rafmacalaba/datause-encoder-data huggingface dataset:
conjecture that if a lender cannot fully adjust the interest rate of a subsidized loan (e.g., an earmarked loan in the context we study), a bank with market power might increase the pricing of other loans to the same client, weakening the transmission of the government subsidy. We establish a series of hypotheses related to the use of cross-product pricing strategies and test them using granular loan-level data. We evaluate two alternative hypotheses that might explain the average increase in interest rates: the credit reallocation and credit risk channels. Our findings are consistent with the view that banks with market power transfer part of the cost of earmarked lending into other lending products.

We provide novel evidence about the limits of government programs that rely on subsidies to reduce the cost of credit. These programs typically require banks to set interest rates in certain ranges. Our analysis illustrates why, even under such strategy, lenders with market power can prevent the transmission of subsidies to final borrowing costs.

# **References**

<mark>Ayyagari, M., Demirguc-Kunt, A. and Maksimovic, V., 2011. Small vs. young firms across the world: contribution to employment, job creation, and growth. The World Bank.</mark> <mark>Bartik, A., Cullen, Z., Bertrand, M., Glaeser, E.L., Luca, M. and Stanton, C., 2020. How Are Small Businesses Adjusting to COVID-19? Early Evidence from a Survey.</mark> _<mark>(April 6, 2020). Harvard Business School NOM Unit Working Paper</mark>_ <mark>, (20-102), pp.20-102.</mark> <mark>Berger, A.N. and Udell, G.F., 1995. Relationship lending and lines of credit in small firm finance.</mark> _<mark>Journal of Business</mark>_ <mark>, pp.351-381.</mark> <mark>Bharath, S., Dahiya, S., Sauders, A., and Srinivasan, A. (2007). “So what do I get? The bank’s view of lending relationships.”</mark> _<mark>Journal of Financial Economics</mark>_ <mark>85 368-419.</mark> <mark>Bikker, J. A., & Haaf, K. (2002). “Competition, concentration and their relationship: An empirical analysis of the banking industry.”</mark> _<mark>Journal of Banking & Finance</mark>_ <mark>, 26(11), 2191-2214.</mark> <mark>Bonomo, M., Brito, R. D., & Martins, B. (2015). The after-crisis government-driven credit expansion in Brazil: A firm level analysis.</mark> _<mark>Journal of International Money and Finance</mark>_ <mark>,</mark> _<mark>55</mark>_ <mark>, 111134.</mark> <mark>Canales, R. and Nanda, R., 2012. A darker side to decentralized banks: Market power and credit rationing in SME lending. Journal of Financial Economics, 105(2), pp.353-366.</mark> <mark>Carvalho, C. & Martins, B. (2019). Cross-subsidy in credit markets: micro level evidence from directed lending in Brazil.</mark> _<mark>Mimeo</mark>_ <mark>.</mark> <mark>Carvalho, Daniel (2014). The real effects of government-owned bank: evidence from an emerging market.</mark> _<mark>Journal of Finance</mark>_ <mark>, 69 (1) 577-609.</mark> <mark>Claessens, S., Feijen, E., Laeven, L., (2008). Political connections and preferential access to finance: the role of campaign contributions.</mark> _<mark>Journal of Financial Economics</mark>_ <mark>, 88(3), 554-580.</mark> 16
11 receipt of severance pay among eligible employees across firm ownership, however, in the Sakernas (2008) survey. Generally, eligible employees who received severance pay steadily increases with the wage level in both surveys.

While only a limited number of job separators actually received severance pay, those who did were generally satisfied with their payment. Only the IFLS (2007) survey specifically addresses employee satisfaction with respect to severance pay. Of the four levels of satisfaction identified in the survey, 68.6 percent stated that they were either very satisfied or satisfied with their compensation while only 28.9 percent indicated they were dissatisfied and just 2.5 percent stated that they were very dissatisfied with the severance pay they received.

On the first aspect of non-compliance, the proportion of eligible formal sector employees who receive a payment, two-thirds of all eligible employees who separated from a job did not receive their severance payment. The second aspect is the proportion of the entitlement amount that is actually received. It is to this aspect that we now turn. The conclusion we draw from the survey data is that employers who make a severance payment actually pay less than the amount to which their employees are entitled.

Table 3 shows the amount of legally entitled severance pay and actual payments by a set of employee and job characteristics. These calculations are based on weighted data from the IFLS (2007) survey. The mean monthly wage among recipients was 968,892 IDR; the severance entitlement amounted to 9,361,576 IDRs of which about 40 percent had actually been received to date (3,607,587 IRDs). Part of both totals, however, could be associated with long-service compensation as well as severance pay.

