rafmacalaba/datause-encoder-data huggingface dataset:
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# ANNEX A - DATA AND METHODOLOGY

Time-series data for official and market prices were compiled from a variety of sources including World Bank and IMF documents, consultant's reports, the FAO, the USDA, the International Federation of Agricultural Producers, and direct contacts with World Bank Resident Missions and country economists. 22/ Producer and consumer price data have been compiled for the most important agricultural commodities in each of 35 countries; for up to four majot export commodities, up to three traded food crops, and up to two nontraded staples.

Data for agricultural production and trade are from FAO. National accounts data are from the World Bank data files and IMP. Transport costs per kilometer-ton have been estimated based on the large number of existing detailed studies, and are applied to distances from the major producing and consuming region for each commodity via the most commonly used mode. Historical transport cost data, where unavailable, are extrapolated on the basis of the domestic CPL Processing costs have been estimated similarly. Ocean freight costs are estimated based on World Bank figures.

The critical distinction between official prices and open market or parallel market prices has been maintained throughout. Where the available information indicates that most farmers receive the official price for their product, that price is also assumed to ba the market price. '.Vhere data on open or parallel market prices are available these prices are used as being more representative of what producers receive. Averages across countries for export or food crops are taken as weighted averages (by production value) within countries, and simple averages across country groups. _2Q/_ For comparative analysis between country groups, group averages are simple means. Weighted averages would be inappropriate since the objective is to illuminate a central tendency among countries in the group, rather than to assess their aggregate magnitude.

The producer price data assembled are deflated by the domestic CPI for up to four major export commodities in each country. An average index of export producer prices is computed weighing each commodity's price by its relative value of total production.

The following potential data problems should be borne in mind when interpreting the results of this study:

# 1. Production data and, to a lesser extent, export data, may be inaccurate. Export data be

_29/_ In general, the producer price data reflect the average annual price received by farmers in the most important producing regions, although due to the wide regional and seasonal variations and the lack of specifics from some sources, this level of precision cannot be assured. Data series from different sources have sometimes been spliced when they seemed reasonably consistent.

L _/_ Detailed data on producer prices was compiled for 35 countries, excluding very small countries (Comoros, Seychelles, Cape Verde, Sao Tome, Lesotho, Swaziland), ones where no data were available (Angola), or where agriculture is of minor importance in the overall e.nmomy (Gabon, Djibouti).

1 iI |

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consistent with the idea that bank finance sustains firms over longer periods enabling them to expand over time.

In contrast, neither private credit nor stock market capitalization is significantly associated with investment rates, firm size, or employee training within the sample of highincome countries. Further, there is no strong relationship between stock market capitalization and the other firm outcome variables within the sample of low-income countries. These patterns mimic the ones that we found for labor growth and thus support our interpretation of our main finding regarding the beneficial effects of banking sector development in lower-income countries.

# **VII. Conclusions**

We have combined data from detailed firm surveys with indicators of financial development and structure that cover a broader set of countries than in earlier studies in order to study the relationships between firm growth, financial structure, and stage of economic development. In low-income countries, we find evidence that labor growth is swifter in countries with a higher level of private credit/GDP, consistent with predictions from new structural economics. This conclusion holds using two well-established estimation approaches for identifying the causal impact of financial development on growth: instrumental variables regressions and the RajanZingales methodology. The plausibility of the finding of the beneficial role of banks in poor countries is further boosted by our finding that a more developed banking system is associated with higher investment rates, more employee training, and larger firm sizes only in poor countries, which suggests that banks spur both physical and human capital investments besides boosting job growth in such countries. In high-income countries, we find that labor growth rates are increasing in the level of stock market capitalization, which could also be viewed as being consistent with predictions from new structural economics, though we are unable to provide evidence that the association is causal.

At the same time, when we examine the types of firms that appear to benefit from welldeveloped private credit markets in low-income countries, we find no evidence that it is the small-scale firms that benefit most. Rather, labor growth rates are increasing in the level of private credit market development for larger, capital-intensive firms. This pattern is more 26
<!-- Start of picture text --> 12%<br>10%<br>><br>2<br>Ff=o 9% e GTM<br>E ex ° HND<br>3 _<br>= 4% PCO eects 2 RRY<br>9 ccxtsipeeenn © DOM ® UGA<br>©© og2% haaARG © CRI<br>3 ° ETH © NGA<br>io)<br>0%<br>on MNG 5% 10% 15% 20%<br>Increase :in employment rate<br><!-- End of picture text --> <!-- Start of picture text --> 100% 80<br>2 95%<br>< 95%<br>s 60<br>2®a 19 399 a=]xs<br>5 70% 5<br>2F 86% 40 €<br>= 235% 839 g<br>2 =<br>r=@ 20<br>g 80%<br>3<br>2<br>€ 75% fa)<br>wer<br>May-July Aug-Oct Nov-Jan'21<br>@@mmfemale Mm Male —Stringency (right)<br>Note: Results for 8 countries<br><!-- End of picture text -->
plus the dumping margin found are compared against non-injury prices, which are usually the domestic prices during a period prior to the one being investigated.

