rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 2 4 6 8 10 6 7 8 9<br>Log per capita expenditure Log daily calorie intakes<br>Stunting Wasting Stunting Wasting<br>Underweight Underweight<br>0 1 2 3 4 5<br>Log daily protein intakes<br>Stunting Wasting<br>Underweight<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>Malnutrition status .1 Malnutrition status .1<br>.5<br>.4<br>.3<br>.2<br>Malnutrition status .1<br><!-- End of picture text --> Data source: HIES 2009-2010

# **3.2. Regression Results**

Table 2 shows the regression results of stunting, wasting and underweight status for children under age 5. Household wealth was a significant indicator for children’s stunting status. Children from poorer families are significantly more likely to be stunted. Household head education level has a significant effect on children’s stunting status after controlling for household wealth and caloric/protein intake. Urban children are less likely to be stunted, and boys are more likely to be stunted.

Diseases, particularly infectious diseases, are important determinants of stunting (Marie T Ruel, Harold Alderman et al. 2013). Diarrhea is well documented in the literature as a contributor to stunting because of its association with mal-absorption of nutrients (Stuart Gillespie and Lawrence Haddad 2001; FAO, WFP et al. 2012). The literature also suggests malaria is an important determinant of stunting (Rowland, Cole et al. 1977). In PNG, the regression result did not show significant correlation between diarrhea and malnutrition. However, the regression results clearly showed that in PNG, children with malaria were significantly more likely to be underweight and stunted. It is hard to determine the causal effects of malaria and stunting/underweight. Malaria affects children’s ability to absorb nutrition due to the malfunction of red blood cells after the incidence of malaria, which then has a negative effect on children’s height and weight. At the same time, children with malnutrition may be at a higher risk of getting malaria because of their overall living environment and access to mosquito nets.

Protein intake quintiles also had significant prediction power on children’s stunting status. Children from families with lower protein intakes were more likely to be stunted (Table 2). Studies have shown that people living in urban areas rely mostly on rice and wheat as their staple foods, while the majority of rural populations consume traditional root (tuber) crops of which sweet 10
Table A3: Periods of Economic Crisis and Wars (1905-2013)

|**a. Periods of war**||
|---|---|
|**Country**<br>**Years**|**Type of war**|
|1955|Intra-state|
|Argentina<br>1975-1977<br>1982|Intra-state<br>Inter-state|
|Bolivia<br>1932-1935|Inter-state|
|1912-1916|Intra-state|
|Brazil<br>1932|Intra-state|
|1944-1945|Inter-state|
|1948-1958|Intra-state|
|Colombia<br>1951-1953|Inter-state|
|1989-2013|Intra-state|
|1906|Inter-state|
|Hd<br>1907|Inter-state|
|onuras<br>1924|Intra-state|
|1969|Inter-state|
|Panama<br>n.a.|n.a.|
|1911-1912|Intra-state|
|Paraguay<br>1932-1935|Inter-state|
|1947|Intra-state|
|1932|Intra-state|
|Peru<br>1982-1992|Intra-state|
|1995|Inter-state|
|Uruguay<br>n.a.|n.a.|
|**b. Periods of economic instabil**|**ity**|
|**Name of the crisis**|**Years**|
|Economic recession|1928-1939|
|Latin American debt crisis|1982-1992|
|Latin American fnancial crisis|1998-1999|
|Global fnancial crisis|2008-2013|

_Note_ : Intra-state wars encompass wars that predominantly take place within the recognized territory of a state. Inter-state wars encompass wars that take place between or among the recognized states.

Source: Author’s compilations and Correlates of War.

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Table C11: Results Pooled: Earnings and Work - Heterogeneity by University

||(1)<br>Earned Income<br>Last 7 Days|(2)<br>Main Job Earnings<br>Last 7 Days (USD)|(3)<br>Main Job Hours Worked<br>Last 7 days|
|---|---|---|---|
|Offered Training|0.06<sup>_∗∗∗_</sup>|5.62<sup>_∗_</sup>|3.58<sup>_∗∗∗_</sup>|
||(0.02)|(3.11)|(1.23)|
|Offered * University degree|-0.03|1.26|-0.69|
||(0.05)|(7.01)|(2.35)|
|Control Mean: University degree|0.67|33.65|27.10|
|Control Mean: Diploma|0.74|39.00|29.47|
|Observations|2,597|2,597|2,597|
|p-value University|0.39|0.27|0.14|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level. Earnings are top winsorized at the ninety-ninth percentile; we replace earnings with 0 for participants who do not work.

