rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 10 OLS: Are school and teacher characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grades 3 and 2, UP</u>**

||**Hindi**<br>**Grade 3**|**Math**<br>**Grade 3**|**Hindi**<br>**Grade 3**|**Math**<br>**Grade 3**|**Hindi**<br>**Grade 2**|**Math**<br>**Grade 2**|**Hindi**<br>**Grade 2**|**Math**<br>**Grade 2**|
|---|---|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**5.83**|**5.33**|**5.83**|**5.33**|**4.4**|**3.7**|**4.4**|**3.7**|
||**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|
|Index of infrastructure|0.01|0.00|0.01|0.00|0.01|0.00|0.01|0.00|
||(0.16)|(0.33)|(0.16)|(0.31)|(0.20)|(0.60)|(0.20)|(0.55)|
|% non-sc teachers|-0.01|-0.03|-0.01|-0.03|-0.01|-0.02|-0.01|-0.02|
||(0.93)|(0.54)|(0.93)|(0.53)|(0.78)|(0.52)|(0.78)|(0.52)|
|% male teachers|0.03|0.01|0.03|0.01|0.04|0.00|0.04|0.00|
||(0.44)|(0.78)|(0.45)|(0.76)|(0.18)|(0.89)|(0.18)|(0.93)|
|% teachers with college<br>deg|0.09|0.03|0.09|0.03|0.09|0.06|0.09|0.06|
||(0.01)*|(0.34)|(0.02)*|(0.31)|(0.01)*|(0.06)<sup>+</sup>|(0.01)*|(0.06)<sup>+</sup>|
|% teachers with graduate<br>deg|0.05|0.04|0.05|0.04|0.05|0.04|0.05|0.05|
||(0.29)|(0.35)|(0.29)|(0.34)|(0.19)|(0.18)|(0.19)|(0.17)|
|Av. Years of service|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.38)|(0.84)|(0.40)|(0.91)|(0.97)|(0.83)|(0.95)|(0.73)|
|Av. days of in service<br>lastyear|0.00|0.00|0.00|0.00|0.00|-0.003|0.00|-0.003|
||(0.79)|(0.41)|(0.79)|(0.38)|(0.41)|(0.06)<sup>+</sup>|(0.44)|(0.05)*****|
|% teachers with pre<br>service training|0.04|-0.01|0.04|0.01|0.03|0.02|0.02|0.04|
||(0.40)|(0.91)|(0.47)|(0.84)|(0.53)|(0.59)|(0.61)|(0.31)|
|% teachers doing multi<br>grade|0.01|0.03|0.01|0.03|0.00|0.01|0.00|0.01|
||(0.84)|(0.44)|(0.84)|(0.43)|(0.93)|(0.74)|(0.93)|(0.73)|
|**% teachers actively**<br>**engaged in teaching**<br>**(mean over 4 visits)**|**0.11**|**0.10**|**0.11**|**0.10**|**0.13**|**0.10**|**0.13**|**0.10**|
||**(0.05)***|**(0.03)***|**(0.05)***|**(0.03)***|**(0.00)****|**(0.01)***|**(0.00)****|**(0.02)***|
|% contract teachers|-|-|0.00|0.03|-|-|-0.01|0.04|
||-|-|(0.99)|(0.65)|-|-|(0.88)|(0.45)|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2673|2670|2673|2670|2697|2695|2697|2695|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%;<sup>+</sup> significant at 10%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

