rafmacalaba/datause-encoder-data huggingface dataset:
If the average height-for-age of children who died was twice as far below the age and gender reference mean as that of children who survived (–3.83 rather than –1.91), the height-for-age z-score for the total sample would rise from –1.91( _Zs_ ) to –2.17( _Zall_ ), or by 13.5 percent. If a height-for-age z-score of –5.0 (the lower bound recommended as the cut-off for outliers; WHO 1995) is imputed to a sample of children who died, the overall z-score would rise from –1.91 to –2.32, or by 21.8 percent. Similar tendencies are observed for the 1998 and 2005 samples.

The impact of the imputations for height-for-age on the total sample is proportional to the mortality rates. For example, the imputation of a height-for-age z- score twice as low as the average to the sample of girls who died before age 36 months results in a 13.0 percent change in the overall mean z-score in 1992 but only a 8.4 percent change in 1998 and a 6.6 percent change in 2005, reflecting the decline in girls‘ mortality rates from 0.077 in 1992 to 0.056 in 2005.

But not all deaths before age 36 months were caused by malnutrition. The next simulation is based on the results from the literature that estimates the contribution of malnutrition to child mortality. Puffer and Serrano (1973) found that malnutrition was an underlying cause in 54 percent of deaths for children ages 2–4 years. Pelletier (1994) explored 28 prospective datasets and found that the population-attributable risk of mortality associated with anthropometric deficits varied from 17 percent to 74 percent in eight studies for Asia and Africa. Pelletier and others (1994) applied to prospective surveys in Ethiopia, Guatemala, India, and Malawi a new methodology for determining the association of malnutrition and mortality by the severity of malnutrition. They demonstrated that 42–57 percent of deaths of children ages 6–59 months were associated with malnutrition‘s potentiating effects on infectious disease, 76–89 percent of them attributable to mild to moderate malnutrition. Analysis of data for 53 developing countries for the 1980s found that about 56 percent of child deaths were associated with malnutrition. The proportion is close to 67 percent for India, with 73–74 percent of it attributable to mild to moderate malnutrition (Pelletier and others 1995).

While this approach is based on weight for age and not height-for-age, it makes a good starting point for a simulation of imputed height-for-age, based on the assumption that 67 percent of deaths in children up to age 36 months in India were related to malnutrition (the upper bound for that association; see bottom panel of table 2). The height-for-age of living children by gender in a particular year was used to impute the average height-for-age of children whose deaths were not associated with malnutrition. Of deaths among children related to malnutrition, 70 percent were assumed to have occurred among children with moderate to mild malnutrition, and an average height to 7
**Henderson,** J.V., "The Sizes and Types of Cities," _Amer. Econ. Rev.,_ 1974, 64(4), 640-656.

**Hununels, David,** "Essays on Spatial International Economics," 1995. (unpublished Ph.D. thesis).

**Isard, W.,** _Location and Slace Economy,_ Cambridge, Mass.: MIT Press, 1956.

**Jacobs, Jane,** _The Economy of Cities,_ New York: Random House, 1969.

_-, Cities and the Wealth of Nations,_ New York: Random House, 1984.

- **Jacoby, Hanan** G., "Access to Markets and the Benefits of Rural Roads," _Economic Journal,_ 2000. (forthcoming).

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"Increasing Returns and Economic Geography," _J. Polit. Econ.,_ 1991, _99(3),_ 483-99.

- **Lampard, Eric** E., "The Evolving System of Cities in the United States: Urbanization and Economic Developmenl;," in "Issues in Urban Economics," Baltimore: Harvey S. Perloff and London Wingo (eds.), John Hopkins Press, 1968.

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- **Losch, A.,** _The Economics of Location,_ New Haven, Conn.: Yale University Press, 1954. (Originally published 1939).

