rafmacalaba/datause-encoder-data huggingface dataset:
14 Given these parameters, several alternative scenarios may be constructed to<sup>estimate the demand for formal</sup> agncultural credit The scenarios are driven by assumptions<sup>regarding agricultural growth rates, the propensity</sup> for informal financing of investment, and the capital-output<sup>ratio in agriculture. The results are presented in</sup> Tables 2.1 and 2.2. Table 2.1 assumes that 1 percent growth<sup>requires a ratio of investment to agricultural GDP of 2</sup> percent, whereas Table 2.4 assumes a ratio of 3 percent<sup>The first column in each table gives three assumed</sup> agricultural growth rates. The next three columns estimate the<sup>needed credit for investment assuming investment</sup> is financed through informal sources (farmer's own savings<sup>plus borrowing from informal sources) at rates of 5, 6</sup> and 7 percentage points. The nine estimated values in<sup>the three columns are the needed agricultural investments</sup> as a percentage of agricultural GDP, assuming different<sup>combinations of agricultural growth and rates of credit for</sup> informnlly financed investment. Thus, in Table. 2.1 (which<sup>assumes capital output ratio of 2:1), 4 percent growth</sup> in agriculture requires total investment of 8 percent of the<sup>agricultural GDP. If non-institutional sources (own</sup> savings and borrowings from informal sources) provide<sup>5 percentage points of these, the requirement for</sup> institutional credit is calculated to be 3 percentage points.

Table 2.1. Annual Credit Requirement from Institutional Sources for<sup>Aaricultural Investment</sup> (Percentage of 1992-93 Axricultumal GDP) (Assuming 1 percent growth requires investment/Ag. GDP rate 2 percent)

|Agricultural growth rate|Assumed percentage po<br>|ints of investment financed<sup>through non-formal sources</sup><br> <br>|
|---|---|---|
|....................................................|5<br>..............................................|6<br>7<br>.........................................................................................................................|
|4|3|2<br>1<br> <br>|
|6|7|6<br>5<br> <br>|
|8|11|10<br>9|

Source: Authors' estimates The needed investment credit can be calculated in (1994-95) rupee<sup>values by multiplying the by the projected</sup> 1995-96 agricultural GDP of Rs. 449.1 billion (assuming that the agriculture<sup>will grow at 4% per annum between</sup> 1994-95 and 1995-96). This yields total investment credit need from institutional<sup>sources of Rs. 13.47 billion.</sup>

# Production Credit

According to the 1990 agricultural census of Pakistan, investment<sup>on inputs such as fertilizers and</sup> insecticides constituted about 10 percent of total annual investment<sup>in agriculture. This underestimates the share</sup> of prodution expenditures to total investment because seeds, purchase<sup>of water, and hiring of implements; and</sup> labor are not, included. The evidence from other countries in the region suggests<sup>that total expenditure on inputs</sup>
Table A8: IV regression: The effect of migration on land tenure arrangements: Acreage, Extended Controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|3.371***|2.917***|0.454|-1.759*|-1.700**|-0.229|0.912*|
||(1.087)|(0.865)|(0.585)|(1.000)|(0.742)|(0.885)|(0.538)|
|bank branches (number)|-0.0188|-0.0142|-0.00465|-0.00403|0.0255|0.0179|0.00865|
||(0.0246)|(0.0211)|(0.0130)|(0.0233)|(0.0183)|(0.0185)|(0.0121)|
|road length (km)|-0.00170**|-0.00162**|-8.84e-05|0.000949|0.000549|0.00171***|-0.000988**|
||(0.000813)|(0.000707)|(0.000359)|(0.000649)|(0.000470)|(0.000598)|(0.000434)|
|2012 year|-6.661*|-2.837|-3.824*|-4.403|8.818***|22.22***|5.130**|
||(3.995)|(3.521)|(2.144)|(3.429)|(2.292)|(3.043)|(2.201)|
|Observations|722|722|722|722|722|722|722|

