rafmacalaba/datause-encoder-data huggingface dataset:
financial development (La Porta et al., 1997; Levine, Loayza and Beck, 2000, Djankov, McLiesh and Shleifer, 2005). Countries whose legal systems more effectively prevent looting of corporations by controlling shareholders suffered milder financial crises in 1997-98 (Acemoglu et al., 2003). More effective legal systems provide for more flexible, adaptable and rapid conflict resolution rather than rigid, lengthy statutorily based processes with positive repercussions for firms’ access to finance (Djankov et al., 2003; Beck, Demirguc-Kunt and Levine, 2005). Cross-country comparisons have shown that financial systems that can rely on more effective legal systems, have lower interest rate spreads and are more efficient (DemirgucKunt, Laeven and Levine, 2004; Laeven and Majnoni, 2005). The effect of legal system efficiency on financial intermediation is illustrated in Figures 4 to 6, where we show the positive relationship between creditor rights and Private Credit to GDP (Figure 4), the negative relationship between the time period it takes to enforce contracts and Private Credit to GDP (Figure 5) and the positive relationship between better protection of minority shareholder rights and the stock market capitalization to GDP (Figure 6).<sup>6</sup>

# **2.3. Informational framework**

As financial markets and institutions arise due to information asymmetries that prevent direct interaction between multiple savers and investors, effective financial intermediation depends on tools to reduce these information asymmetries. The informational framework is thus a third fundamental requirement for effective financial intermediation. International experience has shown that the sharing of credit information sharing is important to reduce adverse selection problems and foster competition in the financial system. Credit registries that give easy and reliable access to clients’ credit history and both negative and positive information can

> 6 There is a literature that discusses the direct impact of property right protection and contract enforcement on entrepreneurs’ investment decisions and firm growth (Johnson, McMillan and Woodruff, 2002, Claessens and Laeven, 2003; Cull and Xu, 2005).

9
21 The first branch seeks to exploit spatial variation in the geographic and sectoral patterns of growth and in initial demographic and distributional conditions within countries to shed light on what makes growth more or less “pro-poor,” i.e. to examine its incidence within a country. Datt and Ravallion (1998) and Ravallion and Datt (2002) for India, Ravallion and Chen (2006) and Montalvo and Ravallion (2008) for China, Ravallion and Lokshin (2007) for Indonesia, and Ferreira, Leite and Ravallion (2007) for Brazil all follow this approach. In essence, these studies compute a panel of poverty rates across states (or provinces) and over time, and regress the changes against sector-specific rates of growth in each spatial unit. Control variables typically include differences in initial conditions across state, including pre-sample differences in land or income inequality, literacy, and the like. There may also be time-varying state-level controls, such as changes in various types of public spending in each state.

These studies require relatively long series of repeated cross-section household surveys, and are easiest to conduct in large countries, where spatially disaggregated sub-samples retain statistical representativeness. Looking across the studies carried out so far, a few lessons emerge. First, the sectoral composition of growth does seem to matter for poverty reduction. In all three countries, the growth elasticities of poverty reduction varied substantially and significantly across sectors. But the relative sector ranking varied across countries: agricultural growth was by far the most effective in reducing poverty in China, while growth in the services sector had a higher impact on poverty in Brazil and India. In all three countries, the effect of manufacturing growth on poverty reduction seemed to vary significantly across states, suggesting that diverse geographic, distributional or institutional conditions can affect the growth elasticity of poverty reduction, even within a single country.

It was generally found that less “initial” (i.e. pre-sample) inequality was associated with a greater effectiveness of growth in reducing poverty (as the previous section would suggest). Greater literacy and better initial health conditions (often measured inversely by infant mortality rates) also help make growth more poverty-reducing. In India, about half of the range in longterm rates of poverty reduction across India’s states (between the best performer, Kerala, and the worst one, Bihar) can be attributed to the difference in initial literacy rates (Datt and Ravallion, 1998). The elasticity of poverty to _non-farm_ economic growth in India was particularly sensitive to differences in human resource development (Ravallion and Datt, 2002). In Brazil, one interesting finding was that a greater level of voice or “empowerment”—proxied by the rate of
households surveyed in the second round.<sup>2</sup> Lower and upper bound estimates of mobility are derived from two different sets of assumptions about the correlation between the error term in the first round and in the second round.

