rafmacalaba/datause-encoder-data huggingface dataset:
|HHs with 2 adults and at least 4 dependents|16%|19|6.4|4898|48%|
|---|---|---|---|---|---|
|HHs with 3 or more adults and at least 3 dependents|13%|25|7.2|5358|57%|
|Other HHs|2%|72|1.4|14305|4%|
|Total|100%|29|5.0|7499|31%|

**Source** : Authors’ calculations based on HIECS 2017/18. **Note** : “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

Table 5 complements this analysis of the profile of the poor in 2017/18. Again, children are more affected by poverty than adults. On average, poor households still have a rather large size (5.5 members) and have several dependents (2.6 dependents). Poor households are still very often headed by a person with low education. Finally, the fraction of poor households who owned a private car had barely changed since 2015, but the fraction of poor households who owned a smartphone had increased four-fold, likely reflecting the evolution of communication technologies rather than an improvement in their welfare.

_Table 5: Profile of the poor in 2017/18 (total expenditure)_

|Profiles among poor households (total expenditures)|Value for<br>poor HH|
|---|---|
|Children|36.3%|
|Fraction of<br>Elderly|16.3%|
|poor among<br>Men<br>Women|31.8%<br>30.1%|
|Average HH size|5.49|
|Average number of dependents|2.60|
|Fraction of poor HHs whose head has low education|95.0%|
|Fraction ofpoor HHs whose head is not currentlyworking|23.8%|
|Fraction of poor HHs that have some welfare-relevant assets|-|
|Fraction of poor HHs that have a private car/motorcycle or bike<br>Fraction of poor HHs that have any washing machine (electric, semi-automatic,<br>or automatic)|14.9%<br>-|
|Fraction ofpoor HHs that have a smartphone|34.6%|

**Source** : Authors’ calculations based on HIECS 2017/18. **Note** : “Low education” includes all illiterate individuals. “Currently not working” includes the unemployed and people not actively looking for a job. “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

Overall, the previous tables show that poverty increased over the period 2015 to 2017/18 but that the profile of the poor did not radically change. However, the interpretation of this increase in poverty is complicated by the hybrid nature of the official poverty measurement methodology. We now turn to the interpretation one can give to the increase in poverty.

# **3.3 Decomposition of the poverty trend over 2015 to 2017/18**

We address the difficulty associated with the interpretation of a hybrid definition of poverty lines by decomposing the evolution of poverty into the effect of changes in real total expenditures and the effect of changes in real poverty lines.

To correctly interpret the poverty trend, we apply a Shapley-Owen-Shorrocks (SOS) decomposition (Shorrocks 1999). To simplify its exposition, assume that all households have the same composition and 8
change much, the point estimates shift somewhat. Note, however, that a different interpretation of the data is that the (symmetric) order bias is simply lower in the MPL that compares one and two electricity vouchers vs. the MPLs that compare electricity and cash (see columns (1)-(3) of Table A.2). Note also that this approach to estimating bias is only possible with data from at least 3 MPL, and so in many cases may not be a strategy available to the researcher.

**(Re-)Introducing bias through standard procedures** In Section 2.3 and Table 1, we described some common approaches to addressing MSB, NSB and the interval nature of MPL data. Recall that all approaches discussed above distill the information from a subject’s MPL into an interval or even just a single point; some additionally drop inconsistent information or otherwise restrict the data. Here, we replicate some of these approaches to demonstrate how this influences estimated WTP.

We make our points mostly using interval regression estimates. Interval regression is a generalization of a tobit specification and uses one upper and lower bound for the WTP of each subject to estimate average WTP for an electricity voucher. Therefore the researcher has to assign an interval for WTP to each subject based on the observed MPL choices. This is often done by using the first and last observed “switch”. Subjects with MSB are thus assigned a wider interval to accommodate all of their choices, and switches between the interval endpoints are effectively not used.<sup>25</sup> Subjects with NSB can be assigned one-sided (open ended) intervals. They may alternatively be assigned a maximum or minimum of WTP based on the researcher’s assessment which values of WTP are plausible. For example, in the South Africa data, a WTP below -100 would indicate that a subject would rather pay cash than receive the R100 electricity voucher.