Note: This table uses the shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The agro-ecological controls include soils, length of the growing season (in days) and reference ETo (a measure of water stress). The dependent variable is as percentage of acreage of rented in land. The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
19

|**_Error-correction R_**<br>**_D_**|**_epresentation_**<br>**_ependent vari_**<br>|**_Table 8_**<br>**_for the Selected_**<br>**_able: d Primary_**<br>|**_ARDL models 199_**<br>**_Balance_**<br>|**_1-2002_**<br>|
|---|---|---|---|---|
||AIC|RBSC|SBC|HQC|
|Error-correction term(-1)|-.20*|-.21*|-.13*|-.14*|
|_d_Primary(-1)|.04|.02|||
|_d_Primary(-2)|.17**|.16**|||
|_d_Primary(-3)|||||
|_D_debty|-.014|.007|.018**|.017***|
|_D_debty(-1)|-.038|-.013|||
|_D_debty(-2)|-.027|-.018|||
|_D_debty(-3)|-.081***|-.085*|||
|_D_Output|-1.87|-1.7|-1.27|-1.5|
|_d_Output(-1)|-2.36***|-3.1**|||
|_d_Output(-2)|-3.18**|-3.6*|||
|_d_Output(-3)|-1.98***|-2.27**|||
|_D_reer|-1.49*|-.39|-1.2*|-1.4*|
|_D_REER(-1)|||||
|_D_REER(-2)|||||
|_D_REER(-3)|||||
|Dselicr|-.0004|-.004|-.001**|.006|
|dSelicr(-1)|-.001|-.009|||
|dSelicr(-2)|.001||||
|dSelicr(-3)|||||
|Dembi|.44**|.45**|0.4**|.43**|
|dEmbi(-1)|-.66*|-.60*|-.67**|-.63*|
|dEmbi(-2)||-.30|||
|dEmbi(-3)<br>|||||
|R-Bar<sup>2</sup>|.30|.30|.21|.23|
|D.W.|2.15|2.06|2.09|2.05|
|* Significant at the .01 level<br>** Significant at the .05 level<br>***Significant at the.10level|||||

Figure 16: Cyclical Component of the Primary Balance (in percent of GDP) <!-- Start of picture text --> 3<br>2<br>1<br>0<br>-1<br>-2<br>1 9 92 1 9 94 1 9 96 19 98 20 0 0 20 0 2<br><!-- End of picture text -->
are price sensitive. More interestingly, in all of the credit-related specifications (columns 1 to 3), the interaction of the total cost with a dummy for the simplified format is also negative and significant, suggesting that price sensitivity is enhanced by the simplified KFS. Put differently, comparison shopping is enhanced with more transparent disclosure.

Related, financial literacy also increases price sensitivity in Mexico and using the pooled sample (columns 2 and 3, respectively). Echoing the results of Table 3, the impact of financial literacy is however more muted than that of the disclosure format.

Individuals also seem price sensitive when evaluating savings products (columns 4 to 6). In Peru, using the simplified KFS helps, but not in Mexico. Online Appendix OA4 shows an example of the simplified KFS used in Peru (E) and in Mexico (H). While the format in Peru includes the total yield earned in a month with two usage profiles, the format in Mexico does not contain this information. This is perhaps the reason why the simplified KFS is effective in Peru but not in Mexico. As a result, in the pooled regression only financial literacy matters.

Table 5 computes the price-elasticity from the estimates of Table 4. In practice, we use data from one disclosure treatment and run a simple rank-ordered logit without interactions.<sup>11</sup> Columns 1 and 4 use the sample of all respondents. In columns 2 and 5, the sample is restricted to individuals that are not financially literate, that is, individuals that did not answer the question on interest rate correctly (FinLitik = 0), while in columns 3 and 5 only individuals who correctly answered that question are included (FinLitik = 1).

Panel A of Table 5 contains data from Peru, Panel B from Mexico and Panel C pools data from both Peru and Mexico. The price-elasticities reflect the results discussed in Table 4. For example, according to the elasticities reported in column 1 of Panel C, when individuals compare credit products using brochures, an increase of 1 percent in the cost of the loan leads to a decline in the probability of that loan being chosen first of 1 percent (p-value is 0). In contrast, when individuals use the simplified KFS the decline in

> 11 In particular, we run the following specification: Ordercijk = αj + Pricecijk + εcijk. See Online Appendix 5 for details on how the elasticities were calculated.

