rafmacalaba/data-use-mentions-extended huggingface dataset:
CPI inflation rates and not food price inflation rates, which complicates the issue further.<sup>28</sup> There is also some scope to argue that CPI inflation rates alone should provide a reasonable predictive signal. The earlier references already highlighted that a deterioration in inflation captures broad macro-economic deterioration that has preceded major historical food crises. Second, some degree of substitution is possible within household expenditures so that households can offset increased food prices by lowering expenditures in another part in their consumption basket. Only when price increases are also broad-based, and there is less flexibility to cope with food price rises by adjusting other expenditures, will households be forced to reduce food intake. Third, from a statistical perspective, CPI inflation and food price inflation tend to be strongly correlated so much of the statistical information about the position of one of the variables is contained in the other from a model’s perspective.

> 28Forecasting commodity prices is notoriously difficult. Anyone who would have solved the problem of knowing future prices beforehand would likely also be able to solve poverty and malnutrition by redistribution of personal wealth.
for the same valves manufacturing sector.

Using firm-level data, Bresnahan et al (2002) also suggest that the reduction of ICT prices increases investments in work organization and product and service innovations, which raise the demand for skilled workers enhancing productivity growth. Their research, using US firm level data from 1987 to 1994, found evidence of complementarities among all three types of innovations (IT, complementary workplace reorganization and new products and services). In other words, firms adopting innovations tend to use more skilled labor, and the effects of ICT on labor demand are greater when ICT is combined with organizational investments. In sum, they highlight the importance of ICT as an enabler of organizational change, which leads to productivity growth. Using the same firm level data, Brynjolfsson and Hitt (2003) specifically investigate the effect of computerization on productivity and output growth. According to the authors, ICT affects productivity because firms change their production processes and engender complementary innovations within and across firms. Their main conclusion is that the estimated contributions of computerization to output growth steadily increase in the long run. In the short run, measured output contributions of computerization are approximately similar to computer capital costs, but in the long run their contribution is significantly greater than their costs.

Therefore, one strand of this literature argues that productivity gains are reinforced by ICT investments if complementary investments in organization capital are conducted jointly, as found in Crespi et al. (2006) and Commander et al (2011). Crespi et al. (2006) investigate this issue using UK firm-level data from 1998 and 2000. The authors reached similar conclusions as in the previous literature, such as the fact that ICT and organizational change interact in their effect on productivity growth. In addition, the authors find that organizational change is affected by competition, which leads to more innovation as found in Kretschmer et al. (2012). Commander et al. (2011) evaluate this linkage by utilizing firm-level data from two large

> 5 Another example of sector level analysis is
)|(0.014)|(0.102)|(0.018)|(0.172)|(0.015)|(0.018)|
|Network (0/1)|0.014|0.023|0.051|0.043<sup>_∗_</sup>|0.132|0.159<sup>_∗∗∗_</sup>|0.131<sup>_∗∗∗_</sup>|
||(0.022)|(0.020)|(0.160)|(0.024)|(0.162)|(0.023)|(0.020)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|Observations<br>|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.073|0.027|0.273|0.042|0.179|0.078|0.083|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. Dependent variables are different coping strategies utilized by the household. These include reducing meals, selling the assets, taking loans, receiving transfers from family members, and getting assistance from friends. In all regressions, the household age, household size, child-adult ratio, the urban dummy, a dummy indicating whether the floor is made of dirt, and county, wave, strata fixed effects are adjusted. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
Europe|6- 14|52|91|3,405|14,423|4.9|
|Japan|25|165|163|79,754|3,820|93.1|
|U.S.|5|44|45|1,147|4,602|4.2|

_1 Boeschoten (1992), table 1-1, p.200._ _2 Europe averages CHAPS (U.K.), SAGITTAIRE and TBF (France), EAF (Germany), and ME (Italy); Japan is BOJNET; the U.S. averages CHIPS and Fedwire._ The match between transaction type and the payment instruments used for these transactions is related to the average value of the transaction and is illustrated below. Both check and giro payment methods are seen to be quite flexible in that they are often used for a wide range of payment values and transaction types. In contrast, cash and cards are typically tied to smaller value point-of-sale transactions while wire transfers are almost exclusively used for large value financial transactions.

