rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Gross NPA to Gross Advances Ratio<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Public Private SCBs<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Note: Data is from Reserve Bank of India. Years refer to respective fiscal years.

According to experts, the distorted incentive structure coupled with political compulsions has resulted in allocative and operational inefficiencies of a public sector led banking sector, and to periodic loan write-offs, e.g. to the agriculture sector, and under recovery of the corporate credit. Reconsidering the ownership balance, and the incentive and governance structure may be important in order to alter the perennial cycle of allocative and operational inefficiencies.

Under its current balance sheet situation, the banking sector does not seem well equipped to help finance higher growth or investment rate, and suitable reforms will be needed to reverse this equilibrium.<sup>44</sup> In this direction, the government announced an unprecedented recapitalization of public sector banks on October 24<sup>th</sup> , 2017. The proposed measures include recapitalization of approximately INR 2.11 trillion (around $32 billion) over the next two years. The government proposed to fund it through budgetary provisions amounting to approximately INR 180 billion; recapitalization bonds to the tune of INR 1.35 trillion; and direct capital raising by banks from the market by diluting government share (an estimated INR 580 billion). The recapitalization ensured continuity and stability in the system, but needs to be followed by wider reforms.

The government also implemented and strengthened a new Insolvency and Bankruptcy Code. While an important step toward changing the credit culture, the policy will take time to help clean the balance sheets, and is unlikely, on its own, to improve capital adequacy of banks.

A dynamic measurement of public sector banks’ governance and financial performance metrics could be deployed to systematically address moral hazard concerns. Additional measures to durably enhance the stability and efficiency of the financial sector could include consolidation of public-sector banks; revising their incentive structure to align more closely with their commercial performance; ensuring a level playing field for private banks; and opening the space to greater competition. It will

> 44 These have been discussed in the Nayak Committee report, the Indradhanush plan, and the recently concluded joint IMF-World Bank FSAP report.

41
## **7. THE ADOPTION OF GROWTH-REDUCING POLICIES**

In this section we return to the key questions that motivate this essay: what induces societies to adopt growth-reducing policies and why do such policies persist? Historical analysis and the theory of public choice suggest that inefficient policies are likely to be adopted when the conditions for competitive bargaining between interest groups are not met-an issue that is closely related to material conditions and initial endowments of specific groups. Here, we elaborate on this point and consider the importance of institutions and organizational residues for policy formulation.

# **7.1** Initial conditions

An unequal distribution of initial endowments in environments where financial markets are imperfect and credit is rationed can prevent a large proportion of the population from making productive investments. In fact, a number of recent cross-sectional studies have found a robust negative relationship between initial levels of inequality in the distribution of income or wealth and subsequent growth (Alesina and Rodrik 1994; Persson and Tabellini 1994; Deininger and Squire 1996). Several early studies sought to explain this relationship as resulting from an unproductive but costly redistribution from the rich to the poor which was imposed by the "median voter".39 However this explanation does not accord with the observation that the poor are less likely than others to participate in the political process and with the failure to find an empirical association between the degree of redistribution and lower growth. Recent models that incorporate a richer set of empirical regularities point to the possibility of multiple steady states, which are determined by a combination of initial conditions and endogenous institutional arrangements in the economy.

Bourguignon and Verdier (forthcoming) develop a model in which political participation depends on the educational status of a person (only educated individuals can

> 39 The argument is that the more unequal is the distribution of wealth or income, the further from the average will be the median voter, and the higher is the tax rate he approves on wealth or income. The higher is the tax rate, the lower are the incentives for the wealthy to invest in productive activities, and thus the lower is the growth rate of the economy. Societies with very unequal distribution of wealth or income should therefore be characterized by high tax rates on the rich and low growth.

42
acquisition or construction of regional accounts. This challenge is amplified by two factors. First, regional accounts constructed from primary data are rare. Most regional analysts must rely on one of several methods that can be applied to generate regional accounts from corresponding national accounts using a smaller set of region-specific data to estimate counterparts to national parameters. Second, whereas most of the tools that have been developed for national or regional input-output accounts operate on industry-by-industry (or interindustry) accounting frameworks, it is a reality of the data collection and reporting conventions that most nations publish accounts not in interindustry format, but in what is known as a supply-use framework. Interindustry accounts can be derived from supply-use data, but the necessary data manipulations are not always straightforward and intuitive.

