rafmacalaba/data-use-mentions-extended huggingface dataset:
35 percent of GDP, respectively, in 2000–17), while remittance inflows account for 2.26 percent of GDP (World Bank 2018b). Among the three external shocks influencing the evolution and shifting of the structure of capital flows in Sub-Saharan Africa, the 2011–12 European sovereign debt crisis may have caused a larger decline in flows to Africa than the global financial crisis. For example, other investments inflows to Sub-Saharan Africa increased after 2007. Portfolio investments inflows remain relatively low because of the underdevelopment of domestic financial systems —i.e. the activities in those markets are very limited, especially among fragile countries. However, portfolio investment inflows increased after the global financial crisis in non-resource-rich and resource rich countries in the region. The behavior of capital inflows across Sub-Saharan African countries is heterogeneous, given the country differences in the size and structure of the economy, different availability of resources, level of development, and political systems and regulations.

We use annual data on total gross capital inflows for a sample of 136 countries (of which 45 countries are in Sub-Saharan Africa) from 1980 to 2017. Gross inflows are defined by the sum of foreign direct investment (FDI) inflows, portfolio investment (PI) inflows, and other investment (OI) inflows. We present the empirical results from IV estimation to control for the likely endogeneity of capital flows with instruments such as real output and the commodity terms of trade index. Our regressions include countryand time-effects.

The regression analysis presented in this paper examines the determinants of gross capital inflows and investigates the relative importance of global vis-à-vis domestic factors in driving gross inflows to the region. It is crucial to identify the key drivers of capital inflows because understanding these drivers will help design the policies that address the macro-financial risks arising from these flows. Therefore, the regression analysis estimates the importance of the drivers of gross capital inflows to the region. It distinguishes the role of these drivers in the different components of aggregate gross inflows, such as FDI, PI, and OI
many of the non-income MDGs are unlikely to be achieved by broad groups of countries; in fact, only a handful of low-income countries have met or are likely to meet the MDGs for under-five mortality, access to safe drinking water, and access to sanitation (Figure 7(c) and (d)). These countries need development policies that address the structural bottlenecks for successfully accelerating economic growth and moving toward achieving the MDGs.

**Figure 7(a) – (d):  Progress Towards Meeting the MDGs**

<!-- Start of picture text --> (a) Population living on less than $1.25 a day, % (b) Number of Countries by Progress Status for MDG 1<br>70 50<br>45<br>60<br>40<br>35<br>50<br>30<br>40 25<br>20<br>30<br>15<br>X<br>20 SSA Target 10<br>5<br>Total<br>10 0<br>SSA On Target Close to Being On Far Behind the<br>0<br>Target Target<br>1990 2005 2015<br>(c) Number of LIC s that have achieved the goal (d) Number of LIC s that are on track to achieve the goal*<br>10<br>6<br>9<br>8 5<br>7<br>4<br>6<br>5 3<br>4 2<br>3<br>2 1<br>1 0<br>0<br>Under-five mortality Access to safe Access to sanitation<br>Under-five Access to safe Access to drinking water<br>mortality drinking water sanitation<br><!-- End of picture text --> Source: _Global Monitoring Report_ 2011. Figures c) and (d) compiled
key parameters underlying learning in Section 7.

# **3.3 Data**

We collected four rounds of detailed household surveys, complemented by monitoring data on demonstration plots, to support our analysis; we now discuss these data and their application to our analysis.

**Household surveys** Between 2012 and 2015, we administered four rounds of household surveys of farmer group members across our experimental sample. Each survey round was timed to immediately follow the harvest of the dry season; Figure 1 provides a detailed timeline of these survey rounds. Each survey round collected detailed records of agricultural production and technology adoption during the dry season, including adoption of the new flood-saline-resilient seeds promoted by the project. We leverage data from these surveys at the level of the village, household, household-survey wave, and household-crop-survey wave. Appendix A provides a detailed description of all variables used in our analysis and details their construction, and Appendix Table A6 shows how our sample size changes across survey waves and all sample restrictions used in our analysis.

**Baseline survey** The baseline survey was administered on a sample of 1,407 IAPP farmer group members within 78 randomly selected villages of the 110 villages in our experimental sample.<sup>14</sup> In addition to household characteristics, and the dry season agricultural data collected in each survey wave, the baseline survey included a module on social network linkages, in which we asked each household questions about their interactions with all other farmers in their farmer group (sampled or not). In our analysis, we consider a household to be in another household’s social network if they report speaking to them, or were reported being spoken to by them, about farming in the last three months; to ensure consistent measurement across households, for all regressions leveraging social network relationships we restrict our sample to farmers and villages surveyed at baseline.

