rafmacalaba/datause-encoder-data huggingface dataset:
## 3.1.4 Price and quantity effects

The model used to estimate changes in world prices, imports and welfare is a simple partial equilibrium model for each of the six-digit tariff lines existing in the Harmonized System (HS) classi fi cation. We assume that each tariff line includes a homogenous good and that the world market for each of them is in equilibrium. There are no substitution or income effects on the demand or supply side. Using estimates of import demand and export supply elasticities, we can then solve for the percentage change in world prices associated with a tariff reduction in any (or all) 170 countries in the sample. These changes in world prices are then plugged into individual countries import demand and export supply functions to obtain quantities, using (15), and import revenue and export revenue effects, using (18) in the appendix, where we provide further details of the model.

# 3.2 Data

We focus exclusively on preferences granted by the TRIAD to 48 LDCs under their GSP schemes. In the case of the E.U., these are simply ruled by the Everything But Arms (EBA) initiative, which generally provides for duty and quota free access, as the statistics in Table A3 indicate. In the case of the special treatment granted to LDCs under Japan’s GSP scheme we are using their current preferences as introduced in 2002.<sup>32</sup> Finally, in the case of the U.S., the treatment is not homogenous across LDCs, as the variability in the average preference rate over different countries shows in Table A3. Non-African LDCs bene fi t from the extended preferences granted to all LDCs under the U.S. GSP scheme. We are using the LDCs preference rate as noti fi ed to the WTO in 1999, but there has been no change in this regime since 1997. Most African LDCs bene fi t from additional preferences through the African Growth Opportunity Act (AGOA), which adds 1835 tariff lines to the regular U.S. GSP program. More signi fi cantly, AGOA provides duty free access mostly without rules of origin requirements for apparel products originating in AGOA bene fi ciaries which are also LDCs (or that have an income per capita below $1000).<sup>33</sup> The MFN tariffs for the TRIAD are taken from noti fi cations to the WTO (2003 for Japan and the U.S. and 2004 for the E.U.). Because a number of tariff lines are subject to speci fi c tariffs, and WTO noti fi cations do not calculate ad-valorem equivalents for these tariff lines, we used the OECD

> 32 See Handbook on the scheme of Japan 2002/2003 (UNCTAD/ITCD/TSB/Misc.42/Rev.2), available at www.unctad.org.

> 33 The special apparel provision is subject to ful fi lment of speci fi c origin and visa requirements.

15
India, before the financial crises. Their demand for these commodities is likely to recover when the world economy moves beyond recession. A number of countries in Africa are diversifying their exports. The region no longer relies solely on exports of a few raw commodities. Exports are increasingly composed of light manufactured goods, processed foods, and services such as tourism and call centers. Some countries—such as Nigeria and South Africa—have been increasing their shares of exports in technology-based products, as noted by Broadman (2007). Lowering trade costs to take advantage of future opportunities is part of the context in which African trade and development prospects can strengthen over the long term.

# **References**

Arvis, J.-F., G. Raballand, and J.-F. Marteau. 2007. “The Cost of Being Landlocked: Logistics Costs and Supply Chain Reliability.” Policy Research Paper WPS 4258, World Bank, Washington, D.C.

Amjadi, A., and A.Yeats. 1999. “Have Transport Costs Contributed to the Relative Decline of Sub-Saharan African Exports?” Policy Research Working Paper Series: 1559, World Bank, Washington, D.C.

- Anderson, James E., and Douglas Marcouiller. 2002. “Insecurity and the Pattern of Trade: An Empirical Investigation.” _Review of Economics and Statistics_ 84(2): 342-52.

Anderson, James E., and Peter Neary. 1994. “Measuring the Restrictiveness of Trade Policy.” _World Bank Economic Review_ 8(2):151-69.

Anderson, James E., and Eric van Wincoop. 2003. “Gravity with Gravitas: A Solution to the Border Puzzle.” _American Economic Review_ 93: 170–92.

- _____. 2004. “Trade Costs.” _Journal of Economic Literature_ 42(3): 691-751.

- Balat, Brambilla, and Porto. 2008. “Realizing the Gains from Trade: Export Crops,

- Marketing Costs, and Poverty.” World Bank Policy Research Paper 4488, World Bank, Washington, D.C.

- Balchin and Edwards. 2008. “Trade-Related Business Climate and Manufacturing Export Performance in Africa: A Firm Level Analysis.” Mimeo.

