rafmacalaba/datause-encoder-data huggingface dataset:
**Cointegrating OLS Regressions**

Having established that the variables are integrated of the same order, the next stage is to test for cointegrating vectors which imply that non-spurious long-run relationships exist and super-consistent (Stock, 1987) OLS results. We begin by considering the key relationship between factor ratios and factor prices, before incorporating the range of other variables.

Figure 1 shows that after 1950 there is a remarkably close relationship between the ratio of machinery to labour and the price of labour relative to the price of machinery. In the first few years after the end of the war, the arable area was being extended, which led to increased use of labour (especially since harvesting was not mechanised) as well as machinery. Also, supplies of agricultural machinery and equipment were limited and animal power was still important. Then, for three decades the price of machinery falls relative to the price of labour and the machinery labour ratio rises, as the induced innovation hypothesis predicts. Indeed regressing the factor ratio on the price ratio from 1954 onwards gives an R<sup>2</sup> of 0.94. Then, after 1981 the favourable credit and tax policies had largely gone, the gold price had plummeted and the rand had been drastically devalued (Thirtle et al 1993). These events had a combined effect of making a domestic input like labour far cheaper relative to capital and led to a dramatic reversal of the historical trend, with labour use increasing as it was substituted for expensive capital<sup>2</sup> .

The induced innovation hypothesis appears to be well supported by these data. The lack of lags between price changes and quantity adjustments is partly caused by the fact that the machinery series is the service flow rather than the capital stock. However, the claim that the process is induced innovation rather than factor substitution must rest on Hayami and Ruttan's (1985) argument that long-run factor substitution is only<sup>made</sup> possible by innovation.

2 The main reasons for using South African data to test the inducement hypothesis is that the quality is as good as any for the western countries and there are price reversals of this magnitude, so that we avoid just fitting trends and can look at turning points.

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<!-- Start of picture text --> Columbia, Urban<br>Figure 2. Water consumption by quintile<br>El Salvador, San<br>M iguel<br>50.0 El Salvador, Santa<br>Ana<br>45.0 El Salvador,<br>40.0 Sonsonate<br>Paraguay, Urban<br>35.0<br>Panama, Panama City<br>30.0 and Colon<br>Colombia, Bogata<br>25.0<br>Uruguay<br>20.0<br>15.0 Venezuala, M erida<br>10.0 Croatia<br>5.0 Cape Verde<br>0.0 Nepal, Kathmandu<br>1 2 3 4 5 India, Bangalore<br>Quintile Sri Lanka<br>Cubic meters per month<br><!-- End of picture text --> An intuitively appealing solution to this problem is to modify the tariff structure in such a way that many non-poor households are transformed from being subsidy recipients into net cross-subsidizers. This could be done by reducing the size of the subsidized first block of an IBT or reducing the subsidy threshold of a VDT, while raising the rate charged in the unsubsidized portions of the tariff to more than average cost. However, the prospects for significantly improving the distribution of benefits from quantity-targeted subsidies are slim, even with these modifications. Revising the block sizes of the tariff structure will only subsidize the poor at a greater rate than the rich if there are significant differences in the consumption patterns of the rich and the poor. We have seen that this is not always the case, especially in the water sector. Moreover, tariff modifications do not change the base coverage conditions that tend to accentuate the regressivity of consumption subsidies; they have no direct effect on access, uptake rates, or metering rates. Households that are ineligible for the subsidies because they do not have metered connections will remain excluded from the subsidy. The scope for improving targeting performance via changes in block size or thresholds is therefore very limited in low-coverage areas in particular and more limited for water than electricity.

Empirical studies that simulate the distributional effect of changes in tariff structures support this conclusion (see Komives et al 2005 for detailed empirical results). In the simulations of “improvements” to quantity-targeted subsidies evaluated for this study, beneficiary incidence remained virtually unchanged after the modifications. Benefit targeting performance improved in most cases but only produced a progressive subsidy distribution in two cases: the move from an IBT to a VDT for electricity 15
**Table A.24:** Effects on Village Head Characteristics

