rafmacalaba/datause-encoder-data huggingface dataset:
information asymmetries are higher. Recent studies show that institutions aiming at reducing information asymmetries (such as public and private credit registries) foster the development of private credit markets (Jappelli and Pagano, 2002 and Djankov, McLiesh, and Shleifer, 2006), increase access to credit (Barron and Staten, 2003) and firm performance (Kallberg and Udell, 2003) and reduce non-performing loans and the costs of firm financing (Brown, Jappelli and Pagano, 2006). Galindo and Miller (2001) find that, in countries with more developed credit bureaus, firms face less severe financial constraints. This applies to large firms listed on the stock market as well as for small and medium-size companies.

This paper contributes to the empirical literature by investigating the impact of institutions aiming at reducing credit information asymmetries on the structure of corporate debt maturity. In particular, we focus on public and private credit bureaus and accounting standards as determinants of debt maturity structure after controlling for the impact of the legal framework, financial development and other macro and micro factors.

# **3.  Short-term lending as a hedge against uncertainty**

In order to understand how uncertainty about borrowers’ credit quality may affect optimal debt maturity structure, we begin in this section with a two-period model of a bank choosing loan maturity under incomplete but symmetric information. This stylized framework will then be generalized in Section 4 to a multi-period case with asymmetric information.

The intuition of the model in this section can be briefly summarized as follows. A bank with incomplete information about borrowers’ credit quality compares two lending options: (i) committing to a non-renegotiable long-term contract versus (ii) retaining the flexibility of rolling over renegotiable short-term contracts. The latter option carries higher transaction costs but entails the possibility to exit the investment, thus limiting losses, should the borrower default in the first period. Intuitively, the more the uncertainty, the higher will be in equilibrium the bank’s preference for renegotiable short-term contracts. From the lender’s perspective, short-term lending acts as a hedge against uncertainty.

# **3.1 General set-up and payoff structure**

Assume a two-period risk-neutral setting with perfect competition. A bank faces the choice of lending its initial endowment of $1 to either a long-term project or a short-term project as 5
2019). As income decile shares do not follow a random path but tend to be persistent over time, we opt for a first di↵erence GMM estimator (Roodman, 2009). This class of models has been applied to small T/large N settings. For this reason, we collapse our data and form five-year time windows. In line with expectations concerning the central features of this model class (Wu, Luca, and Jeon, 2011), our point estimates become stronger in comparison to baseline (Table A10).<sup>12</sup> Overall, our results withstand a series of robustness checks giving us more confidence in the viability of our proposed mechanism.

# **5 Results: Mechanisms**

In this section, we present the results of the main mechanisms through which CBI is driving inequality. We start with an in-depth analysis of the policy changes and then present the results for anticipated economic outcomes.

## **5.1 Three Mechanisms**

First, a powerful way to compensate for the loss in monetary policymaking is to deregulate financial markets (Aklin and Kern, forthcoming). To capture such instances of financial deregulation, we rely on two indicators. Besides taking an aggregate financial liberalization index that ranges from 0 to 21, whereby higher values indicate less stringent regulatory practices, we complement our analysis using a measure for the degree of privatization of the banking industry. Whereas our aggregate measure captures the overall ‘aggressiveness’ of financial reform, our measure for the extent of the banking industry’s privatization proxies the degree to which policymakers remove barriers to competition among banks to stimulate financial activity and credit (La Porta, Lopez-de Silanes, and Shleifer, 2002; Epstein, 2016). The data come from Abiad, Detragiache, and Tressel (2010).

Second, we perform an analysis of whether CBI leads to changes in the governance of labor relations. For one, CBI constrains governments’ ability to inflate wages for restoring full employment (Thelen, 2014). Furthermore, wage inflation dynamics can threaten the e↵ectiveness of monetary

> 12Even when considering di↵erent modifications within this class of models, our results remain quantitatively and qualitatively similar (Table A11).

16
21 Nigeria.

Turning to the economic factors, more educated women tend to provide a larger share of the household’s labor input into crop production. Women also contribute a larger share if they own the land. Both effects hold across countries, while the effect of household wealth differs by country (increasing the female share in Malawi, and reducing it in Nigeria and Niger). Access to income from off-farm activities (captured by a dummy variable which is one if the household has income from off-farm activities) only reduces the female labor share in crop production in Nigeria. This may point to specialization across activities along gender lines in more developed economies. There is no effect in the other countries and, with the exception of Niger, there are no statistically significant associations between the outcome of interest and the proxies for market access.