_A ranking is used because only scattered data are available on the actual average value of each of the 4 types of transactions._ _The 11 countries are: Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Sweden, Switzerland, and the U.K._

**_3_** _Japan's average value per wire transfer is high because many of its transfers represent settlement payments for sums of transactions rather than individual transfers. Japan's average check value is high because it is almost exclusively used for a small number of large value business/financial transactions._
|**Tool name:**|**Poverty Analysis Macroeconomic Simulator**|
|---|---|
|**What is it?**|PAMS is an econometric model that links a macro-consistency model or macroeconomic framework to<br>a labor-poverty module.|
|**What can it be used for?**|PAMS can be used to address the impact of macroeconomic policies and exogenous shocks (such as an<br>exogenous rise or fall in output growth or a change in the sectoral composition of output) on individual<br>households.|
|**What does it tell you?**|PAMS can produce historical or counterfactual simulations of (1) alternative growth scenarios with<br>different assumptions for inflation and fiscal and current account balances; these simulations allow<br>tradeoffs within a macro-stabilization program to be tested; (2) different combinations of sectoral<br>growth (agricultural or industrial, tradable or nontradable goods sectors) within a given aggregate GDP<br>growth rate; and (3) tax and budgetary transfer policies.|
|**Complementary tools:**|Stakeholder analysis can be useful for identifying groups to inform the process of selecting<br>microcategories.|
|**Key elements:**|PAMS has three main components. First is a standard aggregate macro-framework that can be taken<br>from any macro-consistency model (for example, RMSM-X, 123) to project GDP, national accounts,<br>the national budget, the balance of payments, price levels, etc., in aggregate consistent accounts.<br>Second is a labor-market model that breaks down labor categories by skill level and economic sectors<br>in which the production total is consistent with that of the macro-framework. Individuals from the<br>household surveys are joined in representative groups of households defined by the labor categories of<br>the heads of household. For each labor
Ward, R. M. and J. Li (1993). Solid-waste disposal in Shanghai. _The Geographical Review_ . 83(1): 29-42.

- Whittington, D., D.T. Lauria, and X. Mu (1991. A study of water vending and willingness to pay for water in Onitsha, Nigeria, _World Development,_ 19(2/3): 179-198.

- Whittington, D., J. Briscoe, X. Mu, and W. Barron (1990) Estimating the willingness to pay for water services in developing countries: a case study of the use of contingent valuation surveys in southern Haiti. _Economic Development and Cultural Change,_ 38(2): 293- 312.

- Yang, Y. (1996). Changzhou, China: Water supply, sewage treatment and waste disposal strategies for sustainable development. _Ambio_ , 25(2), The Sustainable City (Mar., 1996), pp. 86-89.

- Ye, Chunhui and Ping Qin (2008). Provision of residential solid waste management service in rural China. _China & World Economy_ . 16(5): 118-128.

- Zhuang, Y., S-W. Wu, Y-L. Wang, W-X., Wu and Y-X Chen (2008). Source separation of household waste: A case study in China. _Waste Management_ , 28(2008): 2022-2030.

19
2004 - MAcMapHS6 v2.1 dataset – with several key updates. We take into account for some internationally-binding commitments to reform that will affect the tariffs that would have applied in 2025 in the absence of an agreement. The adjustments to the baseline tariff include WTO commitments taken in the accession process by a range of countries, including China and Ukraine. In addition, the Japanese GSP for LDCs has been updated based on 2007 improvements in terms of product

> 5 This assumption neglects the important value that can arise from bindings above current applied rates, but ruling out incidents of higher tariffs in the future (Francois and Martin 2004).

9
This view about the importance of revenue mobilization in countries with low levels of provision of public goods and social and infrastructure gaps is, indeed, in line with the evidence presented in Figures 3 and 4 In a similar vein, and mainly due to the need to reduce the fiscal deficit, Costa Rica’s fiscal authorities sent to Congress in 2016 a draft law to increase the VAT rate by one percentage point in 2016 (i.e., from 13 to 14 percent) and another one in 2017 (from 14 to 15 percent).

# 3 Policy implications II: On revenue mobilization in commoditydependent countries

Since the fall in commodity prices in 2014, most commodity-rich countries (especially those whose fiscal revenue structure depends “excessively”upon commodity revenues) have been facing a growing fiscal challenge as the price fall has increasingly been perceived as more permanent than temporary in nature. Indeed, Figure 5 shows, for a sample of 55 countries for which commodity revenue data were available, that the share of commodity revenues (as percentage of total revenues) tracks very well that of commodity prices.<sup>3</sup> Since commodity prices are expected to remain low in the years to come, so will commodity revenues. This, in turn, shows the serious challenge faced by commodity-rich countries and particularly by those with high commodity revenue dependency.

Figure 6 shows the association between the economy’s dependency on commodities (proxied by the commodity GDP as a percentage of total GDP) and the commodity revenue dependency (proxied by commodity revenues as a percentage of total revenues). Not surprisingly, the fitted line shows that the higher is commodity GDP, the larger is the share of commodity revenues in total revenues.<sup>4</sup> For example, in a country like Kuwait, where oil production represents about 60 percent of total GDP, oil revenues constitute about 90 percent of total revenues. In contrast, in the Philippines, oil production represents less than 1 percent in both GDP and
This resulted in the emergence of a variety of initiatives to provide a credible „fair trade‟ signal in the marketplace – not as a unique product/brand but as label that could be adopted, in selected product markets, by producers and distributors that credibly commit to meet „fair trade‟ standards. In 1997, these initiatives came together to form the umbrella Fair Trade Labeling Organization (a member of the ISEAL Alliance). Sales growth of „fairtrade‟ labeled products has been rapid:

- In 2000, global sales amounted to about $300 million. In the subsequent seven years, global sales rose over ten-fold – an almost 50 percent average annual growth rate --  to reach $4 billion in 2008, including 22 percent year-on-year growth for 2008, despite the  global economic crisis.

