rafmacalaba/datause-encoder-data huggingface dataset:
**Table S13. Change in business revenue loss over time**

||Business Revenue<br>Loss|
|---|---|
|Round 2|-1.010***|
||(0.112)|
|Round 3|-1.026***|
||(0.134)|
|Ethiopia|0.032|
||(0.115)|
|Nigeria|-0.145|
||(0.108)|
|Uganda|-0.569***|
||(0.090)|
|Ethiopia-Nigeria|0.084|
|Ethiopia-Uganda|0.000|
|Nigeria-Uganda|0.000|
|Round 2-Round 3|0.907|
|Observations|7,242|
|PseudoR-Squared<br>_Note_: The table reports|0.030<br>the result from a single|

_<mark>Note</mark>_ <mark>: The table reports the result from a single</mark> ordered logit regression in which the dependent variable equals 1 if revenue is higher than the previous period, equal to 2 if revenue is the same as in the previous period, equal to 3 if revenue is less than the previous period, and 4 if they received no revenue. The ordinal variable is then regressed on indicators for each country, with Malawi as the base case, and indicators for each phone survey round, with round 1 as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country or wave indicator equals the coefficient on an indicator for a different country or round. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

40
53 from the change. Obviously, such changes will face considerable political opposition from the losing governments.

In Pakistan, the 1973 constitution calls for a National Finance Commission to meet every five years to reconsider how provinces are to share federal government tax revenues. The current sharing allocation is based upon recommendations made by the Commission in 1974. Although another Commission was appointed in 1979, its recommendations were never made public, possibly because of the political repercussions which changes in the sharing formula might create.

Practices Vis-a-Vis Principles The discussion in the previous section suggested that there are a number of objectives that can be attained through grant systems. Again, there is often a considerable difference between actual practices and these principles. Here we review,several of these differences.

Revenue Growth. Ideally grant revenues will grow to allow recipient governments to meet increasing spending needs caused by increased input prices and expanding demands for public sector services. The data discussed above regarding GDP elasticities of government transfer suggest, however, that grant programs do not always result in such revenue growth. An example of such a grant program that has created considerable fiscal pressure at the local level can be observed in Bangladesh. In 1981 the Government of Bangladesh abolished one of the more productive and buoyant local revenue sources then used by urban governments--the octroi-on the reasonable economic grounds that the tax on imports into a city inefficiently restrained trade. To help offset the revenue losses
# **Private Chinese Investment in Africa: Myths and Realities**

# By Xiaofang Shen

The recently increased Chinese investment in Africa has grabbed the world’s attention. Press views and opinions triggered by this new development are starkly polarized. At one end of the spectrum, one hears praise for China’s role in filling financial and technological gaps for Africa, a role which western companies have been reluctant to play especially since the 2008 financial crises. At the other end, one hears considerable skepticism regarding the motivation behind Chinese investment and its consequential impact on Africa, including charges that China is recolonializing the continent.<sup>2</sup> At first glance, available statistics do not justify the degree of attention China’s investment in Africa is receiving. Official data from the Ministry of Commerce (MOF) on Chinese overseas investment shows total Chinese outbound foreign direct investment (OFDI) in 2011 reached a record high of about US $74.65 billion, but only $1.7 billion, or a mere 2.2%, went to Africa.<sup>3</sup> Compared against the global foreign direct investment (FDI) flowing to the continent that year, around $42.65 billion according to the United Nations Conference of Trade and Development (UNCTAD, 2012), China’s contribution was a mere 4%. When measured in terms of FDI stocks, Chinese OFDI in Africa appears even more trivial – reportedly standing at $14.7 billion by the end of 2011, or 2.6 percent of the total $570 billion FDI stock for the whole African continent.<sup>4</sup> So why is China’s investment attracting so much attention?

One reason is the pace at which Chinese OFDI has risen in Africa, which, truly, has been breathtaking. Until about 15 years ago, China’s capital flow to Africa was almost all government-aid related. According to the MOC, China saw only a negligible amount of $56 million direct investment in the continent by 1996; this number jumped to $1.5 billion by 2005, and again multiplied 10 times to nearly $15 billion by 2011. Most noticeably, just as the world FDI outflow nosedived following the 2008 financial crisis, China’s overseas investment more than doubled in 2008, with the part going to Africa actually more than tripled that year and increasing steadily thereafter. Given the pessimistic prospects for the western economy, many

> 2 Among the numerous media reports on the recently hiked Chinese investment in Africa, one representative piece on the controversy is “Beijing, A Boon for Africa,” by Dambisa Moyo, _The New York Times_ June 27, 2012.