The new Mexican rule is closer to the one used by the European Commission (EC), but lacks a strong methodological foundation and differs from the approach used in Mexico in the early 90s. The Mexican and European rules are similar in the sense that both are based on an endogenous reference to their domestic production, while ignoring current international prices or the prices relevant to fair competition.

Table 10 shows the cases in which a duty less than the margin has been recently applied.

**Table 10 - Cases in which a Duty Below the Dumping Margin was Applied, 2001-2004**

|**Date of final**<br>**determination **|**Product**|**Country**|**Final dumping**<br>**margin**|**Lesser Duty**|
|---|---|---|---|---|
|22-Jun-01|Polyester filament fabrics|Korea|31.61%|16.03%|
|||Taiwan|11.69%|11.00%|
|2-Oct-02|Steel beams|Brazil|80.00%*|42.49%|
|13-Jan-03|Standard tubing|Guatemala|29.93%**|25.87%|
|10-Jun-03|Monobutyl ether|UnitedStates|54.25%|32.56%|
|17-Jul-03|Steel chains|China|0.80 USD/kg|0.72USD/kg|
|24-Sep-03|Ferro-silico-manganese|Ukraine|95.74%|51.28%|
|21-Apr-04|Seamless steel pipes|Russia|111.37%|79.65%|
|||Romania|42.00%|42.00%|
|13-May-04|Tubulargoods|Venezuela|54.69%***|49.94|
|3-Aug-04|Sodium<br>hexametaphosphate|China|124.96%|102.22%|
|4-Aug-04|Steel connections|China|111.41%|81.04%|

Source: Based on data from relevant determinations published in the DOF.

The diverse ways in which LDR has been applied result from the different criteria held by officials engaged in administering antidumping instruments. This situation also reveals the broad discretionality prevailing in the administration of the antidumping system, despite all past attempts in the opposite direction.

34
On paper, agricultural land is allocated through local administrative processes in Senegal. Since 1964, most of the land (between 95 and 98%) has been part of what is called the national domain. Land use rights are attributed by local land committees on the basis of needs and capacity to farm. This land cannot be sold and in theory cannot be bequeathed either (Caveriviére, 1986).

However, the reality on the ground is clearly rather different. Bequests are common, including land inheritance. The survey data we use indicate that 31% of men, but only 17% of women, report that they inherited land. (If one confines the sample to those whose father has died, the proportions are 43% for men and 28% for women.) Looking at all forms of inheritance (including housing, durables, money and productive assets) 54% of men in the sample inherited something, while 38% of women did. In practice, heirs are given priority to obtain the use rights over any other potential users. Hence, inheritance of paternal lineage land is an important means of access to ownership.

Until the constitution of 2001, women were not allowed to own land in Senegal. Furthermore, until May 2010, they were not allowed to be part of the land committees in charge of the attribution of use rights. Hence, women very rarely received land through this allocation mechanism. On the intergenerational transmission side, several inheritance laws coexist in Senegal that give very different treatment to women. Each individual can choose before his death which law he wants to abide by. The French inspired system of inheritance dictates that wealth be shared equally among children, whatever their gender. By contrast, the Islamic inheritance law (which is the most common choice in practice) limits the inheritance of daughters to half of that of sons.<sup>12</sup> In addition, entrenched tradition favors sons for inheriting land. Daughters are supposedly compensated by their brothers with money or other forms of wealth, for what would have otherwise been their share of land inheritance. The data (described in more detail below) reveal that very few women (about 4%) have any land to transfer to their heirs when they die, while more than a third of the men leave some land. In the sample, we observe very few changes in land ownership over the five years preceding the survey. Nevertheless, half of the cases where the amount of land owned increased are due to inheritance.

In these respects, Senegal is not unusual within Sub-Saharan Africa (Cooper, 2008, 2010). The rights of women to land are mainly indirect (Platteau et al. 2000). As a daughter

> 12 Although some of the ethnic groups in Senegal are of matrilineal tradition, those traditions have mostly been displaced by Islam when it comes to inheritance. As a result, inheritance from adult male other than the father (such as a maternal uncle or foster parent for example) remains a rare occurrence.

