rafmacalaba/datause-encoder-data huggingface dataset:
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Table 8 Impact of ICPGDP, LJS and RTB **oi** Environmental Indicators

|Dependent<br>Variable|Intercept|ln ICPGDP<br>_|PCi|Adjusted R<sup>2</sup>|
|---|---|---|---|---|
|ln Air<br>____________|1.60<br>(2.91)|0.38<br>(6.02)||0.76|
|In Air<br>l___________|3.35<br>(8.81)|0.18<br>(4.07)|0.26<br>(6.18)|0.95|
|In Water<br>l___________|2.59<br>(5.57)|0.29<br>(5.35)||0.72|
|ln Water|4.13<br>(16.68)|0.11<br>(3.73)|0.23<br>(8.37)|0.96|
|ln Land<br>____________|2.79<br>(6.19)|0.27<br>(5.16)||0.70|
|ln Land<br>____________|4.20<br>(13.15)|0.10<br>(2.78)|0.21<br>(5.96)|0.93|
|in Living|2.79<br>('.19)|0.27<br>(5.16)||0.70|
|ln Living<br>l___________|4.05<br>(9.12)|0.11<br>(2.15)|0.24<br>(4.91)|0.90<br>_|
|ln Env|3.77<br>(7.79)|0.31<br>(5.48)||0.73|
|ln Env<br>_L.<br>_<br>____(18.08|5.35<br>)|0.12<br>(3.58)|0.23<br>(7.15)|0.95|

The level of explanation in all regressions improves significantly with the addition of the Business International effectiveness indices for legal/judicial and administrative systems and the Gastil measure of property rights protection. Similar BERI measures are not significant, however. We also obtain insignificant or perverse results for all Gastil measures of degree of popular representation and freedom of information.

20
definition of the project objectives and scope and leave the detailed specification of the various components to later stages. Apart from the need to work out projects in considerable detail in advance, pr-oject development can also be delayed due to sudden policy and leadership changes, complicated inter-donor coordination needs, and a lack of professional capabilities within the host country. Unlike other donors, especially the USAID, the Bank generally provides technical assistance through missions and relies heavily on country counterparts to formulate the project. This is not found to be adequate, especially in many African countries. The project preparation facility hlas helped to provide much needed technical assistance. Other mechanisms used in a few countries to hasten the project development process have been: (1) employment of long-term consultants, (2) project launching workshops to train local counterparts, and (3) appointment of a resident population representative for the first few years.

# 3. Lending Coefficients

PHN project development has become more efficient over time, as shown by decreasing lending coefficients from 1981 to 1985 (Table V-1). Although as compared to other sectors, PHN has historically high lending coefficients, the magnitude of the differences has been generally decreasing. This may be a function of the overall increase in Bank lending reported in Part III, use of the project preparation facility and other such mechanisms, and the increased representation in PHND of senior staff with extensive Bank experience. Despite an improvement in the efficiency of the project development process, it is still perceived by many program managers as too long and complicated.

Table V-1 Lending Coefficients (staff weeks) for Agriculture. Education, PHN and Urban Projects. FY1981-87

|Sector|FY1981|FY1982|FY1983|FYi284|FY1985|FY1986|FY1987|
|---|---|---|---|---|---|---|---|
|Agriculture|<br>147|176|133|138|150|128|136|
|Education|138|124|117|117|122|108|117|
|PHN|294|272|198|190|130|147|138|
|Urban|207|154|156|131|139|112|125|

Sources: A.R. Measham. Review of PHN Sector Work and Lending in Health, 198085, pg. 32; and letter from Lauren Chester.

