rafmacalaba/datause-encoder-data huggingface dataset:
analysis requires a certain degree of homogeneity of the studies it includes to yield interpretable results. Because of this, we chose to make sub-fields the main focus of our analysis, looking at specific sectors and when possible at combinations of sectors and specific outcomes. When doing this, we indeed find evidence of the heterogeneity mentioned above.

Third, moving to the results, we show that studies based on public capital measures yield larger estimates. Cross-sectoral studies, which rely in more than 80 percent of cases on public capital measures, have an estimated average elasticity of 0.16, even after controlling for publication bias. On the other hand, sector studies relying almost exclusively on physical, access, or usage measures yield elasticities that range between zero and 0.06 at most. This is in line with several sector-level literature reviews, which find relatively small effects of the different types of infrastructure. We also note that the reduction in the size of estimates does not seem to be due primarily to a change in methods or more sophisticated identification strategies. The reliance on instrumental variables to address endogeneity does not show up as significant when used as a moderator. Similarly, restricting the sample to estimates addressing endogeneity explicitly does not always lead to smaller estimates, and there is no clear time trend in term of the size of estimates. We remain cautious about interpreting this reduction in the magnitude of estimates, given the change in the type of infrastructure indicators used in the latter studies, from public capital to physical units or access and usage rates. Ultimately, we care about the actual social rates of return of infrastructure, which as discussed below may not necessarily be ordered in the same way as the elasticities reported here.

Which are the combination of sectors and outcomes that yield significant elasticities? When taking sectors as a whole, the average elasticities come out at around 0.02 to 0.03 for energy, transport, and digital. When looking at sector sub-samples, digital-micro output has an elasticity of 0.05, energy macro and micro output of 0.04 and 0.05 respectively, while transport yields larger values for micro output, at 0.08, and 0.16 for labor. Robustness checks excluding outliers based on extreme standard errors and t-statistics values or restricting the samples in various ways do not alter dramatically the general results.

21
effectiveness, i.e. the possibility that the effect of a given policy depends on other factors. Inspired by our model, in particular by equation (19), we will explore the size and the composition of the GDP in the host economy as possible relevant factors.

# **5 Results**

## **5.1 First Stage: Gravity-type Determinants**

Table 2 reports the results of the first-stage Probit model for aggregate data (recall from Section 2 that the M&A data are cumulated from deal level data over the period 2003-09). We report separately the results for total manufacturing (column two) and total services (column three). The effect of distance on the probability of observing cross-border M&A is negative and statistically significant for both the manufacturing sector and the service sector, and the coefficient on the border dummy is roughly the same for services as for manufacturing.<sup>20</sup> Sharing a common language increases the probability of observing M&A in services more than it does in manufacturing. Given the critical role of communication in the delivery of intangible services, this finding is hardly surprising. As another proxy for cultural proximity, we use a common religion<sup>21</sup> . As table 2 shows, the coefficient attached to common religion is positive and highly statistically significant, and the coefficient for services is much larger than the one for manufacturing. A common colonial origin has a positive impact on the probability of observing M&A in services but not in manufacturing. In contrast, a common legal system is quantitatively slightly less important for services M&A than for manufacturing. Trade in goods has a positive and statistically significant impact on both manufacturing and services M&A, and so does the existence of a Bilateral Investment Treaty (BIT) between the two countries. However, sharing a regional trade agreement seems to affect the M&A in services, but not in manufacturing.

> 20See Keller and Yeaple (2013) for a model where the negative relation between distance and multinational activity is explained through the presence of barriers to the diffusion of knowledge.

> 21Common religion is the probability that randomly extracting two people from the two countries, they belong to the same religion. Formally, it is the sum of the products of the shares of population belonging to the same religions in the two countries.

19
goods (Behar & Venables, 2011). For this reason, multilateral and donor organizations have sought to view “aid-for-trade” packages as a promising new developmental tool (Huchet-Bourdon, Lipchitz & Rousson, 2009).