The results so far have only considered the female labor share in crop production. One way to explore the effects of livestock on women’s agricultural labor contribution is to see whether their labor share in crop production is affected by livestock ownership. No effect was found in Malawi or Niger, but there was a statistically significant negative effect in (Northern) Nigeria. This may point to some substitution effects, though given that the average TLU is only 0.03, it would not meaningfully affect the estimated female labor share in crop production.

# **4 CONCLUSION**

Using recent, individual-disaggregated, activity-specific plot level information on labor input into crop production from six Sub-Saharan African countries, this study has revisited the premise that women provide 60 to 80 percent of Africa’s agricultural labor. Average labor contribution to crop production in these six countries is estimated at 40 percent instead, though differences exist across countries. The female labor share amounts to slightly more than 50 percent
|Local Government Development Project|Alan G. Carroll|
|---|---|
|Local Government Development Project|Alan G. Carroll|
|Local Government Development Project|Alan G. Carroll|
|Local Government Development Project|Alan G. Carroll|
|EntrepreneurshipDevelopment and Drought RecoveryProgr|am(EDHartwigSchafer|
|EntrepreneurshipDevelopment and Drought RecoveryProgr|am(EDHartwigSchafer|
|EntrepreneurshipDevelopment and Drought RecoveryProgr|am(EDHartwigSchafer|
|EntrepreneurshipDevelopment and Drought RecoveryProgr|am(EDHartwigSchafer|
|EntrepreneurshipDevelopment and Drought RecoveryProgr|am(EDHartwigSchafer|
|Infrastructure Project(01)|K. John Shepherd|
|Infrastructure Project(01)|K. John Shepherd|
|Infrastructure Project(01)|K. John Shepherd|
|Infrastructure Project(01)|K. John Shepherd|
|Infrastructure Project(01)|K. John Shepherd|
|Infrastructure Project(01)|K. John Shepherd|
|Infrastructure Project(01)|K. John Shepherd|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Population,Health and Nutrition Sector Credit Project|Oscar Picazo|
|Fiscal Restructuringand Deregulation Program|Sudhir Chitale|
|Fiscal Restructuringand Deregulation Program|Sudhir Chitale|
|Fiscal Restructuringand Deregulation Program|Sudhir Chitale|
|Fiscal Restructuringand Deregulation Program|Sudhir Chitale|
|Fiscal Restructuringand Deregulation Program|Sudhir Chitale|
|Fiscal Restructuringand Deregulation Program|Sudhir Chitale|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Education Sector Credit Project(02)|Peter N. Ngomba|
|Agricultural Marketingand Estate Development Project|StanleyHiwa|
|Agricultural Marketingand Estate Development Project|StanleyHiwa|
|Agricultural Marketingand Estate Development Project|StanleyHiwa|
|Agricultural Marketingand Estate Development Project|StanleyHiwa|
|Agricultural Marketingand Estate Development Project|StanleyHiwa|
# **Figures**

Figure 1 Figure 2 _Mirt_ Stove with _Injera_ Cooking Cook pouring _Injera_ batter on _Mirt_ Stove Source:ethiopiaethos.files.wordpress.com/2010/06/comp5.jpg Source:energypedia.info/wiki/Baking_with_Improved_Ovens 32 | P a g e
24 K <!-- Start of picture text --> Chart III<br>INOIA<br>S55<br>50<br>45-<br>40-<br>35 _<br>30 _<br>25 -<br>20 -<br>15 _<br>10 _<br>1965 I 1967 I 1969 I 1971 I 1973 I 1975 I 1977 I 1979 I 1981 I 1983 I 1985<br>196'; 1968 1970 1972 1974 1976 1978 1980 1982 1984<br>Year<br>O Priv.lnvt./GDP + Dom.Cred./GDP<br><!-- End of picture text -->
We measure the non-land costs of managing protected natural areas based on GTAP v.7 database as public expenditures on outdoor recreation services per hectare of protected land.<sup>6</sup> We assume that the non-land cost of managing protected natural areas declines over time with technological improvements in non-land inputs. We characterize the change in non-land costs of managing protected natural areas by the following equation: where the parameters _κR_ reect the annual rate of decline in the non-land cost of managing protected natural areas.

