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Table 4: ORF scores estimates.

_Panel A: Treatment effects_

||Balanced Panel|Unbalanced Panel|
|---|---|---|
||(1)|(2)|
|(Constant)|46_._90<sup>_∗_</sup>|46_._54<sup>_∗_</sup>|
||[43_._98; 49_._92]|[43_._76; 49_._31]|
|T1A|0_._90|1_._29|
||[_−_1_._26; 3_._09]|[_−_0_._70; 3_._30]|
|T1B|1_._60|1_._68<sup>_∗_</sup>|
||[_−_0_._04; 3_._26]|[0_._13; 3_._21]|
|T1C|1_._08|0_._91|
||[_−_0_._72; 2_._92]|[_−_0_._79; 2_._59]|
|T2A|1_._40|1_._85|
||[_−_1_._01; 3_._81]|[_−_0_._42; 4_._04]|
|T2B|1_._41|1_._90|
||[_−_0_._75; 3_._55]|[_−_0_._12; 3_._92]|
|T2C|1_._32|1_._79|
||[_−_1_._06; 3_._71]|[_−_0_._49; 4_._05]|
|_Panel B: Pooled tr_|_eatment efects_<br>Balanced Panel|Unbalanced Panel|
||(1)|(2)|
|(Constant)|46_._91<sup>_∗_</sup>|46_._63<sup>_∗_</sup>|
||[43_._87; 50_._02]|[43_._77; 49_._49]|
|Pooled treatment|1_._31<sup>_∗_</sup>|1_._53<sup>_∗_</sup>|
||[0_._08; 2_._52]|[0_._34; 2_._69]|
|Num. obs.|5469|6701|
|Num. students|1823|2439|

Notes: Reporting means and 95% HPD intervals (in square brackets) of the posterior distributions of treatment effects.<sup>_∗_</sup> : zero outside 95% credible interval. We simulate 4 independent Markov chains of 4,000 posterior draws each and discard the first 2,000 as warmup. The remaining 8,000 draws are used to generate the posterior distributions of the coefficients. The Split- _R_<sup>ˆ</sup> of every coefficient is below 1.01 and there are no divergent transitions.

33
13 it: there was certainly a reduction in the rate of growth of inequality and, towards the end of the period, it actually started to decline.

When considering the last decades of the twentieth century, however, better and more comprehensive data are available, enabling researchers to work with approximations to the world income distribution based on (and only on) fully disaggregated household surveys. Looking at the second half of the century with these new data, three interesting regularities emerge. First, even as (unweighted) intercountry inequality continued to grow between 1950 and 2000, international inequality (when population weighted) began to fall. The disparate behavior in these two inequality concepts has been one of the reasons behind the discordant discourse on globalization and inequality. The continuing rise in intercountry inequality (to which Pritchett, 1997, refers as “divergence, big time”) was due largely to slow growth in most poor (and small) countries, relative to some middle-income and richer countries. The decline in international inequality, which refers to a population-weighted distribution, was due fundamentally to rapid growth in two large nations that started out very poor: China and, to a lesser extent, India. As Figure 5 suggests, once China and India are excluded from the international distribution, the post-1980 trend in that inequality concept changes dramatically, and becomes much closer to the rising trend in intercountry inequality.

The second regularity is that the last two decades in the twentieth century saw resumption in the upward trajectory of aggregate within-country inequality, defined as the contribution of within-country inequality to total inequality. The rise in within-country inequality prevented the decline in international inequality (which began, slowly, around the 1960s) from translating immediately into a decline in global inequality. Recall that global inequality is the sum of (appropriately aggregated) within-country inequality and international inequality. Indeed, Milanovic (2002, 2005) finds that global income inequality between people was still rising between 1988 and 1993, but appears to have fallen between 1993 and 1998. This is confirmed by World Bank (2005), which extends Milanovic’s data set by a couple of years.