Lanjouw et al. (2011) argue that the correlation between both error terms is likely to be non-negative.<sup>3</sup> If we are willing to assume zero correlation between the first round and the second round error terms, Lanjouw et al. (2011) propose to predict consumption in the first round by randomly drawing with replacement for each household _i_ in the second round from the empirical distribution of first round estimated residuals (denoted by ) as follows: (2) Equation 2 allows us to compute estimates of movements in and out of poverty. For example, the fraction of poor households in the first round who escaped poverty in the second time is given by: (3) Since we are randomly drawing from the empirical distribution of estimated errors, we need to repeat the procedure _R_ times and take average of equation 3 in order to estimate movements in and out of poverty.<sup>4</sup> In all likelihood, however, the correlation between error terms will be positive. By assuming no correlation, equation 3 will provide an _upper bound_ estimate of the mobility in and out of poverty. Lanjouw et al. (2011) propose estimating a _lower bound_ on mobility by now assuming a perfect positive correlation between error terms. In this case estimates of residuals

> 2 Section (5) discusses the robustness of changing the forecasting direction.

> 3 Correlation between error terms will be non-zero in two cases: (i) the error term includes an individual fixed effect and (ii) shocks to consumption persist over time. Lanjouw et al. (2011) argue that correlation between error terms will almost certainly be positive if the condition (ii) holds.  In their study using Vietnamese and Indonesian data they present empirical support in favor of this assumption.

> 4 We replicate the procedure 50 times in this paper ( _R_ =50). Section (5) discusses the robustness of replicating the procedure 150 and 300 times.

6
> to requiring the divestiture of a controlling interest in Malawi Telecom by June 2001, the project also had an additional requirement that MTL would increase the number of working telephone lines to 150,000 by the end of 2004 (World Bank, 2000).

<!-- Start of picture text --> 120,000<br>80,000 /:<br>60,000 -.<br>40,000-<br>'j-Ji -, 3,__<br>20,000 *<br>1<br>1990 1992 1994 1996 1998 2000<br>OMain Lines OCellular<br><!-- End of picture text --> Figure 16: Total Telephones in Operation in Malawi Source: Intemational Telecommunication Union (2001) Although Malawi Telecom, with financial support from several donor agencies including DANIDA, successfully increased the number of fixed lines from 37,000 in 1998 to over 54,000 by the end of 2001 (see Figure 16), the actual rate of 6. t has .expansion been slower than |g w. ........ in the 1998 Communications Sector Policy. Combined with the slower than intended progress with privatization, this suggests that Malawi Telecom

'envisaged

> might not meet the ambitious goals specified in the 1998 Communications Sector Policy Statement and the 2000 Privatization and Utility Reform project.

46
**<u>Figure 2: Cost of Domestic and External Borrowing.</u>**

<!-- Start of picture text --> Implicit Interest rate on External and Domestic Debt Implicit interest rate on Domestic Debt<br>HIPC and Non HIPC<br>1995 2000 2005 2010<br>Year 1995 2000 2005 2010<br>Domestic nominal Domestic real year<br>External HIPC NON HIPC<br>Real Implicit Interest rate on Domestic Debt Domestic and External Interest (as % of GDP)<br>Yem en TogoMalawi<br>Sierra_Leone Malawi UgandaGuineaMala wi<br>Burundi EritreaTogoUnioNepHaitial n_o Eritreaf_the_ComorosRwandaHaiti BuruGuinRwaHaitiEritrea nda ndi ea BurundiHaiti Guinea<br>Eritrea Ethiopia<br>Malawi<br>Ghana<br>Guinea_Bissau<br>1995 2000 2005 2010<br>Year<br>Year Domestic External<br>Domestic Interest (as % of GDP) External Interest (as % of GDP) Domestic Interest (as % of GDP)<br>HIPC and Non HIPC HIPC and Non HIPC<br>Ghana Malawi<br>Malawi<br>Ghana<br>Ghana Ghana<br>Malawi<br>Ghana<br>Malawi<br>Sierra_Leone<br>1995 2000 2005 2010 1995 2000 2005 2010<br>year year<br>HIPC NON HIPC HIPC NON HIPC Year<br>1996199719981999200020012002200320042005200620072008200920102011<br>1996199719981999200020012002200320042005200620072008200920102011<br>20<br>20<br>15<br>10<br>15<br>Percent 5<br>Percent<br>0 10<br>-5<br>5<br>50 2.5<br>2<br>0<br>1.5<br>1<br>-50<br>Implicit Interest rate Interest ( as % of GDP )<br>.5<br>-100 0<br>2.5 4 6<br>3<br>2 4<br>2<br>1.5 2<br>Interest (as % of GDP) Interest (as % of GDP)<br>1<br>Domestic Interest (as % of GDP)<br>1<br>0 0<br><!-- End of picture text --> _Source: our elaboration on the LIC domestic public debt dataset._