56
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# **Table 7:  Productivity Effects of Services Liberalization. Instrumental variables approach using Ackerberg et al. TFP**

|**Second stage regressions**||||||||
|---|---|---|---|---|---|---|---|
|Services Index(t-1)|1.277***<br>(0.260)|||||||
|Banking Index(t-1)||1.061***<br>(0.247)|||||0.864***<br>(0.280)|
|Banking Index Rajan-Zingales weights(t-1)|||0.224***<br>(0.056)|||||
|Telecom Index(t-1)||||5.459***<br>(1.469)|||4.199***<br>(1.507)|
||||||2527**||2646*|
|Insurance Index(t-1)|||||.<br>(1.139)||.<br>(1.500)|
|Transport Index(t-1)||||||6.891<br>(4.206)|10.174**<br>(4.288)|
|Tiff|0.0009|0.0004|0.0030|0.0004|0.0002|0.0002|0.0019|
|ars(t-1)|(0.0018)|(0.0017)|(0.0019)|(0.0018)|(0.0018)|(0.0016)|(0.0018)|
|I Tiff|-0.0031|-0.0035|-0.0042|-0.0001|-0.0030|-0.0094|-0.0079|
|nput ars(t-1)|(0.0090)|(0.0093)|(0.0089)|(0.0090)|(0.0093)|(0.0067)|(0.0059)|
|Fi|0.027|0.029**|0.030*|0.032*|0.034**|0.045***|0.038**|
|oregn|(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|(0.016)|(0.0160)|
|Observations|22,558|22,558|22,558|22,558|22,558|22,558|22,558|
|R-squared|0.032|0.030|0.035|0.030|0.028|0.028|0.029|
|Number of firms|3771|3771|3771|3771|3771|3771|3771|
|**First stage regressions**||||||||
|WTO commitments – China|2.970***|3.746***|<br>17.665***|1.471***|2.645***|0.675***||
||(0.229)|(0.288)|(2.282)|(0.199)|(0.598)|(0.196)||
|WTO commitments – Indonesia|0.564***|<br>0.210**|1.675|2.117***|<br>0.398**|4.972***||
||(0.141)|(0.120)|(1.665)|(0.146)|(0.198)|(0.941)||
|Tariffs(t-1)|0.0003|-0.0001|-0.0122***|<br>-0.0000|-0.0000|0.0001||
||(0.0003)|(0.0001)|(0.0018)|(0.0000)|(0.0000)|(0.0001)||
|Input Tariffs(t-1)|0.0006|0.0001|0.0044|-0.0000|-0.0000|0.0004||
||(0.0005)|(0.0001)|(0.0074)|(0.0001)|(0.0001)|(0.0003)||
|Foreign|0.003***|<br>0.001**|-0.001|0.000|0.000*|-0.001||
||(0.001)|(0.000)|(0.009)|(0.000)|(0.000)|(0.000)||
|**Test statistics**||||||||
|F-stat|129.470|151.650|34.440|291.620|16.590|50.410|20.690|
|p-value|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Sargan test|0.068|0.216|0.322|0.763|1.561|6.040|5.345|
|p-value|0.795|0.642|0.570|0.382|0.212|0.014|0.254|

Notes: The dependent variable is the log TFP estimated using the Ackerberg et al. method for each of the 11 industries listed in Table 2. All specifications include firm fixed effects. Robust standard errors, clustered at the industry-year level, are reported in parentheses. *** denotes significant at the 1 percent level, ** at the 5 percent level, * at the 10 percent level.

42
|Upper-secondary<br>school degree|18.03|44.39|37.58|100|27.37|53.71|18.92|100|
|---|---|---|---|---|---|---|---|---|
|Post secondary<br>degree|33.71|43.56|22.73|100|18.37|38.19|43.44|100|
|_Consumption_<br>_quintiles_|||||||||
|Poorest|4.87|26.51|68.62|100|4.11|34.12|61.77|100|
|Near poorest|8.26|37.48|54.27|100|6.81|40.01|53.17|100|
|Middle|13.2|39.79|47.01|100|9.19|32.97|57.84|100|
|Near richest|18.32|43.51|38.17|100|12.13|50.35|37.52|100|
|Richest|32.85|41.88|25.27|100|30.96|45.95|23.09|100|
|Number of<br>observations|34|141|205|380|924|2,471|3,650|7,045|

