rafmacalaba/datause-encoder-data huggingface dataset:
period, 1997/98-2001/02; and the first four years of the Tenth Plan period 2002/03-2005/06. The impact of each determinant, shown in the first column of the table, is annualized.<sup>12</sup>

**Table 2. Factors Accounting for Rising Indebtedness, 1985/86-2005/06**

**<u>(Annual average, % points of GDP)</u>**

||**85/86-**<br>**89/90**|**90/91-**<br>**91/92**|**92/93-**<br>**96/97**|**97/98-**<br>**01/02**|**02/03-**<br>**05/06**|
|---|---|---|---|---|---|
|1.Increaseindebt|3.5|3.6|-2.5|2.5|0.2|
|2. Primary Deficit|5.4|3.8|2.1|4.0|2.2|
|3. Real GDP growth|-3.7|-2.4|-4.7|-3.6|-5.7|
|4. Real interest rate|-0.3|-2.0|0.3|2.8|3.5|
|5. Real exchange rate change|0.2|1.9|0.1|0.3|-0.3|
|6. Financial Sector Recapitalization|0.0|0.0|0.3|0.0|0.1|
|7.Divestment|0.0|-0.2|-0.2|-0.1|-0.2|
|8. Residual(1. minus sum of 2. to 7.)|1.8|2.4|-0.4|-0.9|0.7|

_Source_ : Authors’ calculations based on Handbook of Statistics on Indian Economy, Reserve Bank of India We see that the debt-reducing impact of growth was strongest impact during the Eighth Plan period and the first three years of the Tenth Plan period. The real interest rate impact became positive during the Eighth Plan period and has been strongly positive since as financial repression eased. And in contrast to other emerging market countries, especially the ones suffering a serious crisis, the real exchange rate impact (except for the BoP crisis period and its aftermath 1990/91-1991/92) and costs of financial sector recapitalization have been insignificant.<sup>13</sup> Divestment has not played a major role.

The impact of the growth resurgence is shown in Table 3, which presents results by year for 2002/03-2004/05. Even though the average real interest rate impact over the past 20 years was at its strongest during first four years of the Tenth Plan period, Table 3 shows a small, declining trend as the low marginal interest rates (as a result of the record lows in global interest rates) at last began to kick in.<sup>14</sup> Growth has lowered the debt-to-GDP ratio by almost 7 percentage points per year over the past three years. Equally importantly, by 2004/05, the primary deficit had dropped to levels lower than the Eighth Plan period.<sup>15</sup>

> 12 Thus, during 1985/86-1989/90, the debt-to-GDP ratio rose by 3.5 percentage points per year, with the primary deficit contributing 5.4 percentage points per year; whereas over 1992/93-1996/97, the debt-toGDP ratio fell by 2.5 percentage points per year, etc.

> 13 Debt decompositions for the crisis emerging market countries show that real exchange rate collapses and the fiscal  costs of bank bailouts were the main factors raising the debt-to-GDP ratio.  Growth collapses played a small role.  Budina and Fiess (2004) and IMF (2003).

> 14With India’s long maturity debt, the impact of low marginal interest rates takes time to be reflected in the average interest rate which drives debt dynamics.

> 15 For formal analyses of debt sustainability looking at primary fiscal deficits and the real interest rate - growth rate differential, see Rangarajan and Srivastava (2005), Pinto and Zahir (2004b), and Pinto, Zahir and Pang (2006).

8
very recent literature, focusing on non‐linear or time‐varying alternatives (e.g., Balagtas and Holt 2009), finds even less support for PSH.

All this research is perhaps best summarized by Cashin and McDermott (2002) who concluded that the downward trend in real commodity prices is of little policy relevance because it is small when compared to the variability of prices. Or as Deaton (1999: 27) succinctly put it, “what commodity prices lack in trend, they make up for in variance.” Commodity price variability is at the core of the current policy debate. The difficulty associated with describing past price behavior, and hence with making inferences regarding future trends, can be inferred from Figure 1; the conclusions reached depend on what time period is chosen for analysis. Statisti‐ cally, this difficulty reflects the problem of nonstationarity, i.e. the fact that the average price does not exist in the statistical sense. Table 4 shows the results of an analysis of stationarity for prices of six food commodities (wheat, maize, rice, soybeans, soybean oil, and palm oil). For sensitivity purposes, we report results from two tests, with and without trend, both in nominal and real terms (we also used US CPI in addition to MUV and the results were remarkably similar). All lend strong support to non‐stationarity, thus reaffirming the conclusions reached by Cashin and McDermott (2002) and Deaton (1999).

