rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> <<br>”<br>n<br>—_<br>ba a SL eee<br>~e-- ><br>o<br>18 to 24 25 to 34 Age35 groupto 44 45 to 54 55 to65<br>e Selftemployed ——@ —- Wage worker<br><!-- End of picture text --> <!-- Start of picture text --> <<br>”<br>n<br>er<br>be mek ~ sel<br>: ieee ae =<br>°<br>18 to 24 25 to 34 Age35 groupto 44 45 to 54 55 to65<br>e Selftemployed ——@ —- Wage worker<br><!-- End of picture text --> <!-- Start of picture text --> <<br>n a_<br>SY<br>SY<br>~ NeNL . NS<br>NN<br>NS<br>- Saco@ = ~~~<br>o<br>18 to 24 25 to 34 35 to 44 45 to 54 55 to 65<br>Age group<br>e Self-employed ——@—- Wage worker<br><!-- End of picture text --> <!-- Start of picture text --> nN<br>-<br>@------eeea re<br>o<br>18 to 24 25 to 34 35 to 44 45 to 54 55 to 65<br>Age group<br>e Self-employed ——@—- Wage worker<br><!-- End of picture text --> <!-- Start of picture text --> <<br>”<br>a<br>@ LL<br>~ See ee ee Ue--7 _-*e<br>~~~ 9--<br>°<br>18 to 24 25 to 34 35 to 44 45 to 54 55 to 65<br>Age group<br>—®— Self-employed —~—@-—- Wage worker<br><!-- End of picture text --> <!-- Start of picture text --> <<br>e \<br>oad \ \<br>\<br>\ \<br>\<br>\<br>a \. \<br>\<br>\<br>\<br>\ \<br>- \en<br>ss<br>7 ry<br>18 to 24 25 to 34 35 to 44 45 to 54 55 to 65<br>Age group<br>—®— Self-employed —~—@-—- Wage worker<br><!-- End of picture text --> <!-- Start of picture text --> <<br>2 bane aN SA<br>_—<br>‘e<br>a NS SS XS ‘Sy<br>.<br>~ .<br>------ TaHHHHL<br>°<br>18 to 24 25 to 34 35 to 44 45 to 54 55 to 65<br>Age group<br>—®— Self-employed —~—@-—- Wage worker<br><!-- End of picture text --> <!-- Start of picture text --> ”<br>a<br>eo.<br>= ee —--°@<br>°<br>18 to 24 25 to 34 35 to 44 45 to 54 55 to 65<br>Age group<br>—®— Self-employed —~—@-—- Wage worker<br><!-- End of picture text -->
the adoption of less-efficiency-oriented regulatory regimes. Second, guarantees and various types of insurance can reduce the overall risks of projects, and hence reduce the risk of equity flight from the sector (Erhardt and Irwin, 2003). Third, the level of debt can be limited by other instruments, such as leasing agreements and allowing specific arrangements with infrastructure vendors. In some business lines, creative business development will allow improvements, but in most regulated industries facing low-income wage earners as their main client, cost control of some type is likely to be the solution. The main question that remains to be solved is then the extent to which cost cutting and creative business development appear the best ways to improve the operators’ capability to finance their investments and debts.

**Figure 1: Average book-to-market ratio for the companies in privatised infrastructure sectors**

<!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Transport service Transport infrastructure Electricity Gas Water<br><!-- End of picture text --> Source: Datastream, authors’ calculations.

Overall, these suggestions do not eliminate the fundamental problem that these sectors still need to find the necessary levels of finance to deliver on their investment commitments. Ineffectiveness in reducing the equity flight from the sector is likely to reduce the speed at which countries will be able to rely as much as they had hoped for on the private sector to help them finance their investment needs. But there is also a macroeconomic reason for concern. It is not unreasonable to expect that for some countries foreign participation is highly concentrated in these sectors, and increased international debt levels resulting from a switch from equity in public services may have balance of payment effects.