8
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# Level of mother's education

|None|0.639|0.726|0.711|0.746|-0.087***|-0.072***|-0.107***|
|---|---|---|---|---|---|---|---|
|Primary incomplete|0.202|0.157|0.142|0.176|0.045***|0.059***|0.025*|
|Primary complete|0.068|0.046|0.061|0.027|0.021***|0.007|0.041***|
|Secondary incomplete|0.034|0.025|0.030|0.017|0.010**|0.004|0.017**|
|Secondary complete|0.007|0.007|0.009|0.004|0.000|-0.002|0.003|
|Post-secondary technical|0.003|0.004|0.005|0.003|-0.001|-0.002|0.001|
|University and higher|0.047|0.035|0.041|0.027|0.012**|0.005|0.020**|
|Individual received loan from any source|0.179|0.117|0.147|0.076|0.063***|0.032***|0.103***|
|Log Kilometers from Kampala|4.820|4.698|4.220|5.209|0.122***|0.601***|-0.388***|
|Log Kilometers from regional capital|4.195|3.935|3.443|4.461|0.260***|0.752***|-0.265***|
|Log WRSI maize|4.388|4.372|4.365|4.380|0.017***|0.023***|0.008*|
|Log number of fatalities|0.803|1.735|1.973|1.417|-0.932***|-1.170***|-0.614***|
|Share of one's ethnicity living in urban areas|0.126|0.141|0.161|0.114|-0.014***|-0.034***|0.012***|
|Age rank(highest value for oldest)|3.722|4.424|5.101|3.516|-0.701***|-1.379***|0.206*|

T-test results

*** p<0.01, ** p<0.05, * p<0.1

Source: Authors’ calculations with UNPS 22
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- InMWj,

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Poterba, J. and von Hagen, J. 1999. _Fiscal Institutions and Fiscal Performance_ , University of Chicago Press: Chicago Poterba, J. 1994. “State responses to fiscal crises: the effects of budgetary institutions and politics.” _Journal of Political Economy_ , 102: 799-821.

Powell, G. B., (1986). “American voter turnout in comparative perspective.” _The American Political Science Review._ Vol. 80(1): pp. 17-43.

Roubini, N. and J. Sachs, 1989. “Political and economic determinants of budget deficits in the industrialized countries.” _European Economic Review_ 33: 903-938.

Scheinkman, J. A. and Xiong, W. 2003. “Overconfidence and speculative bubbles.” _Journal of Political Economy_ , vol. 111(6): 1183-1219.

Stromberg, D. 1999. “The Lindbeck-Weibull model in US federal structure.” Mimeo, Institute for International Economic Studies, Stockholm.

Veiga, L., G., and Veiga, F., J., (2004). “The determinants of vote intentions in Portugal.” _Public Choice_ Vol. 118(3-4): pp. 341-364 Woo, J. (2003). “Economic, political, and institutional determinants of public deficits,” _Journal of Public Economics_ , 87: 387-426 Yildiz, M. 2004. “Waiting to persuade” _Quarterly Journal of Economics_ , vol. 119(1): 223-248.

Zaller, J. 2002. “Floating voters in US presidential elections 1948-2000.” Working Paper, Department of Economics, UCLA.

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|Constant|3511.2|***<br>1992.4|***<br>2307.4 ***|-3470.2|
|---|---|---|---|---|
||(328.0)|(517.6)|(528.8)|(2125.1)|
|N (state-years)|911|882|882|833|
|R-squared|0.00|<br>0.02|<br>0.05|0.21|

_Note: Pooled OLS, standard errors clustered on state level, in parenthesis. Significance levels: *** 0.001, ** 0.01, * 0.05. Source: authors_

# 4.1.2. Heat

Electricity grids are sensitive to heat for several reasons. Exceptionally hot days cause high electricity demand, especially due to an increase in air conditioning. Moreover, heat reduces the carrying capacity of electric transmission lines, thus further aggravating the effect of increased demand (Bartos et al., 2016). In line with the assessment above for storms, Table 4.2 presents the results of a pooled Ordinary Least Squares analysis with the step-wise addition of control variables and interaction terms. The single-variable specification (Column I) shows that one additional exceptionally hot day increases blackout time by almost 30 minutes, on average.

