rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

Most countries devote considerable attention and resources to attracting foreign direct investment (FDI). This is done in the hope not only of generating benefits like jobs, foreign exchange, and tax revenues, but perhaps more importantly in realizing dynamic benefits to the domestic economy through so-called “spillovers” from FDI. These “spillovers” generally refer to productivity improvements resulting from knowledge diffusion from multinational affiliates to domestic firms – both in the form of unintentional transmission or intentional transfer if the multinational is not compensated for by the domestic firm – encompassing both technology and all forms of codified and ‘tacit knowledge’ related to production, including management and organizational practices. The existence of spillovers is based on the assumption that multinational firms enjoy technological and other advantages and have, therefore, higher levels of productivity (Hoekman and Javorcik 2006).

Numerous econometric studies show ambiguous effects of FDI on domestic firm productivity within the same sector, also known as horizontal spillovers (see, e.g., extensive literature reviews in Görg and Greenaway 2004; Lipsey and Sjöholm 2005; Smeets 2008, among others). Other studies have shifted the focus to vertical spillovers to domestic firms in upstream and downstream sectors (e.g., Javorcik 2004, Blalock and Gertler 2008, Havranek and Irsova 2011). These studies support the existence of positive backward spillovers from multinationals on local suppliers, while evidence on forward spillovers is mixed.

Significant policy relevant research gaps remain, as identified in a recent survey of the empirical literature (Javorcik 2009). Among the gaps identified, there is the need to (i) determine the conditions under which spillovers are likely to materialize; (ii) understand more specifically the mechanisms behind the observed patterns; and (iii) extend the scope of investigations beyond the manufacturing sector (Javorcik 2009). The second research gap is also a function of the FDI measure being used. The econometric studies above, for example, measure FDI only at the broad sectoral level, but don
likely than project-area officials to report efforts to improve local tax collection.

To offer ideas and recommendations from research for further innovation in the design of such projects in Tanzania and beyond, we closely reviewed project documents to understand the general design and theory of change on which these capacity building projects are founded. We find that these projects share a common, global template that is being applied across different countries and contexts, centered on the role of an Annual Performance Assessment which is expected to verify whether local governments have certain institutional features that are found in high state capacity countries: such as, existence of planning documents, council meeting minutes, audit reports, procurement tenders and the like.<sup>26</sup> Qualitative research has critiqued this approach to building state capacity as “isomorphic mimicry” (Andrews et al., 2017), whereby developing countries are made to produce documents and establish protocols that resemble institutions in donor countries, but fail to effectively perform the functions of a state. The lack of difference in measured outcomes of state functioning in the data from Tanzania offers quantitative evidence that is consistent with such critiques.

Alternatively, the amounts committed or the scope of the capacity building component might be too small to make a dent.<sup>27</sup> Further, the rationale for using a financial incentive approach is

> 26See examples from Egypt (World Bank, 2018a), Ethiopia (World Bank, 2014b), India (West Bengal) (World Bank, 2016a), Indonesia (World Bank, 2005), Uganda (World Bank, 2016b), and Vietnam (World Bank, 2014a).

> 27For the project in Tanzania, the total donor contribution was US$255 million, but only US$54 million was

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At endline (visit 24), households report the total number of days and the average hours per day, per person per plot, since the last weekly visit. The design for this group minimizes recall periods and (mostly) avoids the need for complex aggregations and calculation of averages –farm labor estimates for this group are considered as the benchmark.

- **B. Weekly phone:** The design for this group is essentially the same as for the weekly visit group, what differs is the method of data collection. While households in the weekly visit group received weekly face-to-face visits during the season, households in the weekly phone group received weekly phone calls. The main purpose of this treatment arm was to explore the potential of soliciting high-frequency labor data using phone surveys, as a proof of concept, an aspect of the experiment that will be explored in a companion paper.

- **C. Recall:** For this group, there were no visits between the baseline in March 2015 and the endline in October/November 2015. In the endline survey, the agricultural labor module for this group identified the household members that worked on each of the household’s plots during the season, and for those members, data were collected on (i) total days per person per plot spent across the five activities and (ii) typical hours per day worked per person per plot on each activity. This group hence mimics the way LSMS-ISA and other agricultural surveys collect data on farm labor (see also section 2.2).

