rafmacalaba/data-use-mentions-extended huggingface dataset:
been left unaddressed. We focus on two questions in particular: first, _how many_ deflators should be used? A recurring scenario in practice is the availability of several price indexes with partial coverage – say, food and non-food – alongside an aggregate price index. The paper works out the implications of two alternative price adjustment strategies, namely adjusting the sub-components of household expenditure separately using multiple price indexes, versus using a single index to adjust the total. Second, what is the optimal _sequence_ of temporal and spatial price adjustments? When working with household survey data, the analyst typically wants to adjust for intra-survey inflation – usually with a price index that is available outside of the survey – as well as for spatial cost-of-living differences – often by computing a deflator based on survey information. We discuss the implications of temporally adjusting all expenditures before estimating spatial deflators, versus after.

The findings deliver a few clear-cut suggestions for applied work. While using multiple indexes (MI) to adjust the components of nominal household expenditure may seem like the most accurate way to make use of the available information on price variation, we find that whenever we are able to rank the two methods, the single index approach (SI) comes out on top. This seemingly counterintuitive result depends on the fact that our goal is not to adjust nominal expenditures for changes in purchasing power, but rather to derive measures of welfare that are consistently comparable across individuals. Regarding the sequencing of temporal and spatial deflation, starting with the adjustment of all elementary expenditures for intra-survey inflation ensures that any spatial price index estimated from the survey is based on accurate (non-distorted) unit values and budget shares.

The paper is organized as follows. Section 2 explains the notation used in the rest of the paper; section 3 discusses the implications of the use of multiple indexes or a single index to adjust household expenditures; section 4 constructs a benchmark to determine which approach is better; section 5 tackles the question of the sequence between temporal and price adjustments; section 6 provides an empirical
Blomstrom, M., Kokko, A., (1996): Multinational Corporations and Spillovers. CEPR Discussion Paper, No. 1365.

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Caves, R.E., (1996): Multinational Enterprise and Economic, Analysis, 2nd ed. Cambridge University Press, Cambridge.

Cimoli, M., and Katz, J. (2003): ―Structural reforms, technological gaps and economic development: a Latin American perspective‖, _Industrial and Corporate Change_ , (12) 2, 387-411.

Co, C. Y., (2001): ―Trade, foreign direct investment and industry Performance‖, _International Journal of Industrial Organization_ , (19)163–183.

Coelli, T. J., Rao, P., O‘Donnell, C. J., and Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). Springer.

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green jobs. The most reliable and common data source to estimate the number of jobs is specific firm surveys.<sup>13</sup> Despite its simplicity, the output approach comes with several important shortcomings. First, specialized data is needed to classify goods and services as green or environmentally friendly. There are several working definitions. Some researchers use estimates of CO2 emissions;<sup>14</sup> others, like the Bureau of Labor Statistics (BLS), collect specialized data for that purpose.<sup>15</sup> Once this is known, a second challenge arises as firms might produce both green and non-green goods and services. In these cases, if there is a rich firm survey with detailed input and output variables, assumptions can be made about the proportion of labor utilized for green outputs. For example, that proportion could be estimated by the percentage of working time or wages spent on the production of green outputs, or the share of revenues coming from green outputs.<sup>16, 17</sup> When output data is not available, then granular industry information about the main activity of the firm can be used to approximate it. This implies further assumptions to assign a category or a share of green outputs to each industry code.