38
i.e. whether they are driven by changes in supply or demand conditions in the financial and real sectors. In this paper, to identify (real and financial) demand and supply driven recessions, we follow the literature on the cyclical relationship between output and prices (den Haan, 2000; Cooley and Ohanian, 1991; Pakko, 2000; Smith, 1992 among others). To identify real (demand and supply) driven recessions, we draw upon information on prices and output growth during the recession phase of the crisis episodes identified in our sample. In a model with flexible price-adjustment, during recessions in which supply-side drivers dominate, the correlation between inflation and output growth is negative; when demand-side drivers prevail, the same correlation is assumed to be positive. If the whole process only takes a short period of time, we will typically observe changes in quantities and relatively stable prices so the correlation will be smaller or close to zero. Since we are focussing on changes in demand and supply during the whole recessionary phases it is reasonable to assume that the correlation will reflect long term changes in demand and supply conditions with a resulting non zero correlation between output and prices.

To identify financial (demand and supply) driven recessions, we exploit data on credit growth and on the external finance premium (EFP).<sup>5</sup> The underlying assumption is that a negative correlation between the EFP and credit growth during the recession phase signals the prevalence of supplyside constraints to lending, whereby increases in borrowing costs are accompanied by a contraction in credit flows. On the contrary, a positive correlation between the two variables would signal a demand-side constraint, as contractions in the demand for credit would lead to lower borrowing costs (provided that credit supply remains unchanged). If the whole process only takes a short period of time, we will typically observe changes in liquidity but smaller changes in the cost of liquidity provision. That implies a stable EFP and smaller (or close to zero) correlation between liquidity and EFP. Table 2 provides descriptive statistics on the correlation between price and quantities in the real and financial sectors for our sample of countries.

To investigate the channels of the relationship between credit and output during recoveries, we employ a fixed effects Probit model similar to Abiad et al. (2011), where the dependent variable reflects the probability that the recovery will be credit-less. Let a credit-less episode Yi,t in country i = 1, 2, ..., N in time t = 1, 2, ..., T be modeled by: Where Yit<sup>∗indicatestheoccurrenceofacredit-lessrecoveryincountryiintimet,andYitisan</sup>

> 5The external finance premium (EFP) signals the tightness of borrowing constraints and is calculated by taking the difference between the bank lending rate and the risk-free rate at which short-term government bonds are issued. The variable is constructed using data on bank lending and risk-free rates sourced from the International Financial Statistics (IFS/IMF) database.

13
3 followed by presentations of the trade- and welfare-reduction index numbers for all countries studied in the Agricultural Distortions project and for the world as a whole. The methods of analysis developed in this paper are then used to quantify the contributions which the policies of individual countries have made to the reduction in world trade. Some concluding observations and directions for further research are presented in the final section.

# **The recent literature**

There is a growing theoretical literature that identifies ways to measure the welfare- and tradereducing effects of international trade policy in scalar index numbers. This literature serves a key purpose: it overcomes aggregation problems (across different forms of policy and across industries) by using a theoretically sound aggregation procedure that answers precise questions regarding the trade and welfare reductions imposed by each country’s trade policies. The literature has developed considerably over the past two decades, particularly with the theoretical advances by Anderson and Neary (summarized in and extended beyond their 2005 book) and the partial equilibrium simplifications by Feenstra (1995).

Notwithstanding these advances, few series of consistently estimated indexes have yet been estimated across time, and even fewer across countries. A prominent exception is the work of Kee, Nicita and Olarreaga (2008, 2009). Following the approach of Feenstra, they recently estimated a series for developing and developed countries, but they provide estimates only for a snapshot in time (the early 2000s) and based only on import barriers.3 Most other studies have

> 3 Those estimates, which rely mostly on reported tariff rates but include also estimated tariff equivalents of some non-tariff import measures, have been reported in the World Bank’s _Global Monitoring Report_ (e.g., World Bank 2008, pp.121-23). The present estimates, by contrast, rely on domestic to border price comparisons for each product and so directly capture the effects of all border measures as well as domestic prices subsidies or taxes.
apprenticeship positions.<sup>28</sup> A substantial share of these positions were not filled, and the placement ratio is relatively weak: only 1.398/2.51 = 55.5% of positions offered by firms were effectively filled. These patterns are consistent with substantial drop-out among selected youths. This also suggests that firms face challenges in attracting youths, even despite the program subsidy.

Figure 1 provides additional information about the impact of the program on the inflow of apprentices into firms. The figure shows monthly inflows of apprentices in treated and control firms by date (with zero being the randomization date). The figure makes a distinction between inflows of formal apprentices and traditional apprentices in treated firms. The figure clearly shows a spike of entry of formal apprentices in treated firms shortly after randomization. It also shows that flows of traditional apprentices were weakly affected by the program.