As an aid to regional analysts, computer software applications have been developed primarily for the purpose of facilitating the construction of regional accounts. For example, IO-Snap, described further below, supports accounts generation and standard final demand impacts assessment, but more extensive analytical capabilities beyond multiplier analysis, descriptive economic statistics, and impacts assessment are still being added. While additional analytical features are under development, however, IO-Snap can be used to generate regional interindustry accounts that can then be exported for use with software applications like REAL-I-O that provide a much richer suite of analytical tools. IO-Snap and REAL-IO are described briefly, below.

# **IO-Snap**

IO-Snap, which stands for “Input-Output – State and National Analysis Program,” is a Windowsbased computer software program that was developed to facilitate the use of input-output data from U.S. national supply and use tables. In the process of assembling the various utilities that support national and state-level input-output analysis, the developers also created an environment that facilitates inter-state comparisons of input-output related variables such as employment and worker compensation. Annual U.S. IO tables from the Bureau of Economic Analysis (BEA) for 1998 through the most recently published year are included with the software. In addition to default data covering the U.S. 50 states and the District of Columbia, user-supplied data in supplyuse format can be imported for other geographic areas.

36
Second, the scope of the experiment sheds light on whether discrimination takes place at the top tier of educational attainment, but it cannot formally answer _why_ any discrimination revealed takes place. Third, this study answers questions about average callback rate differences assuming constant, nonadaptive job applicant behavior in their search for jobs. In other words, the experiment does not account for modifications in an application strategy after an applicant receives a certain number of rejections or for modifications that account for specific job seeker requirements (Fix and Struyk 1993). Fourth, the fact that the dependent variable is callback rates (and not wage offers) establishes a nonlinear relationship that could throw results into doubt if employers believe there is potential heterogeneity in the unobserved variance of productive characteristics between males and females (Heckman and Siegelman 1993; Neumark 2012). This experiment, like all correspondence studies, attempts to balance productive characteristics between two similar population groups (high achieving and recent male and female graduates), but it cannot control for employers’ beliefs or the variance in unobserved qualities. If employers indeed believe there is more variance in males, they are more likely to lean toward hiring males even if there are no _average_ differences between observed and unobserved characteristics.

Finally, it is important to note the ethical considerations typically brought up in relation to correspondence studies, the most important of which is the assumption that the benefits gained from the study outweigh the unintended waste of time incurred by employers in screening fictitious applications. To minimize this cost, employer callbacks that entailed the possibility of hiring (whether requests for an interview or for additional information) were politely declined within a week.

# 10. Conclusions

The average gap in the unemployment rate in Tunisia between tertiary-educated men and women reached 17 percentage points in 2014. To what extent is this gap a result of gender discrimination? This study attempts to answer that question. On the one hand, the study finds that, on average, there is positive discrimination in favor of women because they are more likely to be contacted by an employer. On the other hand, subgroup analyses reveal that discrimination both in favor of and against women exists, and the magnitudes are substantial.

41
WPS3784 Conley and Udry 2004, Besley and Case 1994). This literature identifies human capital (measured by education, gender, and age), learning by doing and learning externalities as major determinants of technology adoption. We model the decision to engage in coffee farming as a function of market access community variables, household characteristics (head of household education, demographic profile, level of wealth, etc.), and a set of control variables such as provincial dummies and availability of extension services. The probit regression is performed for heads of households. The switch from subsistence to coffee can be interpreted as technology adoption in agriculture.

Community characteristics strongly influence the probability of participating in coffee farming with access to electricity, road and extension services all being important (table 10). Extension services are a set of technical supports, training and information about markets provided to farmers to increase productivity and profitability through the promotion of new crops, new techniques and new quality-enhancing practices. Other studies on African economies also show the importance of extension services. For instance, Balat et al. (2004) find that households in Zambia that have received extension services are on average more productive in market agriculture than households that have not. Clearly, provision of high-quality extension services is central to facilitating a structural transformation of agriculture in Rwanda.

Demographic profile is the most important household characteristic that significantly influences the likelihood of engaging in coffee farming. Households with many dependents and headed by a male are more likely to be involved in coffee farming in Rwanda. Coffee is a labor-intensive activity and in a situation where labor markets are imperfect, having a large pool of family labor appears to be an advantage. This is important because our earlier broad analysis of the determinants of poverty showed that a high number of dependents is positively associated with the probability of being poor. This suggests that, after controlling for community characteristics, those households currently involved in subsistence activities who have a relatively strong propensity to produce coffee are more likely to be poor. Hence, initiatives that facilitate the movement 14
# Table 4. EFFECT OF IE ON RATE OF DISBURSEMENT UP TO MIDTERM

