rafmacalaba/datause-encoder-data huggingface dataset:
studies emphasize four different mechanisms through which information sharing can potentially impact firm financing: (i) easing adverse selection, (ii) lowering informational rents, (iii) disciplining borrowers by reducing moral hazard, and (iv) reducing over-indebtedness of borrowers (see Klein, 1992; Pagano and Jappelli, 1993; Vercammen, 1995; Padilla and Pagano, 1997; Padilla and Pagano, 2000).

Using multi-year firm-level surveys for 63 countries covering more than 75,000 firms over the period 2002–13, this paper empirically analyzes the impact of introducing credit information sharing schemes on firms’ access to finance. We also examine how the coverage, scope and accessibility of the credit information sharing scheme as well as the strength of the contractual environment affect the impact of credit information sharing reforms on firms’ access to finance. Our analysis also investigates whether credit information sharing reforms impact different firms differently. In particular, we distinguish firms by their size, experience, and opacity. Finally, to test the robustness of our results, we conduct firm-level panel estimations where we are able to control for time invariant firm-level heterogeneity and we run instrumental variable estimations where we explicitly instrument for the likelihood of CB reforms.

Our results reveal that CB reforms, but not CR reforms, have an effect on firm financing. After the introduction of a CB, the likelihood that a firm has access to finance increases, interest rates drop, maturity lengthens, and the share of working capital financed by banks increases. The effects of CB reform are more pronounced (i) the greater the coverage of the CB and the scope and accessibility of the credit information sharing scheme and (ii) the weaker the contractual environment. We also find some evidence that CB reform effects are more pronounced for smaller, less experienced, and more opaque firms. Finally, we find that our results do not change 3
# **Table 3. The Determinants of Property Rights Structure of Chinese SOEs Panel A. Discrete Dependent Variables**

|||||_Dependent_|_Variable:_||||
|---|---|---|---|---|---|---|---|---|
||_Change of_|_management_|_Adopti_|<br>_ng MRS_|<br>_Delegatin_|_g autonomy_|_Delegatin_<br>_wage_|_g managerial_<br>_discretion_|
|Number of Observation|_Probit_<br>_estimates_<br>3463|_Change in_<br>_prob. when Xi_<br>_change by_<br>_one standard_<br>_deviation_|_Probit_<br>_Estimates_<br>3825|_Change in_<br>_prob. when Xi_<br>_change by_<br>_one standard_<br>_deviation_|_Probit_<br>_Estimates_<br>4234|_Change in_<br>_prob. when_<br>_Xi change by_<br>_one standard_<br>_deviation_|_Probit_<br>_Estimates_<br>5418|_Change in_<br>_prob. when Xi_<br>_change by one_<br>_standard_<br>_deviation_|
|Log likelihood|-1338.02||-939.32||-1186.41||-823.16||
|Prob. (the event happens this year)||.145||.107||.090||.040|
|In(value added per capita at the<br>previous year)|-.057<br>(.037)|-.016|.093*<br>(.048)|.019|-.067*<br>(.040)|-.009|-.004<br>(.049)|-.0002|
|Variation coefficient of avg. value<br>added in the industry at year_t_|-6.091**<br>(2.694)|<br>-.030|-7.623**<br>(3.848)|<br>-.032|-6.201**<br>(2.931)|<br>-.017|2.554<br>(3.262)|.003|
|ln(capital-labor ratio)|.045<br>(.045)|.009|-.089*<br>(.053)|-.015|.080<br>(.048)|.009|.029<br>(.055)|.001|
|In(number of employees)|.067*<br>(.034)|.018|.184***<br>(.042)|.042|-.044<br>(.036)|-.007|.139***<br>(.042)|<br>.007|

22
**17**

# _Shock 4: Monetary easing_

There is an endogenous monetary response in the model for each economy where each economy follows a Henderson-McKibbin-Taylor rule as shown in equation (1) with different weights on inflation (π) relative to target, output growth (Δy) relative to potential and the change in the exchange rate (Δe) relative to target.