> 14The baseline survey covered 279 of the 386 eventual demonstrators, including 24 of 41 regular demonstrators, 81 of 115 shared demonstrators,
# **4.5 Price adjustments for poverty measurement**

The shortcomings of price indices used in the computation of real (spatially adjusted) welfare indicators are well known; the most serious and frequently discussed issues have to do with limitations of price data, namely the lack of reliable information on non-food prices, and the flaws of unit values as a proxy for market prices (Deaton 1988, Deaton and Dupriez 2011, Gibson and Rozelle 2005, Gibson and Kim 2019, Amendola, Mancini, Redaelli and Vecchi 2024). The result is that price indices used in poverty measurement typically have limited coverage (they are _food_ price indices) and their reliability is often called into question. As serious as this problem is, viable solutions are few and far between. The proposed paper should assess strategies to overcome the problems raised by limited-coverage price indices in poverty measurement, including, potentially, the calculation of more comprehensive Paasche indices (incorporating for example rent prices), the use of alternative methods (such as the CPD method, as seen in Chen et al. 2020), and even the switch to an implicit approach based on multiple poverty lines (see WP 3).

# **4.6 Consistency and specificity in poverty measurement**

The idea of a trade-off between the consistency and specificity of poverty lines was popularized by Bidani and Ravallion (1994). A strand of literature that includes some recent contributions (Ravallion and Lokshin 2006; Arndt and Simler 2007, 2010; Mahrt, Herforth, Robinson, Arndt and Headey 2022) has investigated possible solutions, related to the imposition of revealed preference conditions to construct utility-consistent poverty lines that vary across households. However, these extensions are not yet widely popular in applied work. The proposed paper should take stock of this literature, assessing the different strategies available and working towards the establishment of a “best practice”.

# **4.7 How sensitive are poverty estimates to poverty measurement methods?**

The results of a poverty assessment are sensitive
pre-specification of a primary outcome accompanied by a link to the relevant sample size calculation conducted prior to trial commencement; and, in cases where appropriate, insistence on the intent-to-treat principle for the primary analysis.

There are a few productive avenues of inquiry we leave to future research. MonteCarlo simulation of the impacts of different types of bias using existing data from economics RCTs and censuses could illustrate the likely magnitude of the biases outlined here. Standards on reporting related to generalizability, discussed elsewhere (Allcott 2015; Vivalt 2015), are arguably of similar importance and there is a rich literature on how to assess this in reports of RCTs (Rothwell 2006).

Lastly, we would like to mention that a major weakness of our study is the number of assessors we used. Our assessment task was a long and tedious one and almost certainly not without some human error. An increase in the number of evaluators for each paper would almost certainly improve the reliability of our results. Nonetheless, our independent initial assessment by multiple individuals follows best practice in systematic review and the high level of agreement in our independent assessments suggests a high degree of objectivity. The application of our assessment tool to ongoing research would shed additional light on how recent efforts to improve the quality of economics RCTs have fared.

# IV. CONCLUSION

In this study, we make two main contributions. First, we identify several lessons from the medical literature on sources of bias in RCT estimates that are applicable to economics RCTs. Second, we use these lessons to assess the adequacy of reporting and risk of six major biases in economics RCTs published in 52 top economics journals 34
upon request. In particular the analysis regresses the ratio on a constant, the log of per capita GDP in 2005 PPP terms, the log of the population and the log of market potential measure as a proxy for the country’s remoteness. GDP and population are taken from the World Development Indicators, while the market potential measures are taken from CEPII database and are computed using the Redding and Venables (2004) or the Head and Meyer (2004) method.

> 20 For example, Luxembourg has a very high ratio because a small economy that is integrated into Europe will naturally buy its imports from overseas. Among other explanations, Australia has a fairly low ratio because it is geographically remote and production must use more upstream inputs that are locally sourced.

29
empirically document it is still very limited.

# **2.3 The main data challenges**

The above discussed unresolved aspects are amplified when combined with the issue of severe data gaps at the microeconomic level. First and foremost, migration – especially international migration – is generally a ‘rare event’, leading most often to small sample sizes in household surveys or administrative sources (Lucas, 2021). This condition is further exacerbated in the case of climate-induced migration, as climate change impacts are normally concentrated at specific hotspots (which is especially true when the push factor is a fast-onset event). Proposed solutions to this problem are not straightforward to implement – at least in the short-run – as they imply alterations to existing samples and availability of adequate sampling frames (or, in their absence, the adoption of second-best alternatives to obtain representative samples) (McKenzie and Mistiaen, 2009; De Brauw and Carletto, 2012; Bilsborrow, 2016). In addition to this, migration is a dynamic phenomenon which can be captured only through repeated observations over a sufficiently long time span. Yet, there is a widespread dearth of such data because collecting panel data on migrants is costly and complex, whether through direct respondents or proxy ones.<sup>17</sup> These are some of the issues explaining the general paucity of migration-related longitudinal microdata from developing countries. When the focus is on climate migration, the challenges become even more problematic. Available evidence shows that contextual factors and transmission channels play a decisive role in determining the direction of the climate-migration nexus, thus rich information regarding all the potential intervening mechanisms is absolutely essential. However, finding a micro dataset combining longitudinal information on adequate samples of migrants and non-migrants with multi-topic and multi-purpose data can be a hard task. Furthermore, even if these data are available, there are still endogeneity issues related to the non-random nature of the migration decision that should be addressed through sound identification strategies or, ideally, experimental approaches (McKenzie and Yang, 2010; McKenzie et
Policy Research Working Paper 10704