> 7 The baseline and endline surveys were timed as per the production calendar of maize, the most important crop in this area.

> 8 We distinguish between five activities - land preparation and planting; weeding; ridging, fertilizing and other nonharvest activities; harvesting; and supervision.

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school (not shown). On average, villages have 1 agro-dealer and the NAIVS subsidy as a share of the total cost of inputs is 48.6 percent.

The villager survey included a couple of questions regarding the social and/or political connections of respondents. In particular, respondents were asked whether they were related to or socialized with government or NAIVS officials such as the Ward Executive Officer (WEO), the VEO, the District Agriculture and Livestock Development Officer (DALDO), or any VVC member or village influential such as the hamlet, village or ward chairs.<sup>6</sup> Column 2 of Panel C in Table 1 reports that 22 percent of villagers were related to government officials or influential, and 21 percent socialized with them regularly. In addition, 68 percent of villagers had used improved seeds during the 2009/10 season and 48 percent had used inorganic fertilizer (mostly urea).

We find no differences across treatment arms in any of the panels in Table 1. We report the p-value of a joint F-test that all the characteristics reported in a given panel do not predict being assigned to a lottery (column 5) or an open meeting village (column 8). None of the p-values reported is significant at conventional levels (the lowest p-value is 0.385). The different treatment arms thus appear to be balanced.

# **4. Results on Targeting and Impacts**

# _4.1 Targeting_

We use the villager survey to first study whether the eligibility criteria successfully identified the poor. While only the eligible household survey instrument collected food security, we use landholdings as a proxy for poverty as they were collected across all surveys. Using the eligible household survey, we find that the correlation between landholdings and food security is 0.13 and highly significant (p-value is 0.000). Figure 2 shows the distribution of landholdings and the average landholdings among eligible households, that is, those that cultivated less than 1 hectare, and not-eligible households.
4 of people exposed to drought. The regular occurrence of natural disasters together with the availability of three highly comparable nationally representative household living standard surveys (2002‐2004‐2006) render Vietnam particularly suitable to illustrate the approach advanced in this paper. The study constructs in particular disaggregated maps of the annual occurrence of droughts, (local and riverine) floods and storms during 2001‐2006 as well as the likelihood of their occurrence using meteorological data from rainfall stations, storm tracks, and satellite images. The occurrence of these geo‐referenced disasters, further augmented with the likelihood of their occurrence derived from the historical weather records, is subsequently linked with household consumption and other household and location characteristics from the repeated nationally representative household surveys and secondary data sources to estimate the effect of extreme weather events on household welfare.

The results suggest that the immediate losses from floods and hurricanes can be substantial, with hurricanes causing most havoc (up to 52 percent consumption loss among households close to large urban centers). Households tend to cope well with droughts, largely through irrigation. Frequent exposure to disasters erodes the standard of living, but reduces the immediate effects of shocks as households become less exposed and better prepared. Households in frequently inundated areas have even been able to turn the floods into an advantage, as long as the flooding is not too severe. There is however no adaptation to hurricanes, rather the contrary, with high frequency of hurricanes exacerbating the losses from particular events. Finally, those further away from the large urban centers are not only poorer, but also tend to suffer less from disasters, likely due to the adoption of less risky (but less remunerative) portfolios and a higher likelihood of receiving disaster relief.

The next section outlines the approach used to obtain geographically disaggregated estimates of droughts, heavy rainfall, river floods and storms with hurricane force. Section three reviews the empirical methodology to estimate the welfare effects. Section four reviews the data used and section five presents the empirical findings regarding the welfare effects of different natural
observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in Panel A, column 1 is a dummy variable (DV) equal to 1 if the respondent’s household received any transfers (in cash or in kind) from another household during the 12 months preceding the survey; in column 2 it is the monetary value of transfers received during the past 12 months; in column 3 it is a DV equal to 1 if the respondent’s household gave any transfers to another household during the past 12 months; in column 4 it is the monetary value of all transfers given by the respondent’s household during the last 12 months; in column 5 it is a DV equal to 1 if the respondent’s household was a net transfer receiver in the past 12 months; in column 6 it is the monetary value of transfers received minus transfers made by the respondent’s household in the past 12 months. All dependent variables in Panel B (C) are identical to those in Panel A, except they refer to within (outside) village transfers.