- The market is overwhelmingly in North America and Western Europe, largest in the United States and the United Kingdom. The Japanese market, though still small, grew at 44% in 2008.

- Coffee is the largest „fairtrade‟ labeled product, with 2008 sales amounting to $1.8 billion, Bananas (at over $550 million, mostly in Europe), are the second largest fair-trade seller. Tea, cocoa, cotton, sugar and flower each had 2008 fair trade sales in excess of $250 million. The US fair-trade labeling organization recently has introduced a fair-trade labor standard for cotton garments, covering the conditions of both cotton production, and cut-and-trim sewing operations.

- A recent large-scale survey found that the proportion of U.S. consumers reporting that they rewarded companies for being socially responsible has risen from 46% in 1999 to 59% in 2009.

_Sources: Global Fairtrade Foundation, “_ Global Fairtrade sales increase by 22%”, press release, June 8, 2009. _Wikipedia, Transfair USA,_ “Fair trade thriving in tough U.S. economy”, <u>press release, April 16, 2009.</u> 20
revealed high shares of toxic chemicals which have known to cause cancer, genetic damage, fetal damage, and severe allergic responses in exposed populations (for example, carbamates and organophosphates in insecticides, and dithiocarbamates and inorganics in fungicides ) (Zahm, Ward and Blair, 1997).

Many pesticides used in Bangladesh are also banned or restricted under international agreements (Meisner, 2004; NOVIB, 1993; SOS-arsenic.net, 2004; SUNS, 1998). Pesticide suppliers in Bangladesh even continue to sell the 12 particularly controversial pesticides known by activists campaigning worldwide as the “dirty-dozen” (SUNS, 1998); SOS-arsenic.net, 2004). In addition, several studies have shown that inadequate product labeling and users’ lack of information have lead to widespread overuse and misuse of dangerous pesticides. Anecdotal evidence suggests that the consequence of these practices are numerous, with pesticide poisonings abound (Ramaswamy, 1992). Although monitored health data on the effects of pesticides is not available in Bangladesh<sup>4</sup> projections suggest an annual incidence of organophosphate poisoning alone is as high as 900/100,000 population (World Bank estimate, 2004).<sup>5</sup>

# **3. The survey of traders**

_Background of the supply chain_ Bangladesh does not produce any active ingredients, it only imports and formulates. The marketing channel of pesticides in Bangladesh consists of pesticide companies, distributors, wholesalers, wholesaler-cum-retailers, retailers and farmers (Sabur and Moila, 2000). At the top, there exist approximately 66 officially registered companies, with 6 of these being multinational in nature. Distributors buy almost all of their products from pesticide companies. However, the pesticide companies also directly sell to wholesalers. Distributors, in turn, sell their products to the wholesalers-cum-retailers, retailers and large farmers. Wholesalers sell to retailers as well as farmers. Retailers, in turn, sell their product to farmers, but farmers frequently buy directly from the

> 4 The 1999 DGHS data, produced by
this variable to be associated with less severe financial constraints (i.e., positively linked to the ̳finance no obstacle‘ variable) and a greater likelihood of survival. The full set of performance variables enters the survival regressions.

For the survival analysis, all firm characteristics are measured at the time of the third round of the BEEPS survey—mostly in late 2008. There are two reasons for this. The first is practical—it allows us to include firms that were no longer operating at the time of the FCS survey in mid-2009 in the survival regressions. The second is that it reduces the likelihood of reverse causality. This is a particular concern for the measures of firm performance— performance is likely to have been affected by the crisis and those firms affected most seriously are also most likely to be forced to close during the crisis. It is important to note that many variables from the 2008-09 survey are actually measured for fiscal year 2007 or earlier (i.e., firms were reporting retrospective data for the previous completed fiscal year). For example, firm growth is for 2005 through 2007, return on sales is for fiscal year 2007 and the dummy variable for investment is for fiscal year 2007.

F is a matrix of variables that summarize firms‘ use of financial services, which are relevant for the firm survival regressions. All else equal, we would expect firms that had readier access to finance to be more likely to survive. Although having access to finance would not affect firms that were basically insolvent, it might allow firms that have temporary liquidity problems due to the drop in demand to survive the crisis.