The fact that commodity price variability overwhelms trends has a num‐ ber of key implications. On the methodological side, analysis involving prices needs to recognize that correlations may not be meaningful unless certain condi‐ tions are met (see next section), and also that because a mean or a trend of the price series cannot be properly defined, the variability in prices is difficult to cal‐ culate.

On the policy side, attempts to introduce mechanisms with price triggers (as has often been proposed recently) are likely to fail. In fact, the absence of trends (or simply put, the non‐existence of an “average price”) may be the key reason why earlier price stabilization (or other) mechanisms failed.<sup>**11**</sup> When prices stay low for long periods, stabilization funds run out of resources, and when prices stay high for long periods, stabilization funds tend to be misused. Consid‐ er, for example, that the agricultural commodity price index (shown in Figure 1) exceeded its period average (equal to 173) in all years during 1948‐71 and fell be‐ low it in all years during 1981‐2007.

# **_Co­movement_**

Because some agricultural commodities can be substituted for one another (e.g. various edible oils), while resources on the input side (e.g., land, labor, and ma‐ chinery) can be shifted from one crop to another, the changes in fundamentals or

**_Page 14_**
# Table 8: Presence of an improved latrine

|VARIABLES|Improved latr<br>shared f|ine (including<br>acilities)|Improved l<br>defin|atrine (JMP<br>ition)|Improved la<br>defin|trine (strict<br>ition)|
|---|---|---|---|---|---|---|
||Reported<br>(1)|Observed<br>(2)|Reported<br>(3)|Observed<br>(4)|Reported<br>(5)|Observed<br>(6)|
|HWWS (β1)|0.038|0.035|0.049|0.046|0.021|0.028|
||(0.040)|(0.039)|(0.034)|(0.034)|(0.029)|(0.030)|
|TSSM ( β2)|0.124***|0.117***|0.157***|0.151***|0.122***|0.086***|
||(0.039)|(0.039)|(0.034)|(0.035)|(0.029)|(0.028)|
|HWWS + TSSM (β3)|0.074*|0.077**|0.103***|0.106***|0.076***|0.057**|
||(0.038)|(0.038)|(0.031)|(0.031)|(0.029)|(0.026)|
|p-values β1= β2|0.023|0.030|0.001|0.003|0.000|0.076|
|for F-test β1= β3|0.337|0.262|0.091|0.058|0.055|0.339|
|β2=β3|0.176|0.283|0.088|0.161|0.111|0.327|
|_Observations_|3,619|3,619|3,619|3,619|3,619|3,619|
|_R-squared_|0.230|0.214|0.156|0.152|0.151|0.093|
|_Control Mean_|0.761|0.749|0.507|0.497|0.373|0.270|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects.

Definition of improved latrine is based on JMP standard definition. Broad definition considers only the latrine type, while the strict definition requires that the slab is suitable for separating humans from excreta (there is only one whole in the slab and it is in good repair).

44
Experience is de...ned as (age�years of schooling�6): Experience in the US is calculated with respect to the middle of the year of immigration bracket.

# A.2.2 Macroeconomic Variables

Variables necessary to calculate the measure of GDP per capita, investment per worker and Open are taken from the Penn World Tables Mark 5.6 revision of Summers and Heston (1991). The measure of GDP is RGDPL, which is per capita GDP expressed in constant year international prices. Open is de...ned as imports plus exports in exchange rate US$ relative to GDP in purchasing-power-parity US$ and de‡ated by international export prices. This measure of openness has been proposed by Ciccone and Alcala (2004). Average educational attainment is measured for the population aged 25 and over, as reported by Barro and Lee (2000). Data on pupil-teacher ratio and real public spending per student are from Lee and Barro (2001). Measures of cognitive skills are taken from Hanushek and Kim (1999).

The index of government antidiversion (GADP) is taken from Hall and Jones (1999). It is based on data from the International Country Risk Guide which rates 130 countries according to 24 categories. The GADP is de...ned as the equal-weighted average of ...ve of these categories (law and order, bureaucratic quality, corruption, risk of expropriation and government repudiation of contracts) for the years 1986-1995. The index is measured from zero to one. The value of the index increases with the e¤ectiveness of government policies in supporting an environment favorable to productive activities.

