rafmacalaba/datause-encoder-data huggingface dataset:
schooling. This could then be gradually extended, through effectively targeted programmes, to facilitate the development of affordable full day basic education for all (DFID, 2002).

Raising and equalizing the quality of learning outcomes across regions and population subgroups will demand a full range of measures. The international education goals do not define specific quantifiable indicators of education quality, student performance or learning outcomes. Quality monitoring is a difficult task and most education authorities turn to input indicators such as the number of teachers or textbooks. The MDGs use literacy as a proxy indicator for educational performance but this suffers from a lack of a clear internationally agreed definition and testing measure. Reliable and scientific methods of measuring education outcomes exists in the form of ‘criterion reference tests’ and Vietnam is one of the few countries that has started to build national capacity in these techniques. A nationally representative sample survey of 70,000 grade five pupils was recently implemented that assessed learning outcomes by testing various dimensions of reading and mathematics. This will help in identifying which actions are paramount in addressing quality deficiencies. Among measures currently under consideration by MOET are: the extension of the instruction time to full-day education; reforming the curricula and teaching methods; upgrading facilities; strengthening education management; professional development for teachers; establishing education and training facilities of international standard; and, expanding the use of information technology.

# Goal Three: Reach gender equality and empower women

## Achievements

Gender equality in education - Vietnam performs well relative to countries of comparable income levels on many indicators of gender equality and this applies to the four internationally-agreed gender equality indicators. Data from the 1998 VLSS survey shows that there is little difference between male and female enrolment in primary, lower secondary and upper secondary school. (Figure 9). Vietnam performs well in comparison with low income country averages in this respect and this success is reflected in a genderbalanced youth illiteracy rate (see education goal). Gender differences in access to education are far more pronounced, however, in some ethnic minority areas and for the lowest quintile of the population.

Figure 9. Girls as a percentage of boys in primary and lower secondary education: Levels in 199398 and targets for 2005 <!-- Start of picture text --> 120 -<br>100, . =u = = = .<br>,<br>60 kg<br>|<br>40<br>20<br>; .<br>1993 1998 2005<br><!-- End of picture text -->

Source: DFID (2002) 19
DC(8) is the cotton damage function, expressing<sup>the fraction</sup> of yield which is lost through pests. In general<sup>8 can have two</sup> elements, a primary pest, S1 and a secondary pest, S2* As stated earlier, we will take only the primary pest,<sup>S,. From dose-response</sup> function studies, D. typically has<sup>the following form:</sup> _DCo_ 1- e-F^s8' (3) The shape of the damage function is described<sup>inFigure 1.</sup> We have not taken explicit account of<sup>uncertainty</sup> in the production function. Again, this is done<sup>to keep the algebra</sup> simple. Since uncertainty plays an important<sup>role in the demand for</sup> pesticides (Feder, 1979, Pannell, 1991) its<sup>role will be discussed</sup> briefly in the concluding section of<sup>this report.</sup> Pest Population Eguation Primary pest equation where k,(.) represents the carrying capacity of pests<sup>of the cotton</sup> crop, and, in this specification, depends upon the amount<sup>of the</sup> primary input used. _Z1_ is the pesticide used and, g1 is the kill function for the pesticide. Again, it must be<sup>emphasised that</sup> equation (3) is a simplified representation<sup>of reality sincethe</sup> 20
Table 5: Returns to Schooling and Skills from IV <u>(male employees 22-45 years; instrument = 1 if age in 1981 between 6 to 18 years)</u>

|Variable|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Years of schooling|0.079|0.053|0.064|0.058|
||(3.9)|(1.9)|(2.3)|(1.9)|
|Experience|0.009|0.008|0.010|0.006|
||(0.6)|(0.5)|(0.6)|(0.4)|
|Experience squared|-0.0002|-0.0002|-0.0003|-0.0001|
||(0.6)|(0.4)|(0.6)|(0.3)|
|Standardized IALS|-|0.158<br>(2.3)|-|-|
|Standardized SCS|-|-|0.132|0.131|
||||(2.0)|(2.0)|
|Standardized NSCS|-|-|-|0.092<br>(1.3)|
|Father >= Post-Secondary|0.256|0.200|0.264|0.110|
||(2.8)|(2.3)|(2.9)|(0.8)|
|Constant|5.52|5.75|5.63|5.72|
||(17.6)|(15.1)|(14.8)|(13.6)|
|Centered R<sup>2</sup>|0.202|0.214|0.235|0.237|
|N|490|490|467|467|
|First Stage:<br>|||||
|Shea’s partial R<sup>2</sup>|0.305|0.239|0.244|0.231|
|F-value|(213.2)|(152.0)|148.6|(137.8)|
|[p-value]|[0.000]|[0.000]|[0.000]|[0.000]|
|Endogeneity Test:|||||
|Durbin-Wu-Hausman statistic|0.507|0.348|0.219|0.170|
|[p-value]|[0.478]|[0.555]|[0.640]|[0.680]|

