rafmacalaba/datause-encoder-data huggingface dataset:
not reduce the negative relationship (Barrett _et al._ 2010). Similarly, data from Rwanda in the 1990s point towards higher intensity of labor use by small farmers who farm land more intensively, e.g., by reducing fallowing, but also invest more in soil conservation (Byiringiro and Reardon 1996). Farm household survey data from four counties (Malawi, Tanzania, Kenya, Uganda) also point towards a negative relationship between farm size and output (Larson _et al._ 2012).

Detailed empirical study of productivity by farm size will be of relevance for Rwanda where efforts to promote agricultural development prompted adoption of policies to encourage consolidation and prohibit subdivision of plots almost entirely.<sup>4</sup> Such efforts have been controversial and difficult to implement even in countries with higher levels of per capita income. Consolidation efforts in Eastern Europe have a mixed record, partly because they failed to address key institutional factors (Deininger _et al._ 2012). In Mexico, subdivision restrictions had little impact on the ground and merely drove farmers into informality (World Bank 2002). They could thus easily undermine the sustainability of Rwanda’s recent, and in many respects exemplary (Ali _et al._ 2011), effort to demarcate and register all of the country’s 10.3 million land plots.

# **3. Data, descriptive statistics, and econometric approach**

Detailed plot-level data from Rwanda allow us to explore determinants of agricultural production and the presence of a farm size-productivity relationship using output as well as measures of profit consistent with types of labor market imperfections at holding and plot level. Descriptive data and graphs point towards large differences in intensity of input use and gross value of output across farm sizes, in line with the notion of small producers using inputs more productively. This relationship disappears if profits that value family labor at market wages are considered.

# **3.1 Data**

We use data from a 2010/11 survey of 3,600 rural households in 300 randomly selected villages of Rwanda to provide evidence on the relationship between farm size, and output and profit per unit of cultivated land. The main purpose of the survey, conducted by the World Bank with support from DFID and IGC, was as a baseline to assess impacts of a program of land tenure regularization (LTR). A threestage stratified cluster sampling strategy was adopted to select study villages from a complete list of enumeration areas provided by the National Institute of Statistics of Rwanda (NISR). First, 100 sectors nationwide (4 in each of the 25 districts) were randomly selected from all sectors that are above the

> 4 Article 20 of the Organic Land Law prohibits subdivision of agricultural plots of less than one hectare and requires administrative approval for subdivision of plots below 5 ha. As per our data, 97.9% of plots are below 1 ha and 99.9% below 5 ha, so that virtually everybody is affected.

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24 a significant portion of foreign exchange earnings raised through trade taxes, and in such a protected environment, trade arrangements have large incidence effects.

# Incidence of Taxes

As Musgrave (1957) long ago pointed out, there is no unique way to execute tax incidence analyses since the counterfactual needs to be carefully specified. He suggested three different tax incidence concepts: "absolute", "differential" and "budget" incidence. Under absolute tax incidence analysis, the distributional impacts of a tax are examined while keeping public expenditures unchanged with foregone revenues left unfinanced (i.e., neither by expenditure cuts or tax increases). Under budget incidence, equal reductions in taxes and exptnditures are considered. In differential incidence analysis, the most widely used of his concepts, the tax being examined is replaced by a yield preserving neutral alternative tax. The counterfactual analyses we perform with our model use this differential incidence concept, as in Shoven and Whalley (1977). We substitute a yield neutral broadly based sales tax for each major component of the tax system such that total real government revenues (and hence expenditures) are kept constant in each of our analyses.<sup>20</sup> Table 3 reports tax incidence results for various components of the Cote d'Ivoire tax system using 1986 data and the central case specification of our model. We analyze the effects on the seven household groups in the model, who we rank from richest to poorest.<sup>2</sup> '

# **20** See de Wulf (1975) for a review of tax incidence analysis in developing countries.

**21** Note, however, that this classification from poor to rich is not identical to what would be used in a more traditional income range tax incidence analysis, since the use of socioeconomic criteria rather than income deciles mean that the poor and the rich households in the
to the OLS estimates. I use the tabulated critical values for the least demanding version of this hypothesis, which allows for a 30 percent maximal relative bias.<sup>12</sup> The null hypothesis of weak instruments is rejected if the test statistic exceeds the corresponding critical value. For all three studies, and in both the differenced and the levels equation, I find that the null hypothesis of weak instruments cannot be rejected, even at the least demanding level of 30% bias.