|||DID|-OLS|||DID|-RE|||DI|D-FE||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**T (quarters with IE==1)**|0.082***|0.056***|0.082***|0.056***|0.063***|0.056***|0.064***|0.056***|0.060***|0.036*|0.060***|0.035|
||(10.0)|(4.59)|(9.99)|(4.56)|(3.43)|(3.16)|(3.47)|(3.15)|(2.99)|(1.69)|(3.00)|(1.64)|
|**D (project with IE==1)**|-0.030***|-0.052***|-0.030***|-0.052***|-0.015|-0.052**|-0.015|-0.052**|||||
||(-5.31)|(-4.17)|(-5.32)|(-4.16)|(-0.96)|(-2.52)|(-0.96)|(-2.51)|||||
|**_TTL switch dummies_**|Y|N|Y|N|Y|N|Y|N|Y|N|Y|N|
|**_TTL UPI dummies_**|N|Y|N|Y|N|Y|N|Y|N|Y|N|Y|
|**_Polynomial of sector averages_**|N|N|Y|Y|N|N|Y|Y|N|N|Y|Y|
|**_Observations_**|7918|7918|7918|7918|7918|7918|7918|7918|7918|7918|7918|7918|
|**_Outcome mean_**|0.147|0.147|0.147|0.147|0.147|0.147|0.147|0.147|0.147|0.147|0.147|0.147|
|**_Effect in %_**|55.8%|38.1%|55.8%|38.1%|42.9%|38.1%|43.5%|38.1%|40.8%|24.5%|40.8%|23.8%|

_Note:_ Standard errors are clustered (sector and quarter level). T-stats are in parenthesis. All specifications include fixed effects for quarters of project life cycle, sector, fiscal year when project became effective, and country.

Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent 31
Moreover, the associated production data are often inaccurate (especially for vegetables and tree products). I thus select _major field crops_ according to the criteria that they cover at least 1 percent of total cropped area nationally _or_ that at least 5 districts had no less than 10 percent of their cropped area planted to them in 2003-4. These 18 crops, listed in Table 2 in descending order of planted area, comprise some 92 percent of area devoted to field crops in 2003-04 in the major states of India.<sup>22</sup> Table 2 also reports national average log-price changes (weighted by the state share of total production) _relative to rice_ . Thus, in the first row, the relative price change for rice is zero, quite negative for several important crops (e.g., cotton, gram, groundnut, mustard/rapeseed) and highly positive for pulses (Urad, Moong, and Arhar).

# **3.3 Wages**

Wage data are derived from the NSS Employment-Unemployment Survey (EUS), normally conducted every five years. The most recent round, the 66th, collected in 2009-10, is the first conducted in the wake of the food price “crisis” of 2007-08, whereas the 61st round of 2004-05 most closely preceded it. Once again, in the spirit of the theoretical model, I focus on _manual_ labor, which constitutes nearly 83 percent of days of paid employment in rural areas.<sup>23</sup> The first-stage of the estimation takes individual log daily wages in the last week and regresses them on district fixed effects as well as a quadratic in age interacted with gender. Thus, I estimate the respective log-wage district fixed effects, _<u>wd,</u>_ 09 and _<u>wd,</u>_ 04, separately for each round, removing, via the constant terms, year effects due to, e.g., general inflation.

> 22One could assume that minor crop prices did not change at all over the five-year period in question, though this would still leave the problem of missing base-year minor-crop prices for calculating total crop value shares. At any rate, the zero price change assumption seems untenable _prima facie_ . More appealing is to assume that the average change in minor crop prices is equal to the average change in major crop prices, where averages are weighted by value shares within the respective crop category. Under this assumption, the average of major crop prices weighted by major crop value shares (which is what I observe) is identically equal to the average of all crop prices weighted by total crop value shares (which is what I require).

> 23The NSS-EUS categorizes jobs in terms of manual and non-manual labor only for rural, not urban, workers. Based on the 61st round sample of nearly 39,000 individuals, the population-weighted proportions in each category are as follows: 58% in manual-agricultural; 24% in manual nonagricultural; 18% in nonmanual (virtually all in nonagriculture). For the 66th round sample of some 30,000 individuals, the corresponding proportions are 51%, 30%, and 19%, respectively.