Does such assistance work? Economists have studied the potential impact of transport and other costs on trade using gravity since at least Tinbergen (1962).<sup>1</sup> This paper makes a substantive contribution to our understanding of the importance of logistics for developing countries because we use a new World Bank index, which o¤ers a number of advantages. It draws on a wide range of criteria, has broad country coverage from a single source, and is based on detailed evaluations provided by logistics professionals (Arvis, Muster, Panzer, Ojala & Naula, 2007).

This paper also makes a methodological contribution to the estimation and interpretation of gravity models and hence our understanding of the importance of transport costs for trade. We develop a new gravity model, which shows that standard approaches would produce an almost three-fold exaggeration of the typical impact of such factors for developing countries.

We uncover this dramatic exaggeration because our novel gravity modelling approach simultaneously accounts for two issues, namely multilateral resistance ...rm heterogeneity. Regarding the ...rst issue, Anderson & van Wincoop (2003) show that it is not just bilateral trade costs, but those costs relative to multilateral trade costs, captured by price indices, that are relevant for predicting bilateral trade ‡ows. In particular, imports by country i from country j, Mij, are an increasing function f (�) of, inter alia, bilateral trade costs tij relative to the product of the two countries’price indices Pi � Pj, such that Anderson & van Wincoop call the price indices multilateral resistance because they work to aggregate trade costs across the two countries’multiple trading partners. Omitting controls for multilateral resistance (MR) can lead to biased coe¢ cient estimates. More importantly, it can lead to grossly misleading comparative static estimates of the impact of changes in trade barriers on trade ‡ows. This is because changes in trade costs a¤ect both the denominator and the numerator of the argument on the right hand side of (1); empirical studies typically ignore the latter. Economically, for the exporter j, it is the trade cost associated with exporting to i relative to those trade costs incurred when trading with all other traders that matters for its exports to i. If a reduction in trade costs reduces tij but also reduces costs associated with trading with other countries, exports by j to i will increase by relatively less compared to the case where multilateral resistance, acting through Pi � Pj, is ignored.

Trade elasticities are approximately proportional to country size because bigger countries are less a¤ected by MR. Intuitively, since larger countries typically trade a smaller fraction of their total output

> 1 See Clarke, Dollar & Micco; Limão & Venables, (2001); Wilson, Mann & Otsuki (2005); Djankov, Freund & Pham (2010) .

2
suggests that the covariates of caste do not explain the caste gap in coordination. We found no support for two other potential explanations: caste differences in trust and self-efficacy.

The poor ability of high-caste men to coordinate efficiently in the Stag Hunt is consistent with the observed pervasive failures in coordination and institution-building in Uttar Pradesh, which Dr`eze and Gazdar (1997, p. 101) characterize as “social and political inertia.” Our theoretical framework gives new micro-foundations for psychological processes that impede economic development and suggests mechanisms to target with interventions. There is evidence from large randomized controlled trials (RCTs) in Chicago and in rural areas of Liberia that the perception of challenges to one’s status, as well as the automatic response of aggressive retaliation, are malleable. In Chicago, Heller et al. (2015) evaluate three RCTs of programs among disadvantaged male youth to reduce automatic retaliation to assertions of authority and to perceived affronts. Such responses may be adaptive in “street life” in disadvantaged areas but are ill-suited to other environments. The programs thus did not discourage participants from retaliating against threatening assertions of authority, but rather encouraged participants to recognize when automatic responses might be maladaptive and to think about what response was called for. For example, when a teacher tells him to sit down and be quiet so class can start, a participant would learn not to “feel like one’s reputation is at stake” (p. 4). An intervention that helped very disadvantaged high school boys in 18 pubic school think deliberately had the effect of reducing their violentcrime arrests in that year by 44 percent (p. 22). A study of another such intervention in a high-risk juvenile detention center found that in the control group, only one-quarter of the young men had not been readmitted to the detention center within 18 months, whereas the treatment increased that rate by two-thirds (p. 27). In post-civil war rural Liberia, Blattman et al. (2012) found that a training program in mediation gave people new conceptual tools for understanding conflict—for instance, seeing the interaction as positive-sum instead of zero-sum—and new norms that improved coordination. A survey of over 10,000 people one year after the intervention found that the training program did not reduce dispute levels, but it reduced the level of unresolved conflicts and increased the proportion of people satisfied with their resolution.