Applying the output approach in developing countries can pose additional challenges. Usually, developing countries do not have detailed data to classify locally produced goods and services as environmentally friendly or not. Measurement of CO2 emissions is not widespread<sup>18</sup> and conducting specific surveys mapping industry to occupation, such as the BLS GGS, might be unaffordable and not exempt from its own challenges. Consequently, most analysts appeal to relying on classifications of EGS and EGSS from other countries and estimate emissions based on the industry and input-output data. In the first case, the analyst must assume that the technology of production of goods and services is similar in both countries and will need a detailed mapping from EGS to the
/sup>|0_._263<sup>_∗∗∗_</sup>|0_._435<sup>_∗∗∗_</sup>|
||(0_._013)|(0_._021)|(0_._015)|(0_._025)|
|PTA-Measures_ij,t ×_ downstreamness parent<sup>_s_</sup><br>_j_|0_._313<sup>_∗∗∗_</sup>|0_._429<sup>_∗∗∗_</sup>|0_._324<sup>_∗∗∗_</sup>|0_._479<sup>_∗∗∗_</sup>|
||(0_._013)|(0_._02)|(0_._014)|(0_._023)|
|BIT_ij,t_|0_._018<sup>_∗_</sup>|0_._018<sup>_∗_</sup>|0_._018|0_._018<sup>_∗_</sup>|
||(0_._011)|(0_._011)|(0_._011)|(0_._011)|
|Country-pair FE|✓|✓|✓|✓|
|Industry-pair FE|✓|✓|✓|✓|
|Shareholder-country-industry-year FE|✓|✓|✓|✓|
|Subsidiary-country-industry-year FE|✓|✓|✓|✓|
|Domestic-year FE|✓|✓|✓|✓|
|Obs.|985,731|985,731|985,731|985,731|
|R<sup>2</sup>|0.568|0.568|0.568|0.568|

Standard errors are clustered at country-industry pairs level and reported in parentheses. Downstreamness have been scaled by 10<sup>_−_3</
percent decrease in the poverty rate. The GEP with respect to GDP per capita is higher, with a 1 percent increase in GDP per capita being associated with a 2.8 percent decrease in the poverty rate, on average within countries (Column (4) of Table 3). If economic growth is measured by HFCE per capita, the GEP is only slightly lower compared to using GDP per capita and ranges from -2.2 to -2.5 depending on whether or not country effects are controlled for (Columns 5 and 6 of Table 3).

The first salient observation from these results is just how robust the estimated elasticities are to different samples. Based on a sample of 120 spells, Ravallion (2004) writes “ _The elasticity of the “$/day” poverty rate to growth in the survey mean is around –2,_ [...]”, which is remarkably close to the GEP with respect to the survey mean we find in our sample of 575 spells.<sup>10</sup> The second salient observation is that in our sample, the GEP with respect to per capita GDP is larger than the GEP with respect to survey means, which is contrary to earlier research on smaller samples. Based on 126 spells, Adams (2004) finds a GEP of -2.3 with respect to GDP per capita, which is statistically indistinguishable from zero (compared to a significant -2.8 with respect to survey means). Ravallion (2001) also finds a lower elasticity when growth is measured by GDP per capita. We however find elasticities based on GDP per capita to be consistently larger than those based on survey means.<sup>11</sup> Next, we add a number of variables that influence the relative pace of poverty reduction (our left-hand side variable). As expected, a higher poverty rate at the beginning of a spell is associated with a significantly slower relative reduction in poverty, as large relative reductions
_GLOBAL FOOD INSECURITY OUTLOOK_ _27_ composition of economies. Future efforts may however still be taken to enrich the list of covariates with climate variables for which climate models can produce annual projections.

A final shortcoming is that the list of variables related to pre-existing vulnerabilities seems rather short, with only historical child mortality providing a proxy that allows distinguishing between the quality of governance and social systems.<sup>29</sup> The choice of the variable was primarily motivated by data availability, but it is reasonable to believe that a country that has ranked worse on child mortality in the recent past also ranked worse on a wide variety of other indicators that are predictive of weak socio-economic systems. Vastly superior indicators of the quality of governance in particular could be obtained from the estimates of Kaufmann et al. (2011) who maintain the annual Global Governance Indicators. The technical challenge would again be that these data may be predictive, but no outlooks can be produced without introducing new assumptions about the future. Forecasting government breakdown, after all, is at least as difficult as forecasting food crises.<sup>30</sup>