Table 3 columns (1) to (4) implement these two variants of interval regression (unrestricted max./min. WTP or restriction to _±_ 100). For all regressions in the table, odd columns use observations with descending value difference, and even columns with ascending value difference, mimicking again typical MPL data. They can therefore be compared with columns (1) and (2) in Table 2, respectively. As in the scaled probit, the WTP estimates are sensitive to choice order, but they are also attenuated toward zero; slightly more so when a maximum and minimum WTP are imposed in columns (3) and (4).<sup>26</sup> Some MPL implementations also suppress MSB observations, either ex ante or ex

> 25This follows the approach in Andersen et al. (2007).

> 26Note that the number of subjects is lower in (1) and (2) because of subjects whose upper and lower bounds are both undefined. This happens if a subject chooses option B (the lower-value option) in _t_ = 1 but option A in _t_ = 10 (again the lower-value option).

29
Policy Research Working Paper 5133

# **Abstract**

What are the main determinants of transport costs: network access or competition among transport providers? The focus in the transport sector has often been on improving the coverage of “hard” infrastructure, whereas in reality the cost of transporting goods is quite sensitive to the extent of competition among transport providers and scale economies in the freight transport industry, creating monopolistic behavior and circular causation between lower transport costs and greater trade and traffic. This paper contributes to the discussion on transport costs in Malawi, providing fresh empirical evidence based on a specially commissioned survey of transport providers and spatial analysis of the country’s infrastructure network. The main finding is that both infrastructure quality and market structure of the trucking industry are important contributors to regional differences in transport costs. The quality of the trunk road network is not a major constraint but differences in the quality of feeder roads connecting villages to the main road network have significant bearing on transport costs. And costs due to poor feeder roads are exacerbated by low volumes of trade between rural locations and market centers. With empty backhauls and journeys covering small distances, only a few transport service providers enter the market, charging disproportionately high prices to cover fixed costs and maximize markups.

This paper—a product of the Finance, Economics and Urban Development Department, Sustainable Development Network—is part of a larger effort in the department to examine the spatial efficiency equity tradeoffs of alternate public investments. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at slall1@worldbank.org. and hwang4@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the median.!° This estimation procedure does not require knowledge about the underlying distribution of the errors, nor does it require the assumption of homoscedasticity. The variancecovariance matrix of the parameter estimates are computed using bootstrap resampling (i.e. repeated resampling of the data to assess the variability of the estimates) because the quantile regressions underestimate the standard errors in the presence of heteroscedasticity (Gould, 1992)."!

- 3 How much of the poverty in rural China is transient?

3.1 Data For the purposes of this study, a new panel data set was constructed from the Household Budget Surveys done by China’s State Statistical Bureau (SSB). Since 1984 this has been a welldesigned and executed budget survey of a random sample of households drawn from a sample frame spanning rural China (including small-medium towns), and with unusual effort made to reduce non-sampling errors.’ Sampled households fill in a daily diary on expenditures and are visitedon average every two weeks by an interviewer to check on the diaries, and collect other data. There is also an elaborate system of cross-checking at the local level. The consumption data obtained from such an intensive survey process are almost certainly more reliable than those ‘0 For the estimators reported in this paper, p, = 2.q if the estimated residuals are positive (where q is the chosen quantile) and p, = 2(1 - q) otherwise.

‘! The standard errors reported in this paper are computed using 20 bootstrap replications. One could increase the number of replications to get more precise estimates for the variance-covariance matrix estimators, however this would increase the computational burden immensely. (For the current sample, i.e. for a sample of 4,743 observations with around 100 regressors, 20 replications take approximately an hour on a Pentium 590 omniplex). To test for the significance of the regressors, 20 replications are generally considered to be sufficient even in the presence of heteroscedasticity.

- 2 Chen and Ravallion (1995) provide a fairly complete discussion of how the survey was done.

10
migrants to be able to return to employment systems that principally reward years of service – Gibson and McKenzie (2012) find this to be an issue for highly skilled migrants returning to public sector jobs in developing countries. However, again there does not appear to be research which demonstrates the impact of policy efforts to better recognize skills earned abroad.

# **4.4. Why is the evidence base so bad on return migration policies?**

Table 3 shows that almost all policies directed toward returning migrants have no rigorous evidence as to their effects. The following example from IOM (2005, p.iii) is representative of the state of most evidence on return migration policies “This evaluation report summarizes the findings, conclusions and recommendations of a multistakeholder team using participatory approaches to an evaluation of IOM’s reintegration projects ... It reflects the application of a “learning-focused” process, aimed more at identifying lessons learnt than at assessing actual performance or impact levels.”. That is, the standard approach, if an evaluation is done at all, is to attempt to examine the process of the policy, with no reference to a counter-factual of what would have happened without the policy.

