rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20 —e— Non-Financial Coorporations —# Financial Institutions (1)<br>0 —#— Public Administration —— Households and NPISH<br>2001 2002 2003 2004 2005 2006 2007 2008<br>30 3<br>& 25 6 ‘S<br>= 20 =<br>a i)<br>Fis 4 &<br>c is]<br>s3<br>=10 E<br>=2=<br>g5 s<br>= =<br>z= «CO Oo =£<br>GS ra}<br>a=] im<br>S -5 \/ a<br>5 2.8<br>SaL -10 — — Nominal Credit —— GDP &<br>-15 -4,<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text -->
Column headers provide the outcome variables studied. Column 1 considers the entry of young plants by their log count in the district, Column 2 considers the log employment in these young plants, and Column 3 considers the log output in these young plants. We weight estimations by the log of district population in 2001, and we cluster standard errors by district. Estimations have 1,248 observations as the cross of four surveys and 312 districts. We winsorize outcome variables at the 1%/99% level to guard against outliers. Our district sample is constructed such that employment, output, and establishment counts are continuously observed. We do not have this requirement for young plants, and we assign the minimum 1% value for employment, output, and establishment entry rates where zero entry is observed in order to model the extensive margin and maintain a consistent sample.

The top row shows a very substantial increase in young firm counts in nodal cities after the upgrades in comparison to districts more than 50 km from GQ. As we have noted, we are very cautious about interpreting these results much given that the upgrades were built around the connectivity of the nodal cities. The imprecision in these estimates is mostly due to the fact that there are only nine nodal districts. As effects for our other distance categories are being measured for each band relative to districts more than 50 km from the GQ network, the inclusion or exclusion of the nodal districts does not impact our core results regarding non-nodal districts.

The key pattern emphasized in our earlier work is shown in the second and third rows. There is substantial increase in organized sector entry in districts within 10 km of GQ that is not reflected in districts farther away at 10-50 km. Our sample includes 76 districts within 10 kilometers, and 42 districts in the 10-50 km band. To some degree (substantiated further in Ghani et al. 2012a), the upgrades of the GQ network can be taken as exogenous for these districts since neither group is in the nodal district set around which the upgrades were based. The districts within 0-10 km of GQ have a 0.4-0.9 log point increase in entry activity after the GQ upgrade compared to districts more than 50 km away. The point estimates for the 10-50 km range are substantially smaller in size and imprecisely estimated.

Panel B extends the spatial horizons studied in Panel A to include two additional distance bands for districts 50-125 km and 125-200 km from the GQ network. These two bands have 48 and 51 districts, respectively. In this extended framework, we measure effects relative to the 97 districts that are more than 200 km from the GQ network in our sample. The special entry 13
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The patterns in Table 1 and Figure 1 raise two basic questions: (i) why has financial dollarization continued to gain ground, on the whole? and (ii) what factors have influenced the contrasting experience of different countries? The first question is apparently all the more puzzling in view of the stark contrast between the rising dollarization and the declining worldwide inflation.

Figure 1. Dollarization and Inflation <!-- Start of picture text --> South America Transition Economies<br>(in percent) (In percert)<br>80 __30 42 B 600 oo-<br>50So - 25 40 500<br>40 20 38 400<br>30 - . -- 1 5 35 300<br>20 10 34 200<br>10 1 32 I 1-00<br>O 0 30 0<br>199419951993199719981999 2000 2001 19941 995 1 996 19971 9S9 1999 2000 2001<br>Foreign Currenog Deposits to Total Deposits tLHS3 Foreign Currenrlg Deposits to Total Deposits LHSI|<br>Annual - x Change in CPI (RHSi] _-3--- _ Annual X Change In CPI (RiHS<br>Asia Middle East<br>(in percert) (in percent)<br>35 -______ _ 25 47 - 47<br>30 . ._.___ '' 20 2 _______ 42<br>25 373<br>20 32 *32<br>1 5 10 27 27<br>I10 22 - . ---- 22<br>5 1 7 1 7<br>a 0 12 12<br>19941995199819971998199920002001 1994 1995 189B 19971998 1999 2000 2001<br>Foreign Currencg Deposits to Total Deposits (LMS| Foreign Currencg Deposits to Total Deposits (LHS|<br>------- Annual X Change In CPI (RHS) ------- Annual X Change in CPI [RHSl<br>Africa<br>40 -t(in percernt Soo<br>35 -- 450<br>30 , " ''5400<br>25 . 300<br>20 - 250<br>15 200<br>10 , - -'- - - - 10<br>0 ~ ~ ~ ~ ~ ~ ~ ~~~5<br>1994 1995 1 996 i 997 1998 1999 2000 2001<br>Foreign CurrenoV Deposits to Total Deposits ILHS)<br>Annual ------- Z Change In CPI (RHSl<br><!-- End of picture text -->
**Appendix B: Linear (forward) regression: Details**