# **V. Conclusion**

This paper developed a statistical model to estimate and project global food insecurity conditions up to 6 years ahead using macro-economic outlooks. The long-term nature of the outlook aligns with the slow replenishment cycle of development finance, but the results can be revised on a semi-annual basis following the periodical assessments of broader economic outlooks. Extreme food insecurity situations are multifaceted in nature and frequently develop as part of multiple interlocking challenges. The paper’s approach anchors the food insecurity outlook to economic assumptions in an attempt to contribute to broader foresight that is internally consistent so that multiple policy actions can be informed jointly by a cohesive set of expectations. The paper concludes that the proposed framework provides a robust and low-cost approach to maintain reliable projections and scenario analyses that can be interpreted within the context of available economic outlooks, contributing to the capacity to monitor global food insecurity trends.

The model is
Table 4: Summary table of cohort trend for attitudes variables (from World Values Surveys)

|**Variable(percent of men and women who agree)**|**Afghanistan**<br>**Bangladesh**|**India**|**Maldives**|**Nepal**<br>**Pakistan**|
|---|---|---|---|---|
|When jobs are scarce, men have more right to a job (%)|83%|69%||92%|
|Decadal change (percentage points)|5|4||0.5|
|University edu. more important for boy than for girl (%)|45%|38%||61%|
|Decadal change (percentage points)|1|-0.2||-2|
|Both partners/spouses should contribute to hh income (%)|12%|16%||28%|
|Decadal change (percentage points)|-7|-4||-15|
|A woman has to have children to be fulfilled (%)|98%|85%||98%|
|Decadal change (percentage points)|2|-2||2|
|IPV justification (% justifying)|80%<br>20%|8%|22%|29%<br>41%|
|Decadal change(percentagepoints)|-2<br>8|-2|-5|2<br>9|

Source: World Value Survey rounds (see table A2 in the annex). Notes: for each question proxying an attitude or belief, the table reports two rows of numbers. The first row represents the most recent level of the variable, and the second row shows the decadal changes. The levels, in the first rows, are
1982m8|
|||||2003m11|2007m12||1987m8|1990m5|
|||||2021m12|||1994m8|1996m7|
|Korea,||1993m5|United|1972m1|1974m7|Netherlands|1972m11|1974m10|
|Rep.|||States||||||
||1997m12|2001m2||1977m1|1981m1||1977m11|1980m6|
||2005m10|2008m10||1983m3|1984m8||1983m5|1985m8|
||2010m7<br>|2012m7||1987m1<br>|1989m6<br>||1988m7<br>|1992m9|
||2021m8|||1994m2|1995m7||1997m3||
|||||1999m6|2001m1||||
|||||2004m6<br>|2007m9<br>|Portugal|1977m3<br>|1985m8<br>|
|||||2015m12|2019m8||1989m3|1993m5|
|||||20223|||||
|||||m||Spain|1973m7<br>|1983m9<br>|
||||||||1988m9|1991m2|
||||||||1995m1|1995m12|

Sources: Authors’ calculations, based on identification of rate cycles described in Section II, with data from January 1970 – May 2024.Notes: Phases for individual countries in the euro area and the euro area as a whole are in the column on the right. Members of the euro area can have national cycles through 1998, after which phases are reported for the euro area as a whole.

80
_τt_ , to account for year-specific shocks at the national level. We cluster standard errors at the district level. Similarly to our estimation strategy described in equations (1) and (2), we instrument _Fds_ with _FCds_ and include the same set of controls as in equation (3) in the first stage.

In Appendix Table A.4, we use the same IV approach to examine whether the fraction of female leaders in the district predicts predetermined individual or district characteristics. The estimates in Panel A indicate that women’s characteristics, including age, residence in a rural area, religion, caste, and years of schooling, are not predicted by the proportion of female politicians in a district who won in a close elec-

> 11In the regressions based on men’s data, we include the same set of individual-level controls that are instead measured for the male respondents.