Table C12: Results Pooled: Work Categories - Heterogeneity by University

||(1)<br>Employee|(2)<br>Apprentice/Intern|(3)<br>Employer|(4)<br>Self-Employed|
|---|---|---|---|---|
|Offered Training|0.02|0.01|0.00|0.01|
||(0.03)|(0.02)|(0.01)|(0.02)|
|Offered * University degree|0.06|-0.01|-0.02|-0.04|
||(0.06)|(0.04)|(0.02)|(0.04)|
|Control Mean: University degree|0.36|0.22|0.03|0.17|
|Control Mean: Diploma|0.36|0.21|0.06|0.20|
|Observations|2,597|2,597|2,597|2,597|
|p-value University|0.09|0.97|0.29|0.53|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

48
while for Hungary they cover January 1995 – December 2004. The data sets contain monthly observations for the following variables: _Oi,t_ = the number of individuals flowing from unemployment in district _i_ during period _[t-1,t] Ui,t-1_ = the number of unemployed in district _i_ the end of period _t-1_ (i.e. beginning of period _t_ ); _Si,t_ = number of individuals flowing into unemployment (the newly unemployed) in district _i_ during period _[t-1,t]_ ; _Vi,t-1_ = the number of vacancies in district _i_ at the end of period _t-1 (_ i.e _._ beginning of period _t)_ ; Although outflow to jobs is a theoretically preferred variable to total outflow, the data on outflow to jobs are available only for the Czech Republic, while data on total outflow are available for all the countries in our study. We have first carried out the estimation for the Czech Republic using both measures and found that the estimates based on total outflow and outflow to jobs are similar.<sup>22</sup> As a result, we assume that the lack of data on outflow to jobs in other countries does not have a dramatic impact on our results (see also Petrongolo and Pissarides, 2001, for similar evidence from other countries).

# **5. Econometric Estimates**

# **5.1 Basic Statistics**

The basic statistics are described in Table 1 and Table 2, with the unemployment and flow data of Table 1 having been discussed above. In Table 2 we provide information on the extent to which shocks and restructuring altered in each country the rankings of districts in terms of various labor market indicators over time. In particular, we present the Pearson’s rank correlation coefficients of specific

> 22 Total outflows and outflows to jobs are positively correlated, with the latter representing about 75 percent of the former in the Czech Republic.

− 20 −
19

# **_The reforms from the early 1970s_**

Disenchantment with Australia’s interventionist trade and related economic policies gradually spread in the 1960s, but it was not until the 1970s that tariff reductions began. A 25 percent across-the-board cut in July 1973, proceeded by some minor cuts in 197071, started the tariff reform process. Apart from temporary reversals for cars and textiles, the reform process was accelerated in the 1980s and continued through the 1990s. As a result, over those three decades the average nominal rate of assistance to Australian manufacturing fell from 23 to 3 percent, and the effective rate from 36 to 5 percent. In the 1990s alone, both the mean and the standard deviation of Australia's import tariffs on goods halved. This brought the average tariff for manufactures down to 4.2 percent in 1999.<sup>15</sup> The only manufacturers with significant tariff protection now are motor vehicles and parts, and textiles, clothing and footwear. Excluding them, the average effective rate of assistance to Australian manufacturing is now less than 3 percent (Productivity Commission 2000a).<sup>16</sup> 15 This average is still higher than for other OECD countries though (World Bank 2006); and Australia’s WTO-bound tariffs average more than twice the applied rates. However, Australia uses non-tariff import barriers less frequently than other OECD countries, apart perhaps from anti-dumping duties (Productivity Commission 2000a, 2000b, 2004).

16 Tariffs on motor vehicle imports fell from 40 to 15 percent over the 1990s and were cut again to 10 percent in 2005; for clothing the decline over the 1990s was from 55 to 25 percent, and for footwear from 45 to 15 percent, with cuts to 17.5 and 10 percent in 2005, respectively. Further cuts, to as little as 5 percent, are scheduled for 2010 (Productivity Commission 2000a, Table 4.4).
2 developing countries turned to the allocation of expenditure made on more specific goods to households based on micro data on household utilization of the public services. Examples include Reynolds and Smolensky (1977) for the United States, Meerrnan (1979) for Malaysia, and Selowsky (1979) for Colombia. The latter two studies led the research on benefits in developing countries away from very general allocation systems and toward more specific benefit assignments based on utilization of public services. The disaggregation of general results made the studies of greater value to policy makers allocating resources among social goods.

The second period studies are distinguished from the first period studies in three ways: (i) use of micro data describing households' utilization of public services to allocate the public expenditure on particular social goods among households; (ii) recognition of the irmportance of accurate income measurement for households, and accounting for the size of the household as weil as its total income in categorizing households into income quintiles, and (iii) disaggregation of services within a social function. For example, separate benefit incidence computations were performed for primary, secondary and post secondary educational spending.