- Brenton, Paul, and Mombert Hoppe. 2007. “Clothing and Export Diversification:

- Still a Route to Growth for Low-Income Countries?” Policy Research Working Paper 4343, World Bank, Washington, D.C.

- Broadman, Harry. 2007. “Africa’s Silk Road: China and India’s New Economic Frontier.” World Bank, Washington, D.C.

- Buys, Piet, Uwe Deichmann, and David Wheeler. 2006. “Road Network Upgrading and Overland Trade Expansion in Sub-Saharan Africa.” Policy Research Working Paper No. 4097, World Bank, Washington, D.C.

- Cadot, Oliver, Jaime de Melo, and Alberto Portugal-Perez. 2007. “Rules of Origin for Preferential Trading Arrangements: Implications for the ASEAN Free Trade Area of EU and US Experience.” _Journal of Economic Integration_ 22(2): 288-319.

- _____. 2007. “Why OECD Countries Should Reform Rules of Origin.”  Forthcoming in _The World Bank Research Observer_ .

- Collier, P., and J.W. Gunning. 1999. “Explaining African Economic Performance.” _Journal of Economic Literature_ XXXVII(March): 64-111.

34
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contrast, the reference group is overwhelmingly urban in the three Latin American countries. In Senegal and Sierra Leone, rural residents make up more than one-half of the reference group and the minority group. However, the rural-urban divide across the minority ethnic groups is still present in Senegal, with 71 percent of the minority group living in rural areas compared to only 49 percent of the reference group.

**Figure 1. Percentage of rural residents in reference and minority groups**

<!-- Start of picture text --> 90<br>76<br>80<br>71 69<br>66<br>70<br>59<br>60 54<br>49<br>50 41<br>40<br>30<br>21<br>18<br>20<br>10<br>0<br>Bolivia Mexico Peru Senegal Sierra Leone<br>Reference group Minority group<br>Percentage of rural residents<br><!-- End of picture text --> Source: IPUMS-I.

Using census data for intersectional analysis has certain advantages and disadvantages. Since most censuses collect only basic socioeconomic information about the population, the range of variables that can be included as control or outcome indicators is limited in our data set. In addition, most census variables are categorical, which limits the analytical methods that can be used. However, census data have some advantages as well. As mentioned earlier, the IPUMS-I samples drawn from each country’s census is representative of the population and harmonized across countries. Therefore, the results reflect actual education gaps in each country and are comparable across countries. In addition, the simplicity of the census variables means that concerns regarding measurement error are not as relevant in comparison to an analysis based on 12
education (see summary statistics in Table A15). Particularly, we estimate the following probit model for each quarter in the 2014q1‐2017q4 sample (see Table A16 for the full regression output): Pr�officially poor� �1��f��residence�, age�, occupational status�, household type�, education�� where _f_ is the cumulative distribution function of the standard normal distribution. Estimation of the equation for each period separately allows certain factors to affect the probability of being (officially) poor differently each period. For example, if the crisis affected the population in rural areas more, the rural residence status would have a greater impact on the risk of poverty in crisis periods. After the probability function is estimated, we define vulnerability as having a probability of being poor in excess of 10 percent.

_Figure 11: Official poverty and vulnerability, by quarter, 2014‐2017._ <!-- Start of picture text --> 35%<br>29.4% 29.2%<br>30%<br>25%<br>20% 16.1%<br>15% Official poverty<br>10% Vulnerability<br>5%<br>0%<br>2014q1 2014q2 2014q3 2014q4 2015q1 2015q2 2015q3 2015q4 2016q1 2016q2 2016q3 2016q4 2017q1 2017q2 2017q3 2017q4<br><!-- End of picture text --> Note: Cash income is measured in real 2014 roubles per capita and converted into an index with the 2014 level set to 100.

Source: Authors’ calculations based on quarterly HBS.

The average vulnerability level, as defined above, during the period 2014q1‐2017q4 is 21.7 percent (Figure 11). Unlike the official poverty measure, vulnerability grows substantially during the crisis. Vulnerability starts growing at the onset of the crisis from 16.1 percent in the beginning of 2014, and it almost doubles to the peak 29.4 percent in the fourth quarter of 2016. By the end of 2017 the vulnerability level still has not recovered to the pre‐crisis levels, suggesting that the crisis has a long‐lasting impact on the well‐being risks for the population.