Table 2 (intermediate panel) documents the impact of the program on exit of apprentices out of firms. Exits from apprenticeship are substantial. Out of 1.08 apprentices who entered firm due to the intervention, 0.467 apprentices had exited by the time of the follow-up survey. This amounts to a 43.2% drop-out rate. As discussed in the previous section, drop-out is a pervasive aspect of apprenticeship. While it is large, it is not higher in the treatment group than in the control group.<sup>29</sup> Table 2 (lower panel) documents net program impacts on the number of apprentices who entered since randomization and are still in firms at the time of the follow-up survey. Results can simply be deduced as the difference between the top two panels. They show that, 20 months after the launch of the program, there are 0.613 total apprentices per firm that entered since the date of randomization, and 0.787 additional formal apprentices per firm. Substitution effects can also be measured at the time of the follow-up survey. They are again positive and significant, but remain of relatively moderate magnitude, with (0.7870.613)/0.787 = 0.221 traditional apprentice substituted per formal apprentice placed. This estimate is very close to the one obtained looking at inflows between the baseline and followup surveys.<sup>30</sup>

> 28There are 911 youth assigned to 361 firms in the full treated sample (see section 2.3), resulting in an average of 2.52 per firm. In the regression sample, 864 youths were assigned to 334 firms resulting in an average of 2.51 per firm.

> 29If we consider youth who entered firms within 6 months after randomization, we find drop-out rates of 43.3% in the treatment group and 47.1% in the control group.

> 30As can be seen in the top two panels of Table 2, there is also some non-compliance with the experimental

20
22 23. This conclusion rests on the assumption that such terms-of-trade losses did indeed occur. This is now generally accepted within CMEA countries and among the majority of Western observers.

- 24, Official Hungarian estimates suggest a deterioration of the terms of trade with the CMEA of about 30 percent by 1991. The resulting balance of payment losses due to the CMEA transition are estimated to be in the order of $600-700 million for 1991 at pre-Gulf prices, compared to a balance of payments deficit of $150-200 million for 1990. Any increase of the price per barrel of $1 would translate into an additional loss of $80-100 million.

25. Sizable repayments in kind (crude oil) from Iraq to the USSR for past credits, which were in part to be rerouted to other CMEA countries, aggravated the shortfall.

26. Alternative estimates for 1989-90 -- all estimates involve unknown margins of error due to the need to resort to short-cut methods -- arrive at numbers of similar orders of magnitude.

27. The size and pattern of actual adjustment will depend upon the specifics of the “transition® arrangements between each of the Five and the USSR, discussed at the end of this section.

28. The proposed Central European Payment Union, mentioned in the last section, is designed as a multilateral solution to this very problem.

29. On the purely statistical level, there is no consensus whether imports from the CMEA should be included or not. More substantive are the conceptual issues. For instance: reported official reserves may be supplemented by large unused external stand-by commitments and lines of credit and can be backed further by understandings regarding arrears of debt-servicing obligations. These additional resources ultimately determine whether the level of reported official reserves is “sufficient." Furthermore, the reserve cushion necessary for an economy facing unresolved systemic issues seems much larger than for an economy that needs only fine-tuning of existing policies with predictable responses.

30. The most thorough work in this area has probably been carried out by the ECE. In its 1989 report it presents statistical evidence that the East European countries’ exports of manufactures have a high and rising degree of “market similarity” with the “southern tier" countries of the EC as well as a high and rising degree of “product similarity” (the same can probably be presumed for the NICs), and concludes that the static effects of European integration have been negative by 1986, particularly for textiles and machinery, the two most important export categories. See ECE, Economic Survey for Europe in 1988-1989, NY 1990; Chapter 2.5 (pp. 64-86).

31. J. Sach (1991), for instance, considers the emphasis of the unknown time dimension of adjustment of export “a rehash of the discarded structuralist doctrines, once so ruinously applied to Latin America." 32. Initially a separate category of “mutually balanced” trade was considered with both quantities and price set in advance through bilateral negotiations, and with the “indicative list" restricted to deliveries of somewhat lower priority.
Japan's Minamata crisis in the 1960's: Hundreds of people were killed or severely damaged by poisonous levels of industrial mercury in fish. Separate attention to metals is clearly warranted, since the rank correlations of metals intensity with the toxic intensity measures are low (See Table 4.2). Separate IPPS intensities for toxic metal emissions to air, water and land are presented in Table 5.6.