Note: Theoretical sampling weights included; _t_ -values in parentheses The evidence could be taken to suggest that the treated group which benefited most from the 1981 “voucher” reform, besides coming mainly from a better socioeconomic background and from urban areas, were enrolled in the early stages of schooling. They benefited from better quality schooling which endowed them with better schooling cognitive skills (compared to the untreated). As a result, given the “abundance” of schooling cognitive skills in this group, the labor market reward for an additional year of schooling reflects the “true” non-cognitive return to schooling.

27
small STI probabilities (see e.g. Kahneman and Tversky, 1979). Thus they may behave as if the expected loss is substantially higher than it truly is.

# **_High Discounting_**

In a society with a generalized AIDS epidemic (Tanzania has an estimated 6% adult prevalence rate), the expected cost of an AIDS diagnosis might be considered far larger than the modest cash rewards offered. For individuals who are present-focused and heavily discount the future though, the prospect of an AIDS diagnosis many years in the future may not be considered a high cost. But if the price of risky sex would be incurred within months instead (the RESPECT study tested and offered cash rewards every four months), then high discounters may perceive an increased (discounted) price of risky sex, and thus behaviorally respond to this shortening of the time horizon. This of course depends on the extent of high discounting; studies in this context (including measures in the RESPECT study) have found extremely high rates of discounting, implying that even a timeframe of months may result in substantial discounting of the potential reward value. And a reward in the timeframe of months may have little impact on those risky sexual behaviors that may be driven by strong hyperbolic discounting (similar to the concept of compulsive immediate gratification used in developmental psychology) as discussed in the behavioral economics literature (O‘Donoghue and Rabin, 2001).

# **_Other “Nudges”_**

A variety of other behavioral economics and psychological hypotheses have been proposed regarding the operation of incentives, now sometimes referred to as ―nudges‖ following the popularization of the term by Thaler and Sunstein (2008). For example, some argue that introducing explicit monetary incentives into the sexual decision-making process may alter the frame within which people assess costs and benefits, resulting in unpredictable deviations from neoclassical theory. Others suggest that the incentives provide individuals with an excuse for deviating from social norms in order to act on underlying preferences for less risky behavior. Several such theories would predict a discontinuity of the dose-response relationship at zero: the first positive reward amount 13
punctuated by large annual variations, and often failed to sustain. Thus, growth has not just accelerated post-liberalization, but has also become more stable.

**Figure 3: India’s long-term growth rate has become increasingly more stable**

<!-- Start of picture text --> 3 A: Coefficient of variation, GDP growth, 3 B: Coefficient of variation, GDP growth,<br>India EM7 median<br>10-year rolling coefficient of variation 10-year rolling coefficient of variation<br>160 160<br>140 140<br>120 120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016<br><!-- End of picture text --> Note: World Development Indicators data. Coefficient of variation is calculated as the standard deviation divided by the mean for rolling 10-year periods. For EM7, it is the median of the cross-country series for every year.

# **2.2 Spatial Trends in growth**

Next, we examine growth patterns at the state level.<sup>12</sup> For this we estimate the trend coefficient of Gross State Domestic Product (GSDP) growth for annual data since 1981. We use the following regression equation, where the subscripts _s_ and _t_ denote variation at the state and year level, respectively: GSDP Growth�� �β� �β�Trend� �γ� �ε�� �equation 2� To control for level differences in growth across states, we include state-level fixed effects in the regressions, denoted by γ�. A positive and significant coefficient estimate for β� presents evidence of growth acceleration. We also allow the trend coefficient to vary by state characteristics (equation 3). The characteristics we consider are the share of agriculture/non-agricultural sectors in the state

> 12 The states included in this analysis are Andhra Pradesh, Arunachal Pradesh, Assam, Bihar, Goa, Gujarat, Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Manipur, Meghalaya, Orissa, Punjab, Rajasthan, Tamil Nadu, Tripura, Uttar Pradesh and West Bengal. We construct a back casted panel on annual real gross state domestic product growth for the years 1981 to 2016.