The assumed parameter values are set out in Table 3.6. Note that China and most developing economies have a non- zero weight on the change in the $US exchange rate. The monetary easing that has occurred is close to the endogenous monetary policy response already built into the model so any extra monetary stimulus is not required. Of course it is possible that authorities, being fearful of raising interest rates too early and pricking the nascent recovery, could end up easing too much for too long and would be an interesting simulation, especially if different countries chose different amounts of ‘over-easing’ which would set up capital flow changes and hence trade flow changes.

# 3.6: **Coefficients in Henderson-McKibbin-Taylor Rules in Each Country**

||inflation (β1)|output growth (β2)|$US Exchange rate (β3)|
|---|---|---|---|
|USA|0.5|0.5|0|
|JPN|0.5|0.5|0|
|GBR|0.5|0.5|0|
|DEU (*)|0.5|0.5|0|
|EUR (*)|0.5|0.5|0|
|CAN|0.5|0.5|0|
|AUS|0.5|0.5|0|
|OEC|0.5|0.5|0|
|CHI|0.5|0.5|-1|
|IND|0.5|0.5|0|
|OAS|0.5|0.5|-1|
|LAM|0.5|0.5|-1|
|LDC|0.5|0.5|-1|
|EEB|0.5|0.5|-1|
|OPC|0.5|0.5|-10|
|(*) Note that<br>common int|Germany (DEU)<br>erest rate with a|and the rest of the Eur<br>weight on European wid|ozone (EUR) have a<br>e inflation and output gap.|

# _Shock 5: Fiscal easing_

There is an endogenous fiscal policy response in the model but the rule is a targeting of fiscal deficits as a percent of GDP. The easing of fiscal policy announced by most economies has been an extra unprecedented stimulus in the modern era and expansion of fiscal deficits and has to be simulated.
- 17 -

# V. Conclusions

This paper has estimated the effect of institutional credit on agricultural output, investment, fertilizer demand, farm-nonfarm employment and real wage using district-level panel da.a from India. In India special credit programs were launched after the nationalization of commercial banks in 1969 to support the country's green revolution in agriculture. An important policy quesu.Jon thus emerges: to what extent low-interest insitutioi l credit has helped increase private investment and output in Indian agriculture and consequently rural employment and wage.

A panel data analysis is used to estimate the output and input effect as well as wage effect of formal credit. The number of branches of lending agencies are determined by the financial intermcdiaries and thus exogenous to farmer demand for credit. They can, therefore, be used as instruments to identify the aggregate supply of formal credit from its demand. These instruments also help solve the simultaneity between the credit supply, output supply, input demand and wage equations. By using panel data we circumvent the unobserved variable problem that could otherwise produce inconsistent estimates in cross-section data analysis.

Econometric estimates suggest that formal credit plays an important role in fertilizer demand, private fixed investment, crop output, farm-nonfarm employment and agricultural real wage in India. A 10 percent increase in formal credit supply increases fertilizer use by almost 3 percent. A similar percentage increase in the supply of institutional credit spurs a 4 percent increase in private investment in irrigation pumps, 5 percent each in draft animals, 6 percent in milk animals, and
82

# Data Sources

Aon, DL and Tybou, J.R (1994): WTh. Scope of Fuel Substtution i Manufcuring Industais: A Cma Study of Chile and Colombia,' Wod Bank Econtic Review 8(1): 49-74.

o, L.A. an" Lora, E. (1987): "Country Study 6: Colombia,' Stabilization and Adjusment Policies and Progaanuns WDER, Helsinid.

World Bank, (1986) :"A Basis for the Formulation of a Colombian Energy Policy," ESMAP ReportNo. 9278, Washington, D.C.

Word Ban, (1987) :"Colombia: Countiy Economic Memorandum," Report No. 6626-CO, Latin American and Caribbean Regional Office, Washington, D.C.

Wold Bank, (1989): "Colombia: Country Economic Memoranduwm Productvity Gromwth ad Sustained Economic DevelopniW Report No. 7629-CO, Latin Ameican and Caribbean Reginal Office, Washington, D.C.