# **4.3. Instruments**

The structure of domestic public debt in terms of type of instruments matters. According to Abbas and Christensen (2010), the development of local government debt markets helps supply a benchmark yield curve for private lending contracts as well as financial instruments that serve as saving assets and collateral vehicles. But these benefits are to be expected from government debt 13
76 Table S tests of SeboeliAS **Cmpleted and Ranted years** of BareLlnent Rntios by Ion. fot legions from 13s0 and 1360: Ielseted Available Countries

|T<br>Region<br>(number of<br>eountries<br>observed)|eare of Sbho<br>C _emyted<br>Vmeo to<br>Hale Ratio<br>(1)<br>.|oling 1S<br> Age 20-2<br>HaloLev<br>is<br>teats<br>(2)|60-19d0<br>4<br>el<br>|**xpsete**<br>remae<br>Hale<br>(3)|**d**<br>**Teats**<br> to<br>Ratio<br>(4)|**6ftno**<br>Halo<br>in<br>(5)|**llment**<br> Level<br>toars<br> <br>(6)|
|---|---|---|---|---|---|---|---|
|1.ighinsane<br>idus-<br>trially<br>advanced<br>countrioe<br>(including<br>Japan.**Israel.**and<br>South Africa)|1.00<br>(24)|**5.S**<br>(24)||.64|1.00|10.6|<br>12.6|
|2. Latin America|.64|5.5||.92|.67|5.6<br>*|<br>.3|
|3. last Asia<br>(excluding Japan)<br>4-. South Asia|.63<br>(5)<br>.45<br>(5)|64<br>**(0)**<br>3.<br>(5)|IAsia<br>(exclud-<br>Iin Arab)<br>J|.65|.75|7.5|<br>9.4|
|5. West Asia<br>(excluding Israel)<br>S. North Africa|.34<br>(2)<br>.39<br>(2)|4 7<br>(2)<br>3<br> <br>(2)|lArab<br>i<br>States<br> J|°|.73|3.5|<br>7.0|
|7. SubUhaaran Afriea<br>(excluding South<br>Africa)|*<br>(7)|(i3|IAfrica<br> <br>(erxlud-<br>ong<br>sAeb)|.56|.73|3.5|7.0|
|World Total|na|n.e.|.75||.62|7.7|9.6|

Source: Coluns 1 and 2 deriva.d by averaging of the available **sample** o? country estimates reported in Sable A-1 by the author. Columne 3-6 derived from UtRSCO estimates of enrollment rates by lovel, with different regionol Saoupings. I 27StettietieYearbook. Table 2.10. **Soe** tfotnoto 27 fto defaintion 6of xpoeted years ot*solln ent.
Interviews were conducted by phone and the primary respondent was the respondent from the initial MRALS study, typically the household head. The labor module asked this respondent about their own labor participation and additionally to report the labor activities of up to two other adult household members ages 18 to 65. These adults were pre-selected randomly in households with more than three adults. While survey protocols allowed interviews to be done with other household members if the respondent was unavailable, in practice this happened very infrequently.<sup>7</sup> Column 1 of Table 1 shows descriptive statistics of study participants who were successfully reached for the full set of quarterly interviews and who constitute the sample used in the empirical analysis. Household level characteristics, those of the designated respondent, and those of proxied respondents are shown in panels A, B, and C, respectively. Panel A shows that our final sample contains 701 households, with an average of just over five members split evenly between children and adults. In Panel B we see that 75 percent of primary respondents are men and their average age is 44. Sixty-nine percent are in a monogamous marriage while 10% are in polygamous marriages with 6.6 years of education on average. Panel C shows the characteristics of the other household members whose data was collected in the survey. These household members are younger, 30years-old on average, and 76% are female with similar levels of education.<sup>8</sup> Appendix Table 1 shows these summary statics from the analysis sample with those targeted from the sampling of the initial MRALS sample. They are nearly indistinguishable.