18
-8-

# EVIDENCE

Has investment shifted away from secondary institutions?

The answer to this question is yes. Moreover., there are differences in the patterns of change beLween general education and VET, across different VET modes, and across countries with different per capita income levels.

Table 1 summarizes the pattern of World Bank education sector lending for two time periods: 1963-76, and 1977-86. Lending for general education of all kinds is distinguished from lending for VET. The latter is presented in two categories. The "narrow definition" category covers investments in secondary, post-seconds ion-formal and vocational teacher training. Ihis is the subset of modes studied intensively in the project review. The "broad definition" adds university VET, diversified secondary schools and teacher training for general education.

In relative terms, these investments in the cast ten years have placed increasing emphasis on general education, with the increase most marked in primary education. This has been in line with World Bank emphasis on poverty alleviation, where expansion and improvement of basic education can play an important role. Proportionally, investments in VET have declined, although they hiave increased in absolute dollar amounts. Lending for diversified secondary schools declined dramatically, with a corresponding increase in university-level VET. The overall pattern is one of increasing emphasis on university -nd non-formal VET, with significant declines in secondary vocational and diversified schools.
_Case_ to identify potential for enhancing human capital and cost effectiveness of project proposals. While the Bank has education policy and public investment expertise, IFC has private sector investment expertise. Continued information exchange between the Bank and IFC, even joint missions under appropriate circumstances would provide opportunities for clearly targeted policies and investments in client countries within the public and private sectors.

- In 1997-98, IFC sponsored a global study on potential investment opportunities in

- education. The study highlights the importance of the World Bank Group's role in assisting governments to create enabling environments through appropriate regulatory framework for the private sector to function in an unfettered manner alongside the public sector in order to enhance the educational and developmental viability of education projects (Tooley 1999:126). More recently, in November-December 1999, in an effort to bring together private sector investors in education, the IFC and the Bank jointly sponsored a conference on Investment Opportunities for Private Education in Africa was held in Abidjan, Cote d'lvoire. _An explicit strategy for private sector development in education:_ The World Bank's 1999 Education Sector Strategy Paper (ESSP) sets out some priorities in the context of changing international trends. More specifically, the Bank's strategy would be to encourage investment in education. ESSP clearly recognizes that it is part of many governments' strategy to expand education supply - especially of secondary and tertiary places and technical and vocational training - by encouraging investment by not-for-profit groups and entrepreneurs (World Bank 1999:x). The World Bank is working in several West African countries (targeting especially Senegal, the Gambia, CBte d'Ivoire and Mauritania), surveying the market for education and helping governments create an enabling framework for private sector development in education (World Bank 1999:35).

- _Facilitating information exchange:_ In February 1999, the World Bank Group (World Bank, IFC and the Multilateral Investment Guarantee Agency - MIGA) created an education investment information facility called Edinvest _[http://www.worldbank orgledinvest!l._

20
9

# _The Main Bottlenecks to Operating Officially: The Entrepreneur's Perspective_

When questioned in the various surveys conducted, entrepreneurs prominently and repeatedly mentioned as the main bottlenecks to operating officially the following: (1) the myriad of regulations in the foreign exchange and trade regimes; (2) high inflation; and (3) the high tax rates and volatile and often retroactive tax regulations. Interestingly, such highly restrictive bottlenecks, in the eyes of entrepreneurs, contrasted sharply with the entrepreneur and manager's relatively nonintrusive assessment of more conventional determinants, such as labor regulations, the skill and motivation of the labor force, enterprise registration, and infrastructure constraints. Not surprisingly, when questioned about the type of measures that would provide a strong incentive to return to the official economy, of paramount importance was the removal of administrative controls in the trade and foreign exchange regimes, as well as a stable and moderate tax structure and macroeconomic stability (that is, low inflation)."<sup>4</sup> From these microsurveys, the empirical link between the lack of liberalization and stabilization and the flight to the unofficial economy appears to be rather strong. In fact, in response to the question of timing and extent to which entrepreneurs would be prepared to switch toward operating officially, about 50 percent stated that they would come back from unofficialdom within two years of implementation of far-reaching economic liberalization and tax reform measures (30 percent would do so during the first year), and over 33 percent would come back thereafter. Only about 15 percent indicated that there would be no coming back to the official economy, some because they did not believe in the government's ability to carry out sustained liberalization reforms, and some because they were doing just fine where they were and intended to carry on regardless."<sup>5</sup> Clearly, however, the bulk of the unofficial economy appears to be shallow and thus amenable to reversal by liberalizing economic policies.

