rafmacalaba/datause-encoder-data huggingface dataset:
# mortality) may be higher than so far demonstrated.

- O Finally, poor nutrition makes good health unlikely; but poor health also exacerbates malnutrition by reducing nutrient absorption.

It is possible that specific nutrient deficiencies as well as protein-calorie malnutrition have profound effects on health that are not easily addressed through health services.<sup>14</sup> A final two-part conclusion does not come directly from the literature, but can be inferred. It concerns public expenditures on health. First, these expenditures are only one input to improved health; many other factors (education, transportation, food prices and distribution, even food habits) matter and may matter more. Second, the effect of public expenditures on health is highly dependent on the allocation of those expenditures between cost-effective activities (basic health services, endemic disease control -- largely but not exclusively preventive) and high-cost hospital services.

# Potential for Breaking New Ground

Many questions critical for improving health policy and thus health in developing countries remain unanswered. Four potentially fruitful areas of new research are:

- Additional studies in more settings of several critical aspects of household behavior, e.g. by what mechanisms mother's education makes a difference to child health (and whether there are quick substitutes for mother's education); and whether and how much the price of health care (and of private insurance) makes a difference to use of services of various kinds;

14 For example, there is growing evidence that vitamin A deficiency increases susceptibility to mortality as a result of measles and that measles precipitates blindness due to vitamin A deficiency. See World Health Organization, 1988.
(ii) ISPA (Instrument for Structural Policies for Pre-accession) finances infrastructure development in the fields of the environment and transport It is the equivalent of the Cohesion Fund for the Member states. (iii) SAPARD (Special Accession Program for Agriculture and Rural Development) aims at the modernization of agricultural and rural development.

_Sources:_ For more on Pre-accession funds see Council Regulation (EC) No 1266/1999 of 21 June 1999 on coordinating aid to the applicant countries in the framework of the pre-accession strategy and amending Regulation (EEC) No 3906/89 at http://europa.eu.int/comm/employment_social/esf2000/regulations/pre-accession_aid/en.pdf Disbursements of payments from the EU budget to Bulgaria are plotted on Figure 1, based on estimates done by experts from the Ministry of Finance. The size of pre-accession aid for the period 1999-2011 is estimated at €2.56 bn current prices, with over a half of these resources going through the Phare program. Pre-accession aid commitments will be terminated for Bulgaria after its suggested EU accession on January 2007, although disbursements under the preaccession instruments submitted before 2007 will continue even after accession. Thus, there are financial implications of these funds in the immediate period after accession which need to be accounted for when estimating the fiscal effect of accession. Based on the experts’ estimates from the Bulgarian Ministry of Finance, pre-accession aid payments are €1.04 bn in the first three years of accession. Figure 1 below indicates that pre-accession disbursements will be actually made to a great extent after accession (although commitments are made only prior to accession). This is due to the gradual build-up of capacity to absorb EU funds and the longer duration of some of the infrastructure projects.<sup>10</sup>

**Figure 1: Total Pre-Accession Aid, 1999-2011, € mn**

<!-- Start of picture text --> 400<br>300<br>200<br>100<br>0<br>Phare ISPA SAPARD<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>EUR mn, current prices<br><!-- End of picture text --> _Source:_ MoF estimates.

> 10 In the Middle-Term Fiscal Framework for 2006-2008 the Bulgarian Ministry of Finance estimates that the completion of the pre-accession projects would roughly coincide with the increase in EU payment appropriations, which would contribute for the smooth transition from financing under the pre-accession funds to the use of resources from the EU post-accession funds.

12
- (iv) Reduce the visa processing time;

- (v) Increase the duration of stay for service suppliers across sectors;

- (vi) Move towards multiple-entry visas for service providers in all sectors;

- (vii) Provide opportunities for e-visas/visas on arrival for service providers in all sectors; (viii) Exempt visas for temporary entry/transit of service providers.

This said, it must be recognized that discussing a TFiS agenda on Mode 4 trade is becoming increasingly challenging given the refugee crisis in the West. While this highlights the need to separate the issue of ‘temporary entry for supply of services’- which is the essence of Mode 4, from the overall migration debate, pragmatic solutions to address sensitive issues, including through specific visas for service suppliers in committed sectors, and implementing the concept of ‘authorized economic operator<sup>7</sup> ’ also need to be discussed. Moreover, innovative ways must also be deployed to address the inherent bias that continues to prevail in any discussion on Mode 4 against unskilled and semi-skilled service providers.