World Ban, (1989): 'Colombia: Energy Pricing Study," VoL I & II, Report No. 8321-CO, Country D-epnnt HL Wahington, D.C.

Woodd Bank, (1992): World Development Report, Washingbt D.C.
provincial level would help address intra-provincial inequity. In addition, provincial-level agencies could develop capacity to establish a viable contracting and performance evaluation arrangement with the providers of care. Provinces could be incentivized to explore alternative service purchasing and payment mechanisms, based on schemes already piloted abroad, to improve efficiency of service provision. Moreover, provincial governments could boost their capacity to monitor the use of public resources by replacing the existing cascading system of transfers (which moves resources through several levels of government before they are actually spent) with direct payments from the provincial treasury system (via a treasury single account that is already operational in many provinces).

Importantly, the central government will have to effectively monitor and evaluate the use of public resources, policy implementation and overall service delivery performance across provinces, holding the provincial governments to account. As an innovative measure, citizen score card surveys could become a useful tool to gather citizens’ feedback regarding their experience with public services (including their ability to utilize primary care and other services, the required fees and out-of-pocket payments, their ability to access relevant information and resolve complaints, and their satisfaction with services and with the performance of service providers, insurance schemes, local government agencies and others). Such a direct mechanism for obtaining citizens’ feedback would allow the central government to better assess policy implementation performance at the local level, particularly with respect to equity and quality in service delivery. (20) The independent assessment could effectively feed into a comprehensive performance management system and help strengthen accountability at the provincial and sub-provincial levels (across government agencies and providers) for the delivery of health care and other public services and their outcomes. The strong monitoring, evaluation and performance management system, internalizing and addressing citizens’ feedback, with respect to service delivery (outputs) and its outcomes as well as public resource allocation at the local level, will boost incentives (and hence allow for a greater autonomy) at the provincial and sub-provincial levels.

In fact, overall improvements in public sector governance reforms are likely to generate equitable improvements in the health of Chinese citizens beyond HSR. This is because public governance reforms would enhance essential public service delivery in line with the national policies across sectors improving the social determinants of health, such as access to safe water, sanitation, basic health education, housing, rural access roads, social assistance, and others. (44)

# **Acknowledgements**

We thank John Langenbrunner (World Bank) and Sarah Barber (WHO) for comments, and Jennifer Fong for assistance in data collection and analysis.

10
WPS3959

# Microfinance Games ∗

Xavier Giné World Bank Pamela Jakiela University of California, Berkeley Dean Karlan Yale University Jonathan Morduch New York University

## **Abstract**

Microfinance has been heralded as an effective way to address imperfections in credit markets. From a theoretical perspective, however, the success of microfinance contracts has puzzling elements. In particular, the group-based mechanisms often employed are vulnerable to free-riding and collusion, although they can also reduce moral hazard and improve selection. We created an experimental economics laboratory in a large urban market in Lima, Peru and over seven months conducted eleven different games that allow us to unpack microfinance mechanisms in a systematic way. We find that risk-taking broadly conforms to predicted patterns, but that behavior is safer than optimal. The results help to explain why pioneering microfinance institutions have been moving away from group-based contracts.

World Bank Policy Research Working Paper 3959, July 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._ ∗ We thank Shachar Kariv, Edward Miguel, Chris Udry, and participants in the University of Groningen 2005 conference on microfinance, NEUDC 2005, and seminars at Yale and UC Berkeley. Antoni Codinas, Gissele Gajate, Jacob Goldston, Marcos Gonzales, and Karen Lyons provided excellent research assistance in the field. We thank the World Bank, Social Science Research Council Program in Applied Economics, the UC Berkeley Institute for Business and Economic Research, and Princeton University for funding.
The survey differentiates between general business functions (Module B), which comprise tasks that all establishments conduct, regardless of the sector where they operate, and sectorspecific business functions (Module C), which are potentially relevant only for establishments in a given sector. All establishments in our sample respond to Module B, but only those belonging to the sectors for which we have developed a sector-specific module respond to C. To attain a wide coverage that allows a meaningful study of sector-specific technologies, we develop sector-specific modules for 12 significant sectors in the economy, including agriculture (crops and fruits), livestock, food processing, wearing apparel, leather goods and footwear, automotive, pharmaceutical, other manufacturing, wholesale and retail, financial services, land transport services, and health services.<sup>4</sup> These sectors have been selected based on their share in aggregate value-added, employment and number of establishments and they cover all three industries (agriculture, manufacturing, and services).

