rafmacalaba/data-use-mentions-extended huggingface dataset:
where _Yi j t_ is the outcome if individual (household) _i_ , in locality _j_ , surveyed in year _t_ . _Catchment Area j_ is a dummy variable set equal to 1 if the community is located within 0-5km from a coffee mill and 0 otherwise. _Postt_ is a dummy defined equal to 1 if the coffee mill is operational at the time of the survey and 0 otherwise. _Catchment Area j × Postt_ is essentially a dummy set equal to 1 for those living within 5km from a coffee mill that is operational during the survey year and 0 otherwise. _λt_ represent location and survey year fixed effects respectively. All other variables remain as previously defined.

# **6.1 Income**

The underlying premise of this paper is that the opening of a coffee mill leads to an income shock to residents within the catchment area of the mill, due to the value-addition of coffee washed in these mills relative to those washed using traditional methods. While the direct effect of the income shock will be borne by coffee farmers, the indirect effect on the local economy cannot be overemphasized.<sup>19</sup> To provide evidence on this channel, we leverage the wealth and asset poverty measures from the DHS dataset, due to the absence of income measures from the DHS dataset.<sup>20</sup> Table 7 presents results on the association between coffee mills and household income/welfare using proximate measures of household poverty status and wealth score from the DHS dataset.<sup>21</sup> The results show a positive income effect of coffee mills on treated households. Households living within the catchment area of coffee mills experience a 0.05 std increase in their wealth score (column 1), and a 4 percentage points (pp) decrease in the probability of being (asset) poor (column 3).

# **6.2 Child Health**

Conditional on the positive income effect of coffee mills in the catchment area, a proximate outcome is an improvement
Policy Research Working Paper 7581

# **Abstract**

Reducing child undernutrition is a key social policy objective of the Ethiopian government. Despite substantial reduction over the past decade and a half, child undernutrition is still high. With 48 percent of children stunted, underweight, or wasted, undernutrition remains an important child health challenge. The existing literature highlights that the targeting of efforts to reduce undernutrition in Ethiopia is inefficient, in part because of the lack of data and updated information. This paper remedies some of this shortfall by estimating levels of stunting and underweight in each woreda for 2014. The estimates are small area estimations based on the 2014 Demographic and Health Survey and the latest population census. It is shown that small area estimations are powerful predictors of undernutrition, even controlling for household characteristics, such as wealth and education, and hence a valuable targeting metric. The results show large variations in share of children undernourished in each region, more than between regions. The results also show that the locations with larger challenges depend on the chosen undernutrition statistic, as the share, number, and concentration of undernourished children point to vastly different locations. There is limited correlation between the shares of children underweight and stunted across woredas, indicating that different locations face different challenges.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aambel@worldbank.org or tpavesohnesen@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
of transport costs

## _2.1 Export performance_

In recent years, South Africa’s export performance has been disappointing. While the commodity boom from 2000 to 2010 translated into huge value creation for many mining countries, South Africa failed to take full advantage of the good times. China, India and Brazil recorded nominal annual growth in mining production value of at least 20 percent while Australia, Chile and the Russian Federation grew annually between 10 and 16 percent. But South Africa achieved nominal annual growth of 8 percent – which is only slightly above average inflation. Crucially, this growth was mainly from the pull of high prices rather than volume growth, which was flat (World Bank, 2014). Poor export performance has resulted in a 15 percent decline in South Africa’s share of world exports since 2011.

**Figure 1: South Africa’s export performance (2005 – 2013) and change in shares of world exports (2011 – 2014)**

<!-- Start of picture text --> 800<br>700 20<br>600 15<br>500 10<br>400 5<br>300 0<br>200 ‐5<br>100 ‐10<br>0 ‐15<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Brazil Chile Hungary Indonesia<br>India Mexico Poland Russian Federation<br>South Africa Turkey<br>Export value index (2000=100)<br>China Turkey India Mexico Poland Colombia Russia Malaysia Thailand Hungary Brazil Chile Ukraine Peru Argentina Indonesia South Africa<br><!-- End of picture text --> _Source: first panel: World Bank Development Indicators and second panel: Comtrade_ _3_
available, individuals’ safety and health concerns might be assuaged and therefore, they are more willing to supply their labor. Alternatively, vaccination can lead to increases in labor demand as the economy recovers and particularly, for those workers who are vaccinated to mitigate spread of the virus at the workplace. While it is not possible to quantitatively distinguish whether supply- or demand-side drivers are behind the findings in Table 1, the supporting evidence seems to be in favor of the latter. Firstly, labor force participation has been declining consistently and has not shown signs of an uptick since India’s vaccine campaign was launched. Unemployment, however, has recovered. This suggests that instead of additional workers joining the labor force, vaccination may have allowed existing job seekers to match with employers. Secondly, there are anecdotal reports that employers are requesting new hires to be vaccinated and are prominently displaying signs stating that all staff are vaccinated. This is corroborated by survey data when we look at the individuals who get prioritized for vaccination in households where not all, but only some eligible adults are vaccinated. In Figure 17, we see that male, employed, and prime-working age adults are more likely to get priority for being vaccinated. This may be due to costs or behavioral constraints in accessing vaccination despite it being freely provided at government health centers. The priority of ‘primeworking’ members over others in the household suggests that they have greater returns from vaccination, likely through the labor market.

