rafmacalaba/datause-encoder-data huggingface dataset:
microfinance is not cash transfer, and its benefits from self-employed activities take time to be realized. Keeping this in mind, the earlier study of Pitt and Khandker (1998) used a cross-sectional survey of households in villages where the program had been in place for at least 3 years prior to the survey. The follow-up study as well as other panel data analysis from Bangladesh confirms the earlier benefits of microfinance (e.g., Islam 2011; Khandker 2005).

Bangladesh’s experience with microfinance has been phenomenal. In 2008, there were some 30 million members of MFIs who received an annual disbursement of US$1.8 billion with an outstanding balance of US$1.5 billion (Khandker et al. 2013). There were some 576 registered MFIs in June, 2011, compared with only a few in 1991/92. With rising competition among MFIs in a market with limited diversification of product design and marketing, continued borrowing by poor households may create conditions for market saturation, resulting in village diseconomies. Thus, microcredit growth may create micro-debt dependencies, where participants have the possibility of borrowing from multiple sources and are likely to be over-indebted or trapped in poverty.

It is alleged that too much credit or too many MFIs are neither good for borrowers nor good for the economy. A recent study based on a long panel survey of 1991/92-2010/11 shows that multiple program membership has increased manifold over the years. Nonexistent in 1991/92, it represented 8.9 percent of borrowers in 1998/99 and 31.9 percent in 2010/11 (Khandker, Faruqee, and Samad, 2013). But the growth of multiple program membership has not yet caused loan recoveries of MFIs to fall (Khandker, Koolwal and Badruddoza 2013), nor resulted in long-term borrowers necessarily being trapped in poverty or debt as many contended in recent years (Khandker and Samad 2013).

This paper, using data from the long panel survey spanning over 20 years, explores dynamic issues beyond what Pitt and Khandker (1998), Khandker (2005) and Khandker and Samad (2013) had explored. More specifically, this paper explores whether the effects of short-term loans vary 3
- 21 -

_Proposition 11. In order to mitigate capture of public procuring agencies, the government has to provide incentive payments to procurement officials in proportion to the size of market._

|Table 6.|Major actors in po<br>|wer sector<br>|corruption<br>|<br>|
|---|---|---|---|---|
||Bangladesh|India|Nepal|Pakistan|
|Meter readers|26|23|24|36|
|Billing employee|7|22|22|24|
|Officers|20|24|13|12|
|Linesmen|12|37|36|9|
|Repairmen||5||1|
|Electricians||7||3|
|Others||||3|

In percentage of respondents reporting corruption with each type of actor. Multiple responses were permitted.

Source: Transparency International; cited in World Bank (2009).

Table 7. Nature of complaints about development project procurement, FY2007

||Number of<br>complaints|%|
|---|---|---|
|Qualification of other firms|41|11.8|
|Technical evaluation|39|11.3|
|Application of evaluation criteria|37|10.7|
|Disqualification of bids|29|8.4|
|Contract administration|25|7.2|
|Allegation of fraud and corruption|22|6.4|
|Payment|20|5.8|
|Combined financial and technical rating|14|4.0|
|Irregularity before bid opening|14|4.0|
|Own qualification issue|14|4.0|
|Quality of bidding document|14|4.0|
|Bid security|11|3.2|
|Technical specifications|10|2.9|
|Bid and proposal submission|8|2.3|
|Eligibility|6|1.7|
|Short listing|6|1.7|
|Irregularity in bid evaluation|4|1.2|
|Performance security|3|0.9|
|Contract award|2|0.6|
|Prequalification|2|0.6|
|Transparency issue|2|0.6|
|Conflict of interest|1|0.3|
|Domestic preference|1|0.3|
|Irregularity in bid opening|1|0.3|
|Terms of reference|1|0.3|
|Other|19|5.5|
|Total|346|100.0|

Source: World Bank (2008).

Despite a number of auction models, another auction model that is of particular interest may be McAfee and McMillan (1986) and Fishe and McAfee (1987), in both of which the principal-agent relationship is examined in the auction contract context. Traditional auction
# **A Spatial Perspective on Booms and Busts:**

**Evidence from Türkiye**<sup>**1**</sup>

**Javier E. Baez** World Bank

**Cigdem Celik** World Bank

**Varun Kshirsagar** World Bank

## **JEL CLASSIFICATION** : E01, I32, O11, O47, R11

**KEY WORDS** : Spatial Inequality, Regional Economic Activity, Measurement and Data on National Income, Macroeconomic Analyses of Economic Development, Empirical Studies of Economic Growth
when we control for domestic competition measured by the Herfindahl index (column (3)), the age of the firm and firm capital intensity (column (4)). The coefficient on input-tariff changes remains negative, significant and stable, however. It is very similar in size to the estimations with only industry-level controls shown in column (1).