The average amount actually received by women and men as a proportion of the legal entitlement is nearly identical: 0.3937 and 0.3816, respectively. Both legally entitled severance pay amounts and actual severance pay received increases with age. The proportion of actual receipt to the legally entitled severance pay ranges from 0.1138 at ages 15-18 years to 0.8883 at ages 50-64 years. These proportions compare to the allage average of 0.3854. The legally entitled amount and the proportion of that amount received both increase with the level of education except for the highest group, those with tertiary education.
number of studies since the early 1990s report low achievement levels at the terminal grades of primary school. Examples include a large national study by the National Center for Educational Research and Training (NCERT) in 1994, which found that children scored an average of 47 percent in language and 41 percent in mathematics (Shukla and others 1994) and state-wise studies with smaller samples in Bihar, Tamil Nadu, Delhi and Madhya Pradesh (Bashir 1994; Hasan 1995; Govinda and Varghese 1993; Aggarwal 2000). A recent survey tested children from almost all districts from India and reports low levels of learning with substantial variation across states: 52 percent of children between the ages of 7 and 10 could read a small paragraph with short sentences at Grade I difficulty levels, 32 percent could read a story text and 54 percent were unable to divide or subtract (Pratham, 2005).

There is little consensus across these studies on the nature and extent of socio-demographic variation in achievement levels. In some studies, boys and students from upper castes perform better (Shukla and others 1994; Aggarwal 2000; Aikara 1997), while in others there are no gender differences (Hasan 1995). As in Pakistan, private schools outperformed government schools (Kingdon 1996; Tooley and Dixon 2006; ASER 2005). Most studies find that home background and school factors were both important in influencing students’ learning (Kingdon 1996; Govinda and Varghese 1993).<sup>4</sup> Thus, even though there has been a greater number of studies measuring learning in India, meaningful comparisons can be made neither across studies nor over time because of differences in sampling methodology, the tests used and the child characteristics collected as part of the survey.<sup>5</sup> As in Pakistan, there is no comprehensive picture of variation in achievement across relevant socioeconomic dimensions. For instance, the lack of consensus on gender and caste differences could reflect different test instruments and different sampling methodologies. In short, in both countries, it is hard to draw any straightforward inferences on either changes in learning over time or the gaps in learning across different groups.

> 4 Although Kingdon (1996) looks at the impact of various student characteristics (wealth, gender, and parental education) and school characteristics on learning, the data is restricted to a sample of urban schools from one city in north India.

> 5 Even though ASER (2005) allows one to compare learning achievements across districts/states in India at a point in time, there is no information collected on household characteristics. If repeated at regular intervals with a standardized instrument, this initiative would be a welcome addition to what we know about learning in Indian schools.

- 8 -
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Tanzania > Road investments > Macro results**

||||Reduction in<br>|Roads (|% variatio<br>|n)<br>||
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
|Ym|Aggregate household income|7,925.60|0.45|0.53|0.54|0.55|0.52|
|EV|Equivalent variation||0.45|0.51|0.53|0.55|0.15|
|s|Wage|1.00|0.57|0.77|0.80|0.82|0.75|
|yg|Government income|668.13|0.87|5.00|5.00|5.00|5.00|
|g|Total government expenditure|516.07|–2.39|2.96|2.96|2.96|2.96|
|ye|Firm income|2,293.57|1.13|0.87|0.83|0.80|0.89|
|It|Total private investment|1,228.04|2.51|2.03|1.73|1.31|1.81|
|GDP|GDP|7,582.43|0.81|0.81|0.81|0.81|0.81|
|e|Nominal exchange rate|1.00|0.57|0.36|–0.41|–1.61|0.39|