25
|TABLE 2. TWO-STAGE L<br>Dependent variable:**_LIBE_**|EAST SQU<br>**_RALIZATI_**|ARES EST<br>**_ON_**|IMATES.||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**_CONSTANT_**|0.19***|0.12**|0.11**|0.11***|0.10***|0.10**|
||(3.15)|(2.73)|(3.14)|(3.14)|(2.75)|(3.10)|
|**_LIBERALIZATION_**<br>**_(-1)_**|0.75***|0.76***|0.77***|0.78***|0.78***|0.78***|
||(15.18)|(17.3)|(18.9)|(19.1)|(20.0)|(21.6)|
|**_FREEDOM_**|-0.004|-0.001|0.01|0.002|0.002|0.002|
||(0.72)|(2.39)|(0.62)|(0.65)|(0.58)|(0.60)|
|**_GOVCHANGE_**|0.02<br>(0.48)|0.03<br>(0.57)|0.02<br>(0.36)|-|||
|**_FREESTAB_**|0.004<br>(0.62)|0.005<br>(0.67)|0.003<br>(0.45)|0.001<br>(0.34)|-|-|
|**_COHESIDEOL_**|0.02|0.02*|0.02**|0.02**|0.02**|0.02**|
||(1.57)|(1.79)|(2.36)|(2.34)|(2.36)|(2.39)|
|**_COHESION_**|-0.03*<br>(1.92)||||||
|**_AID_**|0.004***<br>(2.91)|0.005***<br>(3.90)|0.004***<br>(3.70)|0.004***<br>(3.71)|0.004***<br>(3.68)|0.004***<br>(3.70)|
|**_WAR_**|-0.05***<br>(2.70)|-0.05***<br>(2.64)|-0.05***<br>(2.82)|-0.05***<br>(2.84)|-0.05***<br>(2.87)|-0.05***<br>(2.96)|
|**_INITMACRO_**|-0.00<br>(0.92)|-0.00<br>(0.68)|-|-|-|-|
|**_INITDEVELOP_**|0.00<br>(0.47)|0.00<br>(1.44)|-|-|-||
|**_INITREGIME_**|0.04<br>(1.56)|-|-|-|-|-|
|**_INITFREEDOM_**|0.02***|0.01*|0.01**|0.01**|0.01**|0.01**|
||(3.43)|(1.68)|(2.36)|(2.37)|(2.23)|(2.42)|
|**_FSU_**|-|-|-|-|0.001<br>(0.04)|-|
|**Adjusted R-squared**|0.90|0.90|0.91|0.91|0.91|0.91|
|F-**statistic**|1 00***|117***|151***|174***|173***|204***|
|**White test for**|112.1**|58.3|46.7|43.8|38.5|34.0|
|**heteroscedasticity**|||||||
|**Number of observations**|<br>125|125|125|125|125|125|

Absolute value of t-statistic in parenthesis. *, **, and *** mean that the coefficient is significant respectively at 10%, 5% and 1 % level of confidence. The t-statistics in equation (1) are obtained using the White heteroscedasticity-consistent standard errors.

15
_Ilbadve tabk with population trends_ _l,C>._ x

# _Explanation_

**_A._** thousnds as of the official date of the last census. **0i**

- ThimW g8 ross national product (in U.S. dollars) per capita, from World Bank sourco9.

- Estimated de facto total population at mid-year, in thousands. A

0. Pouation In four non-overiapping age groups, in thousands.

- _._ opulation **pr** squaro kilometer ' land area.

- f total population living In urban areas. definition of urtn areas varies

- sMoewht from country to count.y. See U.N. 1988, p. 1.

- _¢._ **Popuation aged** 611 and 12-17 years, in thousands.

- K _Number_ of womrn'; of reproductive ago (15.49), In thousands.

- **_L._** Annual wth rate for populatiorn in the quinquennium, in percent.

- . Total number of births that each woman would have if, at each age she had 8S many births as women of that age in the current population, i.e., if current age-specific feIitty schedules ware applied.

- **_K._** Annual births par thousar.d persons in the population.

- _L._ Average number of years tha woLid be lived by those born into the population, if they experienced prevailing age-specific mortality risks (separate estimates provided for males and females).

- V. Of very thousand live births, the number that would die before their first birthday.

- _N._ Of every thousand live births, the number that would die before their fifth binhday.

_0._ Annual deaths per thousand persons in the population.

- P. Amount of net intemational migration in thousands (i.e., number of immigrants minus emirabnts over the quinquennium).

_0._ Officil ovemment evaluation of the population growth rate and of fertility as too high or too low, as reported to the U.N.

- _R._ Percentage of currently ii.arried women aged 15 to 44 (or 49) who are using contraception, in the year given immediately below. This is the year of a specific survey or Investigation.

- **S.** O 11a cuirently married women, percent using specified contraceptive methods. For many developing countries, these data are not available or are available for only one period only. All contraceptive mix data are given for the latest period for which data are available.
# **_Section II: Does it matter for growth who borrows?_**

This section analyzes the relationship between borrower type and economic growth. The growth regressions are of the following form (using either share of household credit over total credit or using household credit to GDP):

- (1) GDP p.c. growth=a+ b (initial GDP p.c) + c (household credit/GDP) +d (household credit/GDP)^2 +e X

Where X encompasses the usual variables used in growth regressions, including foreign direct investment, secondary school enrollment, inflation, institutions, and the quality of banking supervision, among others. Dummy variables for the proportion of years in a banking crisis and to indicate the region of the world where the country is located, are also added.

These regressions are run using annual data as well as five-year averages. The growth regressions use the standard set of control variables and various specifications (not all shown) and are estimated using OLS, 2SLS and GMM.<sup>6</sup> Similar to the results in Beck et al (2008, 2012), I find that per capita growth is not significantly related to the share of household credit in GDP. This is true in OLS, 2SLS, and fixed effects analysis (not all reported here). In the GMM analysis, it is significant, but enters with a negative sign. In contrast, the linear relationship between the share of firm credit in GDP and per capita growth is significant and positive in the 2SLS specification.