# **5 Discussion**

Covid-19 was an unprecedented health crisis across the world and in India. This paper examines the impact of the crisis on labor market outcomes in India, which has mostly been in the form of various downward transitions. Individuals affected by Covid19 transitioned out of employment or the labor force altogether. People who remained employed moved into more vulnerable employment arrangements like casual-wage work or self-employment. This is also accompanied by greater volatility in the probability of positive wages and reduced earnings for the Covid cohort. The reduction in median wages holds even
less|<br>-|...-<br>5|----------------------|------------------<br>58|------------<br>63|
|13-35 months|-|79|-|1|80|
|3 years|118|572|31|-|721|
|4 years|347|-|22|-|369|
|Totalsample|465|656|53|59|1233|

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

_______ In **a** series of regressions, each relating to one of the years 19691976, earnings (EARN) are regressed against type of training program (TP), holding constant the effects of training trade (TT), length of course (LEN), 8
implementation of the Uruguay Round agreements, and China’s progress towards WTO accession (which alone contributed to the ratio of global exports plus imports to GDP rising from 44 to 46 percent over those four years).

According to this dataset, the average import tariff for agriculture and food in 2001 was 16.0 percent for high-income countries and 17.7 percent for developing countries, while for manufactures other than textiles and clothing it was 8.3 percent for developing countries and just 1.3 percent for high-income countries (Table 1). The averages of course obscure large variations across countries and commodities, and are poor indicators of overall assistance to farming. For example, if high-income countries’ tariffs on temperate farm products are at a near-prohibitive 100 percent, but zero on tropical products such as coffee, those countries’ import-weighted average agricultural tariff could be quite low even though agricultural value added in those rich countries had been enhanced substantially. Consider also the case of a developing country with a strong agricultural comparative advantage in all but one small farming industry, and with high tariffs to stave off import competition for that industry and for all manufacturing industries. Overall agricultural value added would be depressed by that structure of protection, yet the import-weighted average tariff protection for agriculture would be high and possibly above that for manufactures. A third case is where the non-agricultural primary sector receives a similar level of import protection as the farm sector and less than the manufacturing sector, but is much more export-focused: trade reform may cause it to expand at the expense not only of manufacturing but also of farming. Hence it is not possible to say from the tariff data in Table 1 whether developing country policies have overshot in terms of moving away from an antiagricultural bias, even though the ratio of agricultural to all goods tariffs in that table is well above unity for each of the regions shown. What is needed to address that issue is a general 6
produce an extremely volatile and time-inconsistent poverty profile, resulting in a continuous re-ranking of provinces in terms of poverty rates ( Figure 1). However, the different (and arguably superior) design of the IAF 1996/97 produced higher statistical reliability and, as a consequence, greater coherence between poverty figures and other welfare indicators.<sup>8</sup> The more complete food list in the IAF 1996/97, which included 50 items, most likely led to better consumption measurements, reduced underreporting, and more limited concentration of reported food purchases in the pre-printed items.

**Figure 1: Official Poverty Rates, by Province, by Survey Year**

<!-- Start of picture text --> Official Poverty rates<br>1997 2003 2009<br>Niassa & Cabo Delgado-ruralNiassa & Cabo Delgado-urbanNampula-ruralNampula-urbanSofala & Zambezia-ruralSofala & Zambezia-urbanManica & Tete-ruralManica & Tete-urbanGaza & Inhambane-ruralGaza & Inhambane-urbanMaputo Provincia-ruralMaputo Provincia-urbanMaputo City<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text -->

**Official:** 2008/09 PLs (MPD-DNEAP, 2010); **Simulated:** 1996/97 official PLs deflated by the consumer price index from World Development Indicators (2011)