_Source: Estimation from the 2006 VHLSS_

# **4. Impacts of parental disability on child education**

## **4.1. Estimation method**

To measure the impact of disability of parents on child education, we assume the follow function: where _Y_ is an indicator of educational performance of children, _D_ is the dummy indicating the disability of parents of the children, and _X_ is a vector of control variables including individual and household characteristics which can affect educational performance. In this paper we use two education indicators. The first is school enrollment, which is equal 1 for children attending school, and 0 otherwise. The second is their academic record, which is equal to 1, 2 and 3 for „excellent‟ record, „good‟ record and „normal and bad‟ record, respectively.<sup>2</sup> When the dependent variable is school enrollment, we will use a probit model to estimate equation (1). A problem in non-linear functions is that the meaning of the coefficients is not clear. Thus to estimate the impact of parental disability on school

> 2 As mentioned, we combine pupils with the „bad‟ record and pupils with the „normal‟ record, since there are only around 1 percent of pupils having the „bad‟ record.

9
In terms of our prior classification of countries by income groups, both transitional (BA) economies, Equatorial Guinea and Sudan, meet our four criteria. The remaining countries are drawn equally from the growing economies of both the AA and BB classifications (table 4). Angola, Botswana and Cape Verde are growing AA economies; Chad, Mozambique and Tanzania are growing BB economies. Angola, Chad, Botswana, Equatorial Guinea and Sudan are resource rich. Cape Verde, Mozambique and Tanzania are more diversified, coastal economies. Botswana is the only land locked country.

**Table 7: Growth rate during acceleration, by country**

||||||||**Deviation f**|**rom growth**|
|---|---|---|---|---|---|---|---|---|
|**Country**|**Gr**|**owth rate (**|**%)**|**Growth rat**|**e during acce**|**leration (%)**|**acceleratio**|**n trend (%)**|
||**Average**|**1975-1994 **|**1995-2005**|**1975-2005**|**1975-1994**|**1995-2005**|**1975-94**|**1995-05**|
|Angola|0.70|-3.34|5.84|3.93|1.55|6.30|-60.49|60.49|
|Benin|0.60|0.07|1.50|1.60|0.22|2.07|-86.43|28.81|
|Botswana|6.24|6.50|5.78|6.87|7.41|6.24|7.85|-9.16|
|Burkina Faso|1.21|0.96|1.66|1.39|-0.06|1.66|-104.39|18.98|
|Burundi|-0.46|0.58|-2.26|1.48|1.48|na|na|na|
|Cameroon|0.81|0.14|1.97|2.21|na|2.21|na|0.00|
|Cape Verde|3.26|3.14|3.40|3.57|3.19|3.73|-10.52|4.51|
|Central African Republic|-1.27|-1.81|-0.34|0.89|na|0.89|na|0.00|
|<br>Chad|1.34|-0.30|4.18|7.66|na|7.66|na|0.00|
|Comoros|-0.14|-0.36|0.13|0.11|na|0.11|na|0.00|
|Congo, Dem. Rep.|-3.95|-4.85|-2.38|na|na|na|na|na|
|Congo, Rep.|0.61|0.75|0.37|10.05|10.05|na|0.00|na|
|Cote d'Ivoire|-1.57|-2.47|-0.02|1.82|-2.12|2.61|-216.47|43.29|
|Equatorial Guinea|10.55|0.66|19.45|20.88|3.09|26.81|-85.19|28.40|
|Eritrea|1.96|17.24|-0.82|na|na|na|na|na|
|Ethiopia|0.42|-1.37|2.54|3.71|8.16|1.49|119.81|-59.90|
|Gabon|-0.91|-1.43|-0.01|na|na|na|na|na|
|Gambia, The|0.29|0.04|0.73|na|na|na|na|na|
|Ghana|0.60|-0.40|2.31|2.15|1.30|2.31|-39.67|7.21|
|Guinea|0.97|0.31|1.46|1.78|0.28|2.39|-84.30|33.72|
|Guinea-Bissau|-0.70|0.08|-2.05|0.76|0.76|na|0.00|na|
|Kenya|0.48|0.39|0.64|1.76|1.76|na|0.00|na|
|Lesotho|3.27|3.78|2.39|3.83|3.83|na|0.00|na|
|Madagascar|-1.38|-2.26|0.14|na|na|na|na|na|
|Malawi|0.22|-0.89|2.15|1.68|-3.54|5.59|-310.57|232.93|
|Mali|0.86|-0.29|2.85|2.75|-1.75|3.25|-163.73|18.19|
|Mauritania|0.10|-0.42|0.99|na|na|na|na|na|
|Mauritius|4.22|4.67|3.65|5.65|5.65|na|0.00|na|
|Mozambique|2.07|-0.61|5.49|5.08|3.08|5.37|-39.32|5.62|
|Namibia|0.15|-1.12|1.75|2.14|na|2.14|na|0.00|
|Niger|-1.00|-1.55|-0.07|na|na|na|na|na|
|Nigeria|0.28|-0.57|1.74|1.99|2.01|1.97|1.09|-0.85|
|Rwanda|1.68|-0.51|5.47|2.27|na|2.27|na|0.00|
|Senegal|0.36|-0.64|2.08|1.75|0.22|1.96|-87.61|12.52|
|Seychelles|2.47|3.58|0.54|4.01|4.27|3.58|6.43|-10.72|
|Sierra Leone|-0.57|-1.56|1.15|7.95|na|7.95|na|0.00|
|South Africa|0.12|-0.61|1.38|1.96|na|1.96|na|0.00|
|Sudan|1.72|0.39|4.02|3.90|na|3.90|na|0.00|
|Swaziland|1.15|1.56|0.45|4.63|4.63|na|0.00|na|
|Tanzania|1.69|-0.69|2.98|3.69|na|3.69|na|0.00|
|Togo|-0.60|-1.39|0.76|4.27|12.03|2.71|182.01|-36.40|
|Uganda|1.92|0.73|3.21|3.69|2.67|4.45|-27.66|20.74|
|Zambia|-1.23|-2.60|1.13|2.35|na|2.35|na|0.00|
|Zimbabwe|-1.26|-0.18|-3.14|2.61|2.95|2.45|12.89|-6.45|

