World Bank. _Note_ : Bias is defined as the difference between the actual price and the predicted price. The forecast period is 2015Q12022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency.
Bourguignon, François, and Christian Morrison. 1998. “Inequality and development: The role of dualism.” _Journal of Development Economics_ . 57: 233-257.

- Bourguignon, François, M. Bussolo, Pereira da Silva, Hans Timmer, and Dominique Van der

- Mensbrugghe. 2004. _MAMS: Maquette for MDG Simulations_ . Washington, DC: World Bank.

- Bourguignon, François, and Mark Sundberg. 2006a. “Absorptive capacity and achieving the MDGs.” UNU-WIDER Research Paper 2006/47. Tokyo: United Nations University.

- ———. 2006b. “Constraints to achieving the MDGs with scaled-up aid.” DESA Working Paper 15. New York, NY: United Nations Department of Economic and Social Affairs.

- Breisinger, Clemens, Xinshen Diao, James Thurlow, and Ramatu M. Al Hassan. 2011. “Potential Impacts of a Green Revolution in Africa: The Case of Ghana.” _Journal of International Development_ 23: 82–102.

- Chirwa, Ephraim and Andrew Dorward. 2013. _Agricultural Input Subsidies: The Recent Malawi Experience._ Oxford: Oxford University Press.

- Christiaensen, Luc, Lionel Demery, and Jesper Kuhl. 2011. “The (Evolving) Role of Agriculture in

- Poverty Reduction: An Empirical Perspective.” _Journal of Development Economics_ 96: 239–54.

Clemens, Michael A, Steven Radelet, Rikhil R Bhavnani and Samuel Bazzi. 2012. “Counting Chickens when they Hatch: Timing and the Effects of Aid on Growth.” _The Economic Journal_ 122 (June): 590-617.

Collier, Paul, and Stefan Dercon. 2013. “African agriculture in 50 years: Smallholders in a rapidly changing world?” _World Development._ 63: 92-101.

31
The paper proposes an econometric estimator for the mean BWR for an antipoverty program. To assure that our proposed method is operational, we constrain it to use essentially the same data as the statistical accounting method. We do assume that the micro data are available. Our key identifying assumption can be thought of as a more general, and more plausible, version of the fixed income assumption described above, widely used in the literature. We relax the fixed-income assumption to allow only certain income components to be fixed, which become the instrumental variables for total income net of transfers/taxes. Correlated incidence heterogeneity can still leave a bias in our estimator, by creating correlations between the instrumental variables and the error term. We argue that this extra bias can be signed under the assumption that if the data we have are consistent with the program’s aim of reducing poverty then the unobserved differences in incidence (stemming from heterogeneity in BWRs) will also be consistent with that objective. In other words, if what we observe indicates that the program reduces poverty then it is assumed that this is also true of the things we do not observe.

These ideas are applied to a major new antipoverty program in China. In an effort to address new concerns about unemployed and vulnerable workers, and the social instability that they might create, the central government introduced the _Minimum Livelihood Guarantee_ program, popularly known as the _Dibao_ (DB) program, in 1999.<sup>9</sup> The program’s design is outlined in various documents of the State Council and it is administered by the Ministry of Civil Affairs (MOCA). By 2009 the program covered 23 million people.<sup>10</sup> This is China’s version of _Speenhamland_ .<sup>11</sup> The DB program aims to provide locallyregistered urban households with an income per person below predetermined local DB “poverty lines” ( _Dibao xian_ ) with a transfer payment sufficient to bring their incomes up to that line.<sup>12</sup> So this is a program for which one’s prior, based on the scheme’s deign, would be that there are large incentive effects. Indeed, taken literally, the program’s design implies that participants face a 100% marginal tax rate in that a small increase in non-program income will result in an equal reduction in program receipts. Incentives to escape poverty will be weak or absent.

> 9 _Dibao_ started in Shanghai in 1993, spread to other cities, and became a national policy in 1997, with formal State Council regulations issued in 1999. On the history and politics of the program see Hammond (2009, 2011).

> 10 In 2007 a new rural version of the _Dibao_ program emerged. World Bank (2010) studies this program in its early stages in four provinces.

> 11 It is also reminiscent of Britain’s Supplementary Benefit introduced after the Second World War, whereby income top-ups aimed to assure that all incomes reached the poverty line. 12 Obtaining registration in a new location is generally a difficult and lengthy process in China (not least for the poor), so in practice DB eligibility is confined to well-established local residents.

5
5) _Put options for ITMO sellers_ . The host may purchase put options to sell ITMOs near 2030, at a given (contracted) ITMO price. The options are executed when the host’s GHG emissions rate is below its NDC target. The World Bank has created a Pilot Auction Facility (PAF) for auctioning of options to sell verified climate assets (emissions reductions certificates) at a later date, at an auctioned price where the party offering the lowest sales price wins the auction. The winner is provided a guarantee that the climate asset can be delivered at a future date and price, but has no obligation to sell. See Vivid Economics (2018), World Bank (2017a). In our context, hosts would be given the option to supply ITMOs near 2030 at a fixed price, without obliging the host to sell. Such options purchases can be useful not only for net ITMO sellers, but possibly also for net ITMO buyers. For the latter group, put options could be combined with forward ITMO purchases, which could be excessive and potentially “undone” later by exercising acquired put options. Such transactions however appear as less attractive to low-income countries by imposing early costs (when put options are purchased, and investments made for later GHG abatement), while revenues (from ITMO sales when exercising the put options) come later. Another transaction structure with similar impacts for low-income countries (likely net ITMO suppliers) involves climate finance. An ITMO buyer country can finance mitigation in the low-income country via climate finance, and have the climate finance payment reimbursed when the ITMO transaction takes place ex post (as put options are exercised).

6) _Call options for ITMO sellers_ , combining fixed-price forward ITMO sales with state-contingent buyback of ITMOs using call options. This is a somewhat more complex mechanism than put option contracts considered above; no such trades have so far been proposed. Consider a host which has engaged in fixed-price forward ITMO sales but may feel insecure about fulfilling its NDC requirement when uncertainty is unfavorable ( _z_ takes high values). This host can then contract a sufficient number of call options, for ITMO purchase near 2030, to stay in NDC compliance and also deliver its ITMOs contracted forward. The relevance of this instrument requires that forward ITMO sales, combined with call options for ITMO buyback, are available to the host at an acceptable ex-post contract price.

7) _Call options for late ITMO purchases by net ITMO buyers_ . Forward ITMO contracting is not the only possible forward trading mechanism for net ITMO buyers. Having the option to buy 18