The authors of the NPA (DNEAP, 2010), after carefully examining the problems in the design and implementation of household surveys, arrive at similar conclusions regarding the magnitude of underreporting and its spatial concentration. They propose, as a preliminary solution, the re-estimation of expenditures for households whose calorie intake falls below a certain threshold. These adjustments produce a substantial downward revision of poverty figures at the regional level in the South and a 3 percent lowering of the poverty rate at the national level. However, since the 2002/03 data also experienced similar problems in estimating poverty in the South, the NPA’s authors argue that even though there is some regional variation, ‘the trend between the two surveys results is very
between 1986‐2017). To circumvent these problems, we build on a new class of spatial statistical network model for stream data to interpolate and fill in missing observations across monitor‐year pairs (ver Hoef and Peterson, 2010). The model takes advantage of the fact that water quality in a station downstream depends on environmental conditions and human activities upstream of the station, and on the water quality “received” from upstream (directed network). Therefore, spatial covariates in a well‐defined upstream area and the spatial dependence between observations based on stream distance allow to model water quality and predict it in unsampled locations. We train such model to fill‐in missing observations. We then collapse monitor level observations to the district level. A more complete description of the model is provided in Appendix 1.

We focus on cumulative exposure to nitrate‐nitrogen when the concentrations exceed safety thresholds of 10mg/l from the year of birth to age 3. Prior work suggests that the first 1,000 5
# _Regime shift of type B: Marked increases in real interest volatility_

In order to identify shifts of this type, we estimated an econometric model of each country's _ex ante_ real interest rate and looked for large forecast errors. Specifically, we fitted a simple error-correction model for each country's real interest rate, assuming that it could be modeled as a function of changes in the world interest rate,'<sup>8</sup> of the gap between the world and the local interest rate, and by some autoregressive dynamics.'<sup>9</sup> The estimates were by recursive least squares, and we tested the fitted equations for break-points indicated by systematic failure of one-month ahead recursive forecasts.<sup>20</sup> Examples of the procedure are shown in Figures 7 (a) and (b) for India and Kenya. The recursive residuals from the dynamic interest rate model are shown, bracketed by _5_ per cent confidence intervals. In the lower panel is plotted the probability level at which the hypothesis of no structural change can be rejected in each period. For the Indian data, March 1990 is identified as the break point, and for Kenya, March 1993. Plots for all the countries, together with details of the estimated regressions on the full period, and on the before- and after-liberalization sub-periods are provided in Appendix 2. Note that this method cannot detect a gradual increase in volatility.

As the method identifies regime change events with short-term increases in volatility, the subsequent finding that volatility remained high after the change is not an inevitable and tautological consequence of the identification method, but represents an independent observation. (Indeed, to verify this, we also computed post-event volatility removing a six-month window after the event.)

> 1 We used the first principal component of the 8 large industrial country real TB rates as a proxy for the world rate. '9 The model employed is equivalent to that of equation (1') in the next section
For each disaster type and each event, there is uncertainty on the impacts. We therefore build three different scenarios, which are described in Table 1.

_Table 1 Low, medium and high values for the impact of disasters_

||Low|Medium|High|
|---|---|---|---|
|Vulnerability to floods,<br>storm surges,<br>cyclones, earthquakes,<br>tsunamis|Medium ‐30%|Combination of the<br>USGS PAGER building<br>inventory and simple<br>vulnerabilitycurves|Medium +30%|
|Exposure to storm<br>surges, cyclones,<br>earthquakes,tsunamis|Medium ‐30%|GAR|Medium +30%|
|Exposure to floods|Medium ‐30%|GLOFRIS|Medium +30%|
|Exposure to drought|Medium ‐30%|GLOFRIS|Medium +30%|
|Impact of droughts on<br>farmers’ income|0|10|20%|
|Number of people<br>affected by higher<br>food prices for each<br>farmer affected by a<br>drought|Medium ‐30%|50|Medium +30%|
|Impact of drought on<br>local foodprices|10%|55%|100%|

_Table 2 Reconstruction time after disasters by return period and for the three scenarios_

|||R<br>|et|urnperiod<br>||
|---|---|---|---|---|---|
|Scenarios|0‐10|10‐100||100‐500|>500|
|Low|0.5|1||2|3|
|Medium|1|2||3|5|
|High|3|3||5|10|

Note that we are building a counterfactual scenario
WPS3798

# **What Have We Learned From a Decade of Manufacturing**

**Enterprise Surveys in Africa?** *

Arne Bigsten** and Måns Söderbom***

## **Abstract**

In the early 1990s the World Bank launched the Regional Program on Enterprise Development in several African countries, a key component of which was the collection of manufacturing firm-level data. In this paper we review the research based on the data sets generated by these and subsequent firm surveys in Africa, with a special view to what we think are the most important policy implications. We survey the research on the African business environment, focusing on market size, risk, access to credit, labor, and infrastructure. We cover the research on how firms choose to organize themselves and how firms do business. We review the research on firm performance, including firm growth, investment and technology acquisition, and exports. We conclude with an extended discussion of the policy lessons.