27
<!-- Start of picture text --> ber<br>NL<br>N N<br>N<br>SA<br>SA<br>N<br><q<br>NN<br>~Y<br>SS<br>nd<br><!-- End of picture text --> <!-- Start of picture text --> ah<br>-<br>Hza oe<br>= ——<br>iTwo ewefo.mf s m<br>= ee foN,<br>ou ra : ‘,<br>= oy Po ~<br>ge Pr ane are<br>ao \ a ? WoeSe<br>= a Y<br>3m “<br>o a<br>3 a<br>=<br>Oey<br>1985 1990 1995 2000 2005<br>Year<br>Predicted Trend Plus —---—.- :<br>Quarticin Years Since IDs Issued Predicted Trend<br>wea... Observed Rate of Continuation<br>on to High School<br><!-- End of picture text -->
medical and especially credit insurance suffer large technical losses. Motor insurance experienced a large turnaround in profitability in 2002 as a result of the significant increase in the tariff for compulsory third-party liability. It is not clear what lies behind the improvement in medical insurance, given the tendency of companies to compete aggressively for this line of business.

**Table 9: Evolution of Underwriting Results, 1997-2002**

|(% of NEP)|1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Motor|-9.6|-6.6|-6.0|-11.6|-7.9|0.7|
|Fire|73.5|48.2|76.4|20.6|28.7|151.0|
|Marine|59.7|64.1|57.3|48.6|74.2|103.3|
|Gen Accident|12.7|27.1|15.0|23.7|-24.9|4.5|
|Credit||||-21.0|-298.1|-233.3|
|Medical||-5.6|-9.2|-10.8|-9.5|0.6|
|Total|-3.3|-1.8|-3.3|-8.0|-6.2|4.2|

Data for 2002 are preliminary Source: Insurance Commission

# **E. Motor Insurance**

Motor insurance is the largest branch of general insurance with more than 50 percent of total premiums, but because it relies less on reinsurance, it represents an even higher share of net premiums. Motor insurance accounted for 73 percent of net earned premiums (NEP) and 74 percent of net incurred claims (NIC) in 2002.