# C.1.12 Other Goods and Services

The growth of TFP is described by the following equation: where the parameters _θ_ 0<sup>_o_and</sup><sup>_κo_reecttheinitiallevelandannualgrowth</sup> rate in the TFP of the economy. The initial values for the production of other goods and services and economy's output per capita are based on the value of output at agents' prices from GTAP v.7 database. The production of other goods and services is obtained from GTAP v.7 sectors 12, 14-15, 18, 26-29, 33-42, 45, 47-54 and 56-57. We set total factor productivity growth rate using Jorgenson and Vu (2010) projections based on econometric estimates for 122 economies over the 1990 - 2008 period.

# C.1.13 GHG Emissions

The value of the GHG emission coecient from combustion of liquid fossil fuels comes from the US Energy Information Administration (EIA) website<sup>7</sup> . The GHG emission coecient from production of ammonia from fossil fuels comes from IPCC (2006a) Tier 1 estimates. We compute GHG emissions per ton of anhydrous ammonia applied to crop lands as follows. First, we calculate the

> 6Following Antoine et al. (2008), we dene outdoor recreation services sector based on GTAP v.7 database. This sector comprises of hunting and shing, wildlife viewing in reserves, and other wildlife viewing activities.

> 7See http://www.eia.doe.gov/oiaf/1605/coefficients.html, last checked in August, 2013.

22
# _b. Level of activity: mergers and acquisitions analyzed in the previous 12 months_

This question gives an idea of the workload a competition agency has in the area of mergers. Figure 2 shows that competition agencies can be divided in two groups, one that had to analyze more than 50 mergers last year and the other that dealt with less than ten. In ECA, the majority of agencies had to consider more than 50 mergers while in LAC most of the competition agencies analyzed less than 10 mergers. In EAP, half of the responses indicated a workload of more than 50 mergers and half less than 10.

<!-- Start of picture text --> Figure 2<br>Mergers analyzed in the previous 12 months<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>< 10 11-20 21-50 > 50 < 10 11-20 21-50 > 50 < 10 11-20 21-50 > 50<br>ECA LAC EAP<br><!-- End of picture text --> In the three regions there seems to be a clear dichotomy between agencies that analyze few cases and those that have to analyze a large (more than 50) quantity of mergers per year<sup>6</sup> . Can this result be driven by the varying size of the economies?. To answer this question, we plot the size of the economy (measured by the gross national product<sup>7</sup> ) against the quantity of mergers. The trend shows a very weak relation between these two variables, implying that the

> 6 This argument assumes that last year was an “average year” and so gives an indication of the approximate number of mergers a competition agency analyzes every year.

> 7 Source: World Bank database.

6
Table A7: The Complementary Effect of Internet and Roads on Agricultural Employment

||(1)<br>**All sam**|(2)<br>**ple**|(3)<br>**Rural sa**|(4)<br>**mple**|
|---|---|---|---|---|
||**Farming self-employed**|**Farming employee**<br>**and self-employed**|**Farming self-employed**|**Farming employee**<br>**and self-employed**|
|Submarine internet|-0.147***|-0.112***|-0.104***|-0.128***|
||(0.0395)|(0.0363)|(0.0327)|(0.0353)|
|Proximity to road (KM)|0.00209**|0.00270***|0.00312***|0.00601***|
||(0.000842)|(0.000802)|(0.000610)|(0.000710)|
|Submarine internet * road|-0.0284***<br>(0.00804)|-0.0263***<br>(0.00779)|-0.0133***<br>(0.00397)|-0.0169***<br>(0.00424)|
|N|752131|752131|465918|465918|
|ymean|0.208|0.284|0.304|0.415|
|r2|0.253|0.220|0.332|0.165|
|r~~2~~<br>~~a~~|0.253|0.220|0.331|0.164|
|Kleibergen-Paap F-statistic|45.74|45.74|64.41|64.41|
|_P > F_|0|0|0|0|
|Cragg-Donald Wald F-statistic|885.1|885.1|1219.3|1219.3|
|Windmeijer F-statistic|35.21|41.74|30.01|31.85|
|Kleibergen-Paap Wald F-statistic|7.590|7.590|12.39|12.39|
|Age|✓|✓|✓|✓|
|Gender|✓|✓|✓|✓|
|Place of residence|✓|✓|✗|✗|
|Education|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|
|Grid (20*20) connectivity FE|✓|✓|✓|✓|