One drawback of the Stock‐Yogo approach is that the weak instrument set in the null hypotheses is defined in terms of a weighted average of the relative biases in all of the estimated coefficients. This may disguise the possibility that the coefficients on some variables are more strongly identified than others. To remedy this problem, I report conditional F‐statistics for each first‐stage regression proposed by Sanderson and Windmeijer (2015), that can be compared with the Stock‐Yogo critical values coefficient‐by‐coefficient to test the null hypothesis of a weak instruments, where “weak” is defined in terms of maximal size distortions for a conventional Wald test of a null hypothesis involving that parameter only. In all but one case in Tables 2‐4, the Sanderson‐Windmeijer test fails to reject the null of weak instruments, even at the least demanding tabulated critical values corresponding to a maximal 25 percent size distortion. The only exception is in the levels equation of HOZ, where the null of weak instruments is rejected for the coefficient on lagged inequality (but not twice‐lagged inequality).

A shared shortcoming of both the Stock‐Yogo and Sanderson‐Windmeijer tests for weak instruments is that they both are valid only under the assumption of i.i.d. errors. In contrast, the SYS‐ GMM estimator and the 2SLS estimates of the levels and differenced equations allow for the error terms to be heteroskedastic and correlated with each other within countries. For this reason, these two tests for weak instruments should only be interpreted as suggestive evidence of weak identification in this particular setting. Rather than pre‐test for weak instruments, a more direct approach is to base inferences on statistics that are robust to the presence of weak instruments. In Tables 2‐4, I report confidence sets for the coefficients on the main inequality variables of interest: lagged inequality and redistribution, in the case of OBT, lagged and twice‐lagged inequality in the case of HOZ, and lagged human capital inequality in the case of CC. These confidence sets are based on the AR statistic, the KJ statistics, and the CLR statistics. As discussed in the previous section, these one‐dimensional confidence sets are obtained by projecting the multi‐dimensional confidence set into each axis, and therefore are

> 12 Stock and Yogo (2005) tabulate these critical values only for the case of 1, 2, and 3 endogenous regressors, and for up to 100 instruments.   The critical values decline slightly with the number of regressors, and I linearly extrapolate them to the case of 5 (HOZ) and 6 (CC) endogenous regressors.

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# Conclusion

My emphasis this afternoon is on the need for<sup>increasing expenditures on female</sup> education. Those who advocate such policies also<sup>argue for programs directed at enhancing</sup> family planning and women's health services as well<sup>as measures to combat discrimination in</sup> labor and credit markets. I have little doubt that<sup>such actions would also be constructive, but</sup> I believe that they are less important than improvements<sup>in female education. Hard statistical</sup> evaluations fairly consistently find that female<sup>education is the variable most highly correlated</sup> with improvements in social indicators. And the<sup>benefits of education have multiplier effec3s</sup> because they empower women to bring about other<sup>necess&ry changes. The greatest emphasis,</sup> therefore, should be put on closing the education<sup>gaps that I have described.</sup> Lectures and papers that plead the importance<sup>of a particular type of investment in</sup> developing countries are hardly uncommon. Reflecting<sup>the biases of an economist, I have tried</sup> to concentrate on the concrete benefits of female<sup>education and explicitly contrast it with other</sup> proposed investments. Expenditures on increasing<sup>the education of girls do not just meet the</sup> seemingly easy test of being more socially productive<sup>than military outlays. They appear to be</sup> far more productive than other social sector outlays<sup>and than the vastly larger physical capital</sup> outlays that are projected over the next decade.

In making an economic argument for investing in<sup>the education of women, I have tried</sup> to steer clear of the morl and cultural aspects<sup>unavoidably implicated in any gender related</sup> question. Partially this reflects my comparative<sup>advantage as an economist. But it also reflects</sup> a conviction that one's viewpoint on feminism<sup>should not affect one's evaluation of the</sup> arguments for educating girls. Helping women<sup>be better mothers to their children is desirable</sup> on any view of the proper role of women in society.

There are those who say that educating girls is a<sup>strategy that pays off only in the long</sup> run. I am reminded of a story that John F. Kennedy<sup>used to tell of a man asking his gardener</sup> how long it would take for a certain seed to grow<sup>into a tree. The gardener said it would take</sup> 100 years, to which the man replied, "Then plant<sup>the seed this morning. There is no time to</sup> lose."
where Yi represents the probability to die in 1994 and assumes value 1 or 0. _X_ is a vector of sibling and family characteristics. The regressors of interest are the gender and dummies for the age of the sibling in 1994 and the rural and the educational background of the family. The data are weighed with the population weights provided in the DHS as well as with the inverse of the number of surviving females age 15-49 in each family and the standard errors are clustered by enumeration area.