These concerns do not imply that regulators should regulate the financial structure of the company, but it certainly implies that it may be important for regulators to better monitor the leverage rates and their evolution to minimize the risks of unexpected shocks. It will also require a much serious commitment by all stakeholders to deliver the regulatory accounting systems needed to increase the transparency of the monitoring of the financial viability of companies that ultimately are responsible for delivering basic services in the poorest countries of the world.

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# participation

|Share of total land cultivated in crops (crop<br>mix)|||||
|---|---|---|---|---|
|Grains<br>63.34|26.30|66.47|25.99|0.000|
|Legumes<br>23.73|22.88|21.82|22.79|0.004|
|Tubers<br>1.69|8.23|1.31|6.89|0.577|
|Oils crops<br>0.41|3.85|0.47|3.88|0.403|
|Horticulture crops<br>3.95|10.95|4.17|11.35|0.862|
|Cotton<br>1.21|7.18|1.44|7.47|0.493|
|Tobacco<br>4.76|12.69|3.26|10.08|0.004|

Source: Generated by authors using LSMS data Note: PSLC=Primary School Leaving Certificate. JCE=Junior Certificate Examination. MSCE=Malawi School Certificate of Education Examination.

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**Figure 7: Impact of reaction shocks on EMDE financial and fiscal variables, by investment rate rating**

A. Impact of 25 basis point reaction shock on EMDE interest rates after one quarter B. Impact of 1 percent reaction shock on EMDE short-term bond yields after one quarter <!-- Start of picture text --> Basis points Percent<br>70 2<br>0<br>50<br>-2<br>30<br>-4<br>10 -6<br>-10 -8<br>3-month yield 10-year yields EMBI+ Equity prices REER Capital flows<br>C. Impact of 25 basis point reaction shock on D. Impact of 25 basis point reaction shock on<br>EMDE primary fiscal balance after 8 quarters EMDE gross government debt after 8 quarters<br>Percent of GDP Expenditure Percent of GDP<br>Revenue 3 Mean 90% confidence interval<br>Primary balance<br>3 2<br>1<br>2<br>0<br>1<br>-1<br>0<br>-2<br>-1 -3<br>-2 -4<br>-3 -5<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 -6<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Investment grade Noninvestment grade<br>Investment grade Noninvestment grade<br>E. Impact of 25 basis point reaction shock on F. Impact of 25 basis point reaction shock on<br>EMDE real GDP after 8 quarters EMDE consumer price inflation after 8 quarters<br>Percent Percent<br>1.5 Mean 90% confidence interval 0.3 Mean 90% confidence interval<br>1.0 0.2<br>0.1<br>0.5<br>0.0<br>0.0 -0.1<br>-0.5 -0.2<br>-0.3<br>-1.0<br>-0.4<br>-1.5 -0.5<br>-2.0 -0.6<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Investment grade Noninvestment Investment grade Noninvestment<br>grade grade<br>Non- Non- Non- Non- Non- Non-<br>Investment investment Investment investment Investment investment Investment investment Investment investment Investment investment<br><!-- End of picture text -->

# _Source:_ World Bank

_Notes:_ Panel non-linear local projection model with fixed effects and robust standard errors. Dotted lines reflect 90 percent confidence intervals. See table A3 for details. Models exclude observations during global financial crisis (2008Q4-2009Q4) and the COVID-19 pandemic. EMBI = emerging market bond index; REER = real effective exchange rate. A.B. Impact on first quarter ( ). Orange whiskers reflect 90 percent confidence intervals. B. Positive values of capital flows reflect an increase in net liabilities of portfolio and other investments for EMDE. Positive tt+1 values of the real exchange rate reflect an appreciation in the exchange rate. yy D. Reported as cumulative multiplier ( ). tt+h tt−1 yy −yy 28
instrumenting, we find a strong effect of migration: being in a migrant household is estimated to increase health knowledge by 0.65 standard deviations.