# **Tanzania > Road investments > Sectoral results**

||||Reduction in<br>||Roads<br>|||
|---|---|---|---|---|---|---|---|
||||other blic||Imort|||
|Variable|Sector|Base|pu<br>expenditure|VAT|p<br>duties|Foreign aid|Income tax|
||Crop agriculture|1,964.70|0.83|0.62|0.66|0.66|0.51|
||Export agriculture|1,311.08|1.25|0.95|0.87|0.79|0.92|
|Va|Mining and gas|141.43|1.52|1.46|1.46|1.49|1.47|
|Value added or|Industries|1,242.16|0.82|0.35|0.57|0.81|0.58|
|output|Construction|780.66|2.05|1.79|1.52|1.24|1.86|
||Private services|1,672.10|0.61|0.56|0.54|0.52|0.51|
||Public services|470.30|–2.06|1.51|1.54|1.68|1.51|
||Crop agriculture|1.02|–0.30|–0.04|–0.08|–0.13|–0.24|
||Export agriculture|1.02|–0.70|–0.41|–0.24|–0.43|–0.68|
||Mining and gas|1.06|–0.05|–0.02|–0.05|–1.46|–0.31|
|pq<br>Market prices|Industries|1.10|0.19|0.72|0.57|–0.45|0.12|
||Construction|1.00|0.94|1.00|0.74|0.25|0.84|
||Private services|1.00|0.42|0.58|0.51|0.37|0.40|
||Public services|1.00|0.51|0.73|0.70|0.57|0.62|
||Crop agriculture|1.00|0.46|0.38|0.46|0.48|0.20|
||Export agriculture|1.00|0.38|0.10|0.00|–0.10|0.04|
|r<br>|Mining and gas|1.00|–0.08|–0.80|–0.63|–0.27|–0.64|
|Rental rate of<br>capital|Industries|1.00|0.91|0.21|0.66|1.14|0.64|
||Construction|1.00|3.40|2.91|2.18|1.44|3.08|
||Private services|1.00|1.02|1.08|1.03|1.00|0.92|

41
Policy Research Working Paper 8930 Do Improved Biomass Cookstoves Reduce PM Concentrations? ? If So, for Whom? 2.5 Empirical Evidence from Rural Ethiopia _Randall Bluffstone Daniel LaFave Alemu Mekonnen Sahan Dissanayake Abebe Damte Beyene Zenebe Gebreegziabher Michael A. Toman_ Development Economics Development Research Group June 2019
_Intertemporal Adjustment and Fiscal Policy in a Fixed Exchange Rate Regime_ Over the period of our analysis, inflation in Argentina dropped dramatically, falling below the U.S. rate by 1996 (figure 1.2). And, during the entire currency board period, Argentina’s economy was spared a liquidity shortage because of increases in net foreign assets (figure 1.3).

Nevertheless, over the period, Argentina’s external competitiveness worsened because fiscal policy was unable to offset deterioration in the current account by a voluntary increase in the tax rate or a slowdown in public expenditures (figure 1.4).

In 1991, when the currency board was adopted, the real exchange rate stood higher its long-run equilibrium (Carrera 2002), so competitiveness was already a problem. Subsequently, the peso continued to rise further in real terms, boosting imports and discouraging exports, which served to increase current account deficits (figures 1.5 and 1.6).

Figure 1.2 Inflation in Argentina (solid line) and United States, 1990–2001 Percent <!-- Start of picture text --> 0.075<br>0.050<br>0.025<br>0.000<br>-0.025<br>1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> Source: IFS- IMF.

5
where group status is defined by country, rural or urban residency, educational status, and poverty status in order to compare health measures for babies born to mothers who are similar based on observable characteristics. Also, HIPC at Decision Point is interacted with child and mother characteristics to investigate distributional differences in line with the findings on infant mortality at Decision Point. I estimate the following specification, where the dependent variable, _Health Measureigacy_ , equals one if a baby (or mother) has received or has access to any of the above-mentioned health variables. The mother-group fixed effect is _αg_ , and all estimations include country-specific time trends, mother’s-birth-cohort (five-year) by child’s-birth-year dummy, and exogenous controls for girl infant, birth-order dummies, and dummies for quarters of birth.

As can be seen in Table 13, the probability of a baby born to a poor mother, a mother with no formal schooling, or a mother residing in rural areas receiving the BCG vaccine goes up by 4.2-5.0 percentage points at Decision Point. The pattern is similar but the effect is slightly smaller in size for the first shot of Polio vaccine (3.5-4.2 percentage points). Furthermore, poor, uneducated, or rural mothers are more likely to have access to the Tetanus vaccine during their pregnancies after Decision Point. The effect sizes vary between 5.4 to 6.2 percentage points. These point estimates among the babies translate into four to six percent of the sample means and about nine percent among the pregnant mothers. The results on BCG, Polio1, and Tetanus for pregnant mothers for babies born to mothers who are non-poor, educated, or live in urban areas and the mothers themselves are statistically insignificant.

Table 14 shows that Decision Point increases the likelihood of babies with diarrhea getting oral rehydration solution treatment with 6.3 percentage points if they are born to poor mothers. This effect is large (77.9 percent of the sample mean) partly due to the very low coverage in the sample. Only four percent of the children born to poor mothers have access to flush toilets.