At the same time, providing empirical estimates of the costs imposed by specific regulatory and administrative barriers on Mode 4 trade would also inform the discussion on an MNP TFiS agenda and enable a prioritization of TFiS measures for policy makers. The WTO Secretariat has recently released services trade data by modes of delivery, which could be deployed in regression analysis to estimate the regulatory costs on Mode 4 trade, using the quantitative measures of MNP restrictiveness constructed in this paper, at least for the country sample in the OECD STRI database. This could be an agenda for future research on this subject.

Finally, one additional input into this analysis, which would substantively inform a TFiS agenda on MNP, would be constructive feedback from the actual stakeholders - i.e. sector organizations or representatives and services professionals that cross borders frequently to deliver services – on the actual regulatory, compliance and even investment climate issues that impose unnecessary costs on them. Conducting country- and sector-specific Mode 4 surveys<sup>8</sup> to elicit such feedback would be another agenda for further work on this subject.

> 7 Authorized economic operators (AEOs) are pre-recognized entities whose compliance with an importing country’s regulations could be certified by the exporting country to facilitate market access.

> 8 One example of this is the surveys undertaken of IT sector exporting firms in India by the country’s Central Bank, which inter alia, enables it to provide estimates of IT services exports by modes of delivery.

15
Indeed, inspection of Haque and Montiel's 15 country regressions reveal that only three of the countries have estirmated money demand functions with a significant interest rate variable. The estimated equations are consistent with what one would expect if _kP,_ i* and y were just uncorrelated noise, and the postulated monetary equilibrium and UIP were not true. In effect, what is being estimated is a noisy identity. In particular note that for India, where _kP_ has low variance, the estimated coefficient 7r4 is insignificantly different from unity, and all the other variables insignificantly different from zero.

# (iii) What does the variable m' measure?

One may also question the theoretical basis for constructing the weighted average interest rate using (2'), based oIn money market equilibrium computed at hypothetical money stlock _m'_ instead of (2), based on the actual money stock mt. For suppose that 0 = 0 is true. Then the HM model asserts that (2') prevails. But the actual money stock is m, not _n,'._ So the postulated relationship (2') actually implies monetary disequilibrium (since in general _m,"_ **_mi,)._** As such, the KM model is not consistent with domestic moneymarket equilibrium.

Nlote that, if we try to modify HM's approach by simply substituting (2) for (2') we cannot proceed to recover the parameter q , which is no longer identified in the new estimating e,quation (5'): _mt - p, =ao0 + aii +a2 y, +±a3_<sup>_(m,l - pI)+w,_</sup> The only leverage one has here is that (5') is derived under the condition ( # _0._ A finding that the coefficient of _i*_ in (5') was non-zero would thus be a test against complete closure of the capital market.

_(5')_

# (iv) UIP doesn't fit well for most countries

Finally there is a broacder problem with both of these approaches, and that is that, although well-founded theoretically, UIP and a stable money demand function are both conditions for which there is comparatively little empirical support even for industrial countries with open cariital accounts. Indeed, UIP is generally rejected in empirical studies.<sup>3 8</sup> Demand for money functions often shift, and need to be quite complex to have a chance of remaining stable over lengthy periods. Therefore, however solid the theoretical basis of these tests for capital integration, they rest on rather flimsy empirical underpinnings.

There are possible explanations which allow us to believe in UIP even though it fails most stringent tests. McCallum (1994) proposes that if UIP is augmented with a disturbance termn, and if that error should be correlated with the interest differential (as it would if policymakers responded to the disturbance by liquidity policy) then one would observe failure of the usual UIP tests. Kaminsky (1993) suggests that UIP fails in small

> " Though it may pass weak tests, such as stationarity and non-zero mean of deviations from UIP (Tanner, 1998).

45
# **3.4 Farm structure**

Larger farms, especially if they have foreign connections, may be able to overcome war-related challenges in terms of accessing inputs and liquidity more easily than smaller ones. To test if this is the case with our data, we use 2020 data from by the Ukraine State Statistics Service’ reporting ‘Form 29’ to compute, for every rayon, the share of crop area by farm enterprises that cultivate between 500-2,500 ha or more than 2,500 ha nationally, i.e., either in the rayon or in other parts of the country. Results in table 4 illustrate that 78.5% of area in the average rayon is cultivated by farms operating more than 500 ha and 45.5% by ones operating more than 2,500 ha, a share that is slightly higher in rayons that experienced direct crop damage.