The structure of the motor insurance market is affected by the existence of the Unified Motor Insurance Bureau. This is a market control mechanism, operated by the Federation of Insurance Companies, that allocates third-party policies to insurance companies on a rotational basis. This method of allocating compulsory motor business favors small companies (Table 10). Even after including comprehensive policies, which are contracted freely, no insurance company has a market share that exceeds 7 percent of the total. The Unified Bureau tends to perpetuate the fragmentation of the sector.

**Table 10: Institutional Structure of Motor Insurance, 2002**

|% of|
|---|
|Motor Premiums|
|8 Large<br>46.5|
|9 Medium<br>32.3|
|8 Small<br>21.2|
|HHI<br>450|
|Source: Insurance Commission|

Motor insurance reports relatively low loss ratios, especially by the standards of developing countries (Table 11). These were less than 80 percent in 2002. Acquisition costs amounted to 18 percent, resulting in a combined ratio of 95 percent. The combined ratio was significantly higher than 100 percent in earlier years, but the 2002 adjustment in 13
# PPR Working Paper Series

||Title|Author|Date|Contact|
|---|---|---|---|---|
|WPS65|<br>Interaction<br>of Infant Mortality and<br>Fertility and the Effectiveness<br>of<br>Health and Family Planning Programs|Howard Barnum|July 1988|S. Ainsworth<br>31091|
|WPS66|Slowing the Stork: Better Health for<br>Women through Family Planning|Anthony R. Measham<br>Roger W. Rochat|July 1988|A. Manciano<br>33226|
|WPS67|Price and Tax Policy for Semi-<br>Subsistence<br>Agriculture In Ethiopia|Robert D. Weaver<br>Saad All Shire|August 1988|D. Gustafson<br>33714|
|WPS68|A Comparison<br>of Lamps for Domestic<br>Lighting In Developing<br>Countries|Robert van der Plas|||
|WPSS9|Does Local Financing Make Public<br>Primary Schools More Efficient?||||
||The Philippine Case|Emmanual Jimenez<br>Vicente Paqueo<br>Ma. Lourdes de Vera|August 1988|T. Hawkins<br>33678|
|WPS70|Vocational Education and Economic<br>Environments: Conflict or||||
||Convergence?|Arvil V. Adams<br>Antoine Schwartz|August 1988|T. Hawkins<br>33678|
|WPS71|School Effects on Student Achievement<br>in Nigeria and Swaziland|Marlaine Lockheed<br>Andre Komenan|August 1988|T. Hawkins<br>33678|
|WPS72|The Relative Efficiency<br>of Public||||
||Schools in Developing<br>Countries|Emmanuel Jimenez<br>Marlaine Lockheed<br>Vicente Paqueo|August 1988|T. Hawkins<br>33678|
|WPS73|Taxation and Output Growth in Africa|Jonathan Skinner|August 1988|A. Bhalla<br>60359|
|WPS74|Fiscal Stabilization<br>and Exchange Rate<br>Instability: A Theoretical<br>Approach<br>and Some Policy Conclusions<br>Using<br>Mexican Data|Andrew Feltenstein<br>Stephen Morris|August 1988|A. Bhalla<br>60359|
|WPS75|Welfare Dominance and the Design of||||
||Excise Taxation in the Cote d'lvoire|Shlomo Yitzhaki<br>Wayne Thirsk|August 1988|A. Bhalla<br>60359|
A comparison of Oaxaca and Ransom's (1993) earlier results with ours, and our own analysis of staff who entered between 1980 and 1986, show that overall salary and grade differences have declined, as have the components unexplained by differences in characteristics. Recent Bank policies appear to have successfully reduced differences across staff groups. However, significant unexplained differentials remain.

