rafmacalaba/datause-encoder-data huggingface dataset:
(compared to plots measured during “partly cloudy” or clearer weather). In all other countries and specifications the cloudy or rainy weather does not have a statistically significant effect on the probability of area being measured with high bias.

# _2.Comparison of SR and Objective Measurements_

While Table 7 illustrates the degree to which farmer self-reported estimates differ from compass and rope measurements, it does not offer any explanation as to why the two systematically diverge. For this we turn to regression analysis. Table 10 presents the results of four OLS regression models: the first on the measurement bias (farmer self-reported estimate minus CR measured area), the second on the absolute value of this bias, the third on the relative bias, and the fourth on the absolute value of the relative bias (in percentage terms).

The claim of plot area affecting the direction and degree of error associated with self-reported area estimates is supported by the regression results. In the first specification (on bias) the coefficient on plot area is negative quadratic and positive in the cubic term in Ethiopia, Nigeria and the pooled data. In the second specification (on absolute value of bias), the coefficients on plot area are positive suggesting that as plot size increases the degree of farmer over-reporting shrinks while at the same time the absolute value of the bias increases. In this second specification, the Tanzania data exhibit a negative quadratic term and positive cubic term. When looking at the relative bias and absolute value of relative bias, the linear term is negative and the quadratic term positive in each country but at very different magnitudes, potentially driven by the difference in average plot size observed across the countries.

The distance from the plot to the dwelling holds significant explanatory power in the Ethiopia data, but not in Tanzania. The results from Ethiopia suggest that self-reported estimates of area diverge more from compass and rope measurements on those plots that are further from the household. This could be theoretically explained by assuming the farmer spends less time on plots more distant from the household and does not have the opportunity to view these plots to make his/her area estimate, should he/she prefer to do so. Consistent with Carletto et al. (2015), the existence of property rights (proxied here by the possession of a title or certificate of ownership or the ability to sell or use the plot as collateral) has a significant, negative relationship with the relative bias in Ethiopia and the pooled data, suggesting that on plots where the household has some form of property rights they are better able to estimate the area.

Household characteristics such as the gender, age, and education of the household head play out differently across countries. Results from Nigeria and Tanzania (but not Ethiopia) suggest that measurement bias is greater in households with older household heads. Contrary to expectations, the 25
3 MThR II HEALTH This chapter reviews input and output health indicators. Life expectancy is regarded as the best outcome indicator of the health status of a population. It is a summary measure of many other variables, including infant, child and adult mortality and morbidity. Though of a lesser .ope, child and infant mortality provide a more detailed picture of the health situation of the youngest part of the population and are directly related to other social variables such as nutrition. water supply, and even parents' education.1 Child mortality is also determined by government's efforts to immunize their population, and to provide them with health services, including physicians and nurses. Immunization coverage and physicians per population will therefore also be reviewed in this chapter.

More than other social indicators, health indicators suff3r from a critical lack of good quality data. Ir.deed, while it does not require excessive research for a state to know how many childreu are enrolled in its schools for example, gathering information on births and deaths, on the other hand, can raise considerable problems, especially in remote, rural areas. There may te no incentive for population to travel long distances to report a relative's death. If a birth or death is belatedly or not reported, not only 1Susan Cochrane, "The Effects of Education on Fertility and Mortality." Educatiov and Training Series Discussion Paper EDT26 (Washington, D.C.: World Bank, 1986).
Trujillo, L. and Nombela, G. - _Privatization and regulation of the seaport industry_ port authority. This risk could be shared by making concessionaires' payments dependent on their level of activity, with some minimum payment guaranteed. Perhaps the optimal system to determine concession fees should be a mixed combination of opportunity costs and risk allocation objectives.