If the availability of foreign intermediate goods induces firms to start importing capital goods, we would expect the effect of lower input-tariffs to be greater for firms that actually import intermediate inputs. Columns (5) and (6) carry out this test. First, we inlcude a dummy variable equal to one if the firm imports intermediate goods. Firms sourcing inputs from abroad are more likely to also import capital goods (column 5) confirming the previous descriptive evidence on technological complementarity between imported inputs and foreign technology. 30 Next, we introduce an interaction between input tariff and importer of intermediate goods status (column (6)).

Comparing the coefficients of tariffs on capital goods with those on intermediate goods (column (6)) reveals that the indirect effect of input tariffs cuts on the probability of upgrading capital goods is of a similar magnitude to the direct effect of reducing capital goods tariffs. The estimated coefficient of variable input tariff cuts implies that a 10 percentage point fall in input tariffs leads to 1.5% to almost 1.7% increase in the probability of importing capital goods for the average firm and for those actually importing intermediate goods. Between 1989 and 1997, input tariffs declined on average by 27 percentage points, with an associated implied increase in the probability of importing capital goods of about 4.6 for the average firm importing intermediate goods. These findings suggest that the additional gains from input-trade liberalization thanks to the complementarity channel of imported inputs and capital goods are non negligible.

> 30As previously mentioned, unfortunately the Indian firm level dataset does not allow us to test the quality upgrading channel of imported inputs or to distinguish the country of origin of imports of intermediate goods in order to provide a better assessment of the complementarity mechanism.

32
and subsequent volatility as we do in our main estimations. Thus, we present these results only as supplementary to our initial findings. Nonetheless, the results from this procedure confirm many of our initial _findings-PRIVATE_ is negatively related to the variability of GDP, consumption, and investment growth and _BANK_ is negatively related to GDP variability. Consistent with the mixed nature of results we have reported for investment variability, _BANK_ is positively associated with the variability of investment calculated in this manner.

Finally, an additional possibility that we consider here is that our finding of a negative association between financial development and reduced volatility is being driven by the developed countries in our sample which have both developed financial sectors and lower macroeconomic volatility. Although we do not report the results here, we confirmed that our major conclusions still hold even when we drop high income countries from our sample."<sup>4</sup> Our results are also robust to using a logarithmic specification for either of our three dependent variables or for the measure of financial development. Thus, the relationship we identify is not likely to be the result of a an omitted variable common to high income countries that also are likely to have more developed financial sectors.

# 3.3. Further Discussion

So far, our results indicate that the manner in which the financial sector develops could have important implications for the severity of the business cycle. Others have suggested that the financial system is in fact influenced by several important underlying characteristics of the economy (e.g., La Porta et al., 1997, 1998, Levine, 1998, 1999, and Demirguc-Kunt and Levine, 1999).

These characteristics may be linked to macroeconomic fluctuations and it would be desirable to separate them out from the country-specific fixed effect we estimate above. VVhile panel data is not available to allow us to do this in our main estimation, we are able to implement a cross-country regression for a subset of the countries in our panel.

The additional variables we add to the cross-country regressions are from La Porta 1<sup>4</sup> We considered countries that had income in the top quartile to be high-income countries. These countries had real per capita incomes greater than $11,000 in the last period.