Data on the share of agriculture and manufacturing in GDP are taken from Caselli and Coleman (2001) and are based on data from the World Bank. Data on imports and exports are from Caselli and Coleman (2001), who take the original data from Feenstra, Lipsey and Bowen (1997).

# A.2.3 Regional Dummies

Regional Dummies are de...ned for Africa, Asia, Latin America, Transition Economies and Island. Island includes the Caribbean and Paci...c Island States, the African Island States in the Indian Ocean as well as Cape Verde, Malta and Cyprus. Hongkong, Taiwan and Singapore are added to the Asian dummy. Oil exporting countries are Iran, Iraq, Jordania, Saudi Arabia, Syria and Yemen. The base dummy consists of the OECD member countries as of 1990 plus Turkey and Israel.

29
**Figure 3: Infant and U5-child mortality in the country case studies**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Guinea Senegal Mali Niger Kenya Uganda Madagascar Mozambique<br>U5MR year 0 U5MR year 1 U5MR year 2<br><!-- End of picture text --> Source: Authors’ calculation from DHS surveys.

Note: Year 0 refers to the initial value (taken from DHS surveys as close as possible to the year 1990), year 1 to the first survey used in our analysis and year 2 to the second survey used in our analysis (for the respective years refer to Table A1).

Yet, there are not only stark differences in U5M levels. While some countries have achieved considerable child mortality reductions in the respective periods under investigation, progress in other countries has been limited. In one country, Kenya, the U5M rate has even increased. To put achievement and failures into the context of the MDGs, the fourth column of Table 1 reports the hypothetical reduction in the U5M rate, had the country experienced the same point change reduction observed in the interim period between the two surveys for a period of 15 years. This “actual rate” can then be compared to the reduction of the U5M rate by two thirds in the initial year, which is reported in the second column. Although this is not precisely the MDG target for each of the countries, it is in most cases very close to it.

18
For households who continue to rent in, the optimal operational land holding can be obtained from equation (1) and (2) as _A, = A(a,(p,r,T,w)._ SettingA, to _A1,_ yields the identity <!-- Start of picture text --> MI<br>=- dTU da4T ° (A9) (for from (A) and a from (A6)),<br>dT 8A, 8cara<br>aa<br><!-- End of picture text --> implying that as the transaction costs increase more households would change from renting in land to autarky.

> Similarly for the households who continue to rent out land, and based on (1) and (2)', we can derive the following proposition:

<!-- Start of picture text --> 8A1<br>daT 4< 0 (A10) (for 8A > O from (A) and A, > ° from (A7)),<br>dT - 8aac i aT<br>aa<br><!-- End of picture text --> implying that, as transaction costs increase, more households would change from renting out to autarky.

Proposition 3. Increases of the exogenously given wage for off-farm employment will increase the amount of land transacted in rental markets by increasing the amount rented out by households with low agricultural ability (who join the off-farm labor force) and the amount rented in by those with high-ability (who specialize in agricultural production). This will be associated with a decrease in the equilibrium rental rate which, in a risk-free environment, will make everybody better off.

Without loss of generality, we assume that only the households who originally rented land out will take advantage of the increased off-farm opportunities. Those who rented in land originally will continue to rent in land and the their off-farm opportunities are assumed to remain the same as before. In other words, households who rented out land before will face wage increase while those who rented in land before will face the same wage with the increase of the overall off-farm opportunities.

> For those households who rented out land, we take the derivative of both sides of equation (1) or equation (2)' with respect to w, yield
speeches, and in panel (d) with the coefficient of variation of the r-squares between consecutive speeches. Long-term growth is measured as the percentage point change of the GDP per capita of each country (as a percentage of the GDP per capita of the US). The topics were selected by the LDA using pooled data of all the countries, and using a 10% minimum threshold to discard topics with low frequencies.

Figure 13 shows that our alternative measures of policy volatility yield consistent results with those previously shown. All our four alternative measures of policy stability have the expected correlation with long-term growth, and a similar explanatory power. On average, countries with higher policy stability have higher long-run growth rates. In this case, the goodness of fit of our measures is lower. On average, about a fifth of the crosscountry variability of the long-term growth is explained by any of our policy volatility variables.

# **6. Conclusion**

Frequent shifts in economic policies can increase uncertainty, resulting in lower investment and economic growth over the long run. This is an important but particularly difficult premise to test empirically. To begin with, how to measure policy volatility over long periods of time has proven to be elusive.