# **Table B-1: Model Summary**

||**Model Sum**|**mary**<br>||
|---|---|---|---|
|Model<br>R|R Square|Adjusted<br>R Square|Std. Error of<br>the Estimate|
|.317<sup>a</sup><br>.435<sup>b</sup><br>1<br>2|.100<br>.189|.080<br>.151|21.03051<br>20.19663|
|Predictors: (Con<br>(log)<br>a.<br>Predictors: (Con<br>(log), Remittanc<br>b.|stant), Loss<br>stant), Loss<br>es|in percent of<br>in percent of|Capital Stock<br>Capital Stock|

# **Table B-2: ANOVA**

||Sum of|**ANOVA**|<sup>**c**</sup>||
|---|---|---|---|---|
|Model|Squares|df|Mean Square<br>F|Sig.|
|Regression<br>1|2171.570|1|2171.570<br>4.910|.032<sup>a</sup>|
|Residual|19460.421|44|442.282||
|Total|21631.991|45|||
|Regression<br>2|4092.124|2|2046.062<br>5.016|.011<sup>b</sup>|
|Residual|17539.867|43|407.904||
|Total|21631.991|45|||
|Predictors: (Con<br>a.|stant), Loss in p|ercent of C|apital Stock (log)||
|Predictors: (Con<br>b.<br>Dependent Varia<br>c.|stant), Loss in p<br>ble: Difference|ercent of C<br>(year 5)|apital Stock (log), Remittances||

# **Table B-3: Coefficients**

||Unstan<br>Coeffi|**Coefficients**<sup>**a**</sup><br> <br>dardized<br>cients|Standardized<br>Coefficients|||
|---|---|---|---|---|---|
|Model|B|Std. Error|Beta|t|Sig.|
|(Constant)<br>Loss in percent of<br>Capital Stock(log)<br>1|3.254<br>-4.600|3.247<br>2.076|-.317|1.002<br>-2.216|.322<br>.032|
|(Constant)<br>Loss in percent of<br>Capital Stock (log)<br>2|-3.095<br>-5.934|4.276<br>2.086|-.409|-.724<br>-2.844|.473<br>.007|
|Remittances|1.946|.897|.312|2.170|.036|
|Dependent Variable: Diffe<br>a.|rence (yea|r 5)||||

35
18 state in the wake of the 2004 mini-crisis. This blanket guarantee is due to expire at the end of 2006, and an orderly process to prepare for this event is important.<sup>33</sup> Otherwise, the elimination of the blanket guarantee could lead to more turbulence on the Russian banking market with substantial operational implications for the DIA (i.e., liquidation of institutions and a surge in insurance claims). Finally, Russian authorities are well advised to complement the current deposit insurance system with an ambitious financial education and communication initiative. According to an Izvestiia survey in March 2005, only 30 percent of respondents were aware of the existence of the DIS.<sup>34</sup> There is little evidence indicating that this figure has changed substantially.

> 33 See IMF (2004), p. 54.

> 34 See Izvestiia Bizecon Report (2005).
from less than 1 percent for the Central African Republic to over 40 percent for South Africa (Ardic, Heiman and Mylenko, 2011). An important caveat is that these data are supply-side indicators of financial usage and therefore do not correct for double-counting (e.g. one individual with multiple loans). Therefore, these numbers represent an upper bound on borrowing rates in these countries, and actual borrowing rates are likely even lower.

**Figure 3: Borrowing Behaviour, by Country (average 2008-09)**

<!-- Start of picture text --> Loan Accounts with Commercial Banks<br>(as Percentage of Population)<br>Central African Republic<br>Ethiopia<br>Chad<br>Congo, Rep.<br>Comoros<br>Sierra Leone<br>Madagascar<br>Cameroon<br>Angola<br>Tanzania<br>Rwanda<br>Mauritania<br>Ghana<br>Lesotho<br>Gabon<br>Kenya<br>Swaziland<br>Namibia<br>Botswana<br>South Africa<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45%<br><!-- End of picture text --> <u>Source: IMF-IFS Statistics, 2011.</u> It is striking how these borrowing rates vary by per capita GDP: South Africa, Botswana, Namibia, and Swaziland, the four countries with the highest borrowing penetration, are also among the 10 richest countries in Africa, as measured by GDP (WB-WDI, 2011).