14
# **Table 10: Regression Results: Value lost due to outages (% of sales)**

||Log of Va|lue lost due to outages|||
|---|---|---|---|---|
|Procedures Getting Electricity|0.116*|0.112*|0.121**|0.120**|
||(0.056)|(0.056)|(0.045)|(0.045)|
|Private utility||-0272||-0078|
|||.<br>(0.224)||.<br>(0.186)|
|Ln GNI per capita|||-0.502***|<br>-0.497***|
||||(0.07)|(0.07)|
|Constant|0.4<br>|0.513<br>|4.281***<br>|4.277***<br>|
||(0.353)|(0.362)|(0.660)|(0.666)|
|R-squared|0.025|0.038|0.309|0.31|
|Observations|124|124|124|124|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Log of Va|lue lost due to outages|||
|Time Getting Electricity (Log)|0.200|0.193|0.026|0.026|
||(0.146)|(0.144)<br>|(0.122)|(0.123)<br>|
|Private utility||-0.28||-0.098|
|||(0.223)||(0.187)|
|Ln GNI per capita|||-0.498***|-0.492***|
||||(0.08)|(0.08)|
|Constant|0.137|0.265|<br>4.807***|<br>4.798***|
||(0.698)|(0.704)|(0.974)|(0.979)|
|R-squared|0.013|0.027|0.282|0.284|
|Observations|124|124|124|124|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Log of Va|lue lost due to outages|||
|Cost as % of GNI pc|0.373***|0.367***|0.162|0.162|
|Getting Electricity (Log)|(0.051)|(0.051)|(0.087)|(0.088)|
|<br>Private utility||<br>-0.134<br>||<br>-0.095<br>|
|||(0.200)||(0.188)<br>|
|Ln GNI per capita|||-0.332*|-0.327*|
||||(0.13)|(0.13)|
|Constant|-1.502***|-1.421***|2.525|2.521|
||(0.362)|(0.370)|(1.540)|(1.548)|
|R-squared|0.256|0.259|0.299|0.3|
|Observations|124|124|124|124|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Log of Va|lue lost due to outages|||
|Country rank on the ease of getting|0.009***|0.008***|0.004*|0.004*|
|electricity<br>|(0.002)|(0.002)<br>|(0.002)|(0.002)<br>|
|Private utility||-0.21||-0.087|
|||(0.214)||(0.185)|
|Ln GNI er caita|||-0427***|<br>-0423***|
|p p|||.<br>(0.09)|.<br>(0.09)|
|Constant|0.149|0.237|<br>3.937***|<br>3.932***|
||(0.228)|(0.243)|(0.822)|(0.827)|
|R-squared<br>|0.14<br>|0.148<br>|0.31<br>|0.311<br>|
|Observations|124|124|124|124|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||

41
# Figure 1:  Replacement Rates, Incentives and Redistribution in the RGPS

<!-- Start of picture text --> 120 200%<br>Individual earning average earnings<br>100 50% of the average 150% 2 * average earnings<br>average earnings (+1pp growth in salaries)<br>80<br>100%<br>60<br>50%<br>40<br>Individual with average 0%<br>20 earnings 54 56 58 60 62 64 66 68<br>Not eligible Proportional LoC LoC pension Aging pension -50%<br>0 0.5 * average earnings<br>50 55 60 65 70<br>Retirement Age -100% Retirement Age<br>7<br>0.5 * average earnings<br>6<br>30 average earnings<br>5<br>25 Retirement age 65 (without minimum)<br>4<br>20<br>3<br>15 2 * average earnings<br>Retirement (without pension) 2 average earnings (+1pp growth in salaries) Sustainable level<br>age 60<br>10 1<br>5 0<br>0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 54 56 58 60 62 64 66 68 70<br>Contribution Density (fraction of a year) Retirement Age<br>Gross Replacement Rate<br>Change in Pension Wealth (% average wage)<br>Internal Rate of Return (%)<br>Expected Life Time Net Wealth<br><!-- End of picture text --> _Note:_ The change in pension wealth (the present value of future pensions) is computed at each retirement age. A negative value indicates individuals loose pension wealth if they delay retirement by one extra year. Expected life time wealth is defined as the present value of all income flows (wages and pensions). In the calculations it is assumed that earnings in the informal sector (i.e., when the individual is not contributing to the social security represent 80% of earnings in the formal sector. _Source:_ Authors’ calculations based on current legislation.

Overall, the replacement rates offered by the UI system range between 40 and 100 percent depending on the level of income. The benefit formula ensures that replacement rates are higher for low than for high income workers (see top-left panel of Figure 2). The duration of benefits is also higher for the median worker and below. Taking both UI and FGTS together, the median worker can finance between 3.5 and 8 months of salaries depending on the number of months of contributions (see top-right panel of Figure 2). Still, redistribution within the system seems to be regressive as low income workers have lower take-up rates and lower average benefits (see bottom panels of Figure 2).

In terms of incentives the evidence is somewhat mixed. The most recent analysis suggests that UI does not have a major impact on the duration of unemployment spells and, if anything, it is allowing workers to find better jobs (Margolis, 2008). Previous analysis also found that UI does not affect significantly unemployment spells, except for those transiting into selfemployment. Spells in this case are shortened (see Cunnigham, 2002).