We generally agree with the direction the post-1975 literature has taken. By not dwelling on the entire fiscal picture researchers have more rapidly developed the stock of knowledge about specific social expenditures in developing countries. We believe the approach since 1975 has great potential for informing policy choices on shiftng resources within programs to target benefits to the poor more accurately. Nonetheless, researchers studying developing countries may need to pay extra attention to (i) expenditures made through off-budget programs such as public enterprises, (ii) benefit shifting especially for agricultural programs, (iii) differential public service quality, especially between urban and rural areas, (iv) the effects of benefits on inter- and intrafamily transfers, and (v) the effects of benefits on urban-rural migration.

In a recent third wave of benefit incidence, Gertler and Glewwe (1989), Gertler and van der Gaag (1988), Gertler, et al. (1988), and Laraki (1989), estimated demand curves for various social services. Demand curves for particular population subgroups can be used to calculate changes in welfare based measures (or compensating variation) of social services benefits. Studies using welfare-based measures of benefits for a wide range of public functions can yield
37 undernourished population continue to live in South Asia. Finally,<sup>with</sup> growth that has taken in population of the countries grouped<sup>under South</sup> America In the UN report, the number of<sup>undernourished are likely to have</sup> risen in these countries.

Anthropometric data also suggest the regional trends portrayed above. UNICEF assessment of child malnutrition<sup>in 76 countries reveals that during</sup> 1980 to 1984 the prevalence of underweight<sup>children increased in a majority of</sup> the sub-Saharan African countries, remained the<sup>same in Latin America and</sup> decreased in Asia. About 36 percent, or 150<sup>million children under five in</sup> the developing world, excluding China, are underweight;<sup>39 percent or 163</sup> million are stunted; and 8 percent or 35 million<sup>are wasted. By all accounts</sup> about two thirds of the malnourished children<sup>in the world live in Asia.33</sup> The evidence on mlcronutrient deficiencies is mixed.<sup>Assuming that many</sup> poor households are likelv to consume more inferior<sup>food items that contain</sup> less protein but more calories during periods<sup>of falling income, this paper</sup> briefly ) 3oks at the evidence suggested by the<sup>available data in BESD. The</sup> ratio of protein intake as a proportion of caloric<sup>intake during 1980-86 for</sup> all developing countrles remains stable with a very<sup>close relationship between</sup> calorle and protein intakes. This suggests that<sup>the protein consumption</sup> pattern of countrles as a group has not changed<sup>during the eighties. This,</sup> however, has to be weighed against the observation<sup>that: "Protein consumption</sup> "Carlson and Wardlaw (1990).
outcomes (as well as some non-health outcomes) between insured households or individuals and (matching) uninsured households or individuals. They find modest favorable effects on children’s nutritional status, a mild effect on health expenditure and a sizeable effect on nonhealth spending.

A propensity score based approach is not suitable for calculation of a total program effect since the common support requirement in a PSM approach will exclude part of the population in a systematic way. Therefore the Vietnam data are used to estimate the effect of the program using a standard diff-in-diff approach _i.e._ without allowing for heterogeneity (labeled ‘naïve’). The results are compared with an estimate of the TPE. In this case the ‘program’ is a simple intervention.<sup>16</sup> This makes a comparison with a standard approach clearer.

The data are summarized in Table 1. A difficulty is that some of the outcome variables are individual anthropometric measurements while only households can be matched between survey rounds. Therefore the individual measurements have been averaged per household - a crude procedure only suitable for the current purpose of illustrating the TPE. Lacking information on 1992-3 the sampling weights from 1997-8 are used; clustering is also based on the 1997-8 survey round.

The outcome variables considered are changes in arm circumference, height, body weight, health expenditure and total expenditure. The explanatory variables are the other variables shown in Table 1 (insurance status and the controls school attended, currently attending school, gender, age, a farm dummy, household size): and their interaction with the intervention variable are used as explanatory variables.<sup>1</sup> When total expenditure is not a dependent variable it is also used as control variable.

14
Chitwan, Kanha û Phen, Western Ghats: Bandipur - Khudrenukh û Bhadra, Simlipal, Kaziranga – Garampani). At the same time, cost advantages in Vietnam and Lao PDR maintain high rankings for several areas with high priorities in the Species Threat scenario (particularly the Northern and Southern Annamites). The Equal Weights scenario favors many of the same protected areas in Nepal, Vietnam, Lao PDR and India.