14
section.

To test for potential non-random variation in immigrant concentration across grades, we regress our treatment variable, i.e. the fraction of immigrant students, on the characteristics of native students in the same grade, as well as measures of grade-level teaching resources such as average class size, teachers’ experience and whether the grade has supporting teaching staff. Those grade-level measures of teaching resources allow to test whether schools allocate more resources to grades with more immigrant students, a potential threat to our identification strategy. As detailed in Ladd and Fiske (2011), the large majority of additional funding received based on the number of immigrant children in the grade had to be allocated to personnel. Given these allocation rules, schools with higher levels of funding are expected to have more teaching resources, resulting in lower class size, more experienced teachers (as teachers’ salary levels are based on experience), as well as additional support staff.<sup>17</sup> We therefore include these grade-level measures of teaching resources in our balancing tests.

The results of these balancing tests are reported in Table 3. Column 1 presents the results of a na ̈ıve benchmark OLS regression controlling for year and grade effects. The na ̈ıve estimates show a large and significant association between natives’ observable characteristics, in particular parental education, and the percentage of immigrants in the grade. Correlations between immigrant concentration and natives’ parental education are large in magnitude, and significant at the 1 percent level. As evidenced earlier, natives with low parental education tend to concentrate in schools with a high fraction of immigrant students. In addition, there is a significant negative association between average class size and the share of immigrants in the grade, and a positive 17Those patterns have been confirmed empirically by Ladd and Fiske (2011) using administrative data on Dutch primary schools.

21
banks, (e) _Fixed assets by banks_ is the fraction of fixed assets of the firm that is financed by banks.

The primary variable of interest, _Credit information sharing reform_ , refers to either _Credit bureau reform_ or _Credit registry reform. Credit bureau reform_ equals one for the countries that introduced a credit bureau – Armenia, Bulgaria, Croatia, Czech Republic, Ecuador, Georgia, Kazakhstan, Kenya Kyrgyzstan, Macedonia, Moldova, Nicaragua, Romania, Russia, Rwanda, Serbia, Slovakia, Slovenia, Uganda and Ukraine – for the years after the reform. _Credit registry reform_ equals one for all countries that introduced a credit registry - Armenia, Azerbaijan, Belarus, Bosnia Herzegovina, China, Czech Republic, Ecuador, Guatemala, Latvia, Mauritius, and Nicaragua- for the years after the reform. These variables can be thought of as the interaction of a dummy for the set of countries that introduced a reform during the sample period (i.e., the treatment sample) with a country-specific dummy which identifies the years after reform. Thus, this empirical methodology is akin to a difference-in-difference format, comparing countries with reform and countries without reform, and years pre- and post-reform.

In terms of controls, _X_ is a matrix of firm-level characteristics including firm size, age, ownership type (government or foreign owned), exporter status, and sector (manufacturing versus services). Z refers to a matrix of country-level variables that might influence firms’ access to finance, which includes the inflation rate, the GDP growth rate, and a measure of financial sector development (private credit to GDP).

α j are country fixed effects. These are important in the estimations as they allow us to capture any time-invariant country-specific characteristics that can impact access to finance (e.g., such as the quality of the contract laws and their enforcement which usually do not change much over time). Country fixed effects can also capture country specific differences in the demand for 11
3

- Access to credit should not be hampered by policy or mariet failures to resict productvty growth in agriwuum Wben small farmers constite the bulk of the farming community, as they do in Pakistan, insbbtitons need to be created that overcome market failure due to the high risk and high tasaction costs assoated with lending to such farr.

- Credit should be vailable to the nual poor for consumption smoothing as well as to promote sustainable inme genration activities.

- Policy intervtions that create special lending institutions to meet the first two objectives shotld be s$ect to conditions that ensure financial suviability by reduing and ultimately eliminating their depene on subsidy. The conditions are adequate recovey of loans, market interest rates, and mobilization of deposits

- When scarce credit is targeted at special groups of fanrers or the rural poor, transparent criteria must be establshed to continuously evaluate targeting performance.