The paper has examined a number of explanations. One is omitted variable bias, whereby there are characteristics omitted from our model but observed by those making salary and promotion decisions which are correlated with observed variables. For omitted variables to be contributing to the component of unexplained differences in salary and grade between staff groups, they must be distributed across groups such that the bias is strongest for Part I men, followed by Part II men and so on. Our analysis of the YP sample, inarguably a more homogeneous group hired through a non-discriminatory process, indicates that only negligible inter-group differences in mean salaries are left unexplained by the variables included in our model. Based on these results, we would argue that omitted variable bias is not a compelling explanation for differences attributable to structure. The YP sample also suggests that horizontal inequities can be eliminated through a different hiring process.

A second possible explanation for salary and grade differentials which we are unable to explain is discrimination. We have identified two ways in which this could arise. In its attempt to reduce labor costs, the World Bank may be taking advantage of the fact that the four staff groups face different reservation wages. A third explanation puts more onus on individual managers and their preferences for discrimination coupled with imperfect information. We find that although YPs all begin at the same level and at salaries that differ only to reflect differences in characteristics, once they enter the general staff pool, their salary increases and promotions 33
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and create new waves of innovating activity [Van Dujin, 19831, would combine in suggesting that the opposite should be the case - the capital stock at present may be small, but it should embody all the latest technologies. Yet, as also argued in Section III above, a good deal of empirical evidence suggests exactly the opposite conclusion. Process innovations are stimulated by very high rates of capacity utilization; conversely, prolonged slack has strong innovation-deterring consequences [Nelson, 1981].

If both capacity is in short supply and technological progress has slowed down, a sharp and generalized recovery in demand could well encounter selected bottlenecks and generate upward inflationary pressures. This danger, however, should not be exaggerated. First, the available evidence applies only to the manufacturing sector of the economy which accounts for less than 40 (or, in the United States, less than 30) per cent of value added. Output in the service sector can often be increased with little need for additional capital. Second, overtime and shift-work can overcome some capacity shortages. Third, the lack of capacity may well be overstated by entrepreneurial responses: "One can be pretty sure that a considerable proportion of the capital stock not now in use and becoming ripe for scrapping would again come into use with an acceleration of demand" [Bombach. 1985, pp.22-231. Indirect evidence supporting this statement comes from estimates of (gross) ICORs for the pre- and post-oil - shock periods between 1961-73 and 1973-85, the ICOR of the OECD area nearly doubled, suggesting the creation of a good deal of spare capacity. Finally, even if all these arguments were to be discounLed, it should be remembered that strong inflationary pressures have usually
Due to the fact that _Rij_ represents restrictions both for the importing country as well as for the exporting one, equation 2 can be rewritten in the following form The restrictions can be further divided into restrictions on commercial presence, identified with a superscript _c_ in the equation below, and other restrictions, identified with _a_ superscript _o_ . With this under consideration, the fully extended form of the equation is _o c o c_ ln( _M ij_ ) = _C_ + α 11 ln( _Ri_ ) + α 12 ln( _Ri_ ) + α 21 ln( _R j_ ) + α 22 ln( _R j_ ) + β ln( _Dij_ ) +γ ln( _SCij_ ) + _eij_ (4) Kang (2000) obtains measures of the regulatory barriers for 35 economies and considers the differences between developing and industrialized countries by estimating separate regressions for the two groups. After construction of _M ij_ and _SCij_ comparable statistics were used for 23 economies.

The variable representing the restrictions on commercial presence for country _j_ and the variable representing other form of restrictions for country _i_ are highly correlated and because of multicollinearity problems are not included in equation 2 at the same time. In addition, the results of various estimations obtained by Kang (2000) show that restrictions on commercial presence of maritime services in importing countries (<sup>_R_</sup> _cj_ ) have no significant impact on the shipping margins of exported products and the exclusion of this variable slightly improves the fitness. As a result, the obtained estimated coefficients for<sup>_i_</sup> developing countries, and<sup>_i_</sup> industrialized countries based on equation 2 are the following: 57