# **2.2 The Grid**

To design Modules B and C, we determined the business functions covered and the list of technologies, from most basic to most sophisticated, that can be used to implement the key tasks in each function. We call the resulting structure "the grid".

To construct the grid, we followed three steps. First, we conducted desk research reviewing the specialized literature. Second, we held meetings with World Bank Group experts on each of the sectors covered. Third, we reached out to external consultants with significant experience (at least 15 years) in a given sector. For example, the external experts in agriculture and livestock were agricultural engineers and researchers from Embrapa-Brazil. For food processing, apparel, automotive, pharmaceuticals, transportation, finance, and retail, as well as for the GBFs, we relied on senior external consultants selected by a large management consulting organization. For health, our team relied on consultants and physicians with practical experience in both developing countries and advanced economies. In total, more than 50 experts participated in the construction of the technology grid. The resulting grid is composed of 7 general and 56 sector-specific business functions and contains a total of 305 technologies (See Section A.1.1 of the appendix for details on the procedures followed to define the grid).

All technologies in the Grid are precisely described so that respondents and enumerators can objectively establish their use. Figure 1 presents the general business functions considered in the survey and the possible technologies that can be used to conduct each of 4The granular information that can be obtained with the FAT survey allows us to explore central questions on technology policy in developing countries. One example, itself a product of this paper, is the World Bank policy report "Bridging the Technological Divide" (Cirera, Comin and Cruz, 2022).

6
# **Table 6: Impacts of RCEP with the Melitz and Armington Structures**

(results are percentage change from initial equilibrium, unless otherwise indicated)

||||**Meli**|**tz Structur**|**e (FDI an**|**d IRTS G**|**oods)**|||**Armington**|
|---|---|---|---|---|---|---|---|---|---|---|
|||**FDI Services**|**Cross-border**<br>**Services**|<br>**Import Trade**<br>**Facilitation**|**Export Trade**<br>**Facilitation**|**Import Tariffs**|<br>**RCEP**<br>**Partner**|**Import**|**RCEP Partner**||
||Benchmark<br>**RCEP**|**Barriers Only**|**Barriers Only**|**Only**|**Only**|**Only**|**Tariffs Only**|**NTMs Only**|**NTMs Only**|**RCEP**|
|**Scenario definition**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|**9**|**10**|
|RCEP FDI barriers (% reduction)|50|50||||||||50|
|Cross-border services barriers (% reduction)|50||50|||||||50|
|Import Trade Facilitation (% reduction for RCEP countries)|20|||20||||||20|
|Import Trade Facilitation (% reduction for non-RCEP countries)|5|||5||||||5|
|Export Trade Facilitation (% reduction)|20||||20|||||20|
|Export Trade Facilitation (% reduction for non-RCEP countries)|5||||5|||||5|
|Tariffs on Filipino Imports from RCEP (% reduction)|100|||||100||||100|
|Tariffs on Filipino Exports to RCEP (% reduction)|100||||||100|||100|
|NTMs on Filipino Imports from RCEP (% reduction)|20|||||||20||20|
|NTMs on Filipino Exports by RCEP partners (% reduction)|20||||||||20|20|
|Pareto Shape Multiplier|0.98|0.98|0.98|0.98|0.98|0.98|0.98|0.98|0.98|1.00|
|Elasticity of Substitution Multiplier|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.24|
|FDI Elasticity Multiplier|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|30.00|
|**Aggregate welfare**|||||||||||
|Welfare (EV as % of consumption)|2.1|0.9|0.0|0.4|0.2|-0.1|0.2|0.2|0.3|1.5|
|Welfare (EV as % of GDP)|1.7|0.7|0.0|0.3|0.1|-0.1|0.1|0.2|0.2|1.2|
|**Government budget**|||||||||||
|Tariff revenue (% of GDP)|0.4|0.7|0.7|0.7|0.7|0.3|0.7|0.7|0.7|0.4|
|Tariff revenue|-47.5|1.0|0.0|0.8|1.0|-49.4|1.1|0.4|1.3|-47.5|
|**Aggregate trade**|||||||||||
|Real exchange rate|0.3|0.8|0.0|0.3|-0.5|0.5|-0.5|0.2|-0.5|-0.2|
|Aggregate exports|11.4|1.6|0.0|1.4|1.0|2.3|1.2|**1.1**|2.0|10.8|
|**FDI Output**|28.6|**26.8**|0.0|0.5|0.1|0.2|0.1|0.3|0.1|25.9|
|**Factor Earnings**|||||||||||
|Skilled labor|1.5|0.9|0.0|0.4|0.0|0.1|-0.2|0.2|0.0|1.0|
|Unskilled labor|3.3|0.7|0.0|0.1|0.4|-0.2|1.2|0.0|0.5|2.6|
|Capital|2.6|1.1|0.0|0.3|0.2|0.3|0.2|0.2|0.3|2.0|
|**Diagnostics***|||||||||||
|Feenstra Ratio Summary|100.42%|100.27%|100.00%|100.03%|100.01%|100.04%|100.01%|100.01%|100.04%|100.00%|
|Terms of trade|100.15%|99.98%|100.00%|99.63%|100.33%|99.56%|100.40%|99.71%|100.49%|100.36%|