However, in practice port authorities are not often observed using any market criteria to determine the opportunity cost of assets. Concession fees are usually fixed payments per square meter used, which are revised periodically according to some criteria (some examples can be seen in _Box 14)._ Fees are usually dependent on the service provided by the concessionaire, so that the price per square meter is different if the surface is used for container handling than if it is devoted to specialized storage areas. In some cases, fees depend on the volume of demand attended to by the operator, which therefore considers risk allocation. For example, in the case of the concessions of the port of Buenos Aires (Argentina), concessionaires pay according to the total volume of handled cargo, with a guaranteed minimum payment for the port authority.

**_Box 14._ Concession fees for different ports (1997)**

|**Port**|**Annual price per**<br>**sq.m.**_($)_|**Revision frequency**|**Revision mode**|
|---|---|---|---|
|Baltimore|6.5|Annual|Variable|
|Bremerhaven|2.3|5 years|Price index|
|Bordeaux|4.5|Annual|Price index|
|La Spezia|5.7|Variable|Variable|
|Le Havre|3.8|Annual|Price index|
|Lisbon|15.0|Annual *|Price index|
|Oslo|_61.5_|Variable|Variable|
|Rotterdam|3.2|Variable|Variable|

(*) It also charges a variable fee on volumes: $0.3 per Ton or $5.5 per container.

_Source:_ PDE (1998)

## **5.** - **REGULATION ON QUALITY AND SAFETY**

# **5.1. - Congestion problems**

The waiting time of ships is one of the port characteristics that shipping companies value when choosing between alternative ports. The total time that a ship stays at a port is equal to the sum of the time employed to obtain the required services and supplies, and this time must be considered as a cost for port users. The 'generalized cost' paid by a ship using a port is equal to monetary charges (port tariffs+cargo handling charges+other services' prices) plus the time spent in the provision of services _(see Box 15)._ Therefore, the shorter the waiting times, the lower the generalized cost of port use, and the more attractive the port is to users.

The first part of total ship waiting time is the time spent at the port maritime access zone waiting for a berth to be available. There are two possible scenarios. First, it is possible that 42
where xi is the matrix of individual characteristics, β is the returns to those characteristics, and _Empl_ is an indicator variable taking the value one if the individual is employed and zero otherwise.

To simulate labor incomes in 2020, three pieces of information are needed: (i) the probability of being employed; (ii) the wage-determining characteristics of the population in 2020; and (iii) the returns to those characteristics in 2020. For the probability of employment in 2020, we assume that the national-level Europe 2020 targets for the employment rate are met and, hence, based on propensity scores, we move individuals into employment. We adopt a similar approach for education (an important wage-determining characteristic) by assuming that the national-level Europe 2020 targets for reducing the rate of early school leavers and increasing the tertiary completion rate are met, again using propensity scores to select which individuals will have higher education levels in the simulations. Because age is also an important determinant of wages, in the simulations for 2020 we reweight the data such that the age distribution matches that projected by Eurostat. Item (iii), the labor income returns to characteristics, is the most difficult component to simulate, and we assume that the returns to characteristics remain the same as those estimated in the baseline year.

The structural relationship between an individual’s labor income ( yi) and his or her characteristics is estimated by a Mincerian equation: where ln yi is the natural logarithm of labor income earned by individual i , and xi is the matrix of individual characteristics that include age (linear and quadratic), educational attainment (a set of dummy variables for completing different levels), gender, marital status, whether the individual is a migrant, whether the individual has a chronic illness, whether the individual has limited physical mobility, and whether the individual lives in an urbanized area.

Using Equation (3) with the 2008 EU-SILC data, we generate an estimate of the β̂<sup>2008</sup> vector, which is employed later to simulate labor income under the stable structural relationship assumption.

We estimate the probability of employment of a 20- to 64-year-old individual in the baseline year following the Heckman method (1979). Therefore, we estimate the following probit model: where z is a matrix of individual characteristics, i.e. age (linear and quadratic terms), household size, dummy variables for educational level attained, gender, marital status, whether the individual is a migrant, whether the individual has a chronic illness, whether the individual has limited physical mobility, and whether the household lives in an urbanized area.<sup>11</sup> Equation (4) gives us an estimate for γ�<sup>2008</sup> .