We can speculate at several reasons why very little in the way of rigorous evaluations have been done for these types of policies. A first reason is that the size of many of these programs is very small – a program taking 20 or 30 high-skilled workers over a couple of years is going to find it very hard to have sufficient statistical power to detect any impact of the program unless the impacts are massive. A second, related, reason is that many of these programs have not been very popular. As a result, there may seldom be a natural comparison group of people who wanted to participate in the program but who were not able to be accommodated. A third reason is that some of the intended outcomes (such as spillovers from high-skilled emigrants coming back) can be difficult to measure. A final reason may concern the types of organizations carrying out these policies. The International Organization for Migration (IOM) and national government migration agencies are the main implementers of many of these policies, and these agencies have not typically funded or had staff trained in rigorous evaluations. However, we believe none of these obstacles is insurmountable, and, especially given the dire state of existing knowledge, there is ample scope for future work to provide more credible evidence on these types of policies.

37
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said, endogeneity may still be an issue if, for instance, the expectation of further product concentration in a given sector (e.g. a long term decline in a major sub-sector) leads to policy decisions (e.g. tariff changes) or a decline of the sector as a whole.

Our key policy variable on the right hand side is the statutory tariff, computed as the average tariff in each 2-digit category.<sup>22</sup> Variables that capture more the structural changes of the economy include trade volume—which serves as our proxy for sectorspecific output effects<sup>23</sup> —along with measures identified in Section 2 as related to diversification potential.

The partial correlation between export diversification and two of the explanatory variables we use is significantly different from zero across all regressions. Intuitively, one may expect further diversification to be less likely in a sector that has already achieved a revealed comparative advantage, because the product space might already be very dense. However, the density of the product space may also produce synergies that lead to further diversification. Interestingly, the regressions show that an increase in RCA is associated with a reduction in the number of products exported (the coefficient entering with a negative sign). Yet, when using the Herfindahl index and the share indicator as dependent variables, the sign of the coefficient continues to be negative.

Although seemingly contradictory at first glance, tis finding may have interesting implications. The results may in fact suggest that, while revealed comparative advantage in a sector actually hinders the discovery of new products (presumably because the product space is already well populated), it nevertheless may prove conducive to diversification overall (potentially by strengthening the export of existing products previously exported at inefficiently low volumes). Thus, as a country’s relative specialization in a sector rises, diversification in that sector will occur more through exporting existing products to new markets. The finding by others (Amurgo-Pacheco & Pierola 2008; Brenton & Newfarmer 2009) that export growth along the extensive margin is weighted toward the geographic, rather than discovery, channel is thus consistent with the results presented here.

We find an analogous result for the trade volume variable, albeit in the opposite direction: Larger trade volumes are associated both with a higher number of products exported and more concentration in the sector’s exports. Intuitively, it makes sense that a larger sector is better able to discover new products; it is perhaps surprising, however, that the dominating effect appears to be one that strengthens subsectors that are already 22The fact that a full tariff schedule for Syria was only published in 2002 (from the Unctad Trains database) and 2007 (directly from Syrian Customs) determined the starting and ending point of our sample.

> 23Unfortunately, actual production output data were only available at the sectoral level at a highly disaggregated level—for agriculture and mining/manufacturing—and so were not usable for the analysis.