The first contribution of our paper is to provide a new proxy for policy volatility that exploits the information content of the priorities conveyed in presidential speeches. In most Latin American countries presidents are constitutionally mandated to give a ‘state-of-theunion’-type of speech annually. This is typically a programmatic speech which lays out achievements and priorities of the government in question. We built a new data set with 953 such presidential speeches from 10 Latin American countries and Spain. We then applied a common technique used in the literature on topic modeling, the Latent Dirichlet Allocation algorithm, to identify different topics and identify the share of each speech that is devoted to each topic. This allows us to observe the evolution of topics over time and detect significant differences across countries.

The second contribution of the paper is to provide suggestive evidence of the link between policy volatility and long-term growth. We show that our proxy of policy volatility is negatively correlated with long-term growth over the 1940-2010 period. Our results are robust to a large set of changes on how we construct our proxy of policy volatility.

More broadly, as in the case of Baker et al. (2016) who use the words in newspapers, our contribution demonstrates that drawing on text as data can help deepen our understanding of broad economic, political, and historical development.

26
# **Table 11: Prices for Cosmetic Surgeries in Comparison**

|**Treatment**|**Hungary**<br>**“Perfect Profiles”**<br>**Clinic in Budapest**|**Austria**<br>**“EMCO” Private**<br>**Clinic in Salzburg**|**Potential benefit**|
|---|---|---|---|
||Euro<br>(including only an<br>approximate value for<br>anaesthesia and<br>sedation)|Euro<br>(all inclusive without<br>stationary stay)|Approximate value in<br>Euro|
|Face/Necklift|About 3,290<sup>35</sup>|4,000 (5,080<sup>36</sup>)|About 710|
|Rhinoplasty|About 2,490|3,800–4,700 (4,880–<br>5,780<sup>37</sup>)|About 1,310|
|Mammoplasty<br>(augmentation)|About 2,490|4,200 (4,920<sup>38</sup>)|About 1,710|
|Mammoplasty (reduction)|About2,990|4,700 (5,420–5,780<sup>39</sup>)|About1,710|
|Blepharoplasty (eyelid)|About1,190|2,100 (2,820<sup>40</sup>)|About 910|
|Blepharoplasty (undereye)|About1,290–1,490|2,300 (3,020<sup>41</sup>)<br>|About 810–1,010|
|Abdominoplasty|About 3,290|4,700 (5,780–6,140<sup>42</sup>)|About1,410|

Source: These figures have been taken from the homepages of the private Hungarian Clinic “Perfect Profiles” and the Austrian private clinic “EMCO” in Salzburg and verified by telephone, March 2007.

# **6 BARRIERS TO CROSS-BORDER PURCHASING OF HEALTH CARE**<sup>**43**</sup>

Section 5 has shown that there are indeed, though modest, price differentials in selected health care sectors and resulting from that there are potential cost savings for Austrian HIFs and individual patients. However, for the time being, the number of persons traveling abroad to receive treatment is rather low. The reluctance of insurance funds and patients to purchase health care abroad can be explained with multiple barriers to trade in health services. This section will deal with the main factors that inhibit Austrian public and private health insurers as well as individual patients from realizing cost savings through capitalizing on lower health care prices in Hungary.

> 35 The prices for the Hungarian clinic only refer to the actual price for the individual treatment. Anaesthesia costs about 330 Euro, sedation about 160 Euro, according to the needs. Accommodation etc. is not included.

36 The clinical lump sum for a day in the private clinic “EMCO” amounts to 360 Euro. For face/necklift three days in the hospital have to be counted.

37 A stay of three days is necessary.

38 A stay of two days is necessary. The implants, which are not included in the price, cost between 500 and 600 Euro.

39 A stay between two to three days is necessary.

40 A stay of two days is necessary.

41 A stay of two days is necessary.

42 A stay between three to four days is necessary.

43 For a more detailed discussion on the obstacles for trade in health care, see e.g. Brouwer et al. (2003), or Vollaard (2005).