The remainder of Table 8 explores further this increase in health knowledge. If health knowledge is gained directly by the migrant member, then one should expect to see a much larger increase in maternal health knowledge if the mother herself has migrated compared to when her husband or other family member migrates. The problem in attempting to examine this is that households choose whether to send the future mother, father, or both, and households which choose to send a mother may differ along a number of dimensions, including health knowledge, to those which choose to send a father. We do not have a suitable instrument for determining which household member migrates, and so present two sets of results. Columns 3 and 4 compare households in which the mother is a migrant to households with no migrant members, while Columns 5 and 6 compare households in which the father is a migrant to households with no migrant members. We do indeed find that the increase in maternal knowledge associated with the mother migrating is larger than with the father migrating. However, we can not rule out that this result may just reflect a selection effect, whereby households in historically high migration areas will only send a mother rather than a father if the mother has a high level of health knowledge.

Health knowledge may also be transmitted from one migrant family to another, and from migrant households to non-migrant households in the same community. These spillovers can act to reinforce knowledge gained from migrant members, and may contribute to the large gains in health knowledge we observe. Columns 7 and 8 of Table 8 examine whether mothers in _nonmigrant_ households living in communities with a large number of migrants have more health knowledge than mothers in non-migrant households living in communities with fewer migrants in order to examine the evidence for health knowledge spillovers. We restrict our sample to communities where at least 50 households were surveyed for this analysis. After instrumenting we do find significantly higher health knowledge among non-migrant households in higher migration communities. The 2SLS coefficient finds that a one standard deviation increase in community migration prevalence results in a 0.11 standard deviation increase in health knowledge of non-migrant mothers. Since health knowledge is transmitted to non-migrant households, it appears likely that some of the large impact of migration on health knowledge in

- 16 -
firms, but disappointed to find that the experience required to get those jobs was beyond their reach. Finally, while firms seem to have met low educated workers at the fairs and had an interest in hiring them, these firms were used to recruiting such workers very easily, and with low wages, on the open market. The job seekers at the fairs, though ambitious, may have been surprised to hear how little even these formal firms were offering for low skilled positions. In the next section we study how the experience of attended the fairs may have influence the expectations of both workers and firms, and thus may have altered their job search and recruitment behaviour.

# **6 Indirect effects and learning**

The job fairs did not generate very many hires. But workers and firms came into the fairs with mismatched expectations about the types of matches they were likely to make. Did the fairs have some impact on their search and recruitment behaviour after the fairs?

To answer this question, we now explore changes in job-seekers’ search behaviour and in firms’ recruitment activities. We find clear evidence that both job-seekers and firms increased their efforts at search through formal channels. In particular, workers were more likely to visit the job boards during the weeks after the job fairs. Figure 2 below plots the fortnight-specific treatment effect of the job fairs, relative to fortnight 0 (when the first job fairs were held) and fortnight 8 (when the second job fair was held). These effects were estimated using weekly phone call surveys conducted with all job seekers in our sample, throughout the course of the study. We find significant effects — albeit short-lived — on the probability of visiting the job boards after each of the two fairs.

In Table 10, we study the impacts on firms’ recruitment activities outside of the job fairs. That is, we asked firms about their methods of advertising for vacant positions, and whether they conducted interviews with the applicants who applied. We find that firms invited to the job fairs were about six percentage points more likely in the last 12 months to have advertised for new hires (on a control mean of about 79%); they were 12 percentage points more likely to have advertised for professional positions (control mean: about 60%). They were also more likely to be using the job vacancy boards: the main place for attracting formal applications. All three results are significant, including after controlling for multiple hypothesis testing.