At Decision Point, the probability of receiving the DPT1 vaccine goes down with about 4.9 percentage points for babies born to urban mothers (5.5 percent of the sample mean), whereas the effect on babies born to rural mothers is insignificant. Similarly, the likelihood of urban mothers having access to drinking water from a piped water source drops by about 4.3 percentage points at Decision Point (ten percent of the sample mean). Babies born to urban mothers are thus the only group which experience any significant declines in the health measures examined in this exercise. However, it does not seem to translate into higher infant or neonatal mortality in this group. There do not appear to be any large 16
7 Forward sales through the _P VAM_ are in essence mandatory for private exporters because _deblocages_ are mandatory for all exports. One argument for mandatory forward sales is that their benefits cannot now be gained by private agents because of market failure. The reasoning is as follows. Forward sellers usually must put up the crop, or some entitlement to the crop such as a warehouse receipt, as collateral. In the absence of a margin system, collateral is required because sellers have an incentive to renege on the forward contract if prices subsequently rise or if they cannot obtain the quantity for delivery. The existence of performance risk (i.e., the risk of non-delivery) allows forward selling only if the seller has a good credit standing, if collateral is adequate, or if margins are high enough. In the absence of physical inventories, exporters will only be able to sell forward if they have a forward contract to buy from producers. However, domestic forward markets do not exist due to performance risk and poor contract enforcement. Performance risk is higher where there are many small growers, poorly functioning domestic financial institutions and weak legal contract enforcement, all of which are characteristic of the Ivorian economy. Difficulties in reducing performance therefore makes forward selling difficult by private agents and justifies, in theory, some form of public action to reduce that risk.

The performance risk in forward sales is indeed reduced by the intervention of CAISTAB. CAISTAB, by controlling exports through the sale of _dcblocages,_ can be confident of the crop it will have for sale, and hence of the aggregate performance risk, in the coming season. Because of CAISTAB's established reputation in the market as a reliable counterpart, it is able to sell forward well of harvest, sometimes as far as 18 months.

Evidence for this market failure is that forward sales of cocoa have declined, in volume and period of coverage, after liberalization. In Cameroon and Nigeria forward sales have almost disappeared and the few remaining forward sales are for short periods<sup>8</sup> .

# _The producer risk benefit offorward sales_

Ivorian cocoa producer prices have been fixed nominally within seasons and hence stabilization has been successful in that sense (for example, Diop-Boare 1994). The question remains: are the benefits from stable, but lower, prices greater than those from variable, but higher, prices? One way to evaluate the risk-return trade-off to the producer is the Newbery and Stiglitz (1981) approach, which is to calculate the certainty equivalent (CE) producer price. The CE price is that guaranteed price (as is theoretically available for cocoa in Cote d'Ivoire) at which risk-averse farmers would be indifferent compared to a variable market price.

# The CE price is calculated from

_(3)_ **_Pe_** _= P (exp [-{s_ + _0.5 R_ **_(E-1))_ a}]),**

> 8 Also, in coffee, where in almost all producing countries the private sector is solely in charge of marketing and exports, forward sales are relatively low and cover no more than four to five months ahead.
14

# _Table 1: Surveys Used_

||China Health & Nutrition Survey (CHNS)|Gansu Survey of Children and<br>Families(GSCF)|World Bank China Health VIII project baseline survey|
|---|---|---|---|
|Provinces sampled|Guangxi, Guizhou, Heilongjiang (1997 and 2000<br>only), Henan, Hubei, Hunan, Jiangsu, Liaoning (not<br>1997), Shandong|Gansu (1)|Anhui, Chongqing, Gansu, Guizhou, Henan, Qinghai,<br>Shanxi|
|No. counties sampled|36|20|28|
|Rural or urban or both|Both|Rural|Rural|
|Areas covered|Where possible, the provincial capital and a lower-<br>income city were selected, though in two provinces,<br>other large cities had to be selected. Villages and<br>townships within the counties, and urban and<br>suburban neighborhoods within the cities were<br>sampled.|Villages only|Villages and townships<br>|
|Sampling strategy|A multistage, random cluster process was used to<br>draw the sample surveyed in each of the provinces,<br>which themselves sampled vary substantially in<br>geography, economic development, public resources,<br>and health indicators. Counties in the 9 provinces<br>were stratified by income (low, middle, and high) and<br>a weighted sampling scheme was used to randomly<br>select 4 counties in each province. Villages and<br>townships within the counties and urban and suburban<br>neighborhoods within the cities were selected<br>randomly.<sup>7</sup>|A four-stage stratified random<br>sample was used. First counties<br>were<br>selected<br>(20),<br>then<br>townships (42), then villages<br>(100) and finally children. At<br>each stage care a sampling<br>procedure was used to ensure that<br>sampling was done evenly across<br>the income distribution.<sup>8</sup>|Survey was administered only in project counties<sup>9</sup><br>which were deliberately selected on the basis of their<br>high rates of poverty, their capacity to implement the<br>project, and their financial resources (counties were<br>responsible for repaying the loan to the World Bank).<br>Within counties, a random cluster process was used to<br>draw households.|
|Date(s) conducted|1991, 1993, 1997, 2000<sup>10</sup>|2000, 2003|1998|
|Sample size (no. individuals)<br>per wave|14,578; 13,687; 14,181; 15,334|7,581; 7,581|42,799|