||Parent head|Parent served|Age|Male|Educ|Islam|Bahasa|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|New village head|0.071|0.005|-2.124|-0.028|-0.182|-0.188<sup>_∗_</sup>|0.100|
||(0.096)|(0.113)|(2.969)|(0.059)|(0.582)|(0.135)|(0.117)|
|Observations|443|443|443|443|443|443|443|
|Control mean|0.10|0.24|49.9|0.95|13.1|0.86|0.17|
|Robust p-value|0.371|0.909|0.641|0.526|0.700|0.082|0.308|
|<br>MSE-opt. bandwidth|24.5|28.3|16.0|22.0|28.2|15.9|20.8|
|Effective obs.|241|267|176|220|266|176|214|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). The sample includes all village heads. The dependent variable is: in column 1, a dummy equal to one if the village head’s parent was also village head; in column 2, a dummy equal to one if the village head’s parent served in the village government; in column 3, the age of village heads in years; in column 4, a dummy equal to one if the village head is male; in column 5, years of education; in column 6, a dummy equal to one if the village head’s religion is Islam; in column 7, a dummy equal to one if the village head speaks Bahasa as the primary language.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

**Table A.25:** Turnover and Natural Disasters: Correlations (OLS)

||New Leader|Diff. Vote Share|
|---|---|---|
||(1)|(2)|
|Any disaster|0.141<sup>_∗∗∗_</sup>|0.065<sup>_∗∗_</sup>|
||(0.044)|(0.031)|
|Sample mean|0.47|0.47|
|<br>Observations|512|512|

_Notes_ : This table reports OLS estimates of each outcome on an indicator variable for whether a village experienced a natural disaster (a major landslide, flood event, ocean tide, hurricane, or drought) during an incumbent’s term prior to the election. The dependent variable is: in column 1, an indicator for whether a new leader was elected in the most recent election; and in column 2, the difference between the vote share received by the highest-ranking challenger candidate and the incumbent’s vote share, which is the running variable in equation (1). See Section 6 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

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18

# Table 3. DEMOG-RAPHIC AND HEALTH SERVICE INFRASTRUCTURE FOR TWO ILLUSTRATIVE SAFE MOTHERHOOD PROGRAKS (US$)

Firstland District - Moderate Effort in Poor Rural Area Firstland District - Mountainland & Waterland Moderate Effort in Districts - Hore Limited Poor Rural Area Effort in Poorer Areas Population 200,000 1,000,000 Per capita income $350 $180 Crude birth rate 45/1,000 live births 50/1.000 live births Maternal mortality rate 800/100,000 live births 1000/100,000 live births Infant mortality rate 100/1,000 live births 200/1,000 live births Life expectancy at birth 55 years 50 years Contraceptive prevalence 10% 1-2% rate Hospitals 1 50-bed - Health Centers 3 (1 with 2 beds) 2 (Waterland district) 4 (Mountainland district) Health posts 10 10 (Waterland district) 0 (Mountainland Mobile unit - district) 1 (Waterland district)

# Ministry of Health 10-year targets

|Maternal mortality rate|Reduce by 25% in 5 years<br>and 66% in 10 years|Reduce by 10% in 5 years<br>and 20% in 10 years|
|---|---|---|
|Contraceptive prevalence|Increase from 10% to 35%<br>in 5 years and 40% in 10<br>years|Increase to 10% in 5<br>years and 16% in 10<br>years|
|Institutional<br>deliveries|Increase from 10% to 25%<br>in 5 years and 50% in 10<br>years, giving priority to<br>high risk cases||