19
# _(Box 1)_

Will recent trends be an indication of what will happen in these countries? In China, per capita cereals consumption peaked and then declined once people were consuming 2600 or more kcal per person per day. Along with the decline in cereal consumption, the consumption of other foods was increasing. In India, on the other hand, per capita cereals consumption started to decline once people were consuming fewer than 2400 kcal per person per day. This decline in cereals consumption was not accompanied by significant increases in the consumption of other food calories, however: the reduction of calories from cereals was merely made up by increases in the consumption of vegetable oils, milk, sugar and fruit and vegetables. It is not entirely clear why this should be so, although change in tastes and preferences are one reason. But the nutritional transition (increasing then decreasing proportions of cereal calories in diets) seems to have occurred in India without the significant decrease in under-nutrition that normally accompanies it. If anything, the situation may have deteriorated: India still has the world’s largest number of poor people (around 250 million) who do not get two square meals a day. Again, the very high rates of growth in meat consumption in China in recent years obviously cannot continue for much longer.

There are also global as well as regional trends to consider, which will affect consumption patterns. For example, the slowdown in world population growth, and the attainment of peak population during the 2050s, could be expected to ease the rate at which agricultural expansion and intensification will pressurize resources and the broader environment. In the meantime, global agriculture still has a huge job to do, and even if production levels increase as projected, there are still likely to be real problems of persistent food insecurity in some countries, particularly those with continuing low food consumption levels, high population growth rates, and poor agricultural resource endowments. There are considerable challenges in projecting consumption trends to the middle of this century, and the uncertainties associated with what may happen in countries such as India and China only add to these.

Sources: Rosegrant et al. (2009); FAO (2006).

15
rate of one block per week, offering their service to all interested firms in the treatment blocks. Surveys in both the treatment and matched control blocks at 2, 4, 6, 10, 14, 26, and 52 weeks after the offer enable us to trace the trajectory of impacts on both firms offered the service, as well as spillover effects on uninterested firms in these same blocks (who were not offered the service).

We find initial interest in this solution is high, with 66 percent of interested firms (and thus 46 percent of all firms) in the treated blocks making a purchase from Agruppa. To begin with, Agruppa focused on the heaviest and highest rotation products, offering only five products to start with (potatoes, onions, plantains, tomatoes and spring onions). After four months they began adding items, eventually reaching 28 products. We find that firms taking up the Agruppa service did travel less to the central market of Corabastos, going 0.4 days fewer per week, spending less time at the market on each visit, and saving a total of 113 minutes per week in travel time. This reduced travel costs and improved an index measure of work-life balance. The offer of Agruppa lowered purchase prices for these five core vegetables by 6 to 8 percent. There was incomplete pass-through of these savings into consumer prices, with mark-ups increasing, suggesting imperfect competition. Uninterested firms operating in the same blocks as firms served by Agruppa also reduced their prices for these core products. However, those offered Agruppa services also cut back on sales of other products (since they were going to the market less), and their total firm sales fell 9 percent.<sup>1</sup> Usage fell over time, with only 24 percent of interested firms still using Agruppa after six months, and only 16 percent after one year. The difficulty of achieving sufficient economies of scale to both serve a large enough number of firms and to offer them sufficient variety of products led Agruppa to close business in January 2018, two months after our final follow-up survey.

The data collected through this experiment provide a rare opportunity to closely examine how multi-product urban small retailers price their goods and compete with one another. The final part of our paper uses our rich data to establish several key facts for understanding this behavior. We show that prices for fruits and vegetables change very frequently, and closely track the Corabastos prices over time. However, there is large cross-sectional dispersion in prices for the same good on

> 1 Note that a combination of poor record-keeping and distrust about taxes and crime means that item non-response rates for sales and profits are very high, so this sales figure should be viewed as suggestive only.