In these interventions, large social changes were brought about by more or less simultaneous frame switches by many individuals, a mechanism of social change highlighted by DiMaggio (1997). Interventions can target and change the mental models through which people interpret the situations they are in, and the responses that their interpretations trigger. Mental models and rules of thumb are understudied in economics—a gap that our study and other recent work tries to address.

28
For example, geocoded microdata from Pakistan’s Federal Board of Revenue on the nearuniverse of formal firm-to-firm monthly sales transactions over ten years in combination with GPS tracking on trucks can help understand the spillover effects of floods through production network (Balboni et al., 2023). This research finds that firms affected by major floods relocate to less flood prone areas, diversify their supplier base, and shift the composition of their suppliers towards those located in less flood-prone regions and reached via less floodprone roads. Another recent study on India using new data on the universe of firm-to-firm transactions confirms that firms diversify sourcing locations, and that suppliers exposed to climate risk charge lower prices (Castro-Vincenzi et al., 2024). Such swift diversification of suppliers points to a forward-looking action that reduce future vulnerability to flood risk rather than direct effects of flooding are consistent with experience-based updating. It also supports the hypothesis that the impacts of climate change will be mediated as firms learn from the experience of increasingly frequent climate disasters.

# **3.3 Firm and Industry Attributes in Adapting to Risk and Uncertainty**

In the face of increasing climate change _risks_ , the heterogeneity in preparedness for climate change varies with firms and industry attributes, as is also the case of _actual_ shocks (described in Section 2.1). Some specific evidence of such variation driven by firm and industry attributes is presented below.

## **Firm attributes and climate risk**

In recent work, Lin et al. (2020) show that electricity providers increase investments in flexible power plants in response to long-term changes in local climate; (Li et al., 2020) document a negative effect of changes in long-term climate conditions on local employment; and Li et al. (2020) find that firms with high climate uncertainty increase their capital investments.

## _Firm size and supplier base_

Larger firms have lower exposure to risk as they can devote relatively more resources and have better abilities to sustain performance than smaller firms (Birkie et al., 2017). Firms that have a more _diverse supply base_ , both in terms of numbers and geographic dispersion, and hence may better manage climate risk by ensuring faster recovery in the event of unexpected climate shock (Tokui et al., 2017). Sophisticated supply chains can also increase exposure to climate-related disruptions due to potential ripple effect (Gouda and 15
-9-

ci(wi,e) = G,e, (4a) where a measures the degree of scale economies in embedded electricity production and G; is a firm-specific constant which measures the effects of the input prices.<sup>3</sup> The marginal cost calculated from (4a) is: (4b) c1'(w1,e) = a _Gie_ .

Now consider the specification of the firm's primary production function. We will use the Cobb-Douglas form with the two types of electricity entering as essential substitutes.

(5a) Qi(L,M,e,eN) = A,i & * ea°- _efNUN_ We will assume that there is diminishing retums with respect to each input which requires that the exponent be greater than zero but less than one.' Isoquants will be convex to the origin. (5a) implies that the two kinds of power have different qualities, acting as two different inputs. Production cannot occur unless each input is used in positive quantities. The isoquant between e and eN keeping L and M fixed is convex to the origin and does not cut the axes.

The shape of the isocost curve, keeping L and M fixed, depends on whether NEPA offers a decreasing or increasing block tariff. In both cases, the slope of the isocost line is,

||de||atlaeN<br>$B6i eN"||
|---|---|---|---|---|
|6||||< 0|
|(a)||-|=-|;|
||deN||ac/ae<br>a Gie'||

**3** This functional form corresponds, for example, to an underlying embedded technology which is Cobb-Douglas. In that case, **GI** would be a Cobb-Douglas function of the embedded input prices with the exponents of the input prices being the cost-shares of the embedded inputs in total embedded cost. Cobb-Douglas estimates of such cost functions gave a = 0.55407 for Nigeria and a = 0.3240 for Indonesia.