The third step involves changing key characteristics, namely the education completion rates and employment rates such that the Europe 2020 national targets are met. By estimating a probit model in 11 The same set of characteristics is used as explanatory variables in equations (3) and (4).
# **4. GHG Emissions from Biofuel Expansion**

We calculate GHG emissions from two types of activities: (i) consumption of fossil fuels in various economic sectors, such as power, industry and residential and commercial; and (ii) deforestation caused by the expansion of cultivated area for biofuels. We then compare these emissions under a scenario with the corresponding emissions in the baseline to determine whether an expansion of biofuels would cause a net increase or decrease of emissions to the atmosphere.

Figure 4 presents, under both announced and enhanced target scenarios: (i) CO2 emissions reduced due to the replacement of fossil fuels with biofuels; (ii) the emissions released from land-use change, mainly from the conversion of forest lands to crop lands; and (iii) the net result of the aforementioned emissions. If the net value is positive, there is an increase of atmospheric emissions from the baseline; if it is negative, there is a decrease of emissions from the baseline.

**Figure 4: Change in annual CO2 emissions under the biofuel expansion scenarios as compared to the baseline scenario (Million tons of CO2)**

<!-- Start of picture text --> 287 Fossil Fuels<br>Land -Use<br>169<br>98 106 Net<br>-3 -13<br>-71<br>-132 -135 -118 -140<br>-181<br>-234 -246<br>-285<br>-353<br>-403<br>-494<br>AT ET AT ET AT ET<br>2020 2030 2040<br><!-- End of picture text --> 14
negotiations in Cancun and their slow pace since then (Eagleton-Pierce, forthcoming; Bown, 2009; Heinisch, 2006).<sup>23</sup> On the other hand, these two initiatives also underline some of the limits of the dispute settlement system. Indeed, they point at the importance of trade leverage to obtain compliance from the faulty party in a system that rests on the authorization of countermeasures and show how, alternatively resolving disputes through financial compensation, is likely to have adverse side-effects on third parties, unless the funds are used in non-distortionary ways (Baffes, 2011).

Despite these policy shortcomings, however, relief for African cotton producers could come from the market: the world price for cotton has dramatically risen over the past few months (see figure 3). The extent of this price spike is even far greater than what was expected from a removal of American subsidies: while it was anticipated that the abolition of distortionary support would raise prices by 10 to 15 percent at most, the average A index during the six first months of the 2010/11 season was 80 percent higher than the 2009/10 season average (ICAC, 2011). As of February 2011, it is still on the rise and hits all-time highs (World Bank, 2011). This unprecedented increase is attributed to the low levels of stocks worldwide; the limited supply that resulted from floods in Brazil, China, India and Pakistan; a recovery of demand resulting from the improvement in the global economic environment; the depreciation of the US dollar; and, possibly, speculation (Baffes, 2011; ICAC, 2011).

Over the next few months, the price is expected remain at such heights, while, on the longerterm, prospects are more uncertain (World Bank, 2011). The question of the subsidies should therefore certainly not be ruled out. However, the outlook for commodity prices in general is better than it used to be, thanks partly to growth prospects in China and other emerging economies (Kaplinsky, 2006). As a result, the key question in the short to medium term has shifted. The issue now is to know whether African cotton producers are going to benefit from this favorable global price environment.

# 3. THE DOMESTIC DIMENSION

# 3.1 Global competitiveness: the importance of the supply elasticity<sup>24</sup>

> 23 According to Josling (2005), the impact of the Cotton Initiative on the conduct of Doha negotiations is in fact more ambiguous. While, on the one hand, the case has complicated the process by raising the stakes; on the other hand, by creating a successful precedent, it makes future cases more probable hence raising incentives for rich countries to cut support voluntarily.

> 24 This section heavily draws on Shepherd and Delpeuch (2007).