# **4. Results from econometric analysis**

# **4.1 Results and damage estimates from crop cover regression**

Table 5 summarizes results from estimating equation (1) with detailed results in appendix table 1. While area of damaged crops based on imagery is included throughout, other indicators of conflict and an index for ‘any’ exposure are introduced one at a time in columns 2-5 given their collinearity. An index of conflict intensity that accounts for duration is included in column 6, our preferred specification.<sup>19</sup> Three insights are noteworthy: First, time invariant and time-varying variables are all highly significant; the null hypothesis of all village fixed effects equaling zero is decisively rejected. Direct conflict impacts are highly significant: a VC having been exposed to any type of conflict in all 4 periods is estimated to reduce cultivated area by some 95 ha, in addition to a highly significant point estimate (of -0.10, see col. 6) for crop area directly damage. An effect below a 1:1 reduction is plausible result as only a fraction of a VC’s area will be planted to winter crops and some types of damage (such as movement of heavy vehicles or localized explosions) is unlikely to result in total crop loss even if it reduces yield.

Second, the negative and highly significant time dummy for 2022 points towards an independent economywide effect of the war that may be due to manpower shortages, the difficulty of accessing purchased inputs, especially fuel, or an expectation of not being able to go to market when the crop will be harvested. The estimated area reduction of 82.1 ha per VC (col. 6) amounts to about 8.8% of winter crop area, a large effect given that planting decisions for winter crops had been made before the war so that conflict-related effects would essentially involve abandonment of already planted areas. Comparing estimated coefficients across years illustrates that, after controlling for village fixed effects and weather-related factors, winter crop area in 2021 and 2020 exceeded the 2019 level by 42.89 and 56.51 ha, respectively, implying that a simple comparison of 2022 to earlier years would overstate the impact of conflict by 50% or more.

19 This index takes values of 0, 0.25, 0.5, 0.75, or 1, depending on the number of periods when ‘any’ conflict indicator was . 0. 11
52 sub-Saharan Africa, and how will the relative importance of NTBs on different types of export products change. **Using published details on** the agreement (GATT 1994), Table 11 provides an indication by showing the preUruguay Round NTB coverage ratios for individual African countries along with an estimate of what the ratio will be after the agreement is implemented. These estimates were derived by tabulating the value of pre-Uruguay Round **trade that** will **still be subject** to (post-Uruguay Round) NTBs, and then expressing this value as a share of total (pre- **Uruguay Round)** exports.<sup>2</sup> ' In order to more clearly show the impact of the Round, Table 11 classifies countries **into three groups** (highly NTB affected, moderately affected, and largely unaffected) based on their pre-Uruguay Round coverage ratios.

For several countries the estimated change in the nontariff barrier coverage ratios are dramatic. Prior to the Uruguay Round, 83 percent of Reunion's exports (which consist largely of sugar) faced OECD restrictions while this ratio should fall to zero as a result of the agreement. The coverage ratio for Mauritius should decline by almost **60** percentage points (to just over 2 percent) after textile and clothing restrictions are lifted from this countries' exports, while the tariffication of agricultural NTBs causes Cape Verde's ratio to decline from about 40 percent to zero. **Overall,** the share of Africa's non-oil exports that face nontariff barriers should decline from approximately **11 to about 3 percent.** Figure 4 presents another prospective on the impact of the Round by showing how it altered the overall profile of nontariff protection facing Africa. The top most line Nb ranks African countries in terms of increasing pre-Uruguay Round NTB coverage ratios (the ratios increase as one moves rightward along the horizontal scale), **while the** lower line (N,) indicates what the estimated coverage ratio for each country will be after the Round. **Actual values** of NTB coverage ratios are recorded on the vertical axis.