Households were contacted approximately once per quarter over the course of a year. While the surveys were intended to be spaced evenly, three months between surveys, in practice logistical issues affected this timing and led to some variation in the actual interval between interviews. Dates and timing of each survey wave are illustrated in Figure 1. These quarterly surveys were designed to be brief, averaging under 20 minutes, focused on labor activities of adult household members. The labor module followed a similar structure to that used in many household surveys where respondents are asked about their primary work activity, with a recall period of the preceding 90 days. Follow-up questions capture further details about that activity in particular, which months they spent doing it, and how many days and hours they spent on it in a typical month that they were doing it (in that recall window). After reporting that activity, they were asked if

> 7 This occurred in 2.3%, 2.0%, 0.9%, and 0.7% instances across rounds 1 to 4, respectively.

> 8 There is slightly higher attrition among this group resulting from a requirement that individuals were living in the household in all four rounds in order to be included in the analysis sample.
ability are complements, then a one-time increase in study effort that boosts student ability – through the scholarship incentive, for instance – could lead to persistent increases in both school performance and study effort.<sup>9</sup> Complementarities also open up the possibility of multiple classroom equilibria, some with high levels of effort by all and others with a poor learning environment.

The Girls Scholarship Project that we study directly affected incentives to exert effort. Effort increases the perceived probability, _P_ , that an individual will win the scholarship. Winning the scholarship has total monetary and non-monetary value _Vs_ ≥ _0_ , where this value could differ by school due to variation in local non-monetary benefits, such as social prestige from winning. The probability of winning a scholarship is a function of the individual’s test score and of assignment to a program school, _Ts_ , which takes on a value of one for program (“treatment”) schools. The probability of winning the scholarship is zero for all students in comparison schools, as well as for all boys (and for girls in grades other than Grade 6) in the scholarship schools. Independent of the program, ability leads to perceived time discounted future wage and non-wage benefits _B_ , where these benefits are concave increasing in ability. The cost of exerting study effort is the convex increasing function _C(Eist)_ . Thus the student’s maximization problem over effort takes the form: Under fairly general conditions, the introduction of the award leads to greater school effort among those eligible for the award, and among those who will be eligible in future years (since they seek to increase their academic ability to boost future chances of winning). The award can also lead to persistent test gains, since a one-time increase in effort raises future ability.

A simple extension implies that teachers in program schools could also exert more effort than teachers in comparison schools. If teachers face a maximization problem similar to equation 3, in which they experience benefits from having more scholarship winners in their class, then they should also

> 9 Note that other possible channels for persistent effects of the program are the cash grant payment to winners, and the payment of school fees to winners’ schools.

12
-40-

TABLE 10 SMPL 1979 - 1988 10 Observations TSLS // Dependent Variable is bOPY Instr.:.;ent list: C LPXM(-1) RER(-1) SURY(-1) GY(-1) M2Y(-l)

|VARIABLE|COEFFICIENT|STD|. ERROR|T-STAT. 2|-TAIL SIG.|
|---|---|---|---|---|---|
|C|0.0321909|0.|1183711|0.2719492|0.795|
|LPXM.|-0.0748124|0.|0617359|-1.2118149|0.271|
|RER|-0.0979733|0.|1387308|-0.7062122|0.507|
|SURY|C.8981768|0.|1963997|4.5732083|0.004|
|R-squared|0.922|749|Mean of|dependent var|-0.103986|
|Adjusted R-squa|red<br>0.884|124|S.D. of|dependent var|<br>0.064098|
|S.E. of regres|sion<br>0.021|819|Sum of|squared resid|0.002856|
|Durbin-Watson s|tat<br>3.357|176|F-stati|stic|23.88972|
|Log likelihood|<br>26.61|434||||

