rafmacalaba/data-use-mentions-extended huggingface dataset:
efficiency of product and output development and they also train co-workers with lesser skills. In industrialized countries, governments have increasingly emphasized the importance of employer-led training. Training provides workers with the skills necessary for improving competitiveness, adaptability and GDP-growth. Furthermore, skill acquisition may reduce wage inequalities.

**Table 1**

|**Highest Level of Com**|**pleted Educati**|**on**(%)|
|---|---|---|
|All|Women|Men|
|No education<br>1.6|1.6|_1.5_|
|Primary<br>40.9|35.0|42.3|
|Secondary<br>53.5|57.0|52.5|
|Vocational<br>_1.5_|1.7|1.5|
|University<br>_1.5_|2.3|1.3|

Data Source: RPED data.

The incidence of training off-the-job in Zimbabwe is low as compared to industrialized and semi-industrialized countries. Data indicate that a small number of firms in Zimbabwe train workers formally. Around 17 percent of the employees receive formal training in-house or by outside providers. The low number may indicate that manufacturing enterprises in our sample underinvest in formal training. Biggs _et al._ (1995) compare Zimbabwe to Japan and find that only about one-fifth of the number of people trained in Japan are trained in Zimbabwe, and around half of the number in WestGermany. Tan and Batra (1995) find that in Taiwan 38 percent of large firms and 4 percent of small firms train employees in-house.

In Zimbabwe, external training is carried out in technical schools and training courses, and sometimes abroad. The metal sector trains its employees more than any other manufacturing subsector. More than 40 percent of the firms supply in-house training to their employees. The metal sector also engages more than other sectors in external training. This
Policy Research Working Paper 10541

# **Abstract**

Shocks and seasonality may have profound effects on poor households’ wellbeing, especially in contexts like the Sahel where livelihoods depend on rainfed agriculture and pastoralism. Understanding how seasonal variation affects Sahelian households is therefore essential for guiding policies that jointly seek to address chronic poverty, seasonality, and unexpected shocks. This paper uses harmonized household survey data from Burkina Faso, Chad, Mali, Niger, and Senegal, collected in two distinct waves in 2018 and 2019, to examine the extent of seasonal deprivation in the Sahel. These data reveal significant seasonal variation in poverty and wellbeing. Mean real monetary consumption is around 10.5 percent lower in the lean season. Moreover, rather than representing a reduction in dietary diversity, this drop is concentrated in staple foods (especially cereals), implying that seasonality brings about extreme forms of deprivation. Welfare losses may begin early in the lean season, even as early as April. When the data were collected in 2018/19, the climatic conditions were relatively benign and the security situation was more stable than today, so the effects of seasonality shown in this paper likely represent a lower bound. On policy, although initiatives currently focus on responding to unpredictable shocks, seasonal food insecurity could be better tackled by expanding social protection and providing regular transfers early in the lean season, when prices are lower and fewer households have succumbed to extreme deprivation. Seasonal variation happens every year and more can be done to support Sahelian households if there is information on how it will perennially threaten their wellbeing.

This paper is a product of the Social Protection and Jobs Global Practice and the Poverty and Equity Global Practice. 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://www.worldbank. org/prwp. The authors may be contacted at jlain@worldbank.org and
Policy Research Working Paper 9805

# **Abstract**

Do Sahelian countries face specific risks of water-related conflict? Sahelian countries face growing fragility and climate challenges—especially those belonging to the Group of Five Sahel States (known as the G5 Sahel)—Burkina Faso, Chad, Mali, Mauritania, and Niger. This study examines how their relation to water availability and irrigation infrastructure factors in. It documents that the G5 Sahel countries, given their high baseline water scarcity and state fragility, face a higher risk of conflict over water resources compared to the rest of Africa. This is demonstrated through empirical analyses using geospatial data and exploiting (i) climate-induced variation in water availability, and (ii) an event study analysis of conflict trends, which sharply increased post-2010 in the region following the Arab Spring and the rise of the Boko Haram. Irrigated areas are found to be important for buffering against weather shocks but are also more prone to targeting during conflict events compared to non-irrigated regions. The evidence suggests that this reflects increased competition for scarce (fertile) resources between state and rebel groups on this climate frontier with a well-documented history of agropastoral conflict. Other regions of Africa are not found to experience similar conflict related to water resources. These findings are especially pertinent for informing projects and policy interventions in fragile countries as post-COVID-19 recovery and climate action plans are rolled out.