7
size of the circles corresponds to the number of transactions at the circle center. The triangles represent the estimated focal points, and the solid lines mark the unit price (left panel) and expenditure (right panel) schedules. At left, the downward orientation of the unit prices is clear. On the right, the changing slope of the expenditure line represents the drop in unit prices as quantity increases. The clustering of purchases at focal quantities is also clear.

In each district there are some items that do not exhibit bulk discounts. Estimated unit price schedules are flat for 51 of the 126 item-district groups in the data (40%). Some items exhibit bulk discounts in every district, such as maize, cooking oil, kerosene, and tea leaves. Other items, including sweet bananas, cooking bananas, onions, salt, sugar, and dried sardines, have downward-sloping price schedules in three or more districts. Cigarettes, beans, rice, cassava, and matches only exhibit discounts in 1 or 2 districts.<sup>13</sup> A natural concern with price schedules estimated from observational data is that unobserved quality variation could be misinterpreted as bulk discounts, if higher quality versions of each item are disproportionately purchased in smaller quantities, and unit prices factor in quality. There are three reasons to believe that this is not the case here. First, as described in Section 3, we are able to use the item descriptions in the diaries to create highly standardized item groups. This mitigates unobserved variation from different product types being grouped together. Second, there is no quality variation across quantities in the market survey price data, because enumerators asked vendors to provide prices for different quantities of the exact same item. Yet, we see clear evidence of bulk discounts in those data (Table 3, columns 1-3), and there is no clear pattern as to whether the market price data or the survey data exhibit larger discounts (4 out of 10 coefficients in column 3 of Table 3 are larger in
Iraqi households through its targeted cash transfers, was the best placed institution to meet the needs of conflict-affected households vulnerable to future shocks or poverty.<sup>4</sup> One of the main outcomes of the workshop was the agreement to undertake a joint desk review by members of the CLCI, CWG, and the WB to identify the potential for closer alignment between humanitarian cash assistance and MoLSA cash transfer program, as well as possible transitional pathways from the former to the latter. To do so, one of the first tasks was to gauge what share of humanitarian caseloads would be eligible for MoLSA’s cash transfer program were they to be referred to the MoLSA program. Although the proxies used in the models were different, both the national and humanitarian CT programs used proxy-means-test (PMT) method to assess eligibility. Had all the information been available to apply the national PMT on the CLCI’s database, this step would have been straightforward. But it was not possible to evaluate MoLSA’s PMT on the CLCI’s current caseload because not all the indicators necessary for the national PMT were available in the CLCI database.

To overcome the impediment, we develop an analytical tool called pseudo-PMT (PPMT) that provides a bridge between the targeting models of the two CT programs for potential beneficiaries. Like a regular PMT, the PPMT relies on proxies and their weights to calculate potential beneficiaries’ well-being score (PPMT-score) and uses it to identify program eligibility. Unlike in a regular PMT where an optimum set of covariates that correlates best with the household welfare variable (consumption, income, etc.) are selected from an extensive list, the PPMT uses only proxies that are common to both the humanitarian’s and the government’s CTP targeting formulas.

By design the PPMT cannot reproduce the score that would be produced by the full PMT model. Therefore, we can only assess the probability that a case with a certain PPMT score will be eligible for the MoLSA’s program determined using the full
of the sampling process, as well as changes in the industry average wage among workers outside the municipality. This measure is thus not affected by the movements of women into and out of employment and across sectors that may be associated with violence. This measure is referred to as the leave-out measure because it is constructed by leaving out some municipal-specific information.

The raw data of the Annual Social Information Report (Relação Anual de Informações Sociais, RAIS) include one observation for each employment contract that was active at any point during the year under investigation. As in the case of the data that measure violence against women, the analysis restricts attention to individuals ages 15– 49. It also focuses on workers who hold, at most, two jobs during the year and have job

> 8 The source of the data is Annual Social Information Report (Relação Anual de Informações Sociais, RAIS) (dashboard), Ministry of the Economy, Brasília, http://www.rais.gov.br/sitio/index.jsf.