> **21The results** -- the post Uruguay Round coverage ratio (Cpr) -- was derived from,

# (1) C,,, = VI, **Vtb**

**where V. is the value** of pre-Uruguay exports that will still be subject to post-Uruguay Round NTBs and V, is the **total value of pre-Uruguay Round** exports. This measure probably overstates the importance of remaining nontariff **barriers since the removal** of the OECD nontariff barriers should produce a trade response. That is, African exports **should increase as a result of** the liberalization of OECD NTBs which would make the denominator in the above **expression larger, and coverage** ratio smaller. Whether African trade in textiles and clothing expands, however, **largely depends on these** countries adoption of necessary measures to achieve cost competitiveness.
**Table 3. Average Incidence of Circumstances in the Population**

|Circumstances for children under 16<br>years|1985|Survey<br>1998|years<br>2002|2008|
|---|---|---|---|---|
|Urban location (%)|40|42|39|35|
|Gender of the child (male) (%)|51|51|51|52|
|Gender of the head of household<br>(male) (%)|94|89|86|84|
|Age of the head of household<br>(years)|47.68|45.35|46.19|45.08|
|Education of the head of household<br>(schoolyears completed)|2.84|3.59|4.21|3.83|
|Number of children|5.97|5.13|4.70|4.27|
|Mean per capita household income<br>(francs)|73,942.22|272,832.25|287,390.70|413,257.06|
|Region (number of regions<br>represented in the survey)|10|10|10|10|

_Source_ : Authors’ compilation.

# **4. Opportunities for Children: Results from HOI Analysis**

This section presents the results of the HOI for the infrastructure and education opportunities, including the extent to which HOI differs from coverage rates (that is, the extent to which access rates are unequally distributed); how the index changes over time; and what factors contribute to these changes (the decomposition of changes in the HOI).

The vertical bars in figures 2 and 3 represent the HOI for each habitation/infrastructure and education opportunity, respectively; the dots represent the coverage (or average access) rate; and the small vertical line at the top of each bar represents the confidence interval of HOI. The graphs clearly show how the HOI differs from the coverage rate, and whether that difference is statistically significant (whether the coverage rate is within the confidence interval of the HOI).

14
<!-- Start of picture text --> 2000-2007 2008-2015<br>© I<br>if<br>ill 1<br>| i,<br>> | 1 |<br>fa}re®o | I | Nl ]<br>N | 1 [| | !<br>i H '<br>il Pv ML<br>ass<br>-2 0 2 4 -2 0 2 4<br>[|] TFPR (HK) [-___] TFPR (BKR)<br><!-- End of picture text -->

<!-- Start of picture text --> 2000-2007 2008-2015<br>oO<br>5] callil "[lin __allj elit.<br>a a , es es<br>-1 0 1 2 3 -1 0 1 2 3<br>[| TFPR (HK) [-____] TFPR (BKR)<br><!-- End of picture text -->
Table 2 OLS and FGLS Estimates of the Determinants of Dispersion in Firm Sizes <u>(pooled</u> 1982, 1987, and 1992 data)

|Equation (15):|C4i�|<sup>4</sup><br>Ni<br>=b0|(1�RE|Ni)<sup>b1</sup>(1�|DEPi)<sup>b2</sup>|RES<sup>b3</sup><br>i <sup>eui</sup>|
|---|---|---|---|---|---|---|
|Parameter Esti|mates|b0|b1|b2|b3||
|OLS||20.09<sup>���</sup>|2.57<sup>���</sup>|1.99<sup>���</sup>|-.23<sup>���</sup>|R<sup>2</sup>=:20|
|||(.54)|(.41)|(.45)|(.02)|�R<sup>2</sup>=:20|
|FGLS||25.53<sup>���</sup>|1.63<sup>���</sup>|.88<sup>���</sup>|-.10<sup>���</sup>||
|||(.39)|(.31)|(.29)|(.02)||
|Equation (18):|Vi(s)|=c0(1�|RENi)<sup>c</sup>|<sup>1</sup>(1�DE|Pi)<sup>c2</sup>RE|S<sup>c3</sup><br>i <sup>e�i</sup>|
|Parameter Esti|mates|c0|c1|c2|c3||
|OLS||1.77<sup>���</sup>|5.86<sup>���</sup>|4.21<sup>���</sup>|-.55<sup>���</sup>|R<sup>2</sup>=:19|
|||(.56)|(.96)|(1.05)|(.05)|�R<sup>2</sup>=:19|
|FGLS||3.19<sup>���</sup>|3.72<sup>���</sup>|1.67<sup>��</sup>|-.23<sup>���</sup>||
|||(.35)|(.73)|(.68)|(.04)||

Number of Observations = 828 Standard Errors in Parentheses;<sup>���</sup> p < :01 ;<sup>��</sup> p < :05;<sup>�</sup> p < :1 In assessing the impact of intangible sunk costs on ...rm size dispersion, we limit our attention to advertising. The potential role of R&D could not be evaluated due to insu¢ cient data. Table 3 presents the nonlinear least squares estimates of the parameters in equations (20) and (21). We ...nd that in all temporal cross-sections, a1 and d1 are signi...cantly positive and a2 and d2 are signi...cantly negative. These parameter estimates once again con...rm the role of contestability, as signi...ed by low tangible sunk costs, in tightening the distribution of ...rms. We also ...nd that a3 and d3 are signi...cantly positive in all temporal cross-sections, with the exception of d3 in 1992. Thus, higher levels of advertising intensity are associated with increased ...rm size dispersion.