This paper is a product of the Water Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at akhan31@worldbank.org and arodella@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
data. Furthermore, small firms or individuals involved in small scale trade and thus lacking a license are assigned generic codes.

The main shortcoming of using the license number has to do with the identification strategy relevant for this study. In order to deduce firm-level decision-making, firm-level characteristics helping exporters, firm-level risk management strategies etc. we assume that behind the export licenses there are individual firms. This crucial assumption can be invalidated when the licenses belong to brokers, freight forwarders, import-export firms etc., entities that have little to do with the export survival behavior that is of interest here. Moreover, they can reflect other behaviors linked to a firm‟s identity that are also irrelevant for our purposes, such as mergers, divisions, changed names to avoid taxes etc.

When comparing the customs data with international trade statistics such as those collected by COMTRADE, a number of discrepancies appear. This is most striking when comparing the export destination dollar-value amounts, in particular regarding trade with China, but also the actual number of trade partners. Several possible explanations for these differences exist. Firstly, Lao PDR does not report directly to COMTRADE, and the data used for comparison is mirror data collected from the destination countries. This means that some of the information could be skewed by re-exports, and in value terms there could be significant differences between FOB and CIF values. Similarly, electric power does not appear consistently in the customs data and leads to underreporting when compared with the mirror data. Also of particular concern is the fact that a not insignificant sum of border trade goes unreported (smuggling, traditional trade, illegal logging etc.). In addition, errors in the data collection and data entry procedures can lead to _Exporting from a small landlocked economy_ _4_
discrepancies could be lessened with more detailed micro-level household data and more detailed administrative information on how particular elements of the system operate. While acknowledging the limitations of the CEQ methodology with the existing data for Cambodia, the analysis does provide useful insights into the operation of the tax and benefit system in Cambodia.

# **5. Potential Reforms and Policy Direction**

To enhance the poverty-reducing effect of fiscal policy, the following Conditional Cash Transfer (CCT) simulations provide examples of potential policy reforms which could address the challenges of the current system. Each reform scenario has a specific cost and scale of poverty reduction. As presented earlier, the IDPoor equity card presents a useful device for targeting CCT towards households at the lower end of the income distribution. The card can be combined with other features of households, such as their demographic characteristics, location, or access to facilities to further narrow down the groups of recipients. In countries where such a program is not in place, targeting often relies only on the latter information which often makes targeting less precise and means that significant proportions of benefits are often allocated towards richer households.

Increasing the amount of targeted cash transfers to households with children under 3 has a sizable poverty-reducing effect. Simulations demonstrate increasing cash transfers targeted to IDPoor households with young children from about US$ 63 per year (US$ 190 over 1,000 days) to US$ 200 per year would reduce the poverty rate by 0.11 percentage points. Similarly, a targeted cash transfer of US$ 400 per year would reduce the poverty rate by 0.23 percentage points. Policies calibrated to the same total costs but implemented in a universal fashion (i.e., without the use of the IDPoor equity card to identify eligibility), thus allocating lower average values to the households which fulfill the demographic requirements, have a much smaller poverty-reducing effect.

Table 6: Poverty reduction and cost of four CCT scenarios aimed at IDPoor households

|**CCT scenario**|**Design**
that researchers and decision makers are cognizant of the options available, the additional information required for their interpretation, and the risk of creating false certainty through obfuscation of important gaps in evidence, attribution, and valuation.

The use of the Impact Inventory in addition to value aggregation will assist in transparent representation of results, and it is expected that a comprehensive assessment of the Return on Digital Investment will involve a series of quantitative and descriptive outputs.

# 6. Using the DHI Economic Evaluation Framework

## 6.1 Case Example ONE

The overarching concept when applying the framework is to “comply or justify” – that is, users should attempt to adhere to the steps and principles of the framework, but where this is not feasible or appropriate, should provide justification for the methodological choice. In this way, the framework will facilitate methodological transparency over conformity, improving the overall usefulness of economic evaluations of DHIs.

This section details the initial planning steps for application of the framework to the evaluation of an information system and predictive analytics intervention to serve as an example of how the framework can be used to plan the methodological approach. As more evaluations utilize the framework, further examples will enable refinement of methods specifications.

The example analytic plan utilizing the framework is an economic evaluation of the Orbit data capture system and the Zenysis data analytics platform compared to a pre-implementation scenario in a targeted population HIV prevention program in South Africa. Orbit is a webbased data collection and information management system for capturing programmatic performance information. Data entry is completed by health workers and enables the generation of multiple monitoring and evaluation indicators. The pre-implementation scenario against which Orbit is compared is an Excel-based information management system, where health workers would electronically send completed Excel templates monthly, entered
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- Biau, G. (2012). Analysis of a Random Forests Model. _Journal of Machine Learning Research_ 13, 1063– 1095.

