rafmacalaba/datause-encoder-data huggingface dataset:
income effects from increased commercial sales, as well as possible attritions through migration and fostering arrangements are considered. Further, local food price changes were uncorrelated with the share of non‐farming village households and the initial average child nutrition status in the village, suggesting that observed outcomes are directly attributable to market events and livelihood strategies. Interestingly, gender differences were not detected. Our findings imply that the food price crises can have long‐term impacts on child human capital formation, potentially fostering lifelong income inequality among those affected at an early and vulnerable stage of life. Policy implications are discussed in the concluding section.

# **2. Background**

Between the summer of 2007 and June 2008, following decades of relative stability, global food prices spiked. On the heels of a six‐month run‐up in oil prices, wheat prices began to rise, surging 14 between May and June 2007. Maize prices increased 15 percent between December 2007 and January 2008. Rice prices, which had been climbing modestly during the summer, registered month‐over‐month increases of 21, 24 and 42 percent in February, March and April of 2008. Stocks relative to use had fallen for all three grains during the previous years, for a variety of reasons, including a strategic decision by China to drawn down government stockpiles (Headey and Fan, 2008; Piesse and Thirtle, 2009). With inventories low, markets were positioned to react sharply to negative news (Larson, 2007). In the case of wheat, poor harvests in Ukraine and Australia were seen as triggering events, while, in the case of maize, the large diversion of US maize to mandated biofuel quotas was blamed (Mitchell 2008; Timmer, 2010, Headey, 2011). In the case of rice, researchers suggest that the crisis was largely driven by a series of over‐reactive policy decisions, and that interventions meant to insulate domestic markets sometimes led to rounds of counter‐ productive hoarding and speculation (Slayton, 2009; Dawe, 2009; Timmer, 2010). Regardless, a contagion of decisions by large producers to restrict exports clearly exacerbated the crisis (Headey, 2011).<sup>3</sup> From April 2007 to April 2008, the World Bank’s Food Price Index rose by 67 percent, the associated Grain Index nearly doubled, and rice prices nearly tripled.

The scale and suddenness of the price increases unleased widespread social and political unrest (Slayton, 2009; Bellemare, 2015). However, the sharp change in food prices played out differently among households, creating a continuum of outcomes, even among the poor (Swinnen, and Squicciarini, 2012.) In rural areas especially, households produce some or all of their own food, and this key aspect of rural livelihood strategies is thought to have been particularly important

> 3 Wheat exports from Argentina, Kazakhstan, the Russian Federation, and Ukraine were banned or otherwise restricted. China limited maize exports and levied new export taxes, as did Argentina. Cambodia, the Arab Republic of Egypt, India, and Vietnam restricted rice exports (Headey, 2011).

2
**Table 1: Private sector survey: Returned questionnaires per region**

||Number of<br>surveyed countries|Number of<br>surveyed firms|Average|Median|Minimum|Maximum|
|---|---|---|---|---|---|---|
|All countries|69|<br>3,685|<br>53|50|13|<br>124|
|LDC|58|<br>3,431|<br>59|51|<br>13|<br>124|
|DC|11|<br>254|<br>23|20|14|<br>56|
|SSEA|3|<br>139|<br>46|45|41|<br>53|
|MNA|3|<br>109|<br>36|42|15|<br>52|
|CEE|11|<br>771|<br>70|70|46|<br>114|
|LAC|9|<br>474|<br>53|47|17|<br>87|
|SSA|22|<br>1,288|<br>59|48|13|<br>124|
|CIS|10|<br>650|<br>65|62|31|<br>91|

Figure 1 gives an overall summary of the number of questionnaires returned and the 7 regional distribution of countries covered in the survey.

The first part of Figure 1 gives an overview of the number of returned questionnaires in 69 countries and shows the numbers for individual regions. Of the 3,685 returned questionnaires, 3,431 came from developing countries. Figure 1 shows the percentage regional distribution of the countries in the data set. Sub-Saharan Africa countries constitute the largest percentage (33%) of participating regions. Equal shares of the countries are in the following four regions: Developed countries (16%), Central and Eastern Europe (16%), Commonwealth of Independent States (14%) and Latin-American countries (13%). The regions of South Asia, Southeast Asia, and Middle East and Northern Africa are underrepresented with only 4% of total countries each.