Source: Herz and Measham, 1987.
|**Table 13g. Impact of WTO Acce**<br>|**ssion on F**<br>Overall<br>_change from b_|**ederal Okrug**<br>_ase year, %_|**s (calibratio**|**n to househ**|**old factor s**|**hares)**|||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|**Social Welfare Indices (ev/GDP)**|||||||||
|sigma=0|2.3|4.6|4.1|3.1|2.7|1.9|2.5|4.2|
|sigma=1|4.0|5.4|4.5|3.8|3.8|2.6|2.8|4.6|
|sigma=4|4.4|5.9|4.7|4.2|4.4|3.3|3.1|4.9|
|sigma=+inf|4.5|6.0|4.7|4.3|4.6|3.5|3.2|5.1|
|ev/C|8.1|11.3|7.6|7.8|8.7|8.6|5.5|10.0|
|**Aggregate trade**|||||||||
|Regional terms of trade (% change)|3.5|4.7|3.3|2.4|3.3|1.6|1.8|3.6|
|Regional exports (% change)|1.8|2.6|2.5|1.6|1.8|0.7|1.2|2.3|
|Real exchange rate (% change)|2.6|3.3|2.9|2.6|2.6|1.8|1.6|2.8|
|International exports (% change)|8.9|12.0|16.8|10.1|12.1|3.0|9.1|9.3|
|**Return to primary factors**(% change)|||||||||
|Unskilled labor|6.3|8.4|7.4|5.8|5.7|4.8|4.5|7.3|
|Skilled labor|5.4|7.1|5.6|5.2|5.8|4.4|4.1|6.4|
|National capital|2.1|2.8|2.4|2.1|2.1|1.3|1.1|2.3|
|Regional mobile capital|7.3|12.4|10.0|7.6|8.5|4.6|6.7|9.7|
|Energy sector resources|3.0|6.1|16.0|4.8|5.0|1.5|7.8|8.2|
|Specific capital in domestic firms|-25.8|-26.3|-27.9|-27.5|-25.6|-22.8|-22.2|-25.3|
|Specific capital in multinational firms|103.1|67.0|74.3|126.8|147.0|185.2|164.5|121.4|
|**Factor adjustments**|||||||||
|Unskilled labor (% changing sectors)|2.0|2.8|1.6|1.6|2.5|2.0|2.1|1.9|
|Skilled labor(% changingsector)<br>Source: Authors' calculations.|2.7|3.5|2.7|2.0|2.6|2.9|2.5|2.8|

54
**Figure 13: Changing composition of livestock in ECA**

<!-- Start of picture text --> Animals units<br>(thousands)<br>160<br>140<br>120<br>100<br>80<br>60<br>Cattle<br>40<br>20 Chickens<br>Pigs<br>0<br>Sheep<br>1961 1966 1971 1976 1981 1986 1991 1996 2001 2006<br><!-- End of picture text --> Source: FAO (2011) and authors‘ calculations.

67
was estimated to be generated by public sector services, primarily public administration and education (see also Sen, 1996). Other important sectors in terms of employment shares were found to include retail trade, personal services, construction, wood products and furniture, over-land transport, and textiles. While manufacturing activities are often the first that come to mind when discussing the non-farm sector, the study showed that services are easily as important.

A study by Acharya and Mitra (2000) draws on multiple rounds of National Sample Survey data (spanning the period 1984-1997), and also two rounds of the Economic Census (corresponding to 1990 and 1998) to ask whether the positive non-farm employment trends of the 1980s have continued through the 1990s. They find little evidence of further expansion. At the all-rural India level they find that employment in the secondary and tertiary sectors grew from about 22% of the workforce in 1983 to about 25% by 1987-8. They found no evidence of further growth during the 1990s; the last NSS survey they examined (“thin” round for 1997) indicated an employment rate of about 24%. The authors note considerable variation across states in the degree of occupational diversification (with states such as Kerala, Punjab, Haryana, Gujarat and Tamil Nadu clearly more diversified than others), but observe no clear evidence of growth in non-farm employment rates during the 1990s occurring in any state other than Kerala (Acharya and Mitra, 2000).<sup>5</sup> Unni and Raveendran (2007) suggest that non-farm self-employment and non-farm casual wage employment sector grew slightly more rapidly between 1993/4-2004/5 than during the preceding decade. However, they point to evidence of a less rapid growth rate in regular non-farm employment during this period.

An important recent paper by Foster and Rosenzweig (2003) provides a theoretical exposition of how the non-farm economy interacts with the farm economy - building on the great heterogeneity of non-farm activities in rural areas, and highlighting the importance of general-equilibrium relationships. The authors argue that a key distinction has to be made between traded and non-traded goods and services, and they emphasize the significance of wage and salary employment in non-farm activities as opposed to the

> 5 In his exhaustive examination of NSS data between 1977/8 to 1999/0 Vaidyanathan (2001) observes many of the same trends reported here.