Regarding FGTS, the main concern is that it is providing incentives for fake dismissals as workers attempt to cash-out their unemployment savings accounts and/or employers prefer short7
# **<u>Table 6. Transmission Channels</u>**

||**Currency ratio(a)**|**Excess Reserve**<br>**ratio(a)**|**Currency ratio**|**Excess Reserve**<br>**ratio**|**Currency ratio**|**Excess**<br>**Reserve**<br>**ratio**|**Currency rati**|**o**<br>**Excess Reserve**<br>**ratio**|
|---|---|---|---|---|---|---|---|---|
||Fixed|Effects|SU|RE(b)|SURE|(b)|SU|RE(b)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Post-Reform Trend Break**|-0.015*|0.0003|-0.015***|0.0004|-0.026***|0.003|-0.001|0.007***|
||(0.007)|(0.0015)|(0.0003)|(0.001)|(0.004)|(0.002)|(0.007)|(0.002)|
|**(Trend Break)*(Pre-reform number of**|||||||||
|**credit transfers per inhabitant)**|||||0.076***|-0.015|0.097***|-0.0001|
||||||(0.025)|(0.010)|(0.025)|(0.011)|
|**(Trend Break)*(Pre-reform average**|||||||||
|**currency ratio)**|||||||-0.029***||
||||||||(0.007)||
|**(Trend Break)*(Pre-reform average**|||||||||
|**excess reserves ratio)**||||||||-0.085***|
|||||||||(0.022)|
|log real GDP per capita|-0.228*||-0.290***||-0.277***||-0.260***||
||(0.112)||(0.069)||(0.068)||(0.067)||
|Government expenditure over GDP ratio|0.923||0.889***||0.778**||0.603*||
||(0.587)||(0.318)||(0.315)||(0.311)||
|Nominal interest rate|-0.003***|-0.0002|-0.003***|-0.0002|-0.002***|-0.0002*|-0.002***|-0.0003**|
||(0.0004)|(0.0001)|(0.0004)|(0.0001)|(0.0004)|(0.0001)|(0.0004)|(0.0001)|
|Output growth||-0.0003*||-0.0003*||-0.0004*||-0.0004**|
|||(0.0001)||(0.0002)||(0.0002)||(0.0002)|
|Reserves requirement ratio||-0.009**||-0.009***||-0.010***||-0.011***|
|||(0.003)||(0.001)||(0.001)||(0.001)|
|Foreign Exchange reserves/GDP||0.0001||-0.0001||-0.0003||-0.0004|
|||(0.0001)||(0.0003)||(0.0003)||(0.0003)|
|Country specific trends|x|x|x|x|x|x|x|x|
|Year effects|x|x|x|x|x|x|x|x|
|Country effects|x|x|x|x|x|x|x|x|
|Seasonal Dummies|x|x|x|x|x|x|x|x|
|Within R-squared|0.57|0.48|0.93|0.80|0.93|0.80|0.93|0.80|
|Nbr. obser.|396|392|392|392|392|392|392|392|

Note: Robust clustered standard errors in parentheses. Significant at (*) 10 per cent level; (**) 5 per cent level; (***) 1 per cent level.

(a) The currency ratio is the ratio of currency in circulation (oustide the banking system) over demand deposits and the reserves ratio is the ratio of excess reserves held by private banks over total deposists.

(b) SURE stands for seemingly unrelated least squares
incentives in the labor code would lead us to expect. Second, the symmetry of Vs across directions of movement in all sectors of paid work seems more consistent with a well-integrated market where workers search across sectors forjob opportunities, than one where informal workers seekpermanent status in the formal sector and stay until they retire." Finally, the logit results show that in no sector does the probability of moving into the formal sector relative to staying increase in overall experience as would be expected if there were queuing to enter the sector.<sup>19</sup>

# «ITanlow5 hereŽ

# _ii. Self-Employment as an Alternative to Formal Sector Employment_

Self-employment constitutes the largest source of employment (25%), after formal salaried employment (50%). Although it may serve the traditionally postulated holding pattern or safety net for the latter, the data are consistent with it being a desirable sector in itself As a first approximation it may be more correct to assume that small scale LDC firms have origins and dynamics similar to l/(1-Pj).

18 Although matching workers on multiple characteristics in the construction of the panels guards against record substitutions that would appear as transitions, two additional attempts were made to test the robustness of the results from the "protected" sample, neither of which can claim more validity than the raw transitions themselves. The first tabulated the transitions that occur in the third quarter if all other quarters are identical, assumes that these represent miscodings, and corrects the matrix accordingly. The second looks only at "confirmed" transitions where the initial and final positions were recorded twice consecutively. By treating even legitimate rapid turnovers as coding errors, both adjustment methods necessarily impart an upward bias to the tenure estimates and they do increase in all sectors. However, in neither case do the rankings of sectors by turnover or the symmetries of the IRM change substantially. Results available on request.