The three illustrative cases suggest that the priority rankings of some habitat areas are highly sensitive to variable weighting, while others are not. However, these are only three of many possible scenarios, and the correlations in Table 2 suggest that most habitat areas would change priority ordering substantially for some values of the 8 weighting parameters. We conclude that the principal value of our modeling system is educational rather than prescriptive. Undertaking numerous weighting experiments can provide a useful sense of the relationship between decision-makers’ preferences and habitat assistance priorities. In the same vein, the model can be used to reveal the preferences of decision-makers who have assigned priorities to different habitats in resource allocation.

# **6.  Summary and Conclusions**

In this paper, we have developed and implemented a model that translates detailed information about 74 tiger habitat areas into consistently-derived priority scores and potential project budget shares for those areas. Drawing on the database constructed by Dasgupta, et al. (2012a), the model incorporates information about threats to particular tiger subspecies, the quality of remaining habitat areas, the observed effectiveness of habitat protection by country, and the potential costs of protection projects for different habitats. Implementation of the model moves through two stages. In the first, user-specified weights are employed to combine subindices into composite indices of species threat, habitat quality, cost and protection 12
3 oncentrated on the link between optimal tax tbeory and consumer preferences, no attention bas been paid to the question of the robustness of optimal commodity taxes to the presence of quantity constraint on demand viz. rationing. That is the central motivation of this paper.

Not surprisingly, some of the earliest studies on consumer demand under rationing took place during and soon after the Warl (e.g. Rothbarth (1941), Tobin and Houthakker (1951)]. After a period of relative neglect in the sixties and early seventies, there has recently been renewed - interest in the subject e.g. Pollak (1969], Howard 11977], Weitzaan (19771, Neary and Roberts [1980], Sab 11987]. The methodology of generating rationed demand equations rests on the concept of 'virtual prices' introduced by Rothbarth [1941] and developed recently in the elegant analysis of Neary and Roberts (19801 using duality and expenditure functions. The 'virtual price' of the rationed coumodity is defined as that price which, in conjunction with actual prices of the non-rationed commodities and a 'virtual income' i.e. with the consumer compensated for the price change, will induce him to choose the rationed level as the result of (i.e. as if as) free choice. in their important contribution, Neary and Roberts (19801 bave shown that virtual prices must exist, that the support or virtual prices of unrationed goods coincide with actual prices, and that, via the use of the expenditure function, one can generate a matching system of rationed demand equations from an unrationed demand systeo. It is, however, interesting to note that in most of the recent exercises on rationed demand systems (e.g. Neary and Roberts (19801] Sah [19871), the LES has been chosen as an illustrativ **e** xample. This is of course, not without reason, since LES is one of the few demand systeas that 1See Tobin (1952) for a survey of these early studies.
that are implemented in one visit, and therefore the source of the discrepancies should originate somewhere else.

Thus, we focus next on potential implementation differences between the individual-level recall arm (T3) and the one-line recall arm (T1) that could explain the results. In particular, we look into changes in protocols that were introduced to T3 to improve FAFH collection but that could have unexpectedly impacted the at-home module’s reported consumption. Taking advantage of the paradata that were collected through the implementation of CAPI, we explore two hypotheses: interruptions to the interview and “group interviewing”. Both hypotheses stem from the fact that enumerators were expected to collect data from all adult members in the household when implementing T3 but not when implementing T1.

Interruptions to the interview in general - or the at-home module in particular - may have occurred more often in the individual-level recall arm because enumerators may have tried to get adult members’ information as they were leaving or coming into the house as a way to maximize response rates. This practice, in turn, would have extended the interview time. Results do not show, however, support for this hypothesis. The recorded time between the start of the survey and the start of the AH module, as well as the time it took to complete the AH module, are statistically the same in T1 and T3 interviews.

The hypothesis of “group interviewing” also builds on the idea that interviewers would try to get any adult member as they stop by. In this case, however, instead of interrupting the ongoing interview, the interviewer would ask them to ‘stand by’ for a bit. The potential implication of this practice is that extra people may have participated in answering the AH section. We explore this hypothesis by looking into two indicators. First, we test whether the share of at-home items with positive incidence is higher among T3 households, based on the rationale that the presence of more individuals should have reduced omission. Second, we analyze the correlation between the value of AH consumption and the number of days over which FAFH was collected. The more days FAFH collection is spread over, the less likely group interviewing occurred (and thus the lower the chance that that it resulted in higher AH consumption). Neither of the two patterns were observed in the data.

In this section we explored alternative hypotheses that could explain the higher at-home consumption reported in the individual recall arm. Unfortunately, we fail to find strong evidence to substantiate the proposed explanations, and therefore the reason behind this finding remains an open question. The results do open, however, areas of future research that would further examine unintended effects in survey design. Regarding the implications for the current study, we remain conservative in our conclusions regarding the individual recall arm due to this as yet unexplained effect.

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