8
first contact clinical services without fees to screen for severity may be the most serious public policy issue raised by this box. Substantial resources may be used in administrative costs and provider's time in subsidizing relatively minor ailments. In Indonesia, it was found that many of the visits to district hospitals were for muscle aches and skin rashes (World Bank, 1994b). These tended also to be the ailments which fell most significantly when fees were raised. In the U.S., the most rapid increases in Medicare payments have been for home-care services which include housekeeping.

In sum, there is reason to doubt, on theoretical grounds, the likely impact on health of, at the very least, the curative care components of PHC strategies.

# **_B)_** _Public intervention andpoverty_

Public intervention in the health sector, particularly at the primary level, is often justified on the basis of its impact on poverty or on the distribution of resources, or access to resources, within a society. If the poor lack access to services which would allow them to escape poverty, the argument often goes, then governments are beholden to providing these services.

Table 8 shows a compilation of results on how the benefits of public spending on health are distributed across individuals in each per-capita income (or consumption) quintile. Public spending on health is somewhat, but not impressively, pro-poor, as in all but three of 11 cases the fourth quintile received larger per person benefits than the bottom quintile. Typically the 43
that commonly co-occur within the data. Te model outputs (1) a set of topics, which are defined as mixtures of words, where each word has a probability of belonging to each topic, and (2) for each document analyzed, the proportion of the document associated with each topic. As such, each document is can be characterized by a vector of proportions, representing the share of the document associated with each topic. Using STM, we identify a set of 25 topics<sup>10</sup> discussed within the _gram sabhas_ , and explore how these topics vary with the identifiable characteristics of speakers and villages — including the gender of the speaker, the position of the speaker, and the reservation status of the village council president (female and/or Scheduled Caste). Te generated topics are presented in Appendix Table A.1, which lists the highest probability words in each topic, as well as the FREX words, which are both frequent and exclusive, thereby identifying the words that distinguish topics. We also validate these topics in Appendix 3A using two tests of predictive validity. Below, Figure 3 presents the distribution of these topics across the full corpus.

# _3.4 Measures_

Having interpreted and validated the topic model output, we now turn to our measures of deliberative participation and influence — that is, whether women are able to speak and how well they are heard. As a measure of their participation, we estimate the effect of PVP both on women’s atendance (measured in raw numbers and as a percentage of female voters), as well as the frequency and volume of speech. For measures of frequency, we examine the share of all speech delivered by women, as well as the share of female speeches among only citizens (excluding politicians and administrators). As a measure of volume, we look at the length of speeches — in terms of the number of words — to capture the amount of floor time enjoyed by women versus men. Collectively, these measures capture the extent to which PVP encourages women to be present and active participants in the civic space.

> 10Since this method assumes a fixed, user-specified number of topics, we first assess the relative performance of models under a range of values ( _K ∈_ 5 _,_ 50), and choose _K_ = 25 for the preferred specification. Tis specification performs relatively well on a number of empirical tests (residuals fit, held-out likelihood, semantic coherence, and exclusivity of topics), and yields topic clusters consistent with our substantive understanding of village assembly discussions. We also re-ran the analysis for 15, 20, and 30 topics, and results remain largely robust to these alternative specifications.