The third regularity is that there are signs of _inequality convergence_ over time, whereby inequality has a tendency to rise in low inequality countries, and fall in high inequality ones. This was first noticed by Bénabou (1996), although his tests did not deal with the concern that the signs of convergence may stem solely from measurement error. Subsequent tests by Ravallion
-40-

------------------------------------------------------------------------

__-TABLE 3.6 Volume and Unit Value Movements in the USA, 1975 as a percentage

**f 1977**

----------------------------------------------------------------------

||1975 Volume<br>1977=100|__----<br>1975 Unit Values<br>1977=100|
|---|---|---|
|Grain Mill Products|87.06|130.41|
|Sugar & Sugar Products|80.48|188.25|
|Malt and Malt Beverages|87.82|99.22|
|Tobacco and Tobacco Products|105.01|84.80|
|Textiles|83.39|84.10|
|Footwear and Leather Products|96.83|86.09|
|Pulp and Paper|84.37|94.12|
|Soap and Detergents|92.63|89.00|
|Paints|84.87|91.53|
|Agricultural Fertilizers|84.94|114.75|
|Petroleum Refining and Products|<br>85.02|83.71|
|Tires and Inner Tubes|90.94|87.56|
|Cement|90.70|84.59|
|Bricks|91.16|82.39|
|Iron and Steel|95.67|87.23|
|Radio and TV Receivers|72.77|106.52|
|Motor Vehicles|67.81|87.71|

Source: Figures for the quantity adjustment are from US Department of Commerce, 1982 US Industrial Outlook; figures for unit value adjustment from 1977 Census of Manufactures, after quantity adjustment from 1977 to 1975, and Annual Survey of Manufactures 1975-1976.

Since we prefer to uvse the method which assumes the price relationships for covered output in the industry to be representative for the industry as a whole, we restate formulae (3.5a) and (3.5b), as a consequence of the US quantity ard unit value adjustments, as follows. In formula (3.7a) US gross quantities for 1977 are adjusted to 1975 using the factor "q" derived from the first column in table 3.6: In formula (3.7b) gross quantity of output in Brazil and Mexico is weighted at 1977 US unit values, so that we had to apply the term **"p"** in table 3.6 to convert the comparison to 1975 US unit values:
We thus define different sub-samples according to these indicator variables. For example, the whole sample can be split by the income group indicator into 3 sub-samples, the high income sub-sample (19 observations), the middle (94 observations) and low income subsamples (46 observations). As mentioned, the limitation of higher order effects is mainly due to the decreasing number of observations within sub-groups. Table 8 shows the tests for the different main factors as well as their interactions with the indicators.<sup>10</sup> Full output details can be found in Appendix C.

Table 8: GLM Findings: tests of between-subjects effects

|Dependent Variable: Difference(year 5|)<br>|||||
|---|---|---|---|---|---|
|Source|Type I Sum of<br>Squares|df|Mean<br>Square|F|Sig.|
|Corrected Model|21220<sup>a</sup>|40|531|6.446|.023|
|Intercept|1337|1|1337|16.243|.010|
|Literacy rate|244|1|244|2.969|.145|
|Aid (capital formation)|13|1|13|.162|.704|
|Aid (percent of import and exports)|764|1|764|9.284|.029|
|Capital Stock loss (log) [logCapLoss]|1802|1|1802|21.888|.005|
|Aid (percent of GNI)|2230|1|2230|27.093|.003|
|Remittances [Remit]|1849|1|1849|22.467|.005|
|Capital Stock (log)|20|1|20|.238|.646|
|GDP (log)|80|1|80|.971|.370|
|Land Area (log)|0|1|0|.003|.956|
|I_debt * Remit|4108|2|2054|24.959|.003|
|I_Income * Remit|1|1|1|.008|.931|
|I_SIDS * Remit|97|1|97|1.174|.328|
|I_debt * I_Income * Remit|965|1|965|11.723|.019|
|I_debt * I_SIDS * Remit|653|1|653|7.932|.037|
|I_debt * I_Hazard * Remit|4155|8|519|6.310|.029|
|I_Income * I_SIDS * Remit|369|1|369|4.483|.088|
|I_Income * I_Hazard * Remit|106|1|106|1.291|.307|
|I_SIDS * I_Hazard * Remit|245|3|82|.991|.468|
|I_debt * logCapLoss|727|2|364|4.418|.079|
|I_Income * logCapLoss|698|1|698|8.475|.033|
|I_SIDS * logCapLoss|5|1|5|.063|.812|
|I_Hazard * logCapLoss|1805|4|451|5.482|.045|
|I_debt * I_Income * logCapLoss|82|1|82|.998|.364|
|I_debt * I_SIDS * logCapLoss|140|1|140|1.706|.248|
|I_debt * I_Hazard * logCapLoss|63|2|31|.381|.702|
|I_Income * I_SIDS * logCapLoss|0|0|.|.|.|
|I_Income * I_Hazard * logCapLoss|0|0|.|.|.|
|I_SIDS * I_Hazard * logCapLoss|0|0|.|.|.|
|Error|412|5|82|||
|Total|22969|46||||
|Corrected Total<br>R Squared = .981 (Adjusted R Squ<br>a.|21632<br>ared = .829)|45||||