- Boeing, G., Besbris, M., Schachter, A., & Kuk, J. (2021) Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots _? Housing Policy Debate_ , 31(1), 112-126.

- Borba, J. O. & Dentinho, T. P. (2016). Evaluation of Urban Scenarios Using Bid-rents of Spatial Interaction Models as Hedonic Price Estimators: An Application to the Terceira Island, Azores. _The Annals of Regional Science_ 56(3), 671-685.

- Borde, S., Rane, A., Shende, G., & Shetty, S. (2017). Real Estate Investment Advising Using Machine Learning _. International Research Journal of Engineering and Technology_ 4(3), 1821-1825.

- Borio, C., Kennedy, N. & Prowse, S. (1994). Exploring Aggregate Asset Price Fluctuations across Countries: Measurement, Determinants and Monetary Policy Implications. BIS Economic Papers, Basle, Switzerland: BIS.

- Bossuyt, D., Salet, W. & Majoor, S. (2018). Commissioning as Cornerstone of self-build Housing. Assessing the Constraints and Opportunities of self-build in The Netherlands. _Land Use Policy_ 77, 524533.

Breiman, L. (2001). Random Forests. _Machine Learning_ 45(1), 5-32.

- Bricongne, J-C., Turrini, A., & Pontuch, P. (2019). Assessing House Prices: Insights from ‘Houselev’, A Dataset of Price Level Estimates. _European Economy Discussion Papers 101_ .

- Bricongne
certain plot characteristics. We are unable to consider the role of behavioral factors, due to a lack of good measure of these in the panel data survey. In addition to inorganic fertilizer and hybrid seeds (for which we analyzed the financial returns in Section 4), we consider the use of pesticide, also critical in increasing overall agricultural productivity.

First, improved access to economic markets should enable input use by farmers. Better access to economic markets should facilitate the availability of agricultural inputs for farmers, and should also be an incentive their use as it facilitates the commercialization of the final output by lowering transportation costs. Following a common approach in the literature (see Blankespoor et al. 2016), for every household in each year of the panel, we use a domestic market access index that is a function of the weighted sum of the population of cities with a population larger than 100,000 inhabitants, where the weight decreases with transport time.<sup>16</sup> More specifically, the definition of market access in a household location _i_ at time _t_ ( MA�,� ) is the following:

> 16 We thank Brian Blankespoor for the support in estimating the market access index.

22
Policy Research Working Paper 5717

# **Abstract**

Although good and timely information on agricultural production is critical for policy-decisions, the quality of underlying data is often low and improving data quality could have a high payoff. This paper uses data from a production diary, administered concurrently with a standard household survey in Uganda to analyze the nature and incidence of responses, the magnitude of differences in reported outcomes, and factors that systematically affect these. Despite limited central supervision, diaries elicited a strong response, complemented standard surveys in a number of respects, and were less affected by problems of respondent fatigue than expected. The diary-based estimates of output value consistently exceeded that from the recall-based production survey, in line with reported disposition. Implications for policy and practical administration of surveys are drawn out.

This paper is a product of the Agriculture and Rural Development Team, Development Research 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 author may be contacted at kdeininger@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
<mark>collapses on both poverty and cumulative income changes along the distribution. Still, the average outcome is regressive relative to expectations based on an assumption of proportional distribution of losses.</mark> <mark>The remainder of the paper proceeds as follows. Section II presents a short overview of the data used. Section III presents the methodology and discusses potential issues, as well as how we address them. Section IV presents our findings. Section V concludes.</mark>

# **II.** **<mark>Data</mark>**

<mark>To better understand the social costs of default, we use three main types of variables: those measuring the social costs, those measuring the default crisis events, and those used to construct the synthetic controls in our estimations.</mark> <mark>We use different variables to assess the social costs associated with sovereign default. Some of these variables measure demographic aspects such as infant mortality, life expectancy and population. Infant mortality is defined as the number of children who die before reaching their first birthday per 1,000 live births. Life expectancy is measured at birth in years for the total population as published by the World Development Indicators.</mark> <mark>Other variables measure access to resources or income, such as poverty headcount, calorie supply, and energy supply. The poverty headcount measures the percentage of people living below the $1.90 per day poverty line as published by the World Bank (Poverty and Inequality Platform - PIP). Calorie supply measures the average availability of food in the country (not the actual consumption or its distribution) as computed by the Food and Agriculture Organization. Energy supply assesses the amount of energy usage in a country from any source as published by the OECD World Energy Balances.</mark> <mark>Our data on sovereign defaults to private external creditors comes from Farah Yacoub, Graf von Luckner and Reinhart (2022) and takes
et al., 2020).