8
|R<sup>2</sup>|0.18|0.16|0.16|0.30|0.22|0.24|0.25|0.18|0.18|
|---|---|---|---|---|---|---|---|---|---|
|#obs.|18751|18751|18751|6284|6284|6284|2184|2184|2184|

Significance level, *: p<0.10, **: p<0.05, ***: p<0.01. KCPE = Kenya Certificate of Primary Education.

45
approaches, and we expect that future research will provide greater clarity as to which are the most effective in particular circumstances.

# **_4.3 Simulation of Possible Gains from Improved Trade Policy Transparency_**

Results from our gravity equation suggest that higher levels of trade policy transparency, particularly in relation to importing, are indeed associated with stronger bilateral trade links. From a policy point of view, it is also important to be able to gauge the strength of that effect relative to other policy options. To provide some first indications in this direction, we now use the gravity model results in column 2 of Table 6 to conduct some simple counterfactual simulations (cf. Wilson et al., 2005). For each simulation, we specify the counterfactual in terms of a given exogenous “shock” to a single policy variable. We then map that shock to trade impacts using the elasticities we have estimated. Comparing impacts from one simulation to another gives an idea of the relative trade gains involved.

Concretely, we consider three simulation scenarios, each of which represents an ambitious but, we believe, feasible medium-term objective within APEC:

- Scenario I: Improve importer transparency within the APEC region such that no economy is below the current regional average (0.54).

- Scenario II: Reduce applied tariffs within the APEC region such that no economy applies a higher level of protection than the regional average for each HS Chapter.

- Scenario III: Reduce the ad valorem equivalents of non-tariff barriers within the APEC region such that no economy applies a higher level of protection than the regional average for each HS Chapter.

In line with our estimations, trade impacts for these scenarios refer to intra-APEC trade only, and exclude raw materials (HS Chapters 1-27). Results show that APEC member economies can indeed boost intra-regional trade significantly by cutting tariffs, reforming NTBs, or promoting transparency. Relative to other ready alternatives, policies aimed at increasing trade policy transparency in the APEC region would appear to have the potential for high impact: improving importer transparency to the regional average is 27
# **Figure 1: Percentage of children stunted, by wealth quintile, India, Sri Lanka and poorest countries in Latin America circa 2006**

<!-- Start of picture text --> 75<br>60<br>54<br>49<br>50 48<br>46<br>43<br>41<br>34 34<br>32<br>31<br>28<br>27<br>25 26 25<br>24<br>25 22<br>20 20<br>17<br>16<br>15<br>14<br>13<br>11<br>8<br>7<br>5 5<br>0<br>All Poorest Second Middle Fourth Highest<br>Household Wealth Quintile<br>India 2005-6, GDP $3,213 Haiti 2005-6, GDP$1,562 Bolivia 2008, GDP $4,695<br>Honduras 2005-6 GDP $3,962 Sri Lanka 2006-7, GDP $6,527<br>% Stunted<br><!-- End of picture text -->

# **_Notes:_**

1. The countries selected are from countries with surveys around 2005-06, for easy comparison with the last DHS (NFHS) survey data available for India. These are all low or low-middle income countries, as classified by the World Bank (2005:291).

2. GDP per capita in 2005, PPP (constant 2011 international $). Source: World Bank, World Devt Indicators http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD 3. Child stunting data from the DHS surveys (IIPS 2007, Cayemites et al 2007, Coa et al 2009, DCS 2009, Secretaria de Salud 2006). Stunting is defined as <mark>a height-for-age which is -2 or more Standard Deviations below the 2006 WHO reference population median in their child growth standards.*</mark> 4. <mark>The DHS Household Wealth Quintiles are estimated from a list of household assets.</mark>

- Studies find similar genetic potential for growth in height across populations (Natale and Rajagopalan 2014:6). <mark>C</mark> _<mark>ross-population</mark>_ <mark>differences in height seem related largely to non-genetic, environmental factors, while</mark> genetic factors play more of a role in _intra_ **_-_** _population_ differences (NCD-RisC 2016:1).