22
The median value of the infrastructure index, Ki(j)t, is equal to 0.13 for Central Asian countries, while it is equal to 0.26 for other landlocked countries, and to 1.19 for coastal countries in our sample. For Central Asian countries, moving from their median infrastructure indicator to that of other landlocked countries would promote imports and exports by 3.1% and 2.4% respectively.<sup>20</sup> In the same way, moving from their median infrastructure indicator to that of coastal countries would enhance imports by 19.6% and exports by 14.5%.<sup>21</sup> Finally, we find that landlocked countries are significantly more disadvantaged with regard to trade than coastal countries. According to the predictions of the gravity model, and all other things being equal, a country with no direct sea access imports 22% less and exports 34% less<sup>22</sup> than a coastal country. This estimation is in the same range as others studies (e.g. Carrère, 2006, or Soloaga and Winters, 2001). However, this impact is significantly lower than the impact calculated by Raballand (2003). However, it is difficult to compare results, as Raballand (2003) used a much smaller sample and, most importantly, defined his landlockedness dummy as being equal to 1 for bilateral trade between _two landlocked_ countries.<sup>23</sup> To check the robustness of these findings, we introduced explanatory variables which are often used in empirical literature which apply the gravity model, even if these variables are not formally justified by the gravity derivation used here. In the third column, population variables, _Ni_ and _Nj,_ are introduced and are significant at the 5% level (e.g. Bergstrand 1989; Soloaga and Winters, 2001; Brun et al., 2005). The coefficients of the other variables remain unchanged. In the fourth column we introduce the bilateral real exchange rate indices between _i_ and _j_ , _RER_ ijt (cf. Soloaga and Winters, 2001, Bayoumi and Eichengreen, 1997, Brun et al., 2005, or Carrère, 2006). The RER is defined as the importing country _i_ currency value

> transit infrastructure, but not especially on the returns of their own infrastructure. Hence, Central Asian countries have the same average coefficient for infrastructure as other countries in the sample. 20 1.031=(1.264/1.127)^(0.269) and 1.024=(1.264/1.127)^(0.204).

> 21 1.196=(2.190/1.127)^(0.269) and 1.145=(2.190/1.127)^(0.204).

> 22 -0.217=(e-0.245-1) and -0.335=(e-0.408-1).

> 23 One could expect to observe the impact of landlockedness on Central Asian countries by simply including an interacted variable between the two dummies: landlockedness and Central Asian countries. However, since all these countries are landlocked, we cannot distinguish in this interacted variable between the impact of being landlocked specific to Central Asia and any other constant specific features of this region. However, the definition of alternative variables of landlockedness in the next section will enable us to avoid this shortfall.

20
analysis of over 100 studies, Estache and Garsous (2011) find that the effect of infrastructure on growth depends on three main factors: (i) the specific indicators used to approximate infrastructure;<sup>14</sup> (ii) the time period analyzed;<sup>15</sup> and (iii) the level of development of the country in question.<sup>16</sup> Since there are no specific growth elasticities with respect to infrastructure for the MENA region or countries, it is necessary to rely on a survey of international experience in developing countries to obtain an estimate of the output elasticity with respect to infrastructure. Although there is quite a large literature on the topic, relatively few studies cover developing countries. The basic average elasticity used for the MENA sample, and an associated confidence interval, draw on the estimation results from ten studies focusing on developing countries presented in Table 10. These are relatively high elasticities when compared to those used for developed countries – a fact consistent with one of the lessons in Estache and Garsous (2011). The variance is however large enough to be concerned about the reliability of the average elasticity derived from this sample.

**Table 10 Output elasticities with respect to infrastructure for developing countries**

|**Authors**|**Estimated Elasticity**|
|---|---|
|Dessus & Herrera (2000)|0.13|
|Gwartney, Helcombe & Lawson (2006)|0.17|
|Khan & Kumar (1997) (Africa)|0.32|
|Khan & Kumar (1997) (Asia)|0.26|
|Nazmi & Ramirez (1997)|0.129|
|Odedokun(1997)|0.033|
|Ram(1986)|0.372|
|Ram(1996)|0.299|
|Ramirez (1998)|0.58|
|Sanchez-Robles (1998)|0.003|
|Sridhar& Sridhar(2004)|0.098|

To assess the robustness of the elasticity, we build a confidence interval for the mean _μ_ of this sample. The empirical mean and the standard error of this sample are respectively given by =0.22 and _s=0.17_ . Therefore, assuming normality, we have: .

Consequently, there is only a 5% chance that _μ_ does not fall into the following interval: .

> 14 Using energy as a proxy guarantees a much stronger impact than using water or even telecoms, and a synthetic indicator provides an intermediate level of impact as expected.

> 15 The impact was stronger in the 1950s and 1960s than in the last two decades.

> 16 The less developed the country, the higher the likely impact. However, this result is not as statistically robust as expected.

14