3
# **Table 1. Long-Term Influences on Emerging Market Spreads, 1991M1-2004M6**<sup>**15**</sup>

**-** **<u>Coefficient values (absolute t ratios)</u>**

|**explanatory variables**|**full sample**<br>**(1)**|**crisis periods**<br>**(2)**|**non-crisis periods**<br>**(3)**|**non-crisis**<br>**periods**<br>**(4)**|
|---|---|---|---|---|
|**(1) US variables**|||||
|**long-term interest rate**|**-.0192(0.94)**|**.2261(1.18)**|**-.0143(.74)**|**..**|
|**hi-low corporate**<br>**spread**|**.0739(9.08)**|**.0601(0.57)**|**.0794(10.0)**|**.0796 (12.4)**|
|**(2) country specific**<br>**variables**|||||
|**trade openness**|**-.9260(24.2)**|**-.0640(.08)**|**-.918(23.6)**|**-.913 (23.9)**|
|**debt/gni**|**.0093(7.75)**|**.0200(2.82)**|**.0087(7.77)**|**.0089 (8.09)**|
|**reserves/debt**|**-.0212(15.9)**|**-.0622(8.01)**|**-.0194(16.0)**|**-.0191 (16.0)**|
|**short-term/total debt**|**-.0291(19.8)**|**-.1055(4.37)**|**-.0283(20.2)**|**-.0280 (20.1)**|
|**(3) interaction**<br>**variables**|||||
|**US Tbill rate times**<br>**country’s debt/gni**|**.00066(3.21)**|**.0011(0.69)**|**.00084(4.31)**|**.00081 (4.35)**|
|**contagion dummy**|**.0115(1.20)**|**-.2123(2.56)**|**.0186(1.97)**|**..**|
|**constant**|**6.944(37.5)**|**7.213(6.36)**|**6.779(38.9)**|**6.707(89.1)**|
|**(4) statistics**|||||
|**no. of obs.**|**1551**|**54**|**1497**|**1497**|
|**R2**|**.5785**|**.8243**|**.5976**|**.5960**|
|**p-value for zero coeffs**|**.0000**|**.0000**|**.0000**|**.0000**|

> 15 Period of estimation depends on the country’s data availability.  Static regression of the log of spreads on the explanatory variables listed above, with all coefficients constrained to be the same for all countries.

16
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 3.5 . Net imports as a share of domestic demand (%)**

<!-- Start of picture text --> 100<br>75<br>50<br>25<br>0<br>-25<br>-50<br>-75<br>-100<br>Trade-expansion Trade-stagnation<br>Imports as % of domestic demand Chad<br>Cameroon Gabon<br>Republic<br>Central African Congo, Rep. Equatorial Guinea<br><!-- End of picture text --> Source: Derived from Rosnes and Vennemo 2009. _Note:_ CAPP = Central Africa Power Pool; TWh = terawatt-hour.

When the Democratic Republic of Congo is mapped to the SAPP rather than the CAPP, the CAPP relies primarily on Cameroon for cost-effective hydropower generation. Nevertheless, in reality, the Democratic Republic of Congo could potentially supply the CAPP with some of the power to be developed at the Inga site. Analysis suggests that with the development of 9,500 MW of additional hydropower capacity under the Inga III and Grand Inga 1 projects, the Democratic Republic of Congo could export as much as 52 TWh to the SAPP. Since demand growth in the CAPP region over the same period is not projected to be much more than 10 TWh, a small share of the Democratic Republic of Congo exports could be diverted northward to the CAPP, making a significant contribution to the power supply in this area.

To illustrate the implications, a scenario in which the Democratic Republic of Congo exports 5 TWh of power via the Republic of Congo into the CAPP is considered. These imports displace some 550 MW of hydropower capacity for export in Cameroon and a further 300 MW of hydropower capacity for domestic consumption in the Republic of Congo that would otherwise have been developed. But even though imports from the Democratic Republic of Congo to the Republic of Congo replace imports from Cameroon to some extent, they do not eliminate them. Congo still imports 1.3 TWh from Cameroon, and Gabon still relies heavily on Cameroon. Allowing the Democratic Republic of Congo to export to the CAPP provides overall annual savings of $220 million. Of this total, $180 million is from reduced investment in generation capacity in the CAPP region, while the rest is due to lower operating costs and fewer cross-border transmission investments. This, however, does not account for costs incurred in the Democratic Republic of Congo.

40