In addition, the paper explores the possibility of a nonlinear relationship between household or firm credit and GDP growth. Tables 5a and 5b present some key results. A number of additional control variables are used to check the robustness of the results. The IV regressions use legal origin and religion as instruments. GDP per capita growth is associated with household credit- in the nonlinear specification in OLS, FE and 2SLS estimations. GMM estimation shows no significance or a negative association. Estimations using firm credit on the other hand indicate a consistent nonlinear relationship. When both household credit and firm credit as a ratio to GDP are included, household credit is still insignificant, though firm credit maintains its significant, nonlinear relationship (2SLS shown).

> 6 The growth regressions are initially run with total private credit and the results usually found in the literature prevail. Credit and growth are positively associated, but up to a point.

17
<!-- Start of picture text --> a. By percentiles of initial Gini coefficient<br>—Changes in inequality Changes in poverty<br>5s<br>anagpa<br>Boa<br>Ze<br>BB<br>3B<br>Be<br>Ssg4<br>0 25 50 75 100<br>Percentile of municipalities’ Gini coefficient in 1992<br><!-- End of picture text --> <!-- Start of picture text --> b. By percentiles of initial food poverty rates<br>—Changes in inequality Changes in poverty<br>By<br>anagaS<br>Boe<br>Ze<br>ag<br>3B<br>Be<br>a0g<br>0 25 50 75 100<br>Percentile of municipalities’ food poverty headcount rate in 1992<br><!-- End of picture text -->
ASIA AND OCEANIA, BORROWERS ONLY

# 1. Estimates **and** Projections

|**_yaw_**|**PtpAetin**|**kp.**<br>**ratio**|Period|**Grth**<br>**rate**|C4|CDR|TF4|'o<br>Not"|**0**<br>**Fmtft**|in|xlO00|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965|2366143|64.4|1985-89|1.79|27.0|8.9|3.29|62.7|64.4|69|91|
|1990|2609929|59.8|1990-94|1.63|24.8|8.3|2.97|64.1|66.0|60|78|
|1995|2832160|58.4|1995-99|1.43|22.3|7.8|2.68|65.6|67.8|51|65|
|2000|3042748|56.2|2000-04|1.27|20.2|7.4|2.50|67.1|69.7|44|55|
|2005|324205|52.3|2005-09|1.12|18.6|7.?|2.35|68.2|71.0|38|48|
|2010|**3429263**|48.7|2010-14|1.01|17.4|7.2|2.22|69.4|72.4|32|41|
|2015|**3607194**|46.7|2014-19|0.94|16.7|7.3|2.16|70.6|73.9|27|33|
|2020|3781033|47.0|2020-24|0.87|16.1|7.3|2.13|71.8|75.4|21|26|
|2025|3949143|48.0|2025-49|0.57|14.2|8.5|2.10|74.8|79.1|11|14|
|2050|4552196|57.9|2050-74|0.23|12.8|10.5|2.07|78.2|83.2|5|6|
|**207**|**4823248**|66.0|2075-99|0.12|12.3|11.1|2.06|80.1|85.8|3|5|
|2100|4966048|69.7|2100-24|0.08|11.9|11.2|2.06|81.6|87.9|2|3|
|2125<br>2150|5060340<br>5107063|72.2<br>73.4|2125-49|0.04|11.8|11.4|2.06|82.1|**88.7**|2|3|

# 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> I0 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 75+<br>70-74<br>65-69<br>80-84<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20.24<br>15-19<br>10-14<br>5.9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> AN (92 n)<br><!-- End of picture text --> <!-- Start of picture text --> : ( am C94<br><!-- End of picture text --> 27
A noted growing trend on high altitude hydroclimatological observations, however, shows potential for future climate change studies with multi-temporal scale temporal analyses taking into account recurrent large scale climatic cycles.

# **2.2 Spatial variability in precipitation trends**

Unlike temperature, most of the literature reports a lack of spatially consistent long-term trends in Himalayan precipitation. This lack of homogeneity in trends reflects the influence of local thermodynamic and orographic processes (Dimri and Dash, 2011) over large scale oceanatmospheric processes. Differences in precipitation trends are also observed across seasons.