- Credit must be made available for non-agricultumal rual activities such as micro-enterprises in ural manufauriD&

The paper is stuctured as follows. The rest of this chapter presents a profile of Pakistan's nual crdit Chapte 2 evaluates the credit need of farmers and the performane and lending practices of the formal and infomal sources Of rural credit Chapter 3 presents a profile of rural poverty in Pakistan and reviews the credit componns of the pecial programs for employment generation, income support and poverty alleviation. Finaly, chapter 4 estimates the curret need for mral credit by major categories of rural households and then recommends oost-ctive policies for delivering credit to these groups.

# **Ie Rural** Credit Profile

A detailed profile of the rural financal market, based on nrual credit surves, helps to pinpoint its sgths and weaknesses. The last comprehensive rural credit survey was undertakn in 1985. Other assessments based on **smalr** amples have broadly corroborated the conclusions of the 1985 survey. The most recent of these is the srvey carried out by the International Food Policy Research Institute (IFPRI) with USAID funding. IFPRI conducted several rounds of interviews of rural households in selected districts to elicit information on rural consumption, production, and employment patterns. Although the sample was not randomly drawn and the coverage was relatively small, the survey findings provide many useful insights into the rural credit market in Pakistan.<sup>3</sup> Six policy-relevant dimensions of the credit profile are discussed in this chapter. These are (a) the imporlance of mral credit to agricultunal pefon anc, (b) the wverage of fanners by the rural credit system, (c) dffelrences between institutional and noninstitutional credit sources with respec **t** o (d) oDsts and (e) objectives, (f) the differnces in acs to

> 3 This discussion is adapted from Joachim von Braun, Sohail Mahlik, and Manfred Zeller (1993), Credit M Input Support Policies and the Poor: Insights from Africa And Asia.
Table 5: Effect of girls’ scholarship program on age of test takers

|outcome||||||age > 2|0|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Q)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(1)|
|girls’scholarshipprogram|0.05|0.04|0.05|0.04|0.02|-0.01|0.13**|0.06|0.06**|0.01|O.17***|
|<br>|(0.02)<br>|(0.05)<br>|(0.03)<br>|(0.03)<br>|(0.03)<br>|(0.04)<br>|(0.02)|<br>|(0.03)<br>|(0.03)<br>|(0.04)<br>|(0.02)<br>|
|interaction of program with:|[26]|[53]|<br>{29}|<br>| [31]|[42)|{92}|<br>oa)|<br> [31]|[05]|[86]|—[.0]|
|male|||-0.02|||||||||
||||(0.01)|||||||||
||||[.20]|||||||||
|low enrollment||||0.11||||0.13||||
|||||(0.05)||||(0.02)||||
|||||[.20]||||[1]||||
|rural|||||0.09||||0.06|||
||||||(0.04)<br>||||(0.02)<br>|||
|||||||||||||
|||||||0.10||||0.06||
|||||||(0.06)<br>||||(0.05)<br>||
|||||||||||||
||||||||-0.10||||-0.11|
||||||||(0.02)||||(0.02)|
||||||||[.29]||||[.14]|
|N|7,997|15,811|23,808|7,234|7,234|7,997|7,234|13,813|13,813|15,811|13,813|
|R-squared|0.04|0.03|<br>0.05|<br>0.03|0.03|0.04|<br>0.03|<br>0.03|0.03|0.03|0.03|
|treatmenteffect|||0.04|0.14**|0.11|0.10**|0.03|0.19**|0.12|0.06|0.06|
|<br>withinteraction|||(0.03)<br><br>||(0.04)<br><br>|(0.04)<br>|(0.02)<br>|(0.03)<br>|(0.02)<br>|(0.04)<br>|(0.07)<br>|(0.04)<br>|
|<br>|||[40]<br>|<br><br>[05]<br>|[37]<br>|[02]<br>|<br>[31]<br>|<br>[og<br>|fan)<br>|[.46]<br>|—f.29)<br>|
|meanoutcome|0.35|0.51|<br>0.45|0.36|0.36|0.35|<br>0.36|<br>0.52|0.52|0.51|0.52|