11
Figure A7: Labor force participation rate <!-- Start of picture text --> 2015<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>In the labor force / active<br>Outside the labor force / inactive<br><!-- End of picture text --> <!-- Start of picture text --> 2016<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>In the labor force / active<br>Outside the labor force / inactive<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015 and 2016 Figure A8: Employment and enrollment status <!-- Start of picture text --> 2015<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>Employed Employed and Enrolled Unemployed<br><!-- End of picture text --> <!-- Start of picture text --> 2016<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>Employed Employed and Enrolled Unemployed<
suggests an upward trend in earnings between 2014 and 2016, which seems to be reversed after 2016.

Similarly, Figure 4 (bottom) plots the estimates corresponding to the log hourly wage. The figure shows parallel trends up to 2016 for the groups of cantons defined as having low (solid blue line) and high concentration of Venezuelans (dashed green line) _cantons_ . Though less dramatic than in the case of monthly earnings, we again observe a divergence of the two series after year 2016. As before, the estimates for the cantons with medium concentration of Venezuelans are more erratic and less informative, but also consistent with a reversal in the upward wage trend after 2016.

Taken together, these two figures provide evidence in support of the parallel trends assumption, in the years prior to the arrival of Venezuelans, in _cantons_ that would eventually receive the highest and the lowest arrivals of Venezuelans relative to the size of their populations. Thus, a causal interpretation is reasonably plausible.

# **6.4 Robustness**

As noted earlier, our main estimation sample includes all individuals (age 15 or above) living in Ecuador, regardless of country of birth. It is thus possible that the worsening conditions for low-educated workers in the cantons with a high concentration of Venezuelans might be partly due to a compositional effect. That is, the recently arrived Venezuelan migrants are probably employed in low-quality employment with high rates of informality, as one would expect on the basis of the studies of the labor market effects of Syrian refugees in Turkey (Ceritoglu et al. (2017), Tumen (2016), DelCarpio and Wagner (2015)), which would adversely affect the composition of employment in terms of informality and earnings.

While entirely possible, these compositional effects are unlikely to be quantitatively important in the case of Ecuador. The reason is that new evidence from a new survey on the Venezuelan population in Ecuador indicates that the average education level of Venezuelan workers is substantially higher than for their Ecuadoran counterparts.<sup>24
effect than big city effect is apparent in the test statistics reported at the foot of Table 3, especially for the poverty gap index and in the indirect and total effects.

These differences between big city effects and smaller town effects are not an artefact of less brightly lit fringe areas of big cities being counted as part of the smaller, more dimly lit, towns. If a fixed mask approach is used that excludes areas that ultimately become part of the big city from being ever counted as part of small towns, the results are largely the same (columns (3) and (4)). The elasticities have slightly smaller magnitude than under the variable mask approach in columns (1) and (2) but the dominance of small town over big city effects is just as apparent. Another sensitivity analysis is to use the sum of lights approach, by multiplying lit area by the average DN within that area and the results in columns (5) to (8) confirm significantly larger secondary town effects. The results also hold if the distinction between smaller towns and rural areas is based on a 30% luminosity threshold rather than the 20% threshold used in Table 3 (Appendix Table 2 has these sensitivity analyses).

It might be argued that the “town effect” we identify may be in large part an indirect “city effect,” i.e., that city growth drives town growth, which then helps drive rural poverty reduction. Table 4 takes this argument seriously by providing analogous results to Table 3 but dropping the variables for towns and only reporting results for H since the results have similar patterns to those for PG. (For balance, the table also gives the results when we drop the city lights variables.) We find a total effect of the area of city lights of -0.715. Combined with the results of Table 3, this suggests that 87% of the total big city effect is via induced town growth, and 13% is direct. However, it