> 3 MOC in April estimated the 2011 ODI to the world at $60 billion, and 2011 ODI to Africa at 1.7 billion.  In August, MOC adjusted the 2011 ODI to the world to $74.65 billion, without breakdown adjusted figure of ODI to Africa.

> 4 All figures are calculated using basic data from China’s Ministry of Commerce, China, _2010 Statistical Bulletin of China’s Outward Foreign Direct Investment_ and UNCTAD, _World Investment Report 2012._

3
outlay on the particular service varies across the relevant welfare indicator.<sup>4</sup> Though this approach has its definite uses, it also has drawbacks. These are becoming well recognized (see Selden and Wasylenko 1992) and many of them are discussed in detail throughout PSP. I shall now try to bring together the main points.

In evaluating the distribution of the benefits, individuals are ranked by an often imperfect welfare indicator. It may not adequately capture the true distribution of living standards due, for example, to measurement problems. But it may also be a misleading representation of the welfare distribution in the absence of government spending. The principal aim of incidence studies is to see how the initial "pre-intervention" position of individuals is altered as a result of public spending. Hence, an approximation of the pre-intervention position is required. This is typically estimated by a welfare indicator such as income or consumption which does not include the monetary value of the benefits secured from publicly-provided goods. This may be a poor approximation since the level and composition of public spending affects incomes and expenditures: individuals often spend their incomes in ways that reflect the level of provision of public goods and services. There may also be some shifting of benefits, such as if wealthier households reduce their transfers to poorer ones as a result of the latter benefiting from a public program, as Cox and Jimenez argue (PSP). Similarly, behavioral responses through time allocation can entail that income net of earnings from workfare employment generally underestimates income in the absence of access to such employment, as Ravallion and Datt emphasize (PSP). These are all instances of the general problem of specifying the counterfactual, to which I shall return.

Another limitation of this approach is that costs may be a poor proxy for individual benefits received. The cost represented by a nurse's administration of a polio and DPT vaccine cannot possibly reflect the value to a child of a lifetime free of polio, tetanus, pertussis and diphtheria. Moreover, social benefits are not captured. The impact of a mother's pre- and post-natal good health 7
# Are Health Shocks Different? Evidence from a Multi‐Shock Survey in Laos

## by

## Adam Wagstaff<sup>a</sup> and Magnus Lindelow<sup>b</sup>

a Development Research Group, The World Bank, Washington DC, USA b East Asia and Pacific Region, The World Bank, Washington DC, USA

**Keywords:** Shocks; health; consumption‐smoothing; self‐reported well‐being; Laos.

**Acknowledgements:** The survey tool that was used to collect the data presented in this paper was jointly developed with Caridad Araujo, Valarie Kozel, Shubham Chaudhuri, and Jamele Rigolini. The field work was implemented by Indochina Research. Our thanks go to Natsuko Kiso and Helle Alvesson for assistance with the data analysis, and to Martin Ravallion for helpful comments on an earlier version. The findings, interpretations and conclusions expressed and any errors are entirely ours; the views expressed do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent.
# **Table 2c Trends in Real Unit Labor Costs in Local Currency and US Dollars:2003 = 100, Other High Transition Economies**

||2000-04|2005-08|2009-11|2012-14|2015-17|
|---|---|---|---|---|---|
|BelarusLC|100|108|106|115|121|
|USD|92|164|164|199|151|
|BulgariaLC|101|101|120|127|142|
|USD|91|138|188|190|183|
|CroatiaLC|105|98|101|94|90|
|USD|94|123|134|114|91|
|Czech RepublicLC|97|97|100|102|102|
|USD|86|127|143|132|110|
|EstoniaLC|100|101|105|104|114|
|USD|89|138|160|161|154|
|HungaryLC|97|100|100|102|98|
|USD|85|122|122|113|93|
|LatviaLC|101|97|108|102|112|
|USD|90|155|181|169|158|
|LithuaniaLC|101|95|96|88|97|
|USD|94|125|136|122|115|
|North MacedoniaLC|101|95|108|103|99|
|USD|92|112|135|124|106|
|PolandLC|101|91|90|87|87|
|USD|99|126|122|111|91|
|RomaniaLC|107|91|96|93|98|
|USD|96|160|179|166|153|
|Slovak RepublicLC|99|99|104|103|109|
|USD|88|118|122|112|97|
|SloveniaLC|104|105|109|105|102|
|USD|90|122|133|119|97|
|MeanLC|101|98|103|102|106|
|MedianLC|101|98|104|102|102|
|Mean USD|91|133|148|141|123|
|Median USD|91|126|136|124|110|

Sources: UNECE, ILO and SEE jobs Gateway for wages, WDI for real GDP, persons employed, GDP deflators 13
<!-- Start of picture text --> Figure 2.2. The labor market in SDGSIM<br>5<br>4<br>3 Wage<br>curve<br>Demand<br>2<br>1<br>0<br>85 90 95<br>100 - unemployment rate (%)<br>Wage<br><!-- End of picture text --> Source: Authors’ elaboration.