_The Extent of the Unofficial Economy in Ukraine: The Microevidence_ Given the background of the high cost of operating officially, it is not surprising to find that unofficial activities have mushroomed in Ukraine. Our initial survey work in 1992 on the fledgling nonstate enterprise sector suggested that the extent of unofficial activities was already about 25 percent of the total. Subsequent surveys in 1993, 1994, and 1995, while not perfectly comparable in terms of sample size and characteristics, indicate fairly stable estimates, ranging between 47 percent and 59 percent, for the nonstate sector."6 A rather partial sampling from the state sector suggests that the acknowledged extent of engagement in unofficial activities is lower than what the nonstate sector is prepared to acknowledge in surveys. But even using such limited estimates, the suggestion is that unofficial activities by the state sector grew very significantly, and by 1994 they were likely to approach or exceed one-third of overall activity by the state sector.
each other’s savings. One third of clubs that was assigned to either ordinary or commitment savings accounts was randomly determined to be ineligible to receive raffle tickets (and was not told about the raffle). Another one third of clubs with savings accounts was randomly selected to have raffle tickets distributed privately. The final third of clubs with savings accounts was randomly selected for public distribution of raffle tickets. In these clubs, each participant’s name and the number of tickets he received was announced to everyone that attended the raffle meeting.

Because of the simple formula for determining the number of tickets, farmers in clubs where tickets were distributed publicly could easily estimate how much other members of the club had saved. Private distribution of tickets, though, did not reveal information about individuals’ account balances. The raffle scheme was explained to participants at the time of the baseline survey using a simulation. Members were first given hypothetical balances, and then given raffle tickets in a manner that corresponded to the distribution mechanism for the treatment condition to which the club was assigned. In clubs assigned to private distribution, members were called up one by one and given tickets in private (out of sight of other club members). In clubs assigned to public distribution, members were called up and their number of tickets was announced to the group.

Thus, the final design of the project includes seven treatment conditions: a pure control condition without savings account offers or raffles; ordinary savings accounts with no raffles, with private distribution of raffle tickets, and with public distribution of raffle tickets; and commitment savings accounts with no raffles, with private distribution of raffle tickets, and with public distribution of raffle tickets (see Table 2).

The randomization was carried out at the club level. The list of tobacco clubs in central Malawi (all of which had existing production loans with OIBM) was provided by OIBM in cooperation with the two tobacco buyer companies. Prior to randomization, treatment clubs were stratified by location<sup>11</sup> , tobacco type (burley, flue-cured or dark-fire) and week of scheduled interview. The stratification of treatment assignment resulted in 19 distinct location/tobaccotype/week stratification cells.

> 11 “Locations” are the tobacco buying companies’ geographically-defined administrative units within which extension services and contract buying activities are coordinated.

9
**Table 10 continued System Estimation of Four Equations: Three Stage Least Square**

||Dependen<br>|t Variable<br>|
|---|---|---|
|Independent Variables|Infant Mortality|Life Expectancy|
|Constant|7.389***<br>(1.208)|-4.540***<br>(0.726)|
|Dummy for 1974-83|-0.109<br>(0.173)|0.106<br>(0.105)|
|Dummy for 1984-93|-0.363**<br>(0.182)|0.306***<br>(0.110)|
|Initial Per Capita GDP|-0.765***<br>(0.117)|0.407***<br>(0.071)|
|Per Capita GDP Growth|1.308<br>(6.589)|-1.891<br>(3.952)|
|Social Spending/GDP|-0.376***<br>(0.097)|0.174** *<br>(0.058)|
|Quality of Bureaucracy|-0.855**|0.051|
|(O worst, I best)|(0.436)|(0.264)|
|Political Freedom|0.029|-0.051**|
|(I high, 7 low)|(0.043)|(0.025)|
|Income Inequality|0.037***<br>(0.010)|_-0.015***_<br>(0.006)|
|Latin America Dummy|-0.049<br>(0.232)|0.022<br>(0.139)|
|East Asia Dummy|-0.699***<br>(0.279)|0.135<br>(0.166)|
|Sub-Saharan Africa Dummy|-0.335|-0.262|
||(0.350)|(0.211)|
|Socialist Dummy|0.201<br>(0.325)|-0.234<br>(0.196)|
|Number of Observations|91|91|
|System R-Squared|0.7420||