> 10 A least squares criterion is used to obtain estimates of the parameters models.

22
- 24 -

Table 1

# ORDER OF MAGNITUDE OF MODEL PARAMETERS

|Activity parameter|Order of magnitude|Notes|Reference|
|---|---|---|---|
|Total population<br>Rural population<br>(RP)<br>Urban population outside BKO (UP)<br>Oistrict<br>population<br>(PB)|7,600,000<br>5,300,000<br>1,600.000<br>700.000|1987 General Census<br>1987 General Census<br>1987 General Census<br>1987General Census|(661<br>1663<br>1661<br>[66]|
|Village or section<br>HS and AS utilization|25% of RP|Djidian (AFVP) 1983: 6-10%<br>Bankass and Koro (MOM): _3-40%<br>Kolokani (1986): 24%|r23] pp. 7-l1<br>123] pp. 1-5<br>[23] pp. 11-15|
|Orugs/inhab.|CFAF 30-230 (PPM)<br>CFAF 15 (NS)|Kita, Bafoulabe, Kenieba (PDS)<sup>1985: CFAF 30-50</sup><br>Bjidian (AFVP) 1983: from<sup>CFAF 83 to 230</sup><br>Kolokani (1986): from CFAF<sup>34 to 118</sup><br>Bankass and Koro (MOM): 15<sup>CFAF</sup>|[231 pp. 19 ff.<br>(231 pp. 7-11<br>[23] pp. 11-15<br>(23] pp. 1-5|
|Arrondissements||||
|Utilization<br>with no change<br>existence of drugs|10% of RP<br>12-30% of RP|Djidian (AFVP) 1983-86: 9-13%<br>Nossombougou 1985-87: 8%<br>Bankass (MOM) 1987: 31%<br>Koro (MOM) 1987: 12%<br>Timbuktu Region (NSF) 1987:<sup>13%</sup><br>Gao Region (NSF) 1987: 17%|_[23]_pp. 7-P<br>[231 pp. 160<br>123] pp. 1-5<br>[23] pp. 1-5<br>[23] pp. 43 ff.<br>[231 pp. 43 ff.|
|Proscription<br>per episode|CFAF 1.000 (PPM)<br>CFAF 400 (MS)|Nossombougou<br>1985-87: CFAF 960<br>Niono 1988: CFAF 2,600 (to be<sup>checked)</sup><br>Bankass (MOM) 1987: CFAF 364<br>Koro (KiM) 1987: CFAF 416<br>Timbuktu Region (MSF) 1987:<sup>CfAF 225</sup><br>Gao Region (NSF) 1987:<sup>CFAF 282</sup>|[23] pp. 160<br>!b5l<br>[23] pp. 1-5<br>[23] pp. 1-5<br>(23] pp. 43 ff.<br>(231 pp. 43 ff.|
|Private prescription<br>and self-medicn.|<br>CfAF 800/inhab. (PPM)|Estimation by difference (see<sup>text)</sup>||
|LRIecapita||||
|Utilization<br>wit;h<br>no change|6-30% of UP|Kolokani 1985-86:<br>6% (under-recording)<br>Kita attraction<br>zone (PUS) 1987: 30%<br>Mahina attraction<br>zone (PUS) 1987: 40%<br>Bafoulab& attraction<br>zone (PDS) 1987: 307<br>Kdnieba attraction<br>zone (P0S) 1987: 317|1231 pp. 11-15<br>(58]<br>[58]<br>(58]<br>[58]|
Furthermore, our estimating equation is less susceptible to reverse causality from exports to exchange rates for two reasons: our dependent variable, disaggregated exports, is less likely to affect a macroeconomic variable like the exchange rate; moreover, the latter is the exchange rate of another country. What about reverse causality from the exports to the index of competition? Our count-based index, while derived from theory under symmetry assumptions, has the empirical virtue of being based on the extensive margin and not being measured in value terms; hence being less related to the left hand side variable and less afflicted by reverse causality problem. Our value based index is potentially more vulnerable to the reverse causality problem because it is expressed in values, like the dependent variable. To minimize such endogeneity concerns we compute both indices for the initial period of the sample (i.e. for the year 2000).