# **Abstract**

The World Bank’s analytical work has a strong reputation, but its knowledge products are also perceived to be of varying quality and relevance, and the drivers of this heterogeneity are only partially understood. Building on previous evaluations, this paper adopts a production function approach to assess how budget resources, time to completion, technical skills, and institutional responsibilities affect the internal ratings and external visibility of different types of analytical tasks at the World Bank. To this effect, the paper first matches records from three unconnected electronic platforms—for internal documents, budget codes, and external publications—to assemble a comprehensive database of knowledge products and their key characteristics. With analytical documents as its unit of observation, the exercise shows that: (1) devoting more resources to analytical tasks leads to both better ratings and greater visibility; (2) both outcomes are systematically worse when a greater share of resources comes from trust funds; (3) they are also consistently worse for tasks that take longer to complete; (4) more academically oriented team leaders underperform on ratings and overperform on visibility, whereas technically solid but less stellar team leaders overperform on ratings; and (5) everything else equal, performance varies systematically with the nature of the unit in charge. The findings of the paper can be read as a cautionary note against knowledge management that is based on the counting of analytical tasks. Instead, the findings call for much stronger information systems on knowledge products, a better alignment of incentives for the units in charge, and regular evaluations in the spirit of this paper.

This paper is a product of the Operations Policies and Country Services Vice-Presidency. 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 mrama@
- Department of Foreign Employment (2014). Labour migration for employment a status report nepal: 2013/2014. Technical report, Government of Nepal, Ministry of Labour and Employment, Department of Foreign Employment.

- Foged, M. and Peri, G. (2013). Immigrants' and native workers: New analysis on longitudinal data. Working Paper 19315, National Bureau of Economic Research.

- Greenwood, M. J. (1969). An analysis of the determinants of geographic labor mobility in the united states. The review of Economics and Statistics, 51(2):189194.

- Hägerstrand, T. (1957). Migration and area. In Hannerberg, D., Hägerstrand, T., and Odeving, B., editors, Migration in Sweden: A Symposium, pages 27158. Gleerup: Lund Studies in Geography.

- Hugo, G. J. (1981). Village-community ties village norms and ethnic and social networks: a review of evidence from the third world. In De Jong, G. F. and W, G. R., editors, Migration Decision Making, Multidisciplinary Approaches to Microlevel Studies in Developed and Developing Countires, pages 186224. New York NY Pergamon Press 1981.

- Kaphle, A. (March 24, 2014). Nepal, once known for farming, now exports people; migrants earn big but face risks. The Washington Post.

- Lokshin, M., Bontch-Osmolovski, M., and Glinskaya, E. (2010). Work-related migration and poverty reduction in nepal. Review of Development Economics, 14(2):323332.

- Lokshin, M. and Glinskaya, E. (2009). The eect of male migration on employment patterns of women in nepal. The World Bank Economic Review, 23(3):481507.

- Massey, D. S. (1988). Economic development and international migration in comparative perspective. The
and Sathar (1983), and Hobcraft (1984). There have also been studies looking directly at the effects of fertility on infant mortality. See, for example, Scrimshaw (1979), and Taucher (1982).

3The earliest estimates of the interaction of fertility and mortality rates are those by Nerlove and Schultz (1970) explaining fertility and infant mortality in Puerto Rico, and Heller (1976) in a simultaneous equations model explaining infant mortality and age specific fertility rates in Malaysia. Using cross country data, Wheeler (1980) estimated a set of simultaneous equazions relating various components of a "basic needs' model, including equations explaining fertility rates and life expectancy.