19 The one exception is from Infonnal Salaried to Formal Salaried in the Micro-enterprise sample where the overall impact of Exp and Exp squared evaluated at the mean experience level is positive, but not significant. Gregory (1986) analyzing Mexican data from 1940-1980 also finds no evidence of queuing to enter the formal sector "The empirical evidence...represents the antithesis of the Todaro [dualist] model. Rather than flowing into a queue to await the opening of improved employment opportunities, migrants moved quickly and easily into employment opportunities in both the formal and informal sectors..." p. 267 14
categories: micro and macro output, and labor market. Transport studies is where this inclusion of new topics is more obvious, with 90 outcomes out of 393 not belonging to the three main categories.

Table 2 shows the distribution of the sign of estimates by sectors.<sup>6</sup> Finally, approximately half of all estimates (540 out of 1074) correspond to developing countries sample. This proportion is highest for energy (82 percent of estimates) and transport (60 percent), and much smaller for digital (38 percent) and cross-sectoral estimates (16 percent).

# **3 Methodology**

This section presents the steps taken in the paper to quantitatively analyze the large number of estimates in our of studies. we aim at our gathered sample Specifically, deriving own summary estimates of the underlying “true” effect that can be inferred from existing studies, explain the observed heterogeneity in the existing evidence, and assess to which extent it may suffer from publication bias.

When running estimations, we also look at specific sub-samples combining one sector and one outcome, whenever enough observations are available, because combining homogeneous dependent and independent variables categories is likely to reduce sample heterogeneity. There are eight of these: two for the digital sector (micro- and macro-output), three for energy (micro- and macro-output, and labor), and three for transport (microand macro-output, and labor). Finally, we also break down results between developed and developing countries.

Assessing publication bias is critical to a correct appraisal of the literature. It has been shown to be pervasive across many fields of economic research.<sup>7</sup> When publication bias

> 6Table A.3 shows signs by sector and types of outcomes. Positive estimates represent again a consistently high share of 80 percent or more, with two notable exceptions: only 59 percent of estimates in inequality / poverty related studies and of population effects are reported as positive. This must be considered in a context where the number of estimates included in our data is however relatively small (17 and 34 respectively).

> 7Publication bias stems both from the tendency of editors and referees to favor statistically significant results for journal publication, and from the related “file-drawer” problem, as this generates incentives for

10
Harmon, C. and I. Walker. 1999. "The Marginal and Average Returns to Schooling in the l' _European Economic Review_ 43: 879-887.

- Hartog, J., J. Odink and J. Smits. 1999. "Private Returns to Education in the Netherlands," in R. Asplund, and P.T. Pereira, eds., _Returns to Human Capital in Europe._ Helsinki: ETLA, The Research Institute of the Finnish Economy.

- Haveman, R.H. and B. Wolfe. 1984. "Schooling and Economic Well-Being: The Role of NonMarket Effects." _Journal of Human Resources_ 19(3): 128-140.

Heckman, J. and P. Klenow. 1997. "Human Capital Policy." University of Chicago (mimeo).

- Hossain, S.I. 1997. "Making Education in China Equitable and Efficient." Policy Research Working Paper No. 1814, Washington, DC, World Bank.

- Horowitz, A. and C. Schenzler. 1999. "Returns to General, Technical and Vocational Education in Developing Countries: recent evidence from Suriname." _Education Economics_ 7(1): _5-_ 19.

- Ichino, A. and R. Winter-Ebmer. 1999. "Lower and Upper Bounds of Returns to Schooling: An Exercise in IV estimation with different instruments." _European Economic Review 43:_ 889-901.

- Isacsson, G. 1999. "Estimates of the return to schooling in Sweden from a large sample of twins." _Labour Economics_ 6: 471-489.

- Jones, P. 2001. "Are educated workers really more productive?" _Journal of Development Economics_ 64: 67-79.

- Katsis, A., R. Mattson and G. Psacharopoulos. 1999. "Explaining Educational Development in Pakistan: An Analysis of the 1991 Household Survey." _Journal of Educational Planning and Administration 13(1)._

- Kingdon, G.G. 1998. "Does the Labour Market Explain Lower Female Schooling in India?" _Journal of Development Studies 35(1): 39-65._

- Kling, Jeffrey. 1999. "Interpreting Instrumental Variable Estimates of the Returns of Schooling." Working Paper No. 415, Industrial Relations Section, Princeton University.

- Kroncke, Charles and Kenneth Smith. 1999. "The wage effects of ethnicity in Estonia." _Economics of Transition_ 7(1): 179-199.

- Krueger, A.B. 1993. "How Computers have Changed the Wage Structure: Evidence from Microdata, 1984-89." _Quarterly Journal of Economics_ 108(1): 33-60.

8