4 This condition was satisfied in calibration.
41 _Figure 1._ Summary of the in-service teacher training survey instrument (ITTSI) <!-- Start of picture text --> Overarching<br>Who<br>implemented?<br>Professional<br>implications?<br>Based on a<br><!-- End of picture text --> <!-- Start of picture text --> Based on a<br>diagnostic?<br><!-- End of picture text --> <!-- Start of picture text --> Content<br>Focus?<br>(Content,<br>pedagogy, etc.)<br><!-- End of picture text --> <!-- Start of picture text --> Subject area?<br><!-- End of picture text --> <!-- Start of picture text --> Delivery<br>Core activities?<br>Cascade?<br>Proportion in<br><!-- End of picture text --> <!-- Start of picture text --> Proportion in<br>lecture?<br>Practice?<br><!-- End of picture text --> <!-- Start of picture text --> Perceptions<br>What did<br>teachers like?<br>What do you<br><!-- End of picture text --> <!-- Start of picture text --> What do you<br>think mattered?<br><!-- End of picture text -->
but a much higher machinery export share than China and ASEAN4, but a higher textile export share and a lower machinery and equipment export share than Japan and the United States.<sup>9</sup> Columns 6 and 7 in Table 3 present sectoral shares of exports in gross output and imports in total demand as measures of trade dependence. Because of their geographical location and the smaller size of individual members, ASEAN4 and Asian NIEs have the highest trade dependence, especially for the capital-intensive machinery and equipment industries. Fifteen years of market-oriented economic reform have also led China to become more strongly linked with the world economy, especially in manufacturing products. In 1992, China exported more one third of its labor-intensive textile and apparel output and imported one third of its machinery and equipment from abroad. The United States and Japan, as the two largest economies in the world, are relatively more self-sufficient. However, Japan's poor natural resource base leads it to rely on other countries for nearly half of its total mineral and energy use, while it exported nearly one-fourth of its total machinery and equipment production to foreign markets. Although the United State has relatively low trade dependency, at the sectoral level it exports significant shares of its textile and capital goods output, and imports large amounts of nondurable manufactured goods (textiles and apparel), machinery and equipment, and energy and mineral products.

9 Textiles provides an illustrative example of the point made earlier about the elimination of "intra-regional" trade during the combination of countries into our regional aggregates. If Hong Kong were included with the Asian NIEs rather than with China, then the share of textile exports _rises_ in China (now excluding Hong Kong) but remains _unchanged_ in the Asian NIEs (including Hong Kong). This occurs because the separation of China and Hong Kong means that the enornous export of textiles from China to Hong Kong (presumably for subsequent re-export) is counted as a Chinese export, rather than netted out as part of the consolidation of the individual country data sets.