Using several rounds of Business Pulse Survey (BPS) data of the World Bank for six countries in South Asia, this paper quantifies the heterogeneity in the impact of the COVID-19 crisis and the recovery of firms by their informality level. Our results suggest that: _First_ , there is a huge overlap in the productivity distribution across formal and informal firms, suggesting that the reasons for staying informal at the lower end of the distribution might be very different than at the higher end. This also points to the possibility that the shock may not affect all informal firms uniformly.

_Second_ , in line with the existing literature, our BPS data confirms that compared with formal firms, informal firms are smaller, younger, and in sectors with low entry costs. They have lower productivity, with consistently lower levels of management and technological capabilities. Even the better-managed informal firms do not seem to grow in size, especially 2
price and FX puzzles in many EMEs. We first confirm that a standard VAR model controlling for commodity prices as well as global demand and supply shocks produces the price and FX puzzles in EMEs (but not in advanced economies). Alternative variable orderings, identification via highfrequency external instruments, and a factor-augmented VAR, do not resolve these puzzles. Our key result is that the puzzles disappear or are mitigated when we include forward-looking variables. When we incorporate survey-based expectations from professional forecasters, consumers, and firms (as proxies for central bank forecasts) into the model, the empirical results align with theoretical predictions of monetary policy transmission in EMEs.

The critical role of expectations is confirmed through proxy VAR models, which use instrumental variables for monetary policy shocks based on single-equation policy reaction functions. Our results point to an _omitted variable_ problem: standard VAR models fail to capture future inflation developments, reflecting the high volatility of macroeconomic and financial variables in EMEs (Castelnuovo and Surico 2010). In addition, the opposite directional responses of output and inflation to monetary policy shocks (based on the mis-specified models) suggest that the omitted variables may mainly reflect the supply-driven shocks, which are more prevalent in EMEs.<sup>6</sup> Based on estimation of a model augmented with the survey data—to identify “true” monetary policy shocks in EMEs—a one percentage point contractionary monetary policy shock is followed by declines in output and prices (of around 0.9 and 0.3 percent, respectively), along with an increase in short-term interest rates (roughly 0.5-0.6 percentage point), currency appreciation (0.6 percent), and a fall in stock prices (nearly 2 percent). The significant transmission of monetary policy shocks into different financial markets (bond, stock, and currency) and macroeconomic outcomes (output and prices) are observed in a majority of EMEs.

We also find that, in many EMEs, the price puzzle disappears when the FX channel is controlled using sign
<!-- Start of picture text --> Numbers of Harvest cycle Livelihood<br>HRD Appeals<br>months post Farmers actions (average farmer) protection Early Warning Data Process and<br>harvest threshold and and Response<br>Response<br>survival threshold<br>-2 Look for non-farm work<br>Rainfall fails<br>-1 Eat less preferred food<br>0 Harvest what is there<br>LIAS data available<br>Use savings, sell non-productive assets<br>1 Harvesting Borrow money from those not affected LEAP data available<br>Cut back on durable purchases<br>2 Two-season: planting for Cut back on input investments<br>HRD released<br>next season (if two cropping seasons)<br>3<br>Reduce food intake<br>4<br>5<br>Respond to save<br>6 One season: planting for livelihoods<br>next season<br>7 Sell productive assets Possible Response<br>through PSNP FCB<br>8<br>9 Possible Response<br>through<br>10 Respond to save humanitarian<br>Increased mortality<br>lives assistance<br>11<br><!-- End of picture text --> FIGURE 5: A STYLIZED TIMELINE OF DROUGHT, FARMER COPING STRATEGIES AND RESPONSE VIA PSNP FCB AND HRD APPEALS (ADAPTED FROM CLARKE AND HILL 2012) Typically, if crops are lost due to the failure of rains, during the first two months following the harvesting season households employ relatively low cost coping strategies. These may include changing the composition of their food intake, looking for alternative employment opportunities, selling non-essential assets or borrowing money. When the use of these initial coping strategies is insufficient to ensure adequate food security, households may resort to higher cost coping strategies, such as reducing food intake and selling productive assets<sup>58</sup> , or, for