The basic accounting structure and much of the data needed to implement SDGSIM is derived from a social accounting matrix (SAM). The model also relies on complementary stock, elasticity, and base-year employment data.<sup>2</sup> For this study, a new Yemen SAM was built for 2020, primarily drawing on data from the World Bank, the IMF, and the UN. While the quality of data for Yemen is low, the construction of the SAM had the advantage of imposing consistency between data from different sources, as a result producing a coherent database that captures well-known structural features of the Yemeni economy. The disaggregation of the SAM and the model database is presented in Table 2.1.<sup>3</sup> It distinguishes 17 production activities and commodities (or products); seven primary production factors including two labor categories (male and female), private capital, and four natural resources (agricultural land, fish stock, and mineral stocks for oil and gas and other mining); and one household. The government is defined broadly to include donor expenses and receipts.

In 2020, the country’s GDP was 13,999 billion Yemeni riyals; with an exchange rate of 743 riyals per US$ and a population of 29.8 million, this corresponds to a GDP per capita of US$623. Tables 2.2a and 2.2b show a macro version of the 2020 SAM and its accounts, respectively. The SAM, which is expressed as percent of GDP, is highly aggregate – among other things, it has only one production sector (i.e., one activity producing one commodity) – making it easy to learn about the macro situation in 2020 from the SAM. The government current account deficit was 4.5 percent (of GDP) (cell [cap-gov,gov]) while government consumption and investment amounted

> 2 For additional details on the SAM, see the appendix.

> 3 The disaggregation is primarily based on the most recent UN data. Although it would be possible to further disaggregate the sector assuming that the patterns of the 2014 IFPRI SAM still are valid, this does not seem meaningful: it would not add anything significant to the analysis and, furthermore, the extent to which these patterns also were valid in 2020 is unknown.

4
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24
demand or command more. This is confirmed by tables 2 and 3 which show that doctors and nurses charge more often than the other categories of staff. Doctors also levy a significantly higher amount, almost twice the national average. Though administrators charge less often than nurses, they collect a bit more money from informal payments. Hospital attendants indeed have limited extortion power; they fare best in Khatlon with an average 6.2 somonis a month and do not have access to this source of income in Dushanbe and GBAO. The average health worker supplements her income with 28.8 somonis a month from direct charges on the patients.

Informal payments are important both by their extent and scale. Table 3 shows that the average monthly intake from informal payments represents 61.8 percent of the sector per capita monthly wage bill including allowances and other supplements. Doctors in Dushanbe make almost twice, 1.8 times, their salary from informal payments. Men charge more aggressively and levy about 2.5 times the amount of informal payments collected by women. These numbers appear more in line with the reality of corruption than the amounts involved in experimental studies. It is in fact rare in experiments that the average gain of participants exceeds $10 which probably may not impact on people’s behavior. Finally, the status of GBAO is noteworthy. Only 2.8 percent of the health workers and 4.8 percent of the doctors admit they resort to informal charges for income generation. Also, none of the administrators or hospital attendants confessed charging patients. Moreover, the average monthly levy in GBAO is just 1.5 somonis.

Table 4: Probability <u>of Moonlighting (%)</u>

||**Doctor**|**Nurse/Feldsher**|**Administrator**|**Hosp. Att**|**All**|
|---|---|---|---|---|---|
|**Dushanbe**|25.7|20.8|25.0|0.0|22.4|
|**Sogd**|25.8|19.0|27.7|23.3|22.6|
|**Khatlon**|17.6|9.5|17.9|8.8|12.8|
|**RRS**|33.9|36.5|34.4|9.5|30.3|
|**GBAO**|14.3|2.2|0.0|0.0|3.7|
|**Men**|29.4|37.0|31.1|50.0|31.6|
|**Women**|12.8|13.0|12.4|9.2|12.1|
|**Tajikistan**|23.8|16.9|21.4|10.1|18.5|

**Source** : Authors’ calculations from Tajikistan Health PETS 2006 data **Note** : Gorno-Badakhshan Autonomous Oblast (GBAO) and Rayon under Republican Subordination (RRS) are regions