In a recent study, Bhutiyani et al. (2010) observe a statistically significant downward trend (at 5% significance level) in monsoon and average annual rainfall in the northwest Indian Himalaya (as represented by three stations) during 1866-2006. A similar trend is noted for 19602006 over the western Indian Himalaya region (Sontakke et al. 2009) but without any mention of statistical significance. The literature shows intra-regional differences in winter rainfall trends over Western Indian Himalaya. Dimri and Dash (2011) note significantly decreasing winter precipitation (Dec-Feb) in the region for 1975-2006 amid lack of spatially coherent phases among stations. Guhathakurta and Rajeevan (2008) find statistically significant downward trend in winter precipitation (Jan-Feb) in Jammu & Kashmir and Uttarakhand during 1901-2003. In contrast, statistically significant increasing trends are observed in winter precipitation during 1961-1999 in the upper Indus Basin (Pakistan), but no trend is observed during the longer 18951999 period, (Archer and Fowler, 2004; Fowler and Archer, 2005). In the same basin, Khattak et al. (2011) find spatially inconsistent and generally statistically insignificant seasonal precipitation trends during 1967-2005; however, they note more increasing than decreasing trends.

Increase in pre-monsoon (March-May) precipitation has been observed over the western Indian Himalaya during 1901-2003 (Guhathakurta and Rajeevan, 2008). Literature on precipitation trends in Bhutan’s Himalayan region suggests largely random fluctuations and the absence of trend on annual or seasonal basis (Tse-ring, 2003). Likewise, Shrestha et al. (2000) did not find any significant long-term trend in precipitation data (1959-1994) of the Nepalese Himalaya.

2
Table 4: Backlash mechanisms: Agency and decision-making

|Panel A: Women|(1)<br>Want more decisio|(2)<br>n-making on|(3)<br>|(4)<br>Has input in|(5)<br>decisions reg|(6)<br>arding|
|---|---|---|---|---|---|---|
||Household expenses|Childbearing|Income|Household<br>income|Major<br>household<br>expenses|Childbearing|
|Treated couple|0.06***|0.05**|0.07***|0.06**|0.06*|0.02|
||(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|(0.02)|
|Spillover couple|0.09***|0.01|-0.03|-0.05*|-0.02|0.00|
||(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|(0.02)|
|Observations|1,984|1,775|1,975|1,986|1,987|1,760|
|Baseline controls|No|No|No|No|No|No|
|Control mean|0.21|0.14|0.72|0.58|0.60|0.84|
|Treated vs Spillover p-value|0.21|0.10|0.00|0.00|0.06|0.50|
|Panel B: Men|||||||
||Want more decisio|n-making on||Is sole dec|ision-maker|on|
||Household expenses|Childbearing|Income|Household<br>income|Major<br>household<br>expenses|Childbearing|
|Treated couple|-0.00|0.01|-0.01|-0.01|0.00|-0.01*|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Spillover couple|0.03*|0.02|0.04**|0.02**|0.03**|-0.00|
||(0.02)|(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|
|Observations|1,978|1,728|1,969|1,979|1,978|1,746|
|Baseline controls|No|No|No|No|No|No|
|Control mean|0.07|0.05|0.07|0.02|0.03|0.03|
|Treated vs Spillover p-value|0.05|0.53|0.00|0.00|0.07|0.35|

_Note_ : OLS = ordinary least squares. Strata fixed effects included. Standard errors are clustered at village level, shown in rounded brackets. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Results show OLS estimates of direct treatment and spillover effects, and differences between effects on trained and spillover couples, not controlling for baseline values of dependent variables. Columns 1–2 use dichotomous variables equal to one if the spouse declares wanting more decision-making power. Columns 3–6 use dichotomous variables equal to one if the wife declares having a role in the decision (panel A) or the husband declares being the sole decision-maker (panel B). Control mean is the average value of the dependent variable in the control group.

37
Table 2: Summary Statistics for the Survey

||N|Survey<br>%|Po<br>N|pulation<br>%|% of<br>population|
|---|---|---|---|---|---|
|ICT|126|23.6%|894|0.05%|14.1%|
|Manufacturing|87|16.3%|15,110|14.21%|0.57%|
|Retail Trade|202|37.9%|54,618|51.36%|0.37%|
|Other Industries|118|22.1%|51,091|48.04%|0.23%|
|Total|533|100%|106,338|100%|0.50%|

_Notes:_ the population refers to the number of establishments according to the 2017 census. The last column is the number of interviews in the survey sample divided the number of establishments in the population. _Sources:_ PCBS, authors’ survey.

# **3.2 Other Data**

Jointly with the survey, auxiliary data is used in this paper.