The vulnerability measure does not take income levels into account directly, but it is still highly correlated with income. As we can see from Figure 12, vulnerability levels decline monotonically from poorest to richest decile. For the poorest decile the vulnerability level is 51.8 percent, meaning that more than half of those in the bottom decile have a probability of falling into poverty in excess of 10 percent. For the second decile the share of vulnerable population declines to 35.9 percent, and in the top decile it is only 2.7 percent of the total.

10
# **7 Conclusion**

The fast-evolving skill demand of employers in both developed and developing countries has the potential to leave those less skilled, less connected, and already economically marginalized even further behind. Occupational training programs are a common active labor market policy with the potential to address skill shortages and mismatch, although their effectiveness across contexts varies greatly. In this paper, we evaluate the effects of a recent large-scale technical skill training program in Brazil that took informational input from firms into consideration for the provision of publicly administered vocational technical training. Our empirical strategy and use of comprehensive monthly labor market data allow us to avoid several of the common challenges in the evaluation of occupational training programs. We find that the program has a substantial causal effect on employment appearing shortly after course completion and persisting for at least one year following the course. Program effects are substantially larger than those in a similarly-run occupational training program that does not take input from firms in deciding the content and scale of skills training. The informational input from firms allows the allocation of skill trainings to better match _future_ growth in skill demand rather than the static distribution of employment, which we show likely explains increased program effectiveness.

There may be an important efficiency-equity tradeoff in demand-driven programs. This is particularly germane in contexts where economic inequality is sought to be attenuated through active labor market policies. Such designs have the potential to exclude segments of firms and workers from accessing program benefits. In particular, we find that the distribution of courses provided under the demand-driven program is concentrated in more skill-intensive occupations that are more likely to find employment in industries offering relatively higher-wage formal employment. Participating businesses may also comprise a selected segment of the distribution of firms. For workers, if initial skill levels play a meaningful role in access or ability to complete these courses, this dimension may serve to exacerbate 25
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# Roads

This section looks at the national segments of the regional road network. To this end, the regional road network is defined as the network needed to interconnect all national capitals with one another and with the major deep seaports. Overall, 75 percent of the SADC’s regional network is paved, compared with over 90 percent in the Economic Community of West African States (ECOWAS) region. While several countries have made sizable investments in upgrading their portion of the regional road network, still more need to make additional investments. Most notably, less than 20 percent of the Democratic Republic of Congo’s regional road network is paved, and surprisingly, only 60 percent of the road network in Tanzania is paved. Angola and Mozambique also have to upgrade their regional road network.

The SADC members vary substantially in their track record on the maintenance of the regional road network. Overall, 71 percent of the regional network is in good or fair condition (table 2.3). As a general rule, most member countries maintain their portions of the network in good or fair condition. But there are three important exceptions to this pattern. Countries such as Botswana, the Democratic Republic of – Congo, and Zambia have allowed 30 60 percent of their portions to fall into poor condition. This may be symptomatic of wider deficiencies in the funding and implementation of road maintenance works in these countries, and/or denote that regional routes are not being prioritized in their national road plans.

**Table 2.3 Condition of the SADC’s regional road network, by member country (%)**

|||Co<br>|ndition<br>|||Type<br>||
|---|---|---|---|---|---|---|---|
||Good|Fair|Poor|Unknown|Paved|Unpaved|Unknown|
|Angola|71.4|5.5|23.2|0|71.4|28.6|0.0|
|Botswana|40.5|8.1|51.3|0|100.0|0.0|0.0|
|Congo, Dem. Rep. of|14.2|12.6|61.5|11.7|17.7|82.1|0.2|
|Madagascar|59.5|24.9|15.2|0.3|77.6|22.4|0.0|
|Mozambique|12.5|59.2|15.1|13.2|77.9|21.1|1.0|
|Malawi|61.8|32.7|5.5|0.0|95.5|4.5|0.0|
|Namibia|49.5|41.0|7.8|1.7|100.0|0.0|0.0|
|Swaziland|58.0|42.0|0.0|0.0|100.0|0.0|0.0|
|Tanzania|44.9|35.7|3.7|15.7|60.0|40.0|0.0|
|South Africa|88.2|4.3|0.2|7.2|99.4|0.0|0.6|
|Zambia|51.9|14.5|33.6|0.0|99.3|0.7|0.0|
|Zimbabwe|0.0|100.0|0.0|0.0|100.0|0.0|0.0|
|**SADC**|**46.7**|**24.1**|**22.8**|**6.4**|**74.0**|**25.8**|**0.2**|
|**East Africa (EAC+)**|**29.8**|**26.5**|**11.7**|**32.0**|**57.2**|**25.4**|**17.4**|
|**EAC**|**38.0**|**38.7**|**13.2**|**10.2**|**73.3**|**25.3**|**1.3**|
|**ECCAS**|**29.1**|**18.4**|**39.3**|**13.2**|**42.6**|**56.7**|**0.7**|
|**ECOWAS**|**45.1**|**28.4**|**22.5**|**4**|**91.9**|**8**|**0.1**|