9
# **Table 2 Baseline Differences by Treatment Status**

|In-|**means**|||**p-valu**|**es**<br>vs|
|---|---|---|---|---|---|
|<br>person<br>Weekly|Phone<br>Weekly|Phone<br>Triweekly|vs in-p<br>we|erson<br>ekly|<br>phone<br>weekly|
|(1)|(2)|(3)|(2=1)|(3=1)|(3=2)|
|**A. Variables used in randomization**||||||
|Male<br>0.44|0.41|0.40|0.51|0.34|0.78|
|Age<br>34.26|33.21|34.04|0.17|0.79|0.29|
|Education<br>8.61|9.09|9.11|0.20|0.20|0.97|
|Married<br>0.44|0.41|0.41|0.42|0.45|0.97|
|Self-employed<br>0.47|0.35|0.42|0.00|0.24|0.09|
|Wage-employed<br>0.24|0.31|0.26|0.05|0.60|0.19|
|Accra<br>0.50|0.53|0.57|0.62|0.35|0.65|
|Cape Coast<br>0.09|0.06|0.07|0.62|0.65|0.96|
|Dependency ratio<br>0.38|0.39|0.41|0.73|0.31|0.47|
|Log hh assets<br>7.34|7.44|7.53|0.57|0.23|0.60|
|p-valuejoint F test all variables used in A|||0.29|0.71|0.67|
|**B. Variables not targeted in the randomiz**|**ation**|||||
|Days worked per week<br>4.03|3.64|3.75|0.10|0.25|0.60|
|Hours worked per week<br>37.29|31.77|32.74|0.03|0.07|0.66|
|Manufacturing<br>0.07|0.05|0.06|0.58|0.73|0.80|
|Trade<br>0.36|0.27|0.27|0.02|0.03|0.84|
|Services<br>0.26|0.28|0.32|0.51|0.12|0.30|
|Public sector<br>0.03|0.07|0.03|0.07|0.95|0.08|
|Pay per week<br>88.14|81.21|83.59|0.61|0.75|0.85|
|p-value joint F test all variables used in A|and B||0.43|0.49|0.81|
|Total number of individuals<br>318<br>Note: *** p<0.01, ** p<0.05, * p<0.10<br>(|315<br>. Standard<br>Household)|321<br>errors are clu<br>.|stered at|treatme|nt level|

22
DC: The World Bank, 2003. http://ideas.repec.org/p/wbk/wbrwps/3128.html (Accessed on 28 July 2004).

75. Zohoori, Namvar, Karin Gleiter, and Barry Popkin (2001) “Monitoring Health Conditions in the Russian Longitudinal Monitoring Survey 1992-2000.” Report submitted to the U.S. Agency for International Development. Carolina Population Center, University of North Carolina at Chapel Hill.

24
24

# Table 3: Proriles of East Asian Exports Affected by High, Moderate, and Low US Tariffs

||Products w|ith tariffs over|IS percent|Products w|ith tariffs ovcr|10 percent|Products w|ith tariffs ove|r 5 prcment|All 198|9 Exports|
|---|---|---|---|---|---|---|---|---|---|---|---|
||No. of|Value|% oftotal|No. of|Value|% oftotal|No. or|Value|% ofiotal|No. of|Value|
|EAST ASIA||||||||||||
|tChina, People's Republic<br>|446<br>|2,062<br>|16.1<br>|776|3,274|25.6|2,062|8,270|68.2|3,924|12,793|
|Fiji<br>|19<br>|4|19.9<br>|21<br>|4|20.9<br>|26|6|32.1|93|19|
|French Polynesia<br>|I<br>|--<br>|0.1<br>|2<br>||0.2|6|-|1.4|34|9|
|Hong Kong<br>|416<br>|2,937<br>|28.9<br>|713<br>|3,369|33.1|1,954|6,738|66.2|3,634|10,172|
|Indonesia<br>|190<br>|580<br>|15.0<br>|270<br>|679<br>|17.5|434|1,149|29.7|1,096|3,867|
|Korea, Republic<br>|471<br>|2,654<br>|12.9<br>|806<br>|4,115|20.0|2,127|9,958|48.4|3,955|20,556|
|Macau<br>|131<br>|340<br>|49.0<br>|171<br>|368<br>|53.0|259|431|62.1|540|694|
|Malaysia<br>|176<br>|394<br>|8.0<br>|245<br>|483<br>|9.8|391|763|15.5|1,299|4,906|
|Philippines<br>|241<br>|758<br>|23.0<br>|370<br>|913|27.7|589|1,195|36.3|1,713|3,290|
|Singapore<br>|167<br>|604<br>|6.6<br>|261<br>|693<br>|7.6|857|1,933|21.1|1,859|9.151|
|Taiwan, China<br>|513<br>|3,007<br>|11.8<br>|888|4,591|18.0|2,417|13,069|51.2|4,652|25,511|
|Thailand|253|407|8.8|385|770|16.7|672|1,255|27.2|2,101|4,611|
|NAFTA MEMBERS||||||||||||
|Canada<br>|471<br>|261<br>|0.3<br>|785<br>|820<br>|2.5<br> <br>|2,136<br>|3,056|3.4|6.537|38,819|
|Mexico|334|916|3.3|554|1,190o|<br>5.4|993|3,562|13.0|4,186|27,369|