**Maps and Geographic unit.** The primary geographic unit is the locality or town. The PCBS defined locality as _“a permanently inhabited place, which has an independent municipal administration, or a permanently inhabited, separated place, not included within the formal boundaries of another locality.”_<sup>21</sup> The West Bank is partitioned in 443 villages as of 2007. The shapefiles on the location of the Palestinian buildups and other spatial information has been kindly provided by UNOCHA.

**Population and Establishment Census.** Access to the Establishment Census has been negotiated by the author during several visits to the West Bank. The two Census datasets contain data on population, employment, and establishment. The data used from the Establishment Census include jobs in non-farming private establishments, disaggregated by economic sector (ISIC4) and village. The analysis is restricted to establishments in operation at the moment of the data collection, with national or foreign ownership. Activities not classified as establishments or jobs in the public sector are not included in the job count.<sup>22</sup>

**Mobility restrictions.** The data on the mobility restrictions, the Separation Wall, and the location of settlements and military areas come from the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA). Data are available between 2003 and 2017 at a yearly frequency with gaps. UNOCHA also kindly provided geo-referenced data on the road network. Each road segment is classified as regional, primary, secondary, tertiary, residential, or track. UNOCHA also collects data on the road regime, and it classifies roads as either restricted for Israeli cars or unrestricted. The Applied Research Institute Jerusalem (ARIJ) provided information on the location of mobility restrictions at a yearly frequency since 1995.

> 21PCBS Population Housing and Establishment Census 2007.

> 22The data exclude clusters (industry grouping * village) where the number of establishments was one or two. Moreover, between the two time periods, the ISIC classification changed. Some sectors have been merged, others have been disjointed. Economic sectors are reclassified based on the ISIC Revision 4.

13
emissions from fossil-fuel combustion—a major component of total GHG emissions—and they usually move in the same direction. Cross-country panel data show a robust relationship between the two variables (Heil and Selden, 2001, Ravallion et al., 2000), and country-level panel data tend to confirm this finding (H ̈ohne and Harnisch, 2002, Kim and Baumert, 2002). However, since GDP and emissions time series are usually non-stationary (and often increasing), linear regressions over panel data capture only how the underlying trends correlate over time. They do not capture how, at each point in time, a deviation of emissions with regard to its forecasted level is correlated with a deviation of GDP from its forecasted level.<sup>5</sup> In our model, it is the latter indicator that matters for comparing absolute and intensity targets. This indicator can be measured by the linear correlation coefficient _ρ_ .

# **3.3 Modeling Uncertainty on Marginal Abatement Costs**

Marginal costs of abatement are modeled in many different ways in the literature. First, the argument of the cost function is sometimes the percentage of BAU emissions that has been abated (e.g., Nordhaus, 1992), and sometimes the absolute amount of emissions abated (e.g., Ellerman and Decaux, 1998). Second, marginal abatement costs are usually represented as an increasing and convex function of the level of abatement, but several functional forms have been used, including quadratic (Ellerman and Decaux, 1998), exponential (such as GTEM curves in the CERT model by Gr ̈utter Consulting, 2003) or general power-law functions (Ghersi, 2003).

In this paper, we adopt a general representation of costs. Marginal abatement costs are assumed to be a continuous, increasing and convex function of the abatement effort _R_ , such that marginal abatement costs are zero when the effort is zero, and such that marginal costs remain finite for any finite value of _R ≥_ 0. The abatement effort itself is expressed as a combination of a ‘relative’ and an ‘absolute’ effort, in the form 9
seigniorage for Argentina. The framework is similar to other models of inflation finance (e.g. Bailey (1956), Friedman (1971), Calvo (1978), Anand and Van Wijnbergen (1989), etc.), but we adjust it to incorporate the major stylized facts of the Argentine financial system. This section establishes that due to the structure of reserve requirements and the charge. and compensations that the central bank imposes on the various deposits in the financial system, Ml is the basis for the inflation tax. This discussion is continued in section III where we examine the behavior of the inflation tax and seigniorage in Argentina from 1978 till 1985.

In section IV we conduct an empirical study of the demand for money based on monthly data for the period 1979-85. Our results are of interest for two reasons. First, we split the sample in three different periods for estimation purposes, each of them having distinctive rules for the exchange rate, interest rates and on the mobility of international capital flows. This enabled us to study the robustness of our estimated parameters to regime changes. Second, we were able to overcome the simultaneity bias that arises because of the correlation between the opportunity cost of holding money and monetary shocks. This was possible because during the first period the money stock was truly endogenous and interest rates were determined by the preannounced rate of devaluation and by arbitrage conditions. For the third period we found that the stock of money and rate of inflation were cointegrated and therefore we could obtain consistent estimators of the money demand function's parameters.

We conclude in section V with a discussion of the implications of our empirical results for the inflationary process in Argentina.