4The potential importance of the interaction has been examined in simulation models for Nepal by Barnum and Mohtadhi (1982) and Barnum and Yaukey (1979), and in a survey paper by Gwatkin (1984). Cochrane has also noted that the policy implications of family planning programs are significantly influenced by the effects of mortality on fertility (1983).
study the impact of the refugee inflow in 2015/16 on social cohesion, we use data from a large-scale panel survey as well as regional data on incidents of anti-immigrant violence. We measure social cohesion along several dimensions, namely generalized trust, perceived fairness, attitudes towards foreigners, and anti-immigrant violence. To separate causality from correlation, we exploit the fact that asylum seekers in Germany are assigned across federal states based on a formula that takes into account the state’s population and tax revenues several years prior. This means that, for reasons unrelated to the current economic and social climate, some regions receive more asylum seekers than others. Moreover, the panel data allow us to track individuals’ attitudes over time, and compare _changes_ in attitudes between people residing in high- versus low-inflow areas. This addresses many potential concerns about causality, for example that people who are more welcoming to foreigners are more likely to live in areas that also attract many foreigners.

We document two sets of results. First, in individual-level survey data we find limited evidence that the refugee inflow affected self-reported measures of social cohesion. Neither descriptive graphs nor a difference-in-difference analysis show significant differences in attitudes and values between people living in areas with high versus low inflows of refugees. The only significant effect we find is a deterioration in attitudes towards immigrants. Second, we find that the local presence of refugees increases the incidence of anti-immigrant violence. While the incidence evolved similarly in high and low-inflow counties before the inflow, it increased disproportionately in high-inflow counties. This increase was mainly driven by attacks on refugee accommodation, and is concentrated among the top 10% of municipalities with the largest inflows of refugees. Moreover, the effect is stronger in 1
or distribution of benefits along party lines (Das 2015).</mark> In Orissa, payments to ‘ghost workers’ were found to have accounted to 75-80% of total program cost although expectation of an ability to extract rents in the future may also limit theft according to a ‘golden goose effect’ (Niehaus and Sukhtankar 2013b). Exploiting exogenous wage variation suggests that the marginal rate of corruption was close to 100% (Niehaus and Sukhtankar 2013a).

Where these implementation challenges were dealt with, descriptive accounts suggest that the program had positive impact.<sup>5</sup> Several studies find it improved consumption by the poor. NSS data point towards large season-specific effects for scheduled castes and tribes (SCs and STs) but not the general population who, as a result of MNREGS were able to smooth consumption across seasons (Klonner and Oldiges 2014).<sup>6</sup> In Andhra Pradesh, one of the better performing states, panel data suggest MNREGS was well targeted and affected food consumption and asset accumulation by the poor (Deininger and Liu 2013), with impacts on food security, savings, and health outcomes <mark>(Ravi and Engler 2015). Beyond consumption, M</mark> NREGS was

> 4 Productivity shocks have been shown to cause larger changes in wages when supply is inelastic, e.g. because workers are poorer, less able to migrate, or more credit-constrained. These changes generally hurt workers and benefit land owners (Jayachandran 2006).

> 5 Recent IHDS data suggest that MNREGS increased income and wages for unskilled, reducing poverty by up to 32% and preventing 14 million from falling into poverty (Desai _et al._ 2015). 6 A back-of the envelope cost benefit analysis suggests that consumption smoothing benefits for the SC/ST population would have been sufficient to justify the wage outlays incurred by MNREGS (Klonner and Oldiges 2014). Of course conclusions from such an evaluation might change if the disutility of the
variables are also measured using the number of firms established in different years. Similar to equation 1, we control for the aggregate effect at the region level.

Table 4 reports the regression results for equation 2 separately for light manufacturing, heavy manufacturing, “other industry,” WRT services, and “other services,” and by north versus south. To establish the causal effect of the growth of firms in mining on the growth of firms in other sectors, we conduct placebo tests. We consider the lead effects of new mining firms for two periods, _NewMFirmd_ , _t_ + 1 and _NewMFirmd_ , _t_ + 2 . In the north (panel a), we find weak evidence of the relationship between the entry of new firms in other sectors and in mining, as the results are mostly statistically insignificant. This is primarily due to the limited expansion of mining in the north. In the south (panel b), both contemporaneous and lagged effects of new mining firms on the entry of heavy manufacturing firms are positive and statistically significant. The entry of mining and heavy manufacturing firms in the south appears causal based on the results of the placebo tests, as the lead effects are statistically insignificant for heavy manufacturing. For other sectors, the placebo test results do not support a causal interpretation for the increase in the number of firms due to an increase in the number of mining firms. Appendix table A4 reports the regression results for equation 2 at the national level. For heavy manufacturing, the results at the national level reflect a combination of the results obtained at the regional level.

To conclude, the district-level results reinforce the results in Section 3 based on regional I-O tables that mining has a stronger effect on heavy manufacturing through production networks in the south than in the north.

# **Firm-Level Analysis**

The district-level evidence suggests that employment in heavy manufacturing has grown in mining districts between 2003 and 2013. An increase in the number of heavy manufacturing firms could generate this result, without an increase in average employment