_Source:_ AICD various sources.

_Note:_ ECCAS = Economic Community of Central African States; ECOWAS = Economic Community of West African States; EAC = East African Community.

20
first stage model can also be found in the census data. It is important that the variables are compared beforehand to verify that not only their definitions are in agreement, but also their distributions.<sup>9</sup> In the design of household surveys, clusters are commonly the primary sampling unit. Households within a cluster are usually not independent from one another, to allow for the clustering of households and their interrelatedness it is possible to specify equation (1) for a household as: ,– where the indexes _c_ and _h_ stand for cluster and observation, respectively – and disturbances ( _uch_ ) are assumed to have the following specification (Haslett et al., 2010): where _ηc_ and _ech_ , are assumed to be independent from each other with different data generating processes (Haslett et al., 2010).<sup>10</sup> Therefore the resulting model we wish to estimate is a linear mixed model (Van der Weide, 2014).<sup>11</sup> The literature has suggested different methods for estimating the unconditional variances of these parameters;<sup>12</sup> for the purposes of this paper focus is given to the methods presented by Elbers, Lanjouw, and Lanjouw (2003), and the adaptation of Henderson’s Method III by Huang and Hidiroglou (2003) detailed and expanded upon by Van der Weide (2014). The next section describes in detail these two approaches.

# **3 Estimating the unconditional variance of the residual parameters**

## **3.1 The ELL Methodology**

The methodology for estimating the location’s unconditional variance detailed by ELL (2002 and 2003) is presented in the discussion below. The method consists of two steps. The initial step relies on estimating a welfare model (Eq:1) using household survey data, and then obtaining generalized least square ( _GLS_ ) estimates for the model. Given the interrelatedness between households in a cluster, ordinary least squares is not the most efficient estimator. The second stage consists in utilizing the parameter estimates from the first stage and applying these to census data to obtain small area poverty and inequality measures.

Because the motivation is to implement the methodology into a Stata command, where possible, we also follow the methods implemented by PovMap (Zhao, 2006). It must be noted, however, that the methodology utilized by ELL is not necessarily the one followed by the PovMap software. Places where methodologies will be indicated in footnotes.

> 9This point is crucial, and the quality of a good poverty map is highly dependent on these two criteria being met. For further discussion and motivation refer to: Tarozzi and Deaton (2009).

> 10Note that the interrelatedness of _ηc_ across observations is already a violation of _OLS_ assumptions.

> 11Molina and Rao (2010) follow the naming convention of a nested error linear regression model. Regardless of its name, standard _OLS_ is invalid under this structure.

> 12Rao and Molina (2015).

4
Table 7. Estimated Effect of School Type on Employment and Job Quality, Men, by father’s education

|||**Junior seconda**<br>|**ry or below**<br>||<br>|**Senior secondary**<br>|**or above**<br>||
|---|---|---|---|---|---|---|---|---|
||LFP<br>LPM|Unemployment<br>LPM|Formal<br>LPM|Wage<br>OLS|LFP<br>LPM|Unemployment<br>LPM|Formal<br>LPM|Wage<br>OLS|
|Public Vocational|0.003|-0.014|0.060***|-0.001|0.023*|0.023|0.036|-0.034|
||(0.007)|(0.009)|(0.021)|(0.062)|(0.013)|(0.030)|(0.047)|(0.142)|
|Private general|0.008|-0.011|-0.047*|-0.223***|0.043**|0.003|0.015|-0.129|
||(0.008)|(0.009)|(0.027)|(0.061)|(0.018)|(0.028)|(0.054)|(0.164)|
|Private vocational|0.004|0.008|0.012|-0.287***|-0.009|0.058|0.009|-0.167|
||(0.008)|(0.015)|(0.024)|(0.076)|(0.027)|(0.041)|(0.064)|(0.137)|
|Average among public<br>general graduates|0.971|0.045|0.586||0.960|0.068|0.611||
|R-squared overall|0.089|0.163|0.571|0.252|0.156|0.323|0.661|0.434|
|Observations|4,389|4,285|4,106|3,698|1,037|999|917|799|

Notes: *** 1% significance, ** 5% significance, * 10% significance; standard errors in parentheses, they are robust to heteroskedasticity and clustered at subdistrict level.