- 7 A list of countries in each category is in the appendix.

_5_
Table B.8 shows the results from testing various hypotheses about the coefficient estimates.

We find that de-licensing reduces significantly the geographic concentration of non-traded industries but the total effects on import-competing and export-oriented industries are insignificant. The only significant (and positive)<sup>effectof tradereformsongeographicconcen-</sup> tration occurs, surprisingly, for non-traded industries. With respect to the traditional determinants of -aw1iii **i.-** ti we v. find that most mechanisms are stronger for import-competing industries. In particular, import-competing industries;-, **wi** as a result of greater labor pooling potential while the non-traded industries disperse. This<sup>isconsistent withthestory</sup> that greater import competition forces import-competing industries to cut costs via greater use of localization economies.

# **9 Conclusion**

In this paper we analyze the impact of industrial and trade policies on the w1 tii i: of manufacturing industries in India. Our results show the importance of policies **-** in particular de-licensing and FDI liberalization **-** in affecting the spatial distribution of Indian manufacturing. Our study is one of the few that uses a long time span of Indian plant-level data from **1980** to **1999** that allows us to consider these important changes in industrial policies. Several of our results can inform policymakers, particularly in developing countries. Our results show that both de-licensing and FDI liberalization led to a decline in average 1.eration levels, while trade policy had no significant effect. That is, plants respond to some market-oriented policy reforms but not all. Further, our results point out the importance of an hitherto unexplored policy that can affect :a1iii .ti - FDI liberalization **-** and emphasize the need to examine alternative mechanisms that affect plant location decisions.

Our results are also unique in that most studies for developed and developing countries alike have emphasized how market-based forces tend to increase - 1ii.i-tii. Thus it is important to acknowledge the ambivalence of the theory and the prevalence of a multitude of mechanisms **-** including for example, the existing spatial distribution and congestion costs in existing clusters **-** that affect 1 i i..ij. and hence should inform policy. Our result that policy reforms increase dispersion in Indian manufacturing provides an explanation for why

**31**
**Table 6: OLS estimates of the impact of the PSNP on maternal and child diet diversity**

||(1)<br>Diet diversity<br>index of mothers|(2)<br>Minimum diet<br>diversity dummy<br>for mothers|(3)<br>Diet diversity<br>index of children|(4)<br>Minimum diet<br>diversity dummy<br>for children|
|---|---|---|---|---|
|Post COVID-19 round|0.033|0.008|0.990<sup>***</sup>|0.157<sup>***</sup>|
||(0.068)|(0.014)|(0.102)|(0.030)|
|PSNP*Post COVID-19 round|0.078|-0.013|0.141|-0.051|
||(0.087)|(0.019)|(0.148)|(0.041)|
|Constant|2.795<sup>***</sup>|0.063<sup>***</sup>|1.751<sup>***</sup>|0.060<sup>***</sup>|
||(0.028)|(0.006)|(0.025)|(0.007)|
|Control for locust invasion|Yes|Yes|Yes|Yes|
|Household fixed effects|Yes|Yes|Yes|Yes|
|Sampling weights applied|Yes|Yes|Yes|Yes|
|R-squared|0.01|0.00|0.34|0.10|
|No. observations|2,994|2,994|1,781|1,781|

_Notes_ : The outcome variables in columns 1 and 3 are the number of food categories consumed in the last 24 hours, taking values 0-10 for mothers and 0-7 for children and estimated using OLS. The outcome variables in columns 2 and 4 are dummy variables taking value 1 if mothers (children) meet minimum diet diversity defined at 5 categories or more (4 categories or more), respectively, estimated as a linear probability model. PSNP participation is defined as: Reported receiving PSNP payments in 2019. Child estimates are based on children between 6 and 24 months old. Standard errors clustered at the kebele level in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

27
financial supporters. Fiscal autonomy for a metropolitan area means that it can more easily raise the revenues needed to provide services.

In general, the types of expenditures made by metropolitan governments include: police and fire; water, sewers, garbage; transportation; recreation and culture; social services, health, and housing; and planning and development. In some metropolitan areas, primary and secondary education is also a local responsibility.