_Lewis, Robinson, and Wang_ 9
- 23 -

|**T**|**able 7. 3SLS estimatio**|**n result **|**with risk shar**|**ing variab**|**les only**||
|---|---|---|---|---|---|---|
|Dep. Var.|ln_INV_|ln_SAVE_||ln_DUR_||ln_PAY_|
||Coef. Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef. Std.Err.|
|_RISKTYPO_|-0.463 (0.442)|-0.598|(0.361) *|-0.294|(0.178) *|-0.233 (0.504)|
|_RISKREG_|0.613 (0.226) ***|0.757|(0.184) ***|-0.032|(0.091)|0.506 (0.257) **|
|_RISKINF_|-0.422 (0.310)|-0.716|(0.253) ***|-0.131|(0.125)|-0.332 (0.353)|
|_wINV_|-0.013 (0.024)|-0.073|(0.019) ***|-0.035|(0.010) ***|-0.012 (0.027)|
|_wSAVE_|0.057 (0.019) ***|0.027|(0.015) *|0.021|(0.008) ***|0.084 (0.022) ***|
|_wDUR_|0.030 (0.035)|-0.020|(0.028)|-0.026|(0.014) *|0.066 (0.039) *|
|_wPAY_|-0.082 (0.046) *|-0.078|(0.037) **|-0.007|(0.019)|-0.207 (0.052) ***|
|ln_N_|0.568 (0.233) **|0.668|(0.190) ***|0.107|(0.094)|0.861 (0.265) ***|
|_dSSC_|-0.484 (0.301) *|-0.411|(0.246) *|0.605|(0.122) ***|0.202 (0.344)|
|_dPCKG_|-0.484 (0.305)|-0.368|(0.249)|-0.149|(0.123)|-0.891 (0.348) ***|
|ln_KWH_|0.561 (0.179) ***|0.529|(0.146) ***|0.253|(0.072) ***|0.365 (0.205) *|
|ln_AGE_|0.155 (0.382)|-0.828|(0.311) ***|0.093|(0.154)|0.236 (0.435)|
|ln_AREA_|-0.092 (0.236)|-0.170|(0.192)|-0.265|(0.095) ***|0.166 (0.269)|
|_dOFFC_|0.047 (0.282)|0.458|(0.230) **|-0.075|(0.114)|0.214 (0.322)|
|_dHOSP_|0.238 (0.294)|0.868|(0.240) ***|-0.266|(0.119) **|0.549 (0.336) *|
|_dSCHL_|0.584 (0.180) ***|0.610|(0.147) ***|0.106|(0.073)|0.467 (0.206) **|
|_dLIT_|0.877 (0.284) ***|0.471|(0.232) **|0.096|(0.115)|0.743 (0.324) **|
|_dAC_|-0.555 (0.510)|-1.140|(0.416) ***|0.107|(0.206)|-0.525 (0.581)|
|_dHOTW_|0.209 (0.155)|0.348|(0.127) ***|-0.029|(0.063)|0.042 (0.177)|
|_dCGS_|0.506 (0.172) ***|0.691|(0.140) ***|0.129|(0.069) *|0.474 (0.196) **|
|ln_AWAD_|0.004 (0.033)|0.011|(0.027)|-0.058|(0.013) ***|0.016 (0.037)|
|_dLEAS_|0.282 (0.306)|0.419|(0.249) *|0.057|(0.123)|0.045 (0.348)|
|_dMANU_|0.005 (0.180)|-0.028|(0.147)|-0.095|(0.073)|-0.011 (0.206)|
|_dENEG_|0.877 (0.248) ***|0.819|(0.202) ***|0.072|(0.100)|1.092 (0.283) ***|
|constant|1.486(3.440)|13.257|(2.808)***|2.969|(1.389)**|3.523(3.923)|
|Obs|71||||||
|R-square|0.844|0.914||0.856||0.852|
|No. of dummy variables|||||||
|Bidder origin prefecture|<br>11|11||11||11|
|Contractyear|8|8||8||8|

Note: A system of four equations is estimated by the three stage least squares. The number of bidders is treated as endogenous. Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.
Policy Research Working Paper 10202

# **Abstract**

Motivated by the deterioration in global food security conditions, this paper develops a parsimonious machine learning model to derive a multi-year outlook of global severe food insecurity from macro-economic projections. The objective is to provide forecasts that are internally consistent with wider economic assessments, allowing both food security policies and economic development policies to be informed by a cohesive set of expectations. The model is validated on holdout data that explicitly test the ability to forecast new data from history and extrapolate beyond observed intervals. It is then applied to the World Economic Outlook database of April 2022 to project the severely food insecure population across all 144 World Bank lending countries.

The analysis estimates that the global severely food insecure population may remain above 1 billion through 2027 unless large-scale interventions are made. The paper also explores counterfactual scenarios, first to investigate additional risks in a downside economic scenario, and second, to investigate whether restoring macroeconomic targets is sufficient to revert food insecurity back to pre-pandemic levels. The paper concludes that the proposed model provides a robust and low-cost approach to maintain reliable long-term projections and produce scenario analyses that can be revised systematically and interpreted within the context of available economic outlooks.

This paper is a product of the Development Data Group, Development Economics and the Agriculture and Food 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 author may be contacted at bandree@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