These ...ndings could be interpreted as implying that advertising gives rise to a sunk cost barrier, and thus it augments the role of tangible sunk costs in reducing market contestability and in allowing ine¢ cient industry con...gurations characterized by a large dispersion of ...rm sizes around the optimum to persist. However, our ...nding that a3 and d3 are signi...cantly positive is also consistent with the view that advertising may allow ...rms to operate in di¤erentiated product niches and away from the minimum point of the average cost curve without being vulnerable to displacement by potential entrants.

16
# Appendix I.A.25 Historical Demographic Data, Spain 1877-1981

|Year|1877|1900|1910|1920|1930|1940|1970|1981|
|---|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|15.17|11.50|10.92|10.93|10.22|9.72|4.95|5.58|
|Population aged 20 to 59/<br>population over 60 years old|6.34|5.42|5.11|4.86|5.15|4.93|2.95|3.21|
|Pecentage of population<br>over 60 years old|7.97|9.05|9.40|9.92|9.52|10.15|16.53|_15.59_|
|Percentage of population<br>under 15 years old|32.34|33.46|33.63|32.18|32.17|29.94|27.05|25.64|
|Women over 60 years old/<br>Menover60yearsold|_1.05_|1.08|1.14|1.06|1.21|1.30|1.82|1.37|

Ratios calculated using data found in Mitchell, B. R. 1990 "International Historical Statistics: Europe 1750-1988".

# Appendix I.A.26 Historical Demographic Data, Sweden 18S0-1970

|Yewr|1850|1870|1890|1910|1930|1950|1970|
|---|---|---|---|---|---|---|---|
|Population aged IS to 64/<br>population over 65 years old|12.92|11.16|7.71|7.09|7.16|6.42|4.77|
|Population aged 20 to 59/<br>population over60 yeas old|6.30|5.93|4.04|3.93|4.15|3.71|2.68|
|Percentage of population<br>over60yearsold|7.85|8.11|11.44|11.96|12.83|14.99|19.67|
|Percentage of population<br>under 15 years old|32.88|34.84|33.33|31.72|24.82|23.44|20.82|
|Wornen over 60 years old/||||||||
|Menover60yearsold|1.45|1.36|1.24|1.24|1.21|1.13|1.18|

Ratios calculated using data found in Mitchell, B. R 1990 "Intenational Historical Statistics: Europe 1750-1988".
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> <sup>12</sup>

ethnic party because, for these groups, ethnicity is simply less salient than other issues (contrary to the presumption of most observers of African politics). However, it should still be the case that, after controlling for respondent characteristics that make them susceptible to the appeals of non-ethnic parties, ethnic groups targeted with credible ethnic appeals should exhibit higher rates of partisan preference.

In sum, if ethnic groups are the target of appeals by credible ethnic parties, members of the ethnic groups must disproportionately vote for those parties (exhibit ethnic clustering). If they do not cluster, either parties cannot mobilize the support of the ethnic group (e.g., with credible appeals, by mobilizing ethnically homogeneous clientelist networks, or with fear) or ethnicity is simply not salient. The foregoing arguments then make two predictions. First, those who cluster should be substantially more likely to express a partisan preference than members of other ethnic groups; and, second, alternative party strategies for mobilizing support should have a weaker impact on expressions of partisan interest than membership in a clustered ethnic group.

# **Specification and data**

Equation (1) is estimated to assess the degree to which members of clustered ethnic groups members are more likely to express a partisan preference than members of other ethnic groups. It assumes a logistic distribution and relies on survey data on individuals _i_ from 16 countries _j_ .

(1) _Partisan Preference ij (0,1)_   0   1 _Ethnic Clustering ij_   _ij_  2   _ij_   _j_ The ethnic clustering dummy variable is equal to one for ethnic groups who disproportionately support particular parties; every respondent from an ethnic group shares the same clustering value, regardless of whether the respondent supports the cluster party. If