16
Appendix Table 9: ITT Treatment Effect Estimates, Short-Run Business Outcomes, IMSS Sample OLS

|Outcome variable|ITT trea<br>effect es|tment<br>timates|Control group<br>mean<br>(std. dev.)|
|---|---|---|---|
||(1)|(2)|(3)|
|Full-time paid employees|0.764|0.380|15.932|
||(1.841)|(1.405)|(25.703)|
||229|229|148|
|Avg. sales Dec 2008, Jan and Feb 2009 (1000s USD)|-17.701|-16.381|77.325|
||(23.164)|(14.531)|(161.353)|
||187|187|125|
|Log (Avg. sales Dec 2008, Jan and Feb 2009 in 1000s USD)|-0.182|-0.104|3.045|
||(0.204)|(0.168)|(1.713)|
||187|187|125|
|Feb 2009 costs (1000s USD)|-1.529|-3.804|63.156|
||(23.042)|(19.729)|(141.134)|
||176|176|120|
|Profits (Feb 2009 sales minus costs, 1000s USD)|5.566|5.904|2.321|
||(8.197)|(8.102)|(43.806)|
||160|160|109|
|Log (business assets)|-0.012|-0.008|4.655|
||(0.208)|(0.202)|(1.624)|
||187|187|121|
|Productivity residual|0.080|0.088|0.116|
|Residual from regression of log Feb 2009 sales|(0.181)|(0.170)|(1.154)|
|on log employees and log business assets|152|152|99|
|Return on assets (ROA)|0.173*|0.197*|-0.001|
|Feb 2009 sales minus costs divided by assets|(0.098)|(0.113)|(0.574)|
||140|140|95|
|Controls for baseline value of outcome|No|Yes|-|

Note: This tables included only enterprises that were successfully matched with IMSS data. Each cell in Columns 1 and 2 contains the treatment effect point estimate, robust standard error, and number of observations, for a separate OLS estimation. For the regressions that control for the outcome variable measured at baseline (Column 2), when the baseline outcome variable is missing, the missing value is filledin with zero and a dummy variable indicating that the baseline observation is missing is added to the model. All regressions include controls for strata dummies and re-randomization variables, as well as a dummy for having been surveyed in March 2009 (vs. April, May or June) at follow-up. Column 3 contains means and standard deviations for the control group at follow-up. Significance levels: *10 percent, **5 percent, ***1 percent.

39
of causation. Second, the different results which arise from the use of cross-section and panel data suggest the importance of disentangling short-run from long run effects without throwing away the information in annual data. Quah and Rauch (1990) have made a first attempt to disentangle the impact of cyclical from long-run effects of policies on growth in time series data. Unfortunately, Quah and Rauch use trade shares as their proxy for openness, which is one of the least robust measure of openness in our analysis. Extending Quah and Rauch (1990) to analyze other openness measures would be a useful exercise.

22
|P005883|1987|1994||18 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P005884|1990|1990||1 Closed|Yemen,Republic of|
|P005884|1990|1991||5 Closed|Yemen,Republic of|
|P005884|1990|1992||8 Closed|Yemen,Republic of|
|P005884|1990|1993||11 Closed|Yemen,Republic of|
|P005884|1990|1994||13 Closed|Yemen,Republic of|
|P005884|1990|1995||16 Closed|Yemen,Republic of|
|P005884|1990|1996||20 Closed|Yemen,Republic of|
|P005884|1990|1997||25 Closed|Yemen,Republic of|
|P005884|1990|1998||27 Closed|Yemen,Republic of|
|P005884|1990|1999||29 Closed|Yemen,Republic of|
|P005892|1991|1991||1 Closed|Yemen,Republic of|
|P005892|1991|1992||2 Closed|Yemen,Republic of|
|P005892|1991|1993||4 Closed|Yemen,Republic of|
|P005892|1991|1994||6 Closed|Yemen,Republic of|
|P005892|1991|1995||8 Closed|Yemen,Republic of|
|P005892|1991|1996||10 Closed|Yemen,Republic of|
|P005892|1991|1997||12 Closed|Yemen,Republic of|
|P005892|1991|1998||14 Closed|Yemen,Republic of|
|P005892|1991|1999||17 Closed|Yemen,Republic of|
|P005893|1989|1989||3 Closed|Yemen,Republic of|
|P005893|1989|1990||5 Closed|Yemen,Republic of|
|P005893|1989|1991||9 Closed|Yemen,Republic of|
|P005893|1989|1992||12 Closed|Yemen,Republic of|
|P005893|1989|1993||15 Closed|Yemen,Republic of|
|P005893|1989|1994||17 Closed|Yemen,Republic of|
|P005893|1989|1995||20 Closed|Yemen,Republic of|
|P005893|1989|1996||24 Closed|Yemen,Republic of|
|P005893|1989|1997||29 Closed|Yemen,Republic of|
|P005893|1989|1998||31 Closed|Yemen,Republic of|
|P005893|1989|1999||33 Closed|Yemen,Republic of|
|P005896|1990|1990||3 Closed|Yemen,Republic of|
|P005896|1990|1991||7 Closed|Yemen,Republic of|
|P005896|1990|1992||10 Closed|Yemen,Republic of|
|P005896|1990|1993||12 Closed|Yemen,Republic of|
|P005896|1990|1994||14 Closed|Yemen,Republic of|
|P005896|1990|1995||16 Closed|Yemen,Republic of|
|P005902|1998|1998||2 Closed|Yemen,Republic of|
|P005902|1998|1999||4 Closed|Yemen,Republic of|
|P005902|1998|2000|5/24/2000|7 Closed|Yemen,Republic of|
|P005902|1998|2001|6/29/2001|10 Closed|Yemen,Republic of|
|P005902|1998|2002|6/28/2002|13 Closed|Yemen,Republic of|
|P005902|1998|2003|6/24/2003|16 Closed|Yemen,Republic of|
|P005902|1998|2004|5/5/2004|18 Closed|Yemen,Republic of|
|P005902|1998|2005|4/28/2005|21 Closed|Yemen,Republic of|
|P005902|1998|2006|11/3/2005|22 Closed|Yemen,Republic of|
|P005902|1998|2006|2/3/2006|23 Closed|Yemen,Republic of|
7