The main sources of revenue for local governments are: user fees (for transit, water, recreation, and some social services); taxes (including taxes on property, income, and sales); and intergovernmental transfers (conditional and unconditional). Local governments in most countries are not permitted to borrow to meet operating expenditure requirements. They are, however, often permitted to borrow to make capital expenditures. Nonetheless, local access to capital markets is often heavily restricted in developing countries (Rodden, Litvack and Eskeland 2003). Even in countries with well-developed capital markets, smaller municipalities may only be able to borrow through a financing authority or state/provincial body, in part to reduce borrowing costs by pooling the borrowing requirements of different municipalities.

It is very difficult to find comparable data on municipal revenues and expenditures across countries (and sometimes even within countries). The remainder of this section provides examples from a few cities in developed countries for which municipal public finance data are available.

# _London_

The Greater London Authority has very little local fiscal autonomy. As Table 2 shows, the main responsibilities of the GLA are transport and police; other responsibilities include economic development, planning, and fire and emergency planning. Its revenues sources largely come from central government grants which account for over 80 percent of the revenues of the GLA. Central grants include the distribution of centrally-collected non-domestic rates (non-residential 52
stocks by -1.<sup>12</sup> CARs immediately increase after the announcement date, and they drift slightly upwards until the implementation date (since the implementation date varies across events, this is represented as a shaded area in the figure). We report detailed statistics about the price effect of the rebalancings in the first column of Table 1. In this case, CARs are computed from the day prior to the announcement date to the day of the actual index recomposition.<sup>13</sup> CARs range between 0.87% and 22.32%. The median CAR is 5.45%, while the average is 6.65% and is statistically different than 0 at the 1% significance level.<sup>14</sup> The average price impact of our 18 events is broadly in line with those found in previous studies. For example, Chang et al. (2014) find that inclusions into and deletions from the Russell 2000 have an average price effect of about 5%. This is particularly interesting as the market capitalization of stocks in our sample is close in magnitude to those in the sample by Chang et al. (2014). In a sample of almost 200 stock additions to the S&P 500, Wurgler and Zhuravskaya (2002) instead find the average CAR to be around 3%, which goes up to 5% when focusing on abovemedian demand shocks, that is, additions which are predicted to be associated with larger inflows because of the size of index funds tracking the index.

# **3.2 Investor behavior and traded quantities**

We now analyze the behavior of investors in response to the rebalancings in the MSCI Standard Indexes. As in the previous section, we pool together additions and deletions by considering sales as purchases, and vice versa, in case of deletions. Since MSCI indexes are mostly tracked by international investors, we start

> 12As shown in Chang et al. (2014), index additions and deletions have comparable price effects. Throughout the rest of the analysis we follow this approach and describe our results as if all events were additions.

> 13Since the number of days between the announcement and the implementation dates differ across episodes, the average CAR in Table 1 is slightly different than the one in Figure 1 (where we report the average CAR for each day after the announcement date).

> 14It is possible that the right asset pricing model differs across stocks in our analysis. As an alternative, we estimate abnormal returns using an international factor model with two factors following Bekaert et al. (2014) – a global factor and a domestic factor [ _Rt_<sup>_G, R_</sup> _t_<sup>_D/s_</sup> ]. The two factors are value-weighted market indexes, so that the model potentially embeds a global CAPM, a domestic CAPM, and the mixed case when both global and domestic markets affect the pricing of a particular stock. In this case, returns are measured in U.S. dollars in excess of the three-month U.S. T-Bill rate. The median and mean CAR of stocks in our sample computed using the mixed model are 5.21% and 6.28% respectively. Thus they are very close to the estimates we get with our baseline domestic, one-factor model. In Appendix Table OA3, we report all of our results obtained using the mixed two-factor model.