||Residua|l Plot||obs|RESIDUAL ACTUAL FITTED|
|---|---|---|---|---|---|
||I|*|:|1979|0.00814 -0.15183 -0.15997|
||:|<br>l*|<br>:|1980|0.00142 -0.17372 -0.17514|
|*|:||:|j<br>|1981<br>|-0.03178 -0.16750 -0.13572<br>|
||:<br>|I|*:|I1982<br>|0.01844 -0.13616 -0.15460<br>|
|*I|<br>||:|I1983<br>|-0.02328 -0.13776 -0.11448<br>|
|:|<br>I<br>|<br>|: *|I1984|0.02442 -0.01284 -0.03726|
||:*j<br>|<br>|:||1985|-0.00580 -0.00301 0.00279|
||:<br>I<br>|<br> <br>*<br>|:||1986<br>|0.01075 -0.03939 -0.05014<br>|
||:<br>*I<br>|<br>|:|I1987<br>|-0.00977 -0.08771 -0.07795<br>|
||:<br>I|<br> <br>*|:|I1988|0.00744 -0.12996 -0.13740|
e) Electrodynamic spray equipment, which is the most efficient for a pattern of small land holdings (Matthews, 1987).

f) Addition of synergists (synergists are chemicals which enhance the potency of pesticides, without contributing to resistance build up) such as neem oil, sesame oil, etc.

Table 6 presents the cost of cultivation data from the PEG demonstration plots. Two varieties of cotton have been used, both hybrids. However, L-389 is whitefly resistant, whereas L-861 is not. The column labelled "actual", contains data from the comparator plots of the area, whereas the columns labelled, Set I, Set II and Set III give the data from the PEG plots, with two, three and four sprays of SPs respectively.

In general, pesticide use is much higher and yields much lower on the comparator plots than for the trial plots. In terms of the per unit costs of cultivation, in two cases, Set III plots are far more efficient than any of the others, in one case they are the same whereas in the last case they are somewhat higher as compared to Set II, the next best alternative for the variety. On average, Set III plots are the most efficient with a cost of cultivation of Rs. 339 per quintal of kapas, which is about 27% less than the actual costs of cultivation, also implying that under the most efficient cultivation practices in this trial profits can go up substantially as compared to the prevalent practiceri. Additionally, Set III practices are substantially more efficient than the Set I but only marginally more efficient than Set II plots.

68
# **4. Robustness checks**

# **A. Design-based simulations**

The results in the previous section use just a single sample to compare results from two estimators: a direct estimator, using just survey results, and a small area estimator, which uses auxiliary information to improve the accuracy and precision of the survey results. However, it is difficult to come to a firm conclusion when using just a single sample. Therefore, we simulate the small area estimates 100 times, using 100 different samples and calculating 100 different point estimates for direct and small area estimates. We do not calculate MSE on each simulation due to computing limitations.

Table 8 - Simulation results for municipal estimates

||(1)<br>Male LFP|(2)<br>Female LFP|(3)<br>Male<br>Unemp|(4)<br>Female<br>Unemp|
|---|---|---|---|---|
||mean<br>(median)|mean<br>(median)|mean<br>(median)|mean<br>(median)|
|**Simulated relative bias**|||||
|Direct|0.186|-0.222|0.128|1.451|
||(0.534)|(-0.302)|(-58.406)|(-100.000)|
|SAE|-0.274|1.755|35.627|71.286|
||(-0.656)|(-0.561)|(11.734)|(24.950)|
|**Simulated mean absolute**|||||
|**error**|||||
|Direct|0.060|0.070|0.023|0.015|
|SAE|0.028|0.040|0.009|0.005|
|**Simulated rank correlation**|||||
|Direct|0.443|0.614|0.366|0.361|
|SAE|0.555|0.742|0.449|0.389|

Rank correlations are the average rank correlations across the 100 simulations.

Table 8 presents three separate statistics – bias, absolute deviation from the census value, and the correlation with the true value – for direct estimates and small area estimates. Simulated mean and median bias is quite small for both direct estimates and small area estimates for LFP, with the highest rate being 2 percent mean bias for female LFP. The means are always higher than the medians, especially for the small area estimates for unemployment, as very low base rates can lead to very high values in some simulations.

26