Note: The above tabulations indicate the number of items and value of trade that cncounters a certain minimum tariff level (say 5 percent). By subiracting the corresponding statistics for the nexL highes **t** ariff 'cut-ofr from these totals one can determine the trade that occurs within the tariff range. For example, 42.6 percent (68.2-25.6) of China's exports (1.286 tariff lines) face tariffs of between 5 and 10 percent.

Source: Computed using the World Bank - UNCTAD SMART data base.
For the omitted variable bias concern, the instrument partially helps.The instrument overcomes concerns of a special condition that emerged in a particular district-industry that favored female ownership (e.g., the inspirational female business leader, a favorable local government treatment) if such omitted factor is short-term in duration. That is, the instrument addresses omitted factors that are localized to the 2000-2005 period and do not extend back to the early 1990s. To the extent that an omitted factor was very long in duration and specific to a district-industry (e.g., a local women’s training institute that is specific to an industry), then the instrumental variable approach will not help if the factor was present in 1994 too.

Finally, the instrumental variables will help overcome measurement error in the agglomeration regressors. This measurement error is due to the sampled nature of our data and the imperfect design of our metrics. The instrumental variables can help relieve the standard downward bias in coefficient values that measurement error produces. On a related note, the instrumental variables approach can further help with potential concerns about measuring our entry rates and incumbent conditions from the same surveys, given that we do not observe the universe of Indian firms. The data split that we use of young firms and incumbents has been used by multiple researchers on India, and we have not identified any sampling biases in this approach. Nonetheless, the instrument variables approach will identify off of conditions from the 1994 survey to predict entry in 2000 and 2005.

Table 8 provides the first-stage results. The first two columns provide the first-stage results where we instrument for just the total incumbent employment in the district-industry and the count of female-owned incumbent firms in the district-industry. Columns 3-6 are the firststages from an extended specification where we also include the two Marshallian indices. The construction of the Marshallian indices in 1994 mirrors that in the later surveys. The lagged conditions strongly predict the incumbent conditions in 2000 and 2005, with the strongest elasticity for each agglomeration metric being its direct counterpart in 1994.

Table 9 reports the second-stage outcomes. Column 1 presents results from just instrumenting for the total incumbent employment in the district-industry and the count of female-owned incumbent firms in the district-industry. The results confirm an important role for female-owned incumbent businesses in encouraging female entrepreneurs. The elasticity of 0.063 is about twice the least squares magnitude of 0.027 estimated in Column 6 of Table 6.

Column 2 presents the results from just instrumenting for the total incumbent employment in the district-industry and the two Marshallian indices. The first-stage results for this estimation are similar to those reported in Table 8. We again find evidence for important interactions through both channels. The instrumented elasticities are again about twice the magnitude of those estimated in Column 3 of Table 6.The labor market channel is statistically 19