8
<mark>term growth is likely explainable by fixed effect models not capturing time-variant country characteristics and structural changes due to default, which are captured by the SCM’s counter factual. The striking difference in the first year of default is more puzzling. Importantly, the finding is not primarily driven by the SCM algorithm, or our information-weighting: Using no weights and instead dropping cases with a fit worse than a given threshold (conservatively, we applied 5%;10% and 20% NRMSE thresholds), as well as using a fixed-effect event study methodology (Gourinchas and Obstfeld, 2012). The difference might instead be due to subdued “structural” growth rates during the period studied by Borenzstein and Panizza (2009). And indeed, defaults over this period appear to have been particularly painful: while the average growth rate in the year of default of economies over our whole sample since 1900 is 1 percent, the same was -3.2 percent for the 1972 to 2000 subsample.</mark><sup>22</sup> <mark>Instead, in line with the existing literature (Levy-Yeyati and Panizza, 2011), our results confirm that, likely driven by anticipation of the crisis affecting the private sector, a slowdown in growth of 1.7 percentage points compared to the counter factual precedes the default.</mark> <mark>So far, we have treated all defaults as if they were equal. However, sovereign defaults differ significantly from one another (Asonuma and Trebesch, 2016; Trebesch and Zabel, 2017). Some, such as Côte d’Ivoire’s default spell starting in 1993, remained unresolved and excluded the sovereign from capital markets for decades. Others, like that of Uruguay in 2003, are triggered by a preemptive restructuring in anticipation of debt distress, and are often short-lived, without significant exclusion from capital markets and international trade. And, though less common, there exist</mark> _<mark>hard</mark>_ <mark>default episodes that end swiftly. In the more typical case, default episodes are spells of recurring defaults, with multiple failed attempts at restructuring the debt burden (Graf von Luckner et al., 2021). Regardless of the reason for the duration of the period, these episodes could have very different impacts on economic output (Benjamin and Wright 2018). And especially the average effects in the later part of the sample are thus lowered in magnitude by some countries exiting default and benefitting from the positive impact of debt relief on growth, as is documented by Reinhart and Trebesch (2016). When only considering defaults with durations above the median (6 years), we find defaults to be much more costly in terms of economic output. Over the decade post default, longer defaults have annualized growth rates</mark>

> 22 <mark>See also World Bank World Development Report 2022, Chapter 5, p. 210 showing the great extent to which the lost decade damaged growth in countries entering default between 1980-85.</mark>

19
# **The Climate Implications of Ending Global Poverty**

**Authors:** Philip Wollburg<sup>*</sup> r Stephane Hallegatte<sup>*</sup> r Daniel Gerszon Mahler<sup>*</sup>

Originally published in the Policy Research Working Paper Series on _February 2023._ This version is updated on _February 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Poverty, climate change, greenhouse gas emissions, inequality **JEL codes:** D63, I32, O44, Q54, Q56

*The author ordering was constructed through American Economic Association’s randomization tool (confirmation code: S0bg4jTASKt6). All authors are with the World Bank. Hallegatte may be contacted at <u>shallegatte@worldbank.org, Mahler may be contacted at dmahler@worldbank.org, Wollburg may be</u> contacted at <u>pwollburg@worldbank.org.</u> We are thankful for comments received from Aart Kray, Gero Carletto, Samuel Freije-Rodriguez, participants at a presentation to members of the Poverty and Equity Global Practice of the World Bank and to members of the Global Poverty and Inequality Data team of the World Bank. Mahler gratefully acknowledges financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme and from the World Bank Group SDG fund. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
TABLE 1