_Robust standard errors in parentheses clustered at the grid level. ∗p < ._ 10 _∗∗p < ._ 05 _∗∗∗ p <_ 0 _._ 01 _. We use a strategy that combines difference-in-differences and instrumental variables techniques. The causal effect of Internet is estimated using difference-in-differences, while the effects of the road and its interaction with the Internet are estimated using an instrumental variables approach. Our instruments are: the distance to the hypothetical road, the interaction between the distance to the hypothetical road and an indicator for whether an individual (or a firm) is connected to submarine Internet, lightning, and terrain elevation._ 58
extant literature.<sup>5</sup> Using the events following the 2005 earthquake in Pakistan together with a specially designed household census and survey of more than 28,000 households implemented by the authors in 2009 in four earthquake-affected districts, we are able to provide the first such evidence. We find that mirroring the provision of foreign assistance, trust in foreigners increases dramatically in households living closer to the 2005 earthquake activated fault-line.

The 2005 Pakistan earthquake provides a particularly suitable environment to quantitatively estimate the effects of _particular types_ of foreign assistance on the “hearts and minds” of the local population. The powerful 7.6 magnitude earthquake that struck the NorthEastern region of Pakistan on October 8<sup>th</sup> , 2005 along the Himalayan Frontal Thrust Fault resulted in unprecedented destruction of property and loss of life. An estimated 74,000 Pakistanis died, 70,000 were seriously injured, and over 2.8 million were left homeless. Between October and January, there was a desperate “race against winter”—an all-out effort to get households into some sort of shelter before the harsh Himalayan winter resulted in even higher casualties. Given the severely correlated shock that ruled out any sort of local risk-sharing, it is likely that assistance received during this time had a very high marginal utility and would thus have an especially strong impact on individual attitudes towards the aid provider.

Immediately following the earthquake, there was a remarkable outpouring of aid from all over Pakistan and around the world. This aid came in many shapes and forms. Apart from financial support for the Pakistani government, organizations and individuals provided logistical and technical assistance. A multitude of people ranging from doctors, nurses to trained personnel specializing in excavation arrived from around the world into the affected region. Till March 2006, foreign organizations continued to provide support in terms of emergency shelters and

> 5 In contrast, there is a large and heavily debated literature on the effectiveness of aid in terms of influencing poverty, governance or other economic outcomes. See for instance, Banerjee 2007 and Bourguignon and Sundberg 2007.

3
22 Table 1: Nominal rates of assistance, OECD countries, all covered products, 1960 to <u>2007 (percent)</u>

||1960-69|1970-79|1980-89|1990-99|2000-07|
|---|---|---|---|---|---|
|**Western European Countries**||||||
|**European Union**|**72**|**58**|**79**|**49**|**26**|
|**EFTA**|**62**|**56**|**111**|**178**|**144**|
|Austria|53|21|40|66|33|
|Denmark|41|60|83|48|26|
|Finland|117|90|97|105|32|
|France|64|49|78|52|25|
|Germany|110|72|88|56|30|
|Iceland|—|—|277|219|137|
|Ireland|60|70|131|81|54|
|Italy|40|35|56|36|18|
|Netherlands|107|95|98|53|34|
|Norway|—|—|293|237|147|
|Portugal|11|22|30|29|19|
|Spain|16|-4|28|35|19|
|Sweden|134|90|92|75|32|
|Switzerland|—|—|296|258|143|
|UK|64|56|93|62|33|
|**Europe's transition**||||||
|**economies**|—|—|—|**9**|**18**|
|Bulgaria|—|—|—|-16|2|
|Czech Republic|—|—|—|17|21|
|Estonia|—|—|—|0|20|
|Hungary|—|—|—|16|21|
|Latvia|—|—|—|5|28|
|Lithuania|—|—|—|2|27|
|Poland|—|—|—|15|15|
|Romania|—|—|—|23|45|
|Russia|—|—|—|1|12|
|Slovakia|—|—|—|23|21|
|Slovenia|—|—|—|67|52|
|Turkey|—|—|—|20|24|
|Ukraine|—|—|—|-13|-9|
|**North America**|**6**|**6**|**15**|**10**|**10**|
|Canada|8|11|26|17|13|
|US|6|6|14|9|10|
|**Japan & Korea**|**62**|**87**|**135**|**156**|**143**|
|<br>Japan|73|94|133|148|132|
|<br>Korea|10|61|145|192|189|
|**Oceania**|**8**|**7**|**9**|**3**|**0**|
|Australia|10|7|6|4|0|
|NewZealand|2|10|17|2|2|