# **IV. Data**

We focus on the period 2000-2008, during which concerns about China’s exchange rate policy have been most debated. For this period, we compile disaggregated data on bilateral exports from the UN Comtrade database. We collect data reported by the importing countries, which is generally regarded as more reliable than data on exports (i.e. exports from _i_ to j are measured by imports of _j_ from _i_ ). The data are for roughly 6000 non-oil HS 6-digit lines covering 900 4-digit products. We cover the 57 major importing countries (making sure that we include all countries that together accounted for over 95 percent of total exports of developing countries) and 124 developing country exporters which are potentially in competition with China (summary statistics are provided in Appendix Table A1 and the list of importing and exporting countries covered in Appendix Table A2).<sup>10</sup> The list of developing countries is based on World Bank country classification, and is comprised of all low and middle income countries (2010 GNI per capita of $12,275 or less).

> 10 In principle, we could include all exporting countries in our sample. We choose to restrict the analysis to developing country exporters, largely due to computational constraints. However, this restricted choice also stems from the fact that developing countries compete more with China than industrial countries do: the average index of competition between the former set and China is about 0.4 and 0.9, respectively for the value based and count based indices of competition. The corresponding numbers for industrial countries are 0.1 and 0.7, respectively.

13
though the phenomenon we are modeling (time) is continuous. For our purposes we need a framework that allows a continuous latent variable to be observed as a discrete outcome. Second, as noted, the policy data are missing in many observations, and the missing data are not randomly distributed, which means that discarding observations with missing data will bias the parameter estimates. Our estimation framework addresses both of these problems.

# **4.1 The estimating model**

A first key feature of the data that we confront explicitly is the choice of units in our outcome variable, the reported number of days that a shipment spends in customs. It is likely that the choice of units in the TAB data obscures important variation in countries’ performance, especially when the reported number of days is 1 or 2. For example, the number of days reported for import shipments bound for Romania and Singapore are both 1 day, but trading firms might expect goods to spend considerably less time under border controls in Singapore than in Romania. To address this issue, we apply a discrete-time transition model (Cameron and Trivedi, 2005, p.602), treating the reported number of days necessary to clear customs as a discretization of continuous time. The model estimates the conditional probability that goods will clear customs on a given day, and includes a latent measure of continuous time. This latent variable is useful in our subsequent counterfactual analysis, where policy-related changes can improve expected performance by fractions of a day.

The discrete-time transition model we use is a type of grouped duration model. The model accounts for dependent variables that represent grouped time data, with grouping points, _ta_ = _a_ , _a_ = 1 _,_ 2 _, ..., A_ , where _A_ is the maximum observed time in days, the discrete-time hazard function _λ_<sup>_d_</sup> ( _ta|x_ ) is defined by

where _x_ is a _k ×_ 1 vector of covariates including measures of border policy and other country characteristics.

A discrete-time transition model uses a binary choice model for transition, i.e. the discrete-time 14
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Garcia, M., Pence, A., & Evans, J. L. (Eds.) (2008). Africa's Future, Africa's Challenge: Early Childhood Care and Development in Sub-Saharan Africa. Washington: World Bank.