Outcome is dummy for student age above 20. Sample is students taking grade 12 exit exam in Regions 2-6, 1998-2012. Interaction with male based on individual student. Distant is dummy for above national median distance from school to main road. All other interactions are district characteristics from 1998 household survey. Low enrollment = below national median secondary-age (13-18) enrollment rate. Rural is above national median proportion of 1998 district population living in rural area. Wealthy = at or above national median household assets, by first principal component. “Treatment effect with interaction” is sum of program coefficient and coefficient on interaction term. All regressions include school and year fixed effects. Regression with both male and female students in sample also includes female dummy. Sample sizes differ across regressions because not all districts surveyed in 1998. Standard errors in parentheses, clustered by region. p-values in brackets, calculated from 200 iterations of wild-t cluster bootstrap of Cameron et al (2008) with Webb (2013) 6-point distribution of weights. Starred significance levels are based on these p-values. * significant at 10%; ** significant at 5%; *** significant at 1%.
for the case of UMICs, only the firm’s age is significantly positively correlated with labor productivity, and the managerial experience shows no significant correlation. On the other hand, firms with a top female manager tend to have lower labor productivity, and the association is significant in the pooled sample as well as for the sub-sample of UMICs and HICs. Regulatory environment as captured by _Time Tax_ is not significantly correlated with labor productivity in any of the samples. Having a line of credit, however, has a positive and significant association with labor productivity in all the three samples considered.

For the macro-level variables, higher enrollment rate in tertiary education and better quality of institutions proxied by our _Law and Order_ variable are associated with higher labor productivity, significant at the 1 or 5 percent level. This holds in all the three samples considered. The remaining macro-level variables do not show any significant relationship with labor productivity either in the pooled sample or the sub-sample for UMICs. However, for the subsample of HICs, the enrollment rate in secondary education and market size are significantly (at the 1 percent level) positively associated with labor productivity.

To summarize, the regressions show a significant correlation between labor productivity and several factors in the expected direction, although the results vary somewhat across UMICs, HICs, and the pooled sample. These factors include firm-size, sector of activity, age of the firm, foreign ownership, exports, gender of the top manager, top manager’s experience, quality management proxied by having internationally recognized quality certificate, purchase of capital assets, having a bank loan or line of credit, market size, education attainment, and law and order.

_Heterogeneity_ 17
of indicator (zero/one) variables and average across the variables to create indices for each of these dimensions.<sup>16</sup> Teacher absence was measured separately from this observation and during an unannounced visit to the school. Enumerators recorded the attendance of teachers who, according to the teaching schedule, were supposed to be teaching at the time of the visit. They were then recorded as being absent from the school, present in the school but absent from the classroom, or present in the classroom. We use this to define two indicator variables: (1) absent from the school versus present in the school (which we call school absence), and (2) absent from the school or classroom versus present in the classroom (which we call classroom absence).

In addition to the SDI data, we also make use of data from the UNESCO Institute for Statistics (UIS) to describe trends in the number of primary-level teachers in each country (accessed via the World Bank World Development Indicators database).

# 3.2 Empirical approach

The general approach used here is based on a regression discontinuity design (RDD) approach, which assesses whether there is a break in trend for teachers that were hired before or after the implementation of FPE. The models are estimated on the pooled sample (including all countries) with a specification that includes country fixed effects, and on country-specific samples. In the analysis of student test scores, the empirical specification for the pooled model is: _T_ itc = α + β × ( _Year Hired_ tc – _FPE year_ c) + δ × ( _Hired after FPE year_ tc) + _Country FE_ + εitc (1) where _T_ itc is the test score for student i, of teacher t, in country c. _Year Hired_ tc is the year that teacher t in country c was hired, _FPE year_ c is the year that the FPE reform was implemented in

> 16 The specific variables used are as follows. Good teacher demeanor: Teacher was either sitting or standing in front of the class at any time; teacher visited students in class (1=visited above median number); teacher called out students in class (1=called out above median number); teacher was smiling, laughing, or joking with children; teacher did not hit, pinch, or slap a child. Good pedagogical practices: Teacher asked questions that required learners to recall information; teacher asked learners to carry out a task; teacher asked questions that required learners to apply information; teacher asked questions which required learners to use their creativity; teacher provided positive feedback or praise; teacher introduced the lesson at the start of the class; teacher summarized the lesson at the end of the class; teacher assigned homework to the class; teacher reviewed or collect homework from the class; teacher used the local language as a medium of  instruction at some point in the class. Good classroom environment: there a blackboard and/or whiteboard in the class; there is chalk or a marker to write on the board available during the lesson; classroom has a working electricity connection; children's work is displayed on the walls; other materials were displayed on walls.

9