t** significant at 1% level ** significant at 5% level * significant at 10% level

# Correlation Among Error Terms

||Life Expectancy|Infant Mortality|Growth|Size|
|---|---|---|---|---|
|Life Expectancy|1||||
|Infant Mortality|-0.78|1|||
|Growth|0.30|-0.17|1||
|Size|-0.01|-0.01|0.05|1|

47
NL-2/2-8-88

- 42 -

reserves as a result of capital imports, following the increase in the demand for money creates a deceptive impression of success. In addition, no recessionary side effects emerge as in the case of using the monetary anchor. However, when the exchange rate is fixed in the foregoing scenario the costs appear at a later stage, involving an eventual loss of reserves and a possible balance of payments crisis. The analysis of using the exchange rate as a nominal anchor must therefore focus on issues of sustainability.

From the fiscal point of view, sustainability of a fixed exchange rate stabilization, as of any other stabilization policy, requires government's long term solvency. This means that the program should not induce a rate of growth of public debt which exceeds in the long run the real interest ratei The special feature of exchange rate stabilization is that solvency should be achieved without inflation tax generated by devaluations. Lack of solvency implies that the government does not Intend to repay its debt and therefore this situation must lead to some form of crisis in the asset markets which can be expected to take the shape of a run on the central bank's reserves.

However, since solvency involves long term fiscal policies, its existence is a matter of conjecture. In this type of situation, it is natural for agents (domestic citizens, foreign banks, etc.) to set some limits on the size of public borrowing which is still deemed consistent with solvency2/ The lower is the credibility in fiscal solvency, the tighter the limits. In particular, a low degree of fiscal credibility will reduce the maximal level

- 1/ See discussion in Helpman and Leiderman (1987).

- 2/ In principle this is similar to the public's reaction to a central bank which can conceal its intentions [see Canzonery (1985)1.
**Table 2:** Effects of Turnovers on Bureaucratic Organization

||**Villag**|**e heads**|**Village gov**|**ernment**|
|---|---|---|---|---|
||New leader|Tenure (yrs)|% New appts|% Reshuffing|
||(1)|(2)|(3)|(4)|
|New village head|0.835<sup>_∗∗∗_</sup>|-4.908<sup>_∗∗∗_</sup>|0.182<sup>_∗∗_</sup>|0.089<sup>_∗_</sup>|
||(0.084)|(1.236)|(0.085)|(0.045)|
||[0.101]|[1.527]|[0.100]|[0.052]|
|Observations|442|443|510|510|
|Control mean|0.035|7.96|0.33|0.036|
|Robust p-value|0.000|0.001|0.042|0.073|
|<br>MSE-opt. bandwidth|15.8|31.2|22.0|18.9|
|Effective obs.|172|285|256|235|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observations are village heads in columns 1-2 and villages in columns 3-4. The dependent variable is: in column 1, a dummy equal to 1 if the village head in our survey data is a different individual from the incumbent competing in the most recent village election; in column 2, the number of years spent in office by the current village head; in column 3, the rate of bureaucratic turnover at the village level since the last election, defined as the fraction of new bureaucrats appointed to their current position since the last election; in column 4, the share of bureaucrats “reshuffled” within the village secretariat, defined as the fraction of bureaucrats who experienced a promotion, demotion, or lateral move since the last election. See Section 4 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Conventional standard errors in parentheses and robust bias-corrected standard errors in brackets. Significance levels are based on the robust bias-corrected standard errors.

34