13
# **5 RESULTS**

# **5.1 Macroeconomic CGE**

For economy-wide analysis, a macroeconomic CGE for Vietnam was calibrated to the new 2000 Vietnam SAM for a ‘business as usual’ baseline. This reference scenario was then used to evaluate comparative static experiments provided by GTAP global liberalization results. To implement the latter, we obtained data from GTAP on induced price and external demand changes for the purpose of re-calibrating Vietnamese exports against downward sloping external demand functions. Finally, we assume the so-called Hertel-Keeney medium-run closure. That is, all factors are fully employed before and after experiments, labor and capital are mobile across sectors, but we maintain a specific factor (land) in agriculture. There is no imperfect competition or economies of scale or dynamic gains from trade (Hertel, 1997).

In this paper, we compare three counterfactual scenarios to the baseline, which we call Unilateral Liberalization (UL), Full Liberalization (FL) and Doha Special and Differential Treatment (DSDT)<sup>6</sup> . The baseline scenario corresponds to the ex ante case. In the UL scenario, we assume that Vietnam’s last offer to the WTO is accepted, the country joins the organization. We assume that Vietnam removes all import tariffs and export subsidies. However, Vietnam’s trading partners maintain baseline protection levels with respect to this country and all others. In this case, the benefits of fuller participation in the international economy are severely limited by Vietnam’s inability to penetrate new markets, and the gains of domestic price reform have more limited impact on the growth of income.

The FL scenario includes the same external policy, but embeds this into a larger agenda. This scenario is calibrated to protection rates from the Vietnamese WTO offer, but further assumes that Vietnamese export prices and demand patterns shift according to consensus estimates for a FL scenario obtained using the GTAP global trade database and model. This scenario would greatly expand export opportunities for Vietnam, allowing it to take fuller advantage of efficiency gains arising from border 18
We measure the impact of urbanization on rural poverty in surrounding rural areas, distinguishing between the _location_ and the _economic linkage_ effects, using a large sample of Indian districts for the 1983–1999 period. During this period, the country urbanized at a relatively slow rate: the urban population was 23.3 percent of the total in 1981 and 27.8 percent of the total in 2001 (Government of India 2001). However, given the sheer size of the Indian population, this moderate increase translates into a massive rise in the absolute number of urban dwellers (126 million). This number represents an increase of almost 80 percent in the urban population over this period. These figures mask large variability in urbanization patterns at the subnational level; in particular, districts have urbanized at very different rates (figure S3.1a in the supplemental appendix, available at http://wber.oxfordjournals.org/). For instance, a district such as Idukki in Kerala increased its urban population by 13,000 (+29 percent) between 1981 and 2001, whereas the urban population increased by 1.6 million (+416 percent) in Rangareddi (Andhra Pradesh) and by 2.4 million (+130 percent) in Pune (Maharashtra) over the same period. In the subsequent analysis, we attempt to exploit this variability to identify the impact of urbanization on rural poverty.

India also provides an interesting case in terms of the policy environment and economic performance given the structural changes in economic policy, the rate of growth, and the poverty levels experienced by the country during this period. Despite disagreements regarding the extent to which economic growth increased the welfare of India’s poor, poverty in India declined steadily in the 1990s, particularly in rural areas (Kijima and Lanjouw 2003). The geography of the decrease in the share of the poor, however, is extremely varied (figure S3.1b in the supplemental appendix, available at http://wber.oxfordjournals.org/). Although over 30 percent of the rural 4
**Figure 3.3. Base: Domestic final demands 2022-2030 (bn 2020 YER)**

<!-- Start of picture text --> 1,800 400<br>1,600 360<br>1,400 320<br>280<br>1,200<br>240<br>1,000<br>200<br>800<br>160<br>600<br>120<br>400 80<br>200 40<br>0 0<br>Hhd cons. Gov't cons. Private inv. Gov't inv.<br>Household consumption<br>Other indicators<br><!-- End of picture text --> Source: Authors' calculations based on simulation results.

**Figure 3.4. Base: Real annual macro growth 2023-2030 (%)**

<!-- Start of picture text --> 0.0 0.5 1.0 1.5 2.0 2.5<br>Absorption 2.0<br>Household consumption 2.0<br>Government consumption 2.0<br>Private investment 2.2<br>Government investment 2.0<br>Exports 1.0<br>Imports 1.8<br>GDP at factor cost 2.0<br><!-- End of picture text --> Source: Authors' calculations based on simulation results.

15