Table 8. Estimated Effect of School Type on Employment and Job Quality, Women, by father’s education

|||**Junior secondary**<br>|**or below**<br>||<br>|**Senior secondary**<br>|**or above**<br>|<br>|
|---|---|---|---|---|---|---|---|---|
||LFP<br>LPM|Unemployment<br>LPM|Formal<br>LPM|Wage<br>OLS|LFP<br>LPM|Unemployment<br>LPM|Formal<br>LPM|Wage<br>OLS|
|Public Vocational|0.064*|-0.014|0.022|0.125|0.043|-0.032|0.005|-0.053|
||(0.033)|(0.013)|(0.034)|(0.102)|(0.061)|(0.027)|(0.048)|(0.146)|
|Private general|-0.065*|0.009|-0.089**|-0.200|-0.053|-0.006|-0.037|0.174|
||(0.038)|(0.014)|(0.037)|(0.146)|(0.056)|(0.033)|(0.045)|(0.140)|
|Private vocational|0.010|0.010|-0.027|-0.019|-0.069|-0.008|-0.002|0.170|
||(0.040)|(0.017)|(0.041)|(0.099)|(0.084)|(0.033)|(0.047)|(0.165)|
|Average among|||||||||
|public general<br>graduates|0.642|0.049|0.532||0.700|0.074|0.654||
|R-squared overall|0.176|0.260|0.550|0.332|0.292|0.329|0.710|0.432|
|Observations|3,513|2,238|2,142|1,713|1,370|930|875|739|

Notes: *** 1% significance, ** 5% significance, * 10% significance; standard errors in parentheses, they are robust to heteroskedasticity and clustered at subdistrict level.

28
**Table 5: Do Countries with Higher Passport Costs have less Emigrant Flow?**

Dependent Variable: Emigrant flow per capita to USA, Canada, Australia and New Zealand in 2004 (%)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Passport Cost as % of GNI|-0.002<br>(2.60)**|-0.001<br>(1.36)|-0.014<br>(2.92)***|-0.006<br>(4.01)***|-0.007<br>(2.62)**|
|Passport Cost as % of GNI Squared||||0.000<br>(3.63)***|0.000<br>(2.66)***|
|Log of Population||-0.051<br>(4.13)***|-0.050<br>(4.17)***||-0.051<br>(4.15)***|
|Log of GNI per capita||0.021<br>(0.96)|0.000<br>(0.02)||0.007<br>(0.26)|
|Government Effectiveness||-0.060<br>(1.68)*|-0.057<br>(1.56)||-0.053<br>(1.46)|
|Constant|0.118<br>(5.45)***|0.759<br>(2.69)***|0.948<br>(2.87)***|0.128<br>(5.47)***|0.889<br>(2.81)***|
|Trimmed Sample|no|no|<20%|no|no|
|p-value for test of joint significance:||||0.000|0.032|
|Observations|127|127|121|127|127|
|R-squared|0.01|0.28|0.30|0.03|0.29|

Notes: Robust t statistics in parentheses * significant at 10%; ** significant at 5%, *** significant at 1% Column 3 trims countries with passport costs which are above 20% of per capita national income Tables 4 and 5 have shown that countries with higher passport costs relative to income have lower stocks and flows of emigrants per capita, and that this finding is robust to the inclusion of a number of additional variables such as income and governance which might be correlated with both emigration and passport costs. Nevertheless, one should still be cautious not to interpret this result as necessarily showing that higher passport costs cause less migration to take place. A possible concern may be that the number of migrants a country has determinants the government’s decision on how much to charge for a passport.<sup>12</sup> A large number of migrants may be able to exert political pressure on

> 12 A large absolute number of migrants may also enable economies of scale in the production of passports, lowering the marginal cost of producing a passport in terms of staffing time and office expenses.

- 15 -