7 Details taken from CHNS website http://www.cpc.unc.edu/projects/china.

> 8 Further details are to be found at the GSFC website <u>http://www.ssc.upenn.edu/china/gscf/mainGscf.htm.</u>

9 The survey covers 22 of the 96 counties eventually included in the Health VIII project. The 22 counties are located in the following provinces in central and western China: Anhui, Chongqing, Gansu, Guizhou, Henan, Qinghai and Shanxi.

10 The CHNS also collected data in 1989. This wave was excluded from the present analysis because some of the variables were not collected in the 1989 wave.
# **5.3 Internet use**

_Internet use varies significantly across African countries._ Among those firms using a computer regularly, the percentage of them which has an internet connection is less than 50% (see Table 5); which is below other developing countries like Mexico and Turkey.<sup>13</sup> In this regard, Kenya shows the highest percentage (72.7%) and differences across sectors are low compared to other countries. Less than a quarter of firms using a computer in Tanzania and Uganda have internet access. Manufacturing tends to have a higher proportion of firms with internet, especially in DRC and Zambia.

_Table 8 Firms with Internet Connection by Categories at Country Level (Percentage in terms_ _<u>of Firms Using Computer)</u>_

|_of Firms Using Co_<br>Categories|_mputer)_<br>Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Size**||||||||
|Small|43.8|20.9|18.0|21.3|44.2|64.4|35.5|
|Medium|83.0|58.7|34.2|19.3|56.4|83.1|55.8|
|Large|95.5|63.7|42.9|80.4|93.3|87.6|77.2|
|**Age**||||||||
|age<5|58.4|20.6|16.9|26.4|43.2|66.8|38.7|
|age 5-9|59.8|33.0|15.2|23.4|42.6|81.9|42.6|
|age 10-14|57.2|14.6|18.9|31.1|60.3|69.6|41.9|
|age 15-19|55.9|41.5|29.5|16.5|62.0|69.1|45.7|
|age 20-24|66.1|31.7|33.9|22.5|53.4|83.9|48.6|
|age 25-29|45.6|34.2|23.1|36.6|54.2|59.4|42.2|
|age 30-34|51.0|49.1|38.4|9.1|58.7|78.9|47.5|
|age>34|55.2|45.8|22.2|13.6|27.8|69.9|39.1|
|**Trade**||||||||
|No Trader|51.2|24.8|21.7|21.2|45.2|70.8|39.1|
|Importer|84.1|55.8|16.7|93.7|83.2|95.2|71.4|
|Exporter|74.9|37.3|25.8|27.3|55.8|70.4|48.6|
|Two-way|72.3|100.0|46.6|74.6|100.0|89.3|80.5|
|**Foreign Status**||||||||
|Domestic|52.6|21.4|23.3|23.2|44.3|72.3|39.5|
|Foreign|88.3|60.7|16.1|20.9|67.7|76.9|55.1|

Source: Author’s own elaboration from enterprise surveys _Younger firms are not more connected to the internet compared to older firms._ Regarding firm characteristics, internet use and computer use are highly correlated: larger and more internationalized firms are more likely to adopt internet. One small difference is that middle-aged firms tend to adopt more internet use. In general, we expect that there is a propensity for younger firms to adopt more often new technologies such as internet; however, this pattern is not observed in these African countries. One possible explanation might be the higher cost of internet access. Even for more developed countries like OECD members, costs remain an important barrier for internet adoption as reported in OECD (2004). When looking at firms’ size,

> 13 More than 90% of Mexican (2008) and Turkish (2010) businesses with over 10 employees has access to internet, according to OECD (2012).

17