As expected, Non-Ferrous Metals (3720), and Iron and Steel (3710) have very high metals intensities. Other sectors whose toxic intensity is high are also metals-intensive (e.g., Industrial Chemicals Except Fertilizer (3511); Tanneries and Leather Finishing (3231)). In contrast, Fertilizer & Pesticides (3512), Synthetic Resins and Plastics (3513) and Pulp and Paper (3411) are toxic-intensive but not particularly metals-intensive.

# Table 5.6: Toxic Metal Pollution Intensity by Medium (Pounds/1987 USS Million Output Value)

|Pour Digit ISIC Description|ts1c<br>Code|By Land|By Water|
|---|---|---|---|
|O||||
|opoers<br>|o||t<br>|
|FRUITS€vecErasueS||||
|<br>||||
|O|LC|||
|O<br>||||
|OE||||
|U|<br>|||
|O||||
|a<br>|)<br>|cd||

50
Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2010), “Modeling Services Liberalization: The Case of Tanzania,” _Journal of Economic Integration_ , Vol. 25 (4), December, 644-675. http://dx.doi.org/10.11130/jei.2010.25.4.644

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44
starting year is 1984, reflecting the earliest availability of all relevant data. Data on fiscal rules comes from the IMF’s Fiscal Rules dataset (Davoodi et al. 2022). Data on primary balances, as well as other data on macroeconomic variables is sourced from the IMF’s October 2024 World Economic Outlook (WEO) database.

To estimate the response of the primary balance after the adoption of fiscal rules, we employ the local projections approach following Jordà (2005). Local projections are commonly used in the literature to estimate the dynamic effects of macroeconomic shocks and policy reforms to relevant economic variables.<sup>10</sup> We use the following specification: where _∆hfi,t+h ≡ fi,t+h – fi,t-1_ represents the cumulative change in the primary balance (as a % of GDP) from time _t-1_ to _t+h_ . As we track the response of the primary balance for the first ten years after fiscal rule adoption, _H_ is set to 9. We only include countries with at least ten observations per projection horizon _h_ , ensuring a theoretical rolling window of at _i_ and _τt_ least twenty observations of the primary balance.<sup>11</sup> _μ_ control for country- and timefixed effects (for each projection of the primary balance, time-fixed effects are included _h εit+h_ is the error term. with leads equal to ), respectively, and Following Afonso and Jalles (2019), we set our fiscal rules indicator (FRit) equal to one in the year a fiscal rule is introduced and zero otherwise, modeling rule adoption as a 10 Jordà and Taylor (2024) provide a review of the methodology and examples of its use in the literature. In the literature on fiscal rules, local projections have been used to estimate the effect of _fiscal rule adoption_ on sovereign spreads (Afonso and Jalles 2019) and government borrowing in foreign currency (Apeti et al. 2024). Moreover, research has used local projections to estimate the medium-term effects of the _presence of fiscal rules_ on the government budget balance (Chrysanthakopoulos and Tagkalakis 2024), the response of budgets to recessions (Caselli et al 2022), and how fiscal consolidation episodes impact public investment growth in countries with fiscal rules (Ardanaz et al. 2021).

11 The actual window may be smaller in some cases, as we exclude countries that exhibit highly volatile fiscal policy and filter out episodes of primary balance booms and busts. We omit countries with a standard deviation of the primary balance of 10 or higher, resulting in the exclusion of Kuwait and Saudi Arabia from the sample. We also identify years of extreme fluctuations— ‘booms’ and ‘busts’—as those in which the change in the primary balance falls beyond the lower (1<sup>st</sup> ) and upper (99<sup>th</sup> ) tails of the distribution. We remove observations for the three years after if the boom (bust) in the primary balance relative to the year before stays above (below) the outlier threshold. In total, this leads to the omission of 43 observations of the primary balance across 22 countries. Results that include extreme fiscal volatility and episodes of primary balance booms and busts are consistent with the baseline but appear more volatile.

10