The model specification in Column II introduces three additional control variables: (i) Grid density, as a measure of how dense or spread out the transmission and distribution network of a state is, (ii) for network fragility, i.e. how disruption prone the network is when only considering non-natural external causes of outages, and (iii) the number of power accounts, as an indicator of the complexity of the system and magnitude of demand. In this specification, the additional blackout duration due to an exceptionally hot day remains stable from the single-variable specification. Moreover, the number of power accounts increases blackout duration with strong statistical significance.

The model specifications in Column III introduces an interaction term for the frequency of exceptionally hot days and the number of power accounts. For instance, in New York, with 8.2 million registered grid customers, an additional extreme heat day is associated with an additional 89 minutes of outage duration. As in the case of storms, there is a small adaptation effect, as states that experience hot days more frequently tend to experience fewer outage minutes. This result is robust to the introduction of further state and year level control variable.

**Table 4.2: Linking heat frequency and blackouts across states and time**

|Heat blackouts, annual duration|(I)|(II)|(III)|(IV)||
|---|---|---|---|---|---|
|Heat frequency|27.3|*<br>24.2|*<br>-25.6|**<br>-31.2|***|
|<br>(Number of exceptionally hot days)|(14.6)|<br>(13.9)|<br>(12.7)|<br>(10.2)|<br>|
|Grid density||<br>0.26|0.94|1.53||
|<br>(accounts per km<sup>2</sup>)||<br>(0.69)|<br>(0.88)|<br>(1.67)|<br>|
|Fragility||<br>-10.6|<br>-14.3|420.0||

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**Table 6: Average effects of preprimary interventions on participation and skills** **_after_ the preprimary period**

||Pooled sample|High-income|Low- & middle-<br>income|Expansion|Quality|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|**Panel A: Schoolparticipation andp**|**rogression**|||||
|_School participation and progression_|0.1423<br>(0.0888)|||0.131<br>(0.106)||
|N|22|||20||
|**Panel B: Cognitive skills**||||||
|_Pooled skills_|0.0718***|0.0604|0.0862**|0.0604|0.0865*|
||(0.0236)|(0.0375)|(0.0311)|(0.0336)|(0.0346)|
|N|62|23|39|31|31|
|_Literacy_|0.0626<br>(0.0344)<br>14|||0.0655<br>(0.0515)<br>9||
|_Math_|0.0571*<br>(0.0311)|0.0648<br>(0.0566)|0.0512<br>(0.0361)|0.0535<br>(0.0547)|0.0622<br>(0.0399)|
||29|7|22|9|20|
|_General_|0.0603<br>(0.0476)<br>10|||||
|**Panel C: Executive functions and so**|**cial-emotional s**|**kills**||||
|_Pooled skills_|0.0686***<br>(0.0190)||0.0750***<br>(0.0178)|||
|N|70||34|||
|_Social-emotional skillls_|0.0945**<br>(0.0342)||0.1056**<br>(0.0361)|||
|_otes_: Standard errors estimated using s<br><0.1 Number of observations (N) refers<br>ool skills use one large category for ext<br>eneral cognitive skills) or what is typic<br>ehaviors). See Appendix 1 for details<br>eneral skills; executive functions; socia<br>ble to obtain trustworthy p-values, whe<br>Tipton, 2015).<br>N|mall sample adju<br>to number of stud<br>racted outcomes -<br>ally referred to as<br>on definitions of<br>l-emotional skills;<br>n the degrees of fr<br><br>34|stments as in Tip<br>y-specific avera<br>either cognitive<br>non-cognitive s<br>school participa<br>and behavior. C<br>eedom fall belo|ton (2015) are in p<br>ge effects used in t<br>skills (which comb<br>kills (executive fun<br>tion and progress<br>ells are blank whe<br>w 4 or when all obs<br><br>21|arentheses. *** p<br>he specification.<br>ines language, l<br>ctions, social-em<br>ion; language, li<br>n the sample size<br>ervations came fr|<0.01, ** p<0.05,<br>Specifications tha<br>iteracy, math, an<br>otional skills, an<br>teracy, math, an<br>is too small to b<br>om a single stud|

_Notes_ : Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Specifications that pool skills use one large category for extracted outcomes - either cognitive skills (which combines language, literacy, math, and general cognitive skills) or what is typically referred to as non-cognitive skills (executive functions, social-emotional skills, and behaviors). See Appendix 1 for details on definitions of school participation and progression; language, literacy, math, and general skills; executive functions; social-emotional skills; and behavior. Cells are blank when the sample size is too small to be able to obtain trustworthy p-values, when the degrees of freedom fall below 4 or when all observations came from a single study (Tipton, 2015).