|||||(p|INITIAL<br>eriod<br>avera|CONDITIO<br>ge.<br>197|NS<br>0-80)|||
|---|---|---|---|---|---|---|---|---|---|
||1<br>DEBT ASI<br>OrCOPp&/|2<br>DEBTAS X OF<br>c,port98/b/|3<br>REAL<br>EFFECTIV<br>EXCH. RAIE|4<br>FISCAL<br>E DEFICIT A)<br> _%_OF CDP'/|S<br>ANNUALAVG.<br>RATE OF<br>INFLATION|8<br> <br>RATE<br>OF<br>CROWTH|7<br>DOI.ESIIC<br>SAYINGAS<br>d/ X OF CDPdI|8<br>INVESTMEW<br>AS1<br> <br>OF coPd/|9<br>T EXPORTS<br>AS 1<br>OFCDpd/|
|rA[average<br>|41.5 (3)|204.7 (2)|98.3 (3)|-8.4(2)|23.2 (3)|4.8(2)|18.7 (1)|24.3 (3)|24.7 (2)|
|61edan<br>|48.6<br>|199.8|9e.8|-8.9|13.6|6.8|19.2|23 8|22.3|
|1t quartile<br>|24.2<br>|118.3|94.4|-9.2|9.8|2.6|14.6|19.8|16.6|
|3,d quartilo|**49.3**|**_277.0_**|101.7|**-3.6**|19.8|**8.3**|**22.1**|28.2|29 4|
|04t**average**<br>|**39.8**(2)<br>|**208.2**(3)<br>|97.9 (2)<br>|-7.0<br>(3)|21.3 (2)|**3.9**(3)|13.9 (3)|23.6<br>(2)|24.7 (31|
|Wie:ian<br>|34.9<br>|160.1|99.1|-6.2|11.8|3.8|14.3|21.8|20.4|
|1'quartile<br>|22.3|11.0|96.1|-7.1|8.9|2.7|2.9|_15.7_|_12.0_|
|3<sup>rd quartlto</sup>|46.8|320.8|101.2|-2.1|16.9|4.9|24.0|30.8|34.4|
|NA!**_average_**<br>|29.7<br>(1)|144.8(1)|97.8<br>(1)|-4.4<br>(1)<br>|12.2<br>(1)|6.6(1)|14.8(2)|22.8(1)|29.9<br>(1)|
|Mledidan<br>|28.1|112.8|_97.8_|-3.6|11.4|4.8|16.4|21.8|29.1|
|**1 **<sup>**5t**quartil</sup><br>|**18.9**<br>|**78.6**<br>|94.2<br>|**-7.7**|**9.1**|**3.8**|10.0|**18.8**|**18.3**|
|3 <sup>d quarti</sup><sup>**l**</sup>|37.0|128.8|**100.6**|-1.4|**13.3**|**7.0**|**19.8**|26.8|30e|

'ourco World Eanl dota.

- a. **The** ratios **are** computed using data in current US dollars; the period covered is 1976-80. The data includtotal disbursed guaranteed an-' non-guaranteed debt. f.Iorts of goods and non-factor sorvices **aro** obtained from balance of payments stotistics in **the** World Bank **date file.** 7 1 - column corsiders only the average for the period 1978-80 and is based on IIMF date; **Algeris,** Bolivia, Indonesia **and Jsmsics have** date ave **lab:e** only **.'-.e** 1979, Guinee-Bis..u and Morocco since 1978. and Brazil. B.,rkina Foso China, Congo, Greece, Niger and Portugal since 1917.

- d/ The ratios **are** calculated with data in constant local currency. **Note: The numbers** in **parentheses are the** rankings of **the** country groups for that indicator **(1beat' is one).**

f,l710 tSPiFQ 90 F s
government stimulus programs as well as monthly average income and its volatility plus consumption are all influenced by unobserved community heterogeneity.

At the same time, when we relate monthly consumption to monthly income and its volatility plus some non-income factors, we invariably introduce a common month effect, which affects a household‘s monthly income, consumption and participation. That is, consumption may be completely independent of monthly variations in income and government policies, and still co-vary with monthly income, simply because of a common month effect (Khandker, 2012). For example, there is a common monthly but unobserved factor, such as preference of households for certain foods in a specific month – the New Year, for instance – in which case we would get an effect simply because of this common month factor, even though it has nothing to do with monthly variations in income or prices, or participation in government program.

Note that introducing a monthly dummy in the above equations (2)-(4) does not resolve the problem of a common-month effect, because this monthly dummy does not control for the unobserved, location-specific common time effect (  ). Correcting this sort of bias requires monthly household panel data (i.e., repeated observations across months for each household unit), which we do not have. But we have monthly data across households over years across communities. For example, we have community panel data at the sub-Changwat or sub-province level (which as mentioned earlier is the lowest common sampling unit that can help create the panel across months). Using the community-level panel, we could apply a panel fixed-effects regression, and to control for monthly unobserved bias, we can include month*community interaction variables along with a time trend to control for both  _j_ and  _m_ .

However, a panel fixed-effects model may not suffice to account for time-varying unobserved factors that influence how households respond to stimulus and its associated impacts in the local economy. To account for time-varying unobserved heterogeneity, our approach in this paper is to estimate a dynamic panel model (Arellano and Bond 1991; Jalan and Ravallion, 1998, 2002) using the multiple waves of monthly data. As described below, we also account for a range of 20