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in practice (Beegle, Christiaensen, Dabalen and Gaddis, 2016). With many of our results, especially on consumption, based on a short sample and one country, replication and extension of the findings using longer time periods and across different settings is important to strengthen their internal and external validity. New waves of the LSMS‐ISA surveys in Tanzania and other countries are now becoming available, opening up exciting opportunities to do so, including along a wider range of welfare indicators and indicators of longer term impacts such as child growth and nutrition.

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- Instead, the brunt of the impact appears to be concentrated within sectors. Thus, studies using plant- or firm-level data conclude that major impacts of trade reforms are natural selection among firms and reductions in X-inefficiency: less efficient firms in a sector are forced to downsize, improve efficiency or exit, with more productive (efficient) firms expanding their market shares. Overall total factor productivity increases more in industries that liberalized more.

- Correspondingly, the direct effects of trade reform on aggregate employment are muted. Different models imply different predictions for the long run with the neoclassical ‘no change’ being the frequently held view. The evidence is varied: it does not suggest longrun adverse effects and in some cases suggests long-run employment gains as accessing international markets with their high elasticities of demand permits expansion and accumulation in successful sectors without encountering large declines in prices. In the short run Keynesian employment responses and/or adjustment strains can be adverse, but are not generally very large relative to total employment.<sup>15</sup>

Turning to future research priorities, we would note the following questions:

- **Who/what is protected?** Some of the micro-econometric research to date suggests that the most heavily protected sectors in many developing countries are sectors that employ a high proportion of unskilled workers who earn low wages. A corollary is that trade liberalization, especially when also accompanied by investment liberalization and inward FDI, has a negative impact on unskilled workers in the short- and medium-run—be it in the form of lower wages and/or unemployment. A puzzle stressed by Harrison and Hanson (1999) is why these countries find it optimal to protect low-skill intensive sectors when this is their abundant factor.<sup>16</sup> However, this finding may also be a function of the set of countries that have been analyzed, which in turn has been limited by the

- availability of firm-level datasets. In fact, there is also evidence that countries tend to

> 15 They are
. 2017) but dietary diversity (i.e., not focusing on MDD-W only) has not shown consistent relationship with overweight or obesity and NCDs (Salehi-Abargouei et al. 2016; Vadiveloo, Dixon and Parekh 2013, Verger et al. 2021).

To assess whether a diet is **healthy,** quantitative dietary intake data are viewed as the reference, using detailed data on foods consumed. However, there is only very limited availability of validated metrics based on quantitative data that are well-suited for monitoring healthy diets in LMICs (Miller Micha and Mozaffarian 2020; WHO and UNICEF 2021). When quantitative dietary survey data are not available, the most common alternative approach in LMIC is to assess consumption of specific food groups (both protective and risk-inducing) across the previous day (or other such time interval). These data have only been collected relatively recently, and various alternative metrics have been suggested, such that the methodology for assessing these variables and their link with health and nutritional outcomes is still evolving. Another alternative method of data collection is to use food frequency questionnaires: these have the disadvantages that they are not quantitative and individuals may not recall as accurately over a longer period, but the advantage that better reflect the long-run intake of an individual. What is or is not healthy is frequently defined relative to WHO recommendations for a healthy diet for individuals 15-65 (WHO 2019, reproduced for convenience in Appendix Table 6).

Various metrics have been developed and used to assess whether diets of **adults aged 15 years and older, both men and women** are healthy, a literature which is evolving. Metrics have been developed to assess consumption of specific food groups associated with increased risk of negative nutrition/health outcomes (e.g. ultra-processed foods, sugar-sweetened beverages), as well as combinations of food groups that are recommended either for increased or limited consumption. The strengths and limitations of these indicators for this work are summarized in Table 3.

The Global Diet Quality Project provides new
should be such that all banks, including foreign banks, can compete on an arms-length bases and that foreign ownership of East Asian banks should be encouraged.