13
- 1.35 -

Estimates for two concepts of farm Income are also presented. Concept 1 is utility based in that the cost of labor Is netted from Income, most of wnich Is the estimated disutility of labor by family members. This Is the Income concept that enters Into the multiperlod decislons fo farmers in model solutions, and It Is the concept used for the Income estimates from the solutions of that model. Concept 2 Is more conventional In that labor costs are not netted out. This concept Is practically Identical to the value added concept used In measuring agricultural gross domestic product (GDP).

At this point, it Is useful to note that of the osthuates presented In Table 1.C.3, only the cocoa production and family Income (Concept 2) esthuates can be chocked against reliable, Independent estimates, although cocoa acreage can be checked against the Informal estimates of the COCOBOD. Clearly, the absence of census and survey data that documert the labor force and Its utilization, as well as an agricultural census that Integrates annual and perennial crops has created an information void concerning labor and land utilization that should be corrected.

Turning to the cocoa production estbmates for which validation Is possible, Table l.C.4 presents the relevant comparison. Clearly, simulated production Is acceptably close to actual productlon In 1985 and 1986. Note, however, that both 1984 and 1987 were years marked by exceptional weather that depressed cocoa production. Moreover, the cocoa buying price was still quite iow In real terms In 1984, provIding little Incentive for a complete picking of the cocoa on the trees. As already noted, the model cannot be expected to pick up the effect of short-run weather fluctuatlons. In fact, the simulated level of production shown, with annual cocoa production approxkmately constant over the perlod, Is precisely what should be expected given an IncreasIng price Incentive but a history such that cocoa Investments five to ten years previously were quite low due to weak Investment Incentives then.
small in magnitude to be of importance. Distance from the market is statistically significantly associated with lower normalized losses, but the magnitude is inconsequential.

Perhaps the most interesting results in Table 5 are those related to wealth. When using levels (columns 1-2), losses appear to be positively related to wealth. When using percentages (columns 3-4), losses are negatively related to wealth. This pattern suggests that there may be different types of loss-prone households—wealthy households that suffer large losses in levels but small losses as a percentage of total spending, and poor households that suffer small losses in levels but large losses as a percentage of total spending. In some of the analysis to follow we distinguish between households that are loss-prone in levels only, percentiages only, both, and neither, where a household is “loss-prone” if it is in the highest quartile of losses. Section S4 in the Appendix contains more details on this categorization.

# **5 What explains the observed purchasing patterns?**

We now turn to hypotheses about what might lead households to engage in financially inefficient purchasing by foregoing bulk discounts. We consider a number of possibilities: binding liquidity constraints, utility from frequent shopping, transport costs, storage costs, ignorance of bulk discounts, purchasing in small quantities as a form of self- or other-control, avoidance of social taxation, and coordination costs within the household. These hypotheses arose from three sources: discussions with individuals and focus groups in Tanzania (described in Section 3), our own hypothesizing based on the literature or our knowledge of the context, and suggestions from colleagues or seminar participants.

Our analysis uses observational data, so it is not possible to say that a mechanism is relevant or irrelevant for all households. Rather, we assess which mechanisms do or do not seem to have a substantial impact on the losses that we observe. Our findings provide an important check on certain hypotheses, and should be especially useful as a guide to steer future research.

Because it is important for assessing some mechanisms, it is worth reiterating what it means to buy in bulk in our data. Bulk purchasing in developed countries typically connotes buying several weeks’ or months’ worth of an item at once. In our setting, bulk purchases 18
- 11 -

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|---|---|
|:jmly1992.||
|Numbar<br>47|309|
|Size Range<br>23,789-414,367<br>2,534-270,000|12-18,791|
|Avege Size<br>96,118<br>27,801|6,963|
|Standard<br>Deviation<br>_79,434_<br>49,622|_1,23|_|
|_._<br>=Dcepnt<br>of S<br>_;Ai;_Governmn_of_Aibanl||

21 The size of local goverments and the degree of urbanizadon of a country influence the costs of local governments and the extent to which the latter can capture economies of scale and be<sup>viable</sup> as independent units. Table 3 presents some comparative stdstics in this respect for Albania<sup>and</sup> other Central and Eastern European counties. It is evident that Albania is by far the least urban<sup>of</sup> these countries, using the _Work! Development Report's_ definition of urban as being the percent of<sup>the</sup> population living in cities of more than 10,000. At the most aggregate level of local government,<sup>the</sup> rethe or district level, Albania's local government units are smaller in size than those of most<sup>of its</sup> neighbors in Central and Eastern Europe. On the other hand, Table 3 indicates that the lowest level of local government in Albania, comprising the municipalities and rural communes, has a larger average size than that in other Central and East European countries with the exception of Poland and Bulgaria.2