15
**Table 5.13: Cross-tabulation of incidence of respiratory illness and access to basic services**

<u>(Proportion of children)</u>

|||Urba<br>|n<br>||Rural<br>||
|---|---|---|---|---|---|---|
||Ser|vice|Pearson<br>|Serv|ice|Pearson<br>|
||Yes|No|Test<sup>2</sup>|Yes|No|Test<sup>2</sup>|
|**Infants: 0-12 months old**|||||||
|Use of fuel wood at home<br>Kitchen has a escape for smoke|.53<br>.22|.37<br>.57|*<br>**|.46<br>.35|.46<br>.48|**|
|**Children: 13-59 months old**|||||||
|Use of fuel wood at home|.41|.47||.53|.49||
|Kitchen has a escape for smoke|.39|.39||.47|.55|**|

_Notes_ : Refers to illness during the month previous to the survey. _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala It is interesting to compare these results with those from a second source of data on this issue: the National Maternal and Infant Health Survey 1998/9. This survey, which falls into the broad category of Demographic and Health Surveys, has a much tighter definition of what constitutes an _acute_ respiratory illness. This is defined as a child manifesting at least two of the following three symptoms simultaneously: coughing, fever and breathing in quick short breaths. Given the more stringent definition, the proportion of children reporting respiratory illness in the DHS (at around 20%) is substantially lower than in the ENCOVI (at around 40%). Moreover, the equivalent cross-tabulation for the DHS data, shows a significant correlation between cooking with fuel wood and incidence of respiratory illness. The correlation is particularly strong in the case of infants, who (due to their lack of mobility) tend to spend more time close to the mother while she is cooking (Table 5.14). Unfortunately, the DHS does not include questions about kitchen ventilation and hence it is not possible to make that comparison with the ENCOVI data.

**Table 5.14: Cross-tabulation of incidence of** **_acute_ respiratory illness and access to modern fuels**

<u>(Proportion of children)</u>

||Serv|ice|Pearson|
|---|---|---|---|
||Yes|No|Test|
|**Infants: 0-15 months old**<br>Use of fuel wood at home|.30|.21|***|
|**Children: >15-60 months old**||||
|Use of fuel wood at home|.22|.19|*|

_Notes_ : Refers to illness during the two weeks previous to the survey. Null hypothesis (homogeneity of users and non-users) is rejected at: *** 99%, * 90%. _Source:_ Torres, (2001) based on Guatemala National Maternal and Infant Health Survey 1998/9.

As well as looking at links between specific types of services and specific types of illnesses, it is interesting to consider the overall impact of modern utilities on the production of health at the household level. In the health literature, stunting (or the ratio of height for age in children) is considered a good stock measure of the accumulated health experiences of the child throughout life. Simple crosstabulations of stunting rate against a range of access variables show that children living in households with modern services are significantly less likely to experience stunting. The differences are up to a factor of two in the case of some services.

47
check for this possibility by controlling for two measures of trade openness which include exports plus imports as a ratio of GDP (trade to GDP ratio, log values) and the overall tariff rate weighted by the volume of bilateral trade for each product ( _Weighted tariff_ ).

We compliment the income measure above with the absolute distance of countries from the equator ( _Latitude_ ). One motivation for controlling for _Latitude_ is same as for GDP per capita since _Latitude_ is a proxy for overall development. Another motivation is that geography could be important for trade and hence the quality of trade facilitation. If _Latitude_ is also correlated with _Population_ , then the omitted variable bias problem is implied.

A natural question to ask here is whether the proposed relationship between _Population_ and _Documents_ is specific to trade facilitation or is it part and parcel of a broader relationship between country size and the overall level of regulation. To this end, we control for a measure of the overall level of regulation measured by the World Bank’s Ease of Doing Business (EODB) index ( _Regulation_ ). As expected, in our sample, countries with high values of _Regulation_ (implying heavier regulation) are also the ones that have significantly higher values of _Documents._ However, our results discussed below show that the relationship between country size and the trade facilitation measure is unique in that it goes well beyond any general relationship between the overall level of regulation and country size.

A number of studies have shown that the legal origin of a country has a significant effect on various aspects of the business climate including the regulation of entry, financial development and the quality of courts (see, for example, La Porta et al., 2008). Hence, one might expect some correlation between legal origin and our trade facilitation measure. Although there is little work on how country size varies with the legal origin of countries, if the two happen to be systematically correlated then the possibility of a spurious correlation with our main results cannot be ruled out. To counter this possibility, we control for the legal origin of countries using dummy variables for the English Common Law and the Socialist Law, with the French Civil Law as the omitted category.

8