Table 1 confirms the results from the figures in a multivariate regression framework. The three panels display marginal effects of the coefficients in the above regression. The results indicate that, controlling for age, males and people with an educated and an urban background were more likely to die in 1994. Males were 1.3 percentage points more likely to die, while individuals with a rural background are a little bit more than 3 percentage points less likely to have died. Each panel uses a different measure of education: panel A compares no education with at least some primary education (1.7 percentage point more likely to die), panel B compares at least some secondary education (4.5 percentage point more likely to have died) with lower educational achievements and panel C enters linearly the years of education (each additional year of education increases by 0.3 percentage point the probability to have died).

# **5. The Omitted Variable Problem**

The 2000 DHS has one major drawback: it does not contain the ethnicity of the respondent. The ethnicity variable is determining the probability to die but we cannot include it in the analysis. Before using an econometric strategy to deal with this omitted variable problem, we should first note that Tutsi were not the only victims of the 1994 12
of Labor Statistics. We restrict the sample to all men ages 19 to 63. Next, as in Chiquiar and Hanson (2005), we only use Mexican wages that are between US$0.05 and US$20.00.

We use two samples from the Mexican census. The first is a sample of all workers meeting the above criteria. The second is a sample of primarily urban dwellers that includes Mexico City, the State of Mexico, San Luis Potosí, Leon, Guadalajara, Chihuahua, Monterrey, Tampico, Torreon, Durango, Puebla, Tlaxcala, Veracruz, Merida, Orizaba, Guanajuato, Tijuana, Ciudad Juarez, Matamoros, and Nuevo Laredo. We call this the urban sample.

In Table 3, we report the summary statistics from our data for both the rural/urban and the urban sample from Mexico. The table shows that mean wages in Mexico have been stable from 1990-2010. For the whole sample, we see that wages increased modestly from $1.42 in 1990 to $1.55 in 2000 to $1.59 in 2010. Wages in the urban sub-sample are slightly higher but do not exhibit the same monotonic increase from 1990-2010. There have also been significant gains in educational attainment over this period. For example in the whole sample, in 1990, 32.64% of the sample had between 0 and 5 years of education, whereas in 2010, this number dropped to 13.50%. In the urban subsample, the corresponding numbers are 20.85% in 1990 and 8.41% in 2010.

# _Variance Decomposition_

To explore the predictions of the model about the relative importance of “between” and “within” inequality, we begin with a standard variance decomposition exercise that is common in the inequality literature (e.g. Lemieux 2010) in which we break the total variance of log wages in a given country into its “within” and “between” components. The variance at time t can be decomposed into its “within” and “between” components as follows: where 17
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**Table 4**

**Estimated Rates of Return to an Additional Year of Experience**

|Years:||||19|88|||||19|92|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Education|Experience|_OLS_|Q10|Q25|Q50|Q75|Q90|_OLS_|Q10|Q25|Q50|Q75|Q90|
|2|10|_0.057_|0.047|0.045|0.053|0.062|0.070|_0.046_|0.039|0.038|0.042|0.050|0.056|
|2|20|_0.036_|0.028|0.028|0.034|0.041|0.047|_0.029_|0.023|0.023|0.027|0.033|0.039|
|2|30|_0.016_|0.009|0.010|0.014|0.019|0.024|_0.013_|0.007|0.008|0.011|0.015|0.021|
|2|40|_-0.004_|-0.010|-0.007|-0.005|-0.003|0.001|_-0.003_|-0.009|-0.007|-0.005|-0.002|0.003|
|6|10|_0.055_|0.045|0.046|0.053|0.061|0.068|_0.044_|0.036|0.038|0.042|0.049|0.054|
|6|20|_0.035_|0.026|0.028|0.034|0.040|0.045|_0.028_|0.021|0.022|0.026|0.032|0.037|
|6|30|_0.014_|0.007|0.011|0.014|0.018|0.022|_0.011_|0.005|0.007|0.011|0.014|0.019|
|6|40|_-0.006_|-0.012|-0.007|-0.005|-0.004|-0.001|_-0.005_|-0.011|-0.008|-0.005|-0.003|0.002|
|11|10|_0.053_|0.042|0.046|0.053|0.060|0.065|_0.042_|0.034|0.037|0.042|0.047|0.053|
|11|20|_0.033_|0.023|0.029|0.034|0.038|0.042|_0.026_|0.018|0.022|0.026|0.030|0.035|
|11|30|_0.012_|0.004|0.011|0.015|0.017|0.019|_0.009_|0.002|0.006|0.010|0.012|0.017|
|11|40|_-0.008_|-0.015|-0.006|-0.005|-0.005|-0.004|_-0.007_|-0.014|-0.009|-0.005|-0.005|0.000|
|16|10|_0.051_|0.039|0.047|0.053|0.059|0.062|_0.040_|0.031|0.036|0.041|0.045|0.051|
|16|20|_0.031_|0.020|0.030|0.034|0.037|0.039|_0.024_|0.015|0.021|0.026|0.028|0.033|
|16|30|_0.010_|0.001|0.012|0.015|0.015|0.016|_0.007_|-0.001|0.006|0.010|0.011|0.015|
|16|40|_-0.010_|-0.018|-0.005|-0.005|-0.006|-0.007|_-0.009_|-0.016|-0.010|-0.006|-0.007|-0.002|
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change in the capacity utilization rate (standard injury determinants) with the probability that the U.S. authorizes trade remedy protection to a petitioning industry. On the other hand, we expect a positive relationship between the U.S. protection decision and explanatory variables such as the import penetration ratio, the level of industry employment and the four-firm concentration ratio of the industry. These last two variables are commonly used to proxy for the political importance of the industry, as well as the industry’s ability to overcome the free-rider problem to organize and successfully petition for protection.