Since it is impossible to separate efficiency improvements from excessive risk taking, we have assumed that efficiency is constant during 1992-96 in order to construct our measure of risk taking. Of course, bank efficiency is not constant over time, even for a relatively short period of _5_ years with no significant changes in bank management and bank regulation. However, the increases in estimated efficiency that we see for most banks in our sample are clearly overstated, since a lot of those banks entered financial distress or even bankruptcy after 1997. It seems evident that on average the increase in calculated efficiency was mainly (in most cases even completely) due to excessive risk-taking instead of a true increase in efficiency/performance. Therefore, although we acknowledge that our risk measure is only a rough estimate of bank risk taking, we argue that on average our risk measure should give a good indication of the extent of risk taking across banks.

Traditional bank efficiency measures do not only disregard the risk profile of a bank's business, but they also suffer from the limitation that they do not account for business mix, the quality of service, and exogenous shocks. Although we have made an attempt to take the risk profile of a bank's business into account by assessing a bank's risk-taking behaviour, our measure still suffers from not accounting for business mix and the quality of service. Accurate data on a sectoral breakdown of extended loans might be used to account for business mix, but further research is needed to develop methods that will enable us to assess the performance of a bank given its accounting data.

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Year|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|340|340|340|340|340|340|340|340|340|
|R-squared|0.943|0.843|0.982|0.443|0.892|0.754|0.640|0.883|0.992|

_Note:_ Dependent variables at the 2-digit industry level are, for each column, entry rate, exit rate, share of employment in firms up to 5 years of age, standard deviation of employment growth rate, gross job creation, gross job destruction, net employment change, job reallocation, and the Herfindahl–Hirschman index (HHI). The share of employment in SOEs is the fraction of employment in all SOEs relative to total employment. All models include year and sector-fixed effects. Data includes private sectors from RAIS, 2016-2019. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.
Policy Research Working Paper 7668

# **Abstract**

A survey of participants in a large-scale business plan competition experiment, in which winners received an average of US$50,000 each, is used to elicit beliefs about what the outcomes would have been in the alternative treatment status. Participants are asked the percent chance they would be operating a firm, and the number of employees and monthly sales they would have, had their treatment status been reversed. The study finds the control group to have reasonably accurate expectations of the large treatment effect they would experience on the likelihood of operating a firm, although this may reflect the treatment effect being close to an upper bound. The control group dramatically overestimates how much winning would help them grow the size of their firm. The treatment group overestimates how much winning helps their chance of running a business, and also overestimates how much winning helps them grow their firms. In addition, these counterfactual expectations appear unable to generate accurate relative rankings of which groups of participants benefit most from treatment.

This paper is a product of the Finance and Private Sector 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 dmckenzie@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
generally suggests that overall enrolment and quality (admittedly difficult to measure in the 1980s because of the absence of comparable test scores) did not improve over the 1980s (and divergence among schools increased)<sup>20</sup> . From the decentralization and accountability perspective, we can say that the poor results of the municipal sector were probably mostly due a gap between responsibility and authority to act, the presence of soft budget constraints for the operation of the municipal education sector, the lack of citizen participation mechanisms at the local level (mayors appointed, lack of local participation bodies) and the “missed opportunity” of exploiting the potential of the “client power” relationship as instrument to improve competition between public and private schools, due to the reasons just mentioned plus the lack of municipal school autonomy and the information and selection constraints on the demand-side.

In the 1990s, on the one hand, further constraints on municipal autonomy (teacher statute of 1991) and centrally driven interventions in favour of the poorest/weakest municipal schools complicate even more the working of competition between municipal and private schools (see Di Gropello (2002) for the negative impact of the teacher statute<sup>21</sup> and Sapelli and Vial (2002) for the negative impact of central interventions on competition through the differences in per-capita budget between the two types of schools that they imply); on the other hand, dissemination of standardized school scores (SIMCE scores), increased administrative and pedagogical municipal school autonomy, introduction of school co-financing and better monitoring of student attendance (through the provinces) improve the operation of the voucher system. Additionally, in an attempt to create more national consensus and strengthen “voice”, mayors were elected in 1992 and municipal management was made more participatory in 1995/1996. Overall, average test scores improve during the 1990s for all school categories and there is increasing convergence between all these school categories (i.e. municipal vs. private, subsidized or not, and private subsidized vs