***** The percentage change is the value indicated minus 100. Source: Authors’ estimates

**.**

53
Policy Research Working Paper 10596

# The Enduring Impacts of a Big Push during Multiple Crises

Experimental Evidence from Afghanistan _Guadalupe Bedoya Yulia Belyakova Aidan Coville Thomas Escande Mohammad Isaqzadeh Aminata Ndiaye_ Development Economics Development Impact Group November 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
estimate the impact of expanding the supply of consumer credit in South Africa on consumers‟ well-being, on their employment outcomes and on lenders‟ profitability. They implemented a field experiment that randomly encouraged loan officers to give loans to marginally rejected loan applicants. The loans were made to relatively high-risk borrowers, and had average interest rates of around 200% and default rates of around 20%. A survey was administered 6-12 months after the loan to measure borrowing activity, loan uses and household well-being. Typical reported uses of the loans were to pay off other debt or for transportation expenses. Key impact findings are that individuals in the treatment group were significantly more likely to retain their job and had higher incomes. It seems that the loans helped individuals maintain employment by avoiding shocks that might have affected their ability to travel to work. Individuals in the treatment group also reported more positive attitudes toward their future prospects but also had higher rates of depression and stress. Additionally, individuals in the treatment group were less likely to report experiencing hunger in the last 30 days (14% of the sample reported experiencing hunger). The study did not focus on differences between female and male borrowers but found that along most dimensions there were no significant differences by gender. Lastly, the study found that the loans to the marginally rejected applicants were profitable for the lenders, suggesting that both borrowers and lenders might benefit from an expansion of consumer credit.

# **4.3  Opening Savings Accounts for the Poor in Rural Kenya**

Dupas and Robinson (2009) report results from a field experiment in rural Kenya designed to test whether savings constraints, in terms of a lack of opportunities for formal saving, inhibit selfemployed individuals from expanding their businesses. The treatment intervention opened interest-free savings accounts for a random subsample of poor, daily income earners. The study finds that the take-up of these savings accounts was high among women but low among men, despite the fact that the accounts were interest-free and had relatively high penalities for early withdrawal. It finds that savings accounts were associated with positive benefits for women, in terms of positive impacts on productive investment levels and expenditures, but that they had no effect on men. The authors hypothesize that investment and expenditures increased with the interest-free savings because the women faced negative private returns to savings when they tried to save informally at home.

36