Source: Authors’ calculations based on data in Anderson and Valenzuela (2008)
# **5. Results**

# **_5.1. Bank Finance and Firm Performance_**

Table 4 reports the estimated coefficients from baseline regression (1). Columns (1) and (7) show that the formal financial system is associated with higher sales growth for both the full sample of all firms and a sub-sample of private firms (dropping public and state owned firms). The role of bank financing in the second sub-sample is of particular interest, since private firms are likely to have fewer alternative options than publicly traded firms or state owned firms. Firms that have bank financing also reinvest a higher proportion of their profits in their businesses in both samples (columns (2) and (8)). Thus, there is no evidence that bank financing funds growth that firms are unwilling to undertake using their own capital.

Columns (3) and (8) show that bank finance is not significantly negatively associated with labor productivity growth. When we look at growth in TFP for a smaller sample of firms, again we find that bank finance is positively associated with growth in TFP though not significantly so. These findings suggest that the formal financial system rather than informal sources have a positive association with firm growth and reinvestment and that the growth that results is not inefficient. Columns (4) and (5) for the full sample, and (9) and (10) for the sub sample reinforce this finding by showing that the association holds over a longer horizon.

When we look at the control variables, larger firms, older firms and firms organized as cooperatives or collectives are found to have lower growth rates. Growth rates are lower in highly competitive (>100 competitors) industries. The city dummies show that compared to firms in Benxi, firms in Changchun, Chongqing, Hangzhou, 28
**_Table 3:  Tests of the null hypothesis of equality of medians of per‐capital expenditure_**

|**Broad caste and Jati**<br>**group**|**Number of rejections of**<br>**the null hypothesis of**<br>**equality**|**Broad caste and Jati**<br>**group**|**Number of rejections of**<br>**the null hypothesis of**<br>**equality**|
|---|---|---|---|
|SC: Chamar|8|OBC: Dhanuk|0|
|SC: Dobha/Dobh|5|OBC: Koeri|4|
|SC: Dom|1|OBC: Kurmi|0|
|SC: Dushad|6|OBC: Shershabadia|0|
|SC: Musahar|11|OBC: Yadav|5|
|SC: Pasi|1|Other OBCs|3|
|SC: Sardar|8|EBC: Keuta|1|
|Other SCs|0|EBC: Mallah|1|
|ST: Adivasi|2|EBC: Nat|0|
|Other STs|9|Other EBCs|3|
|Muslim: Ansari|21|FC: Brahmin|23|
|Other Muslims|22|FC: Rajput|24|
|||Other FCs|25|

Notes: (1) Source: Authors' calculations based on data collected by Social Observatory, World Bank and Government of Bihar. (2) We test the equality of median per-capita expenditure between all pairwise combinations, i.e. jati _i_ and jati _j_ ( _i=_ 1,...,24 and _j=_ 1,...,24), using quantile regression with bootstrapped standard errors, with 1000 replications. The null hypothesis of equality is rejected if the coefficient is significant at least the 10% level. The 24 jati groups in our sample have at least 0.5% representation in our sample.

**_Table 4: Inequality in monthly per capita consumption expenditure_**

||**(a) Overall Inequality**<br>**(GE(0))**|**(b) Conventional measure of**<br>**between group inequality**|**(c) ELMO measure of**<br>**between group inequality**|
|---|---|---|---|
||**Panel (a): Cas**<br>|**te**<br>||
|6 broad caste groupings|0.0666|0.0064|0.0083|
|16 jati groupings|0.0666|0.0292|0.0305|
||**Panel (b): Ja**|**ti**||
|SC (8 groups including others)|0.0610|0.0258|0.0297|
|ST(2 groups including others)|0.0660|0.0059|0.0118|
|OBC (6 groups including others)|0.0842|0.0164|0.0207|
|EBC (4 groups including others)|0.0683|0.0107|0.0114|
|Muslims (2 groups including others)|0.0641|0.0011|0.0029|
|FC (3 groups including others)|0.0835|0.0483|0.0652|

Notes: (1) Source: Authors' calculations based on data collected by Social Observatory, World Bank and Government of Bihar. (2) In Panel (b), we restrict the sample to each broad caste category and estimate the inequality between jatis within that broad caste group. The number of jatis per broad caste category is indicated in brackets.

16