## **Table 3: Producti;n Diversification and Growth Performance of Developing Countries (1965-1989)**

||Significantly diversified<br>countries (36)'|<br>Not significantly<br>diversified countries (40)|
|---|---|---|
|Average annual growth rate (%)|5.1|3.2|
|Standard deviation of growth rate|1.38|2.73|

'Couwtries which reduced the share of agriculture ir. total CDP more than 5 percentages points between 1965 and 1989.

Source: World Development Report, 1991 International Financial Statistics, IMF Table 4: Exports **Diversification and Growth Performance of Developing Countries (1965-1989)**

||Significantly diversified<br>countries (31)1|Not significantly<br>diversified countries (37)|
|---|---|---|
|Average annual growth rate (%)|4.6|3.5|
|Standard deviation of growth rate<sup>2</sup>|1.31|2.4|

1 Countries which reduced the share of primary commodities in total exports more than 10 percentage points between 1965 and 1989.

Source: World Development Report, 1991 International Financial Statistics, IMF

# IV. **National Risk Management**

The concept of managing an economy like a corporate firm is not new. One of the remarkable features of high economic growth in East A ian economies has been their rapid growth with low inflation. Aside from the usual explanatory variables such as well educated human capital, outward-looking industrial and trade policies, an "institutional" or "managerial' view is that policy makers managed these economies (and their finances) as if they were single corporations: Japan Inc, Korea Inc, and Malaysia Inc. Some observers argue that resource allocation -,l these economies may behave similarly to large multi-national corporations: they may be guided by market signals, but the allocation of the resources and risks among various
**Figure 11: Corporate Vulnerability Index 2009 vs 2016**

<!-- Start of picture text --> VI 2016<br>0.6<br>VEN<br>0.5<br>0.4<br>BIH<br>0.3 TZA MNE MKD<br>UKR<br>TUN<br>KAZ<br>LVA<br>BRA ARE<br>0.2 KEN EGY MUSSAU IND BGR SVK HRV<br>PAN MAR ZWE OMNCHN GHA<br>HUNCOL ARGNGA RUS KWTPOL ROMISR BOLIDN VNMGEOSRBBGD SVN PAK ZMB<br>0.1 CHL CIV THA<br>ECU TUR<br>PER MYS LTU<br>MEX QAT LKA<br>CRI WBG PHL ZAF JOR BHR<br>0 BWA MWI CZE TTO JAM PRY EST<br>0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4<br>VI 2009<br><!-- End of picture text --> Note: Scatter plot of the Vulnerability Index of 2009 versus 2016 showing that any country at the north of the 45-degree line has seen their corporate vulnerability position deteriorated during this period.

Source: Own calculations At the industry level, corporate vulnerability has evolved unevenly over time: companies in basic materials, consumer goods, consumer services, and industrials increased their vulnerabilities during the financial crisis and this has continued since then. Energy, health care, and telecommunications firms have faced sharp deterioration of their financial conditions since 20112012, a period characterized by accommodative financial conditions and increasing corporate leverage (Figure 12). Since 2014, with the end of the commodity super-cycle, financial conditions for energy companies worsened and their CVI has been high.

18