## **JEL Classification** : D21, O12, O14.

**Key words** : African manufacturing, economic performance, survey data.

World Bank Policy Research Working Paper 3798, December 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We thank the World Bank for funding this research. Many thanks go especially to John Nasir for encouraging us to write this paper and for excellent comments on an earlier draft. We are also grateful to
), STP had a similar increase as the five countries in the comparison group, at the same level as Maldives and the Seychelles. As a result, it seems that STP had an increment mostly based on flights with fewer seats. The ration of seats per flight declined from 109 in 2010 to 67 in 2016. Figure 14, indeed, shows that STP is a unique case for this abrupt decline.

The connectivity pattern is the result of its colonial past and cultural affinity with Portugal. Another barrier, as in the case of STP, the language spoken is Portuguese, which is not widely known outside Portuguese-speaking countries, and which does not represent a large tourist-issuing market. An obvious implication of this is that STP workers in the tourism industry should have appropriate knowledge of foreign languages.

The lack of connectivity can be clearly observed when analyzing flight connections as a full network graph. Take all countries with flight data to be a collection of nodes or vertices, where origin and destination of flights define an edge or link. Consider, thus, an unweighted and undirected network analysis of the flight data for 2016, with special emphasis on STP and related tourism destinations. The unweighted network structure is preferred because the main interest is on the connectivity potential of STP and not in the number of flights, which are related to the node size (i.e., tourist capacity), but on its potential for connectivity (i.e., attracting tourists). Consider also an undirected network as most destinations have the same amount of incoming and outgoing flights. Of interest for a given node is a measure of centrality which determines how important each node is for the full network structure and cohesion.

14
**Table** IV-l ,

||||UsePrivete<br>||Characteriltics<br>of<br>Contract<br>ith Pit|Project|Evaluation|
|---|---|---|---|---|---|---|---|
|Country.<br>Title<br>of Project|Funder,<br>Status<br>of Activity|Project<br>Summary|Distribution<br>S7te.|Employe<br>Benefit|w rvae<br>Providers|Training|Coqamnt|
|FlOIUinWwrjDzU|Sm!SAr<br>VIES(<br>|cont'd)<br> <br> d<sup>i</sup>|Ys|Yes|No. PVO|Yes, of|Assessat|
|Thailanid<br>Factory-Based<br>Family Plaing<br>Services<br>iimuri<br>mv*uno|USAID Enterprise<br>Progra<br>Propoal<br>t=in<br>MFMini<br>RA|Projet<br>WIU extenda<sup>mprove</sup><br>Necha's<br>existing<br>factory-based<br>family planning<br>servicce.<br>KIS<br>for operation<br>and evaluation<br>to<br>cut costs.<br>IUP survey to deter-<br>m<br>d_mnd.<br>Foods will also<br>reorientation<br>nMd Ltrelamling<br>of existin<br>pror<br>**d expur-**<br>dion to maw sites.<br>Mn|||<br>Implementing<br>projet|<br>volunteer<br>motivatora|of progre|
|Indonaesi|||||N/A|
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Information and communication technologies

## **Achievements**

Despite Zimbabwe‘s political and economic situation, it has maintained parity with Sub-Saharan African averages for many ICT indicators. Fixed telephone line penetration exceeded the Sub-Saharan average in 2008 and Internet access penetration was about the same. Mobile penetration has lagged due to chronic network underinvestment as a result of the country‘s economic situation. But the market more than doubled in 2009, lifting mobile subscriptions per 100 inhabitants to 24, reducing the gap between Zimbabwe and the rest of Sub-Saharan Africa (table 14).

**Table 14. Benchmarking ICT indicators**

|||**Zimbabwe**<br>**Zimbabwe**|**Lower-**<br>**income**<br>**group**|**Sub-**<br>**Saharan**<br>**African**<br>**region**|
|---|---|---|---|---|
||Unit|**2000**<br>**2005**<br>**2008**|**2008**|**2008**|
|GSM coverage|% population under signal|48<br>56<br>59|56|56|
|International bandwidth|Bits/person|1<br>4<br>10|24|34|
|Internet|Users/100 people|0.4<br>3.9<br>4.5|4.6|5.1|
|Landline|Subscribers/100 people|2.2<br>2.8<br>2.7|4.6|1.5|
|Mobile phone|Subscribers/100 people|2.7<br>5.9<br