# **5 Estimation Results**

In this section we discuss the results from maximum likelihood estimation of the probit model with selection. Even though the two equations are estimated simultaneously, for ease of exposition we split our discussion of the two separate stages into two separate parts. In the first section we briefly describe the results from the selection equation (reported in table 6a), before turning to the results of the question of interest in section 5.2 – determinants of the foreign country decision of whether to challenge a U.S.-imposed trade remedy at the WTO (reported in table 6b). The first specification (1) in each table uses the baseline sample of U.S. antidumping investigations and measures imposed on GATT/WTO members over the 1992-2003 period, the second specification (2) adds the sample of countervailing duty investigations and measures imposed, and the third specification (3) is a robustness check that allows for industry injury determinants to also affect the WTO-challenge decision.

# **5.1 Selection Equation: Determinants of Which U.S. Investigations Against GATT/WTO Members End in Trade Remedies**

Table 6a shows the results for determinants of the selection equation decision of whether the U.S. investigation of a GATT/WTO member under its antidumping or countervailing duty laws results in affirmative findings (injury and dumping and/or subsidies) and thus a trade remedy. While most of the estimates for the explanatory variables have the sign that is predicted by theory, only three of the estimates are statistically significant at conventional levels. In the sample of investigations of products deriving from WTO members shown in column (1), the U.S. is more likely to impose an antidumping measure the higher the import penetration ratio (2.729) and the industry level reports published in the U.S. Census Bureaus’ series, _2002 Economic Census, Manufacturing Industry Series._ 16
In our analysis below, we settle on 1970 as the starting point for a more empirical reason. Starting the clock in 1990 seems too recent to count as history and actually penalizes countries that started growing rapidly only since then. In other words, it penalizes those very countries that have suffered from not having had large historic emissions.

# _3. Ability to pay_

Ability to pay for mitigation can be most easily captured in a country’s per capita GDP measured in PPP terms.<sup>7</sup> This principle would simply say that a fair share of future per capita emissions should be inversely related to the share of per capita GDP in world per capita GDP.

<!-- Start of picture text --> 1<br>Y  Y i<br> (4)<br>i 1<br> <br> 1 <br><br> <br> Y i <br><!-- End of picture text --> Here Y is a country’s per capita GDP measured in PPP terms.

# _4. Preserving future development opportunities_

Future economic opportunities could relate both to energy needs on the consumption side and growth on the production side, taking into account the possibility of technological progress. These are highly correlated so economic growth is perhaps the best indicator for future economic opportunities. Now, future economic growth has typically been predicted based on some view about each country’s prospects (as in many projections say by the International Energy Agency (IEA), World Energy Outlook (WEO), Cline (2010)). These are necessarily subjective and typically tend to extrapolate the recent past.

Alternatively, one might simply say that projecting growth over a long horizon should be a more objective supply-side exercise. The simplest rule for doing so is economic convergence, namely, that all countries would tend in the long run to converge to similar standards of living and that future _growth_ rates will be inversely related to current _levels_ of per capita income. The post-war evidence is that, although not all countries have shown signs of convergence, the major carbon emitters (especially those in Asia) have, which renders the use of current levels of GDP as an appropriate predictor of future growth rates (see Barro and Sala-i-Martin (2003) and Caselli (2005) in particular for evidence on convergence). More recently, in the period (2002-2008) leading up to the recent global financial crisis, the phenomenon of convergence has become more wide-spread with nearly 70 percent of countries growing faster than the United States (Johnson and Subramanian, forthcoming). In any case, it is appealing to use convergence not just as a predictor of future growth but also as a normative basis for preserving the growth opportunities of the poorest countries.

How would one derive a measure of the share of future emissions based on economic opportunities? Future economic growth rates allow future emissions _growth_ to be predicted on

> 7 Others have suggested the use of the UNDP’s Human Development Index, for example, as a measure of ability to pay (see GTZ, 2004).

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