24
Figure 4: SMEs are more exposed than Large Firms to COVID shocks All firms: Average SME Effect within country-sector <!-- Start of picture text --> Demand Shock: % Firms decreased monthly sales vs 1 yr ago<br>Demand Shock: Average % drop in monthly sales vs 1 yr ago<br>Demand Shock: % Firms increased monthly sales vs 1 yr ago<br>(Export) Demand Shock: % Firms decreased direct/indirect<br>exports vs 1 yr ago<br>Supply Shock: % Firms decrease in supply of inputs<br>Finance Shock: % Firms decreased liquidity or cash flow<br>availability<br>-25 -20 -15 -10 -5 0 5 10 15 20 25<br><!-- End of picture text --> Firms with above average employment growth: Average SME Effect within country-sector <!-- Start of picture text --> Demand Shock: % Firms decreased monthly sales vs 1 yr ago<br>Demand Shock: Average % drop in monthly sales vs 1 yr ago<br>Demand Shock: % Firms increased monthly sales vs 1 yr ago<br>(Export) Demand Shock: % Firms decreased direct/indirect<br>exports vs 1 yr ago<br>Supply Shock: % Firms decrease in supply of inputs<br>Finance Shock: % Firms decreased liquidity or cash flow<br>availability<br>-25 -20 -15 -10 -5 0 5 10 15 20 25<br><!-- End of picture text --> Firms with above average labor productivity growth: Average SME Effect within countrysector <!-- Start of picture text --> Demand Shock: % Firms decreased monthly sales vs 1 yr ago<br>Demand Shock: Average % drop in monthly sales vs 1 yr ago<br>Demand Shock: % Firms increased monthly sales vs 1 yr ago<br>(Export) Demand Shock: % Firms decreased direct/indirect<br>exports vs 1 yr ago<br>Supply Shock: % Firms decrease in supply of inputs<br>Finance Shock: % Firms decreased liquidity or cash flow<br>availability<br>-25 -20 -15 -10 -5 0 5 10 15 20 25<br><!-- End of picture text -->

# _Source:_ World Bank Enterprise Follow-Up Surveys.

_Note_ : Chart reflects the average difference between SMEs and Large Firms within a country-sector with their standard errors. Country-sector fixed effects are included. Stratification weights adjusted for the follow-up enterprise surveys according to median eligibility were used to produce the estimates. The weights account for non-response, assuming that businesses that could not be re-contacted (unobtainable) have exited the market 9
2003/04 season, regular arabica from Bugisu sold at Ush 1,150/kg while organic arabica fetched Ush 1,350/kg, a 17 percent premium. The _2003/04_ UCDA _Annual Report_ cited farmgate premia in the order of 4 and 10 percent for robusta and arabica organic for the 2003/04 season.

Lastly, it is important to emphasize that the way in which statistics on specialty coffee are presented may misguide (often unintentionally) the policy making process. First, several reports present percentage changes without explicitly stating that the starting base is very low. For example, Uganda’s sales to specialty markets grew by 153 percent between 1999/2000 and 2000/01, but this staggering growth rate represents a change from 4,010 to 10,136 bags only, compared with 3.07 million bags of total coffee exports. Second is the stage in which the premia are calculated and presented. While specialty premia can be as high as 100 (or even 200) percent at retail level, their farmgate counterparts are nowhere near these levels, as discussed in the preceding paragraph. Third is selective presentation. For example, the _2003/04_ UCDA _Annual Re‐ port_ cited premia up to $0.42/kg for specialty coffees (p. 18), but only $0.03/kg (robusta) and $0.11/kg (arabica) when it came to organic coffee (p. 19), which accounts for half of Uganda’s specialty coffee sales.

# **_5.2 Introducing Value Addition_**

The objective behind the proposal for value added activities such as roasting and in‐ stant coffee manufacturing is to add income through employment generation. On a few occasions, though, the objective has been stated in terms of increasing prices received by growers through the direct relationship between growers and roasters. The attain‐ ment of these objectives, however, is questionable on several fronts. The first has to do with Uganda’s competitiveness with respect to value‐added coffee. Most roasted coffee requires blends with types of coffee not necessarily produced in Uganda. Therefore, for a successful blending and roasting industry, coffee must be imported from other coun‐ tries, thus subjecting the industry to the same problems currently faced by the export market (in particular delays at the port of Mombasa and higher transport cost due to Uganda’s landlocked status). Moreover, roasted coffee must be consumed quickly, a major impediment for a country where it takes green coffee as much as four (and often six) weeks to reach the port of Mombasa.

Second, development of instant coffee faces a number of difficulties since this type of coffee is typically promoted by large companies with their own trademarks and advertising campaigns. Instant coffee development strategies undertaken by Latin America coffee producers, while they led to some increase in instant coffee exports, evi‐ dence shows that the companies did not use large amounts of local labor force while most of the machinery was imported, thus making the impact to the local economy very limited (Talbot 1997). Hence, Uganda would need to focus on the regional market in light of the extremely competitive nature of the international market. However, even in

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