Gertler, P., et al. (2014). Labor market returns to an early childhood stimulation intervention in Jamaica. _Science,_ 344(6187), 998-1001.

Grantham-McGregor, S. M., Fernald, L. C., Kagawa, R. M., & Walker, S. (2014). Effects of integrated child development and nutrition interventions on child development and nutritional status. _Ann N Y Acad Sci,_ 1308, 11-32.

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37
# **3.3.2 Supply cost**

The supply cost of biodiesel produced from oil-bearing trees is relatively higher than that of diesel due to the high cost of feedstock (Table 8).

**Table 8: Typical cost and related assumptions of jatropha-based biodiesel in China**

|Items|Unit|Jatropha|
|---|---|---|
|**Feedstock cost**|**Yuan/tonne(ethanol)**|**3,000-6,000**|
|Assumed feedstock pricing mechanism||Cost-based pricing<sup>a</sup>|
|Assumed feedstock price|Yuan/tonne(feedstock)|1,000-2,000<sup>b</sup>|
|Assumed conversion efficiency|Tonne (feedstock) / tonne<br>(biodiesel)|3|
|**Non-feedstock Cost**|**Yuan/tonne(biodiesel)**|**2,000**<sup>**c**</sup>|
|Conversion technology||super critical fluid transesterification<br>without catalyst|
|Investment cost|Yuan/tonne(biodiesel)|2,300-2,500<sup>d</sup>|
|**Total production cost**||**5,000-8,000**|
|Share of feedstock cost|%|75|
|Designated biodiesel wholesale price<br>from January 1, 2008 to April 7, 2011|Yuan/tonne|See Table 5|

Note: a. there is no mature market for jatrapha in China, while there are some jatrapha planting contract between company and farmers with protect price range from 1000 to 2000; b. estimated by expert; c. data from field survey based on Hainan project; super critical fluid process is expensive on capital and operation costs compared with conventional acid/basic catalyzed process according to Hainan project; d. data from Wu et al.(2008).

# **3.3.3 Challenges**

The feedstock cost again is one of the major challenges to the development of biodiesel. For example, jatropha has a gestation lag of 3-4 years and the amount of capital required is substantial (Pradip, et al 2010), particularly when it has be grown in marginal lands. Although about 0.2 million hectares of planting base have been built in China, only 40 tonnes of jatropha-derived oil has been produced so far (Wang, 2010). Farmers do not plant jatropha unless they see explicit return from it. According to the case study in Panzhihua, the capital cost of planting base is about 4500-6000 Yuan/ha. (Xin & Zhang, 2011). Due to high risk, the actual development of Jatropha industry in Panzhihua shows sharp contrast to initial expectation (Liu et al., 2010).

# **3.4 Second-generation biofuels**

# **3.4.1 Resource potential**

The cellulosic resource for 2<sup>nd</sup> generation biofuels includes agriculture and forest residues (Table 9).

13
- 22 -

Comparative statie results generated by the model are summerized In Table 8. Proofs can be provided by the authors to Interested readers.

Table 8: Comparative Statlc Results

|Effect on|Productive<br>Investment|Housing<br>Investment|
|---|---|---|
||I|h|
|Change In|||
|Credit (L)|+|+|
|Rlsk (P)||***+**|
|initlal Productive<br>|||
|Capital(K)-||+|
|Initlal Housing (HO)|+||

The effect of additional credit on both types of Investn2ent Is positive, as one would expect when the liquldity constraint Is binding. An Increased risk to land rights will lead to loss farm Investment and higher residential Investment. Higher Initial stocks of productive capital and housing have a negative direct effect on Investment In these Items, but positive cross-effects.

The effect of farm size Is not easy to ascertain In the context of the present model. Typically a larger farm size Is also associated with a larger allocation of credit, thus a ceteris paribus change of farm size Is not meankigful. it can be shown that If returns to scale are Increasing, or If the marginal utility of consumption Is relativoly non-elastic, then It Is more likely that farm size Is mm , . *** _-**