To reduce concerns about reverse causation, it would be best to have measures of use of financial services from before the start of the crisis (i.e., like for the performance measures). Unfortunately, the two best measures in the survey—whether the firm has a loan and whether the firm has a line of credit or overdraft—were asked ̳at the
Figure 1: Cluster-randomization of districts (and circles therein) <!-- Start of picture text --> Chandigarh<br>Punjab Uttarakhand C H I N A<br>Haryana<br>Delhi<br>Sikkim<br>N E PA L<br>Rajasthan<br>Uttar Pradesh<br>Bihar<br>Madhya Jharkhand<br>Pradesh West Bengal<br>Chhattisgarh<br>Odisha<br>Maharashtra<br>Bay of Bengal<br>Telangana<br>Treatment districts (ads) Control districts (no ads) IBRD 48397 | OCTOBER 2024<br><!-- End of picture text -->

This map was produced by the Cartography Unit of the World Bank Group. The boundaries, colors, denominations and any other information shown on this map do not imply, on the part of the World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

of treatment and control districts (and circles therein) over space. While one district can contain multiple circles, all circles in a district are in the same experimental condition. Circles are identified by the coordinates of their centroids and a radius. We passed this information to the MNM advertising team, which used it to exclude the control (red) circles from their nationwide campaign, while running it on the treatment (green) circles as well as on all the remaining parts of the districts and the other targeted states. Due to an implementation issue, we dropped one district from the analysis, leaving 79 study districts in total.<sup>16</sup> We use a cluster design for the following two reasons (Hayes and Moulton, 2017). First, we acknowledge the presence of different types of spillovers.<sup>17</sup> A cluster design allows us to account for the correlation across observations within a cluster and to minimize the risk of contamination between treated and control areas
# Appendix D. Data Collection Timeline

Figure D.1 illustrates the timeline of the data collection. It denotes the timing of the three rounds of the GHS with post-planting and postharvest visits in the autumn and spring, respectively, for each wave.

The telephone survey on conflict was conducted with 717 GHS households that were part of the GHS panel in wave 3, visit 2. It collected recall data on conflict events at the annual level from 2010 to 2016 and for spring 2017. The blue bar in the figure denotes the conflict intensity over time.

Figure D.2, panels a and b, show the merge of the outcome variables and the conflict recall periods for the consumption aggregate as well as food security, respectively. We have merged the outcome variables with the conflict data from the previous year; in cases where there is more than one year between two visits of the GHS, we have used data from several years.

For consumption, we have data from three waves that have two visits each. The consumption aggregate is the median of the consumption level of those two visits. Hence, we have a measure for consumption at three points in time. Conflict events that occurred _during or before each wave_ are the main independent variable. The conflict events that took place before wave 1 (2010–11) are those that occurred in 2010; conflict events between wave 1 and wave 2 (2012–13) occurred in 2011 and 2012; and conflict events between wave 2 and wave 3 (2015–16) occurred in 2013, 2014, and 2015. Therefore, events that occurred in 2016–17 are dropped from the analysis because they have occurred mostly after the end of the last visit of data collection in spring 2016 (figure D.2, panel a).

For the food insecurity analysis, a six-round panel of the GHS, the first round (visit of wave 1, that is the post-planting visit in 2010) was excluded from the analysis because no conflict information existed for 2009
31

**Wage Gaps.** Overall, it does not appear that consumption cities have significantly less human capital than production cities. In other words, for a given level of human capital, the workers of consumption cities are more likely to work in urban non-tradables (and informal employment). Now, inspired by Gollin et al. (2014), we use I2D2 to investigate the productivity gaps between urban tradables (UT) and urban non-tradables (UNT). For individual _i_ and country-year-sample _t_ , we use OLS to estimate the following model for _each_ country one by one, restricting the sample to 18-67 year-old urban workers belonging either to the UT sector or the UNT sector:

where the dependent variable is the log of monthly earnings ( _lnWit_ ) and _Tit_ is a dummy equal to one if the worker belongs to the UT sector. _δ_ measures the wage gap between the UT and UNT sectors. We first examine the gaps estimated without including controls for human capital ( _X_ ); then we re-estimate the gaps after adding human capital as a control and controlling for sorting. If we find large (conditional) wage gaps, that would suggest that the UT sector may provide “better” jobs than the UNT sector.

Focusing on the sample countries (N = data available for 54 countries in I2D2), we find an unconditional wage gap of about 22%.<sup>46</sup> If we control for gender, years of education, and the experience dummies, the gap reduces to 13%.<sup>47</sup> If we additionally control for (spatial) sorting by including first-level administrative area or primary sampling units FEs, we get 15-17% (N = 28-41). In other words, we find significant gaps even when we compare workers with similar human capital levels and workers residing in the same urban regional or local labor market. Of course, these results should be taken with caution since there may still be unobservable factors that we cannot control for.