rafmacalaba/datause-encoder-data huggingface dataset:
temperature (above 32 ̊C) during the second half of the vegetative phase and the reproductive phase reduce yields. The last phase (maturation phase) lasts between 20 to 35 days.

Planting later in the season ensures that the seed germinates quicker, however waiting too long does not allow the crop to complete the maturation stage before the growing season ends. Furthermore, specific to Mexico, in July and August there is a period of mid-summer drought called _canícula_ (Figure 2) affecting farmer‘s planting decisions. In general, farmers want the corn to flower (for the ear formation stage to be complete) before the onset of the _canícula_ in order to better the odds of the crop survival in case it is a drier than normal _canícula_ (Eakin, 2000). This implies that the months leading up to the _canícula_ are of special importance in Mexico.

**Figure 2: Agricultural Cycle in Mexico**

|Feb|Mar|Apr|May|Jun<br>Jul|AugSep|Oct|Nov|Dec|Jan|Feb|Mar Apr|May|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dr|y|||Wet Season||||DryS|eason||Wet s|eason|
|||Pr|e-_caníc_|_ula_<br>_Caníc_|_ula_||||||||
|||||||1999:|ENN||||||
|||2001: M|xFLS I||||||||||
||||||2005:|MxFL|S II(un|til 200|7)→||||

For the household data we use the first two waves of surveys from the Mexico Family Life Survey (MxFLS) (Rubalcava and Teruel, 2006). The first wave of the survey in 2002 interviewed 3,353 rural households<sup>10</sup> in 75 different localities located in all regions of the country and was conducted between March 2002 and August 2002, with the majority of the information collected between April and June. The second wave of the survey was collected between 2005 and 2007 with the majority of the data collection occurring from May 2005 to September 2005. The follow-up survey interviewed 3,271 households in 112 rural localities.<sup>11</sup> Both waves collected detailed information on each household member including information on educational attainment, migration and anthropometric measures, and as well as on household

> 10 Rural households are considered to be those that lived in localities with less than 2,500 inhabitants.

> 11 Given that some households (or parts of households) had moved between the surveys, in Wave 2 the households are spread out over a larger number of localities.

12
Table 10 Impnct of ometmness on growth: synthesis of findings

||Annual<br>|<br>data|Six-year<br>av|erages|Entire Peri|od<br>averages|
|---|---|---|---|---|---|---|
|Openness<br>variable|Levels dif<br>(1)|ferences<br>(2)|Levels<br>diff<br>(3)|erences<br>(4)|Levels<br>dif<br>(5)|ferences<br>(6)|
|Trade liberalization<br>Index (1960-84)|<br>>0*|>0<br>|>0,'*|O|°0|>0|
|Trade liberalization<br>Index (1978-88)|<br>>0<br>)|'O|-|-|°0|<|
|Black<br>market premium<br>1/|<br>>0,**|>0,**|<br>>0,**|>0|>0,**|>0|
|Trade<br>shares|>0|>0,*|0|>0,**|<br>>0|0,**|
|Price distortion<br>measure<br>1/|>0,**|0|>0,**|0,**|<br>O|>0|
|Movement<br>towards|||||||
|world prices||>0,**|<br>-|>0|-|>0|
|Bias against agricultur|e1/40,*|>0|>0,*|>0,**|>0|>0|

** Indicate **s** ignificant at 5 percent level; * indizates significant at 10 percent LeveL.

Notes: ALL regressions except entire period average include country dummies. 1. For purposes of comparison, a value of 1>0" indicates that more openness (Less distortion) positively affects growth. Consequently, for the bLack market premium, price distortion measures, and bias against agriculture, this table wi'L show ">0" when a higher Level of distortion negatively affects growth.

31
Table 2: Summary Statistics

|**Sample**|**Obs.**|**Mean***|**Std. Dev.**|**Min***|**Max***|
|---|---|---|---|---|---|
|_West Manggarai: Restaurants_||||||
|All|18,282|1.889|15.252|0|629.083|
|RPOS|662|1.822|5.434|0|64.805|
|TMD|424|10.370|15.436|0|85.146|
|WS|236|57.744|111.881|0|629.003|
|Untreated|17,506|0.184|0.596|0|4.724|
|_West Manggarai: Hotels_||||||
|All|11,280|4.968|45.170|0|1,659.119|
|WS|37|250.881|417.513|0|1,659.119|
|Untreated|11,243|4.159|35.904|0|1,332.909|
|_Gorontalo: Restaurants_||||||
|All|14,730|4.259|14.415|0|301.368|
|RPOS|2,762|2.219|2.968|0|25.219|
|RPOS-Grab|230|2.103|2.030|0.006|8.921|
|TMD|580|30.961|30.464|0|180.854|
|Untreated|11,158|3.421|13.601|0|301.368|
|_Gorontalo: Hotels_||||||
|All|14,337|2.257|17.103|0|386.770|
|PAC Hotel|109|6.580|2.624|0.490|13.823|
|TMD|218|96.938|86.998|0.570|386.770|
|Untreated|14,010|0.750|6.334|0|168.110|

_*in millions of Rupiah. ’0’ indicates a nonpayment._ 9
- When hit with an agricultural shock, Kenyan households with no mobile money access suffered a 7 percent drop in use of goods and services, while those who did have mobile money experienced no such drop on average.<sup>140</sup>

- In Tanzania, rainfall shocks resulted in 6 percent lower consumption on average, but mobile money users were able to maintain consumption due to improved risk sharing.<sup>141</sup>

# **Digital transactions help people build savings**

People are more likely to build savings when they are paid directly into accounts instead of in cash, in part because the money is protected from theft, loans to family members, and the temptation to spend. This money can be invested in educational and business opportunities and has implications for the World Bank's work on employment and poverty reduction.

- In Afghanistan, workers who automatically deposited part of their salary into a mobile savings account had higher savings and financial security than workers who received a mobile savings account but did not sign up for automatic deposits.<sup>142</sup>

- In India, weekly payments resulted in significantly higher savings for a group that received the payments into accounts, but not for a group that received them in cash.<sup>143</sup>

- In Malawi, farmers who had their earnings deposited into a savings account had higher savings before the next planting season, spent more on agricultural equipment, and increased crop sales.<sup>144</sup>

> 140 Jack, W., and T. Suri. 2014. "Risk Sharing and Transaction Costs: Evidence from Kenya's Mobile Money Revolution." _American Economic Review_ 2014, 104(1): 183‐223.

> 141 Riley, E. 2018. "Mobile Money and Risk Sharing Against Village Shocks." _Journal of Development Economics_ 135: 43‐58.

> 142 Blumenstock, J., M. Callen, and T. Ghani. 2018. "Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan." _American Economic Review_ 2018, 108 (10): 2868‐2901.

> 143 Somville, V., and L. Vandewalle. 2018. "Saving by Default: Evidence from a Field Experiment in Rural India." _American Economic Journal: Applied Economics_ 10 (3): 39‐66.

> 144 Brune, L., X. Gine, J. Goldberg, and D. Yang. 2016. "Facilitating Savings for Agriculture: Field Experimental Evidence from Malawi." _Economic Development and Cultural Change_ 64 (2): 187‐220.

40
**Table 11: Impact of Business Registration Reform on Employment**

||||Depende<br>L|nt variable:<br>ik|Hi|ik|
|---|---|---|---|---|---|---|
||Employe|d dummy|o<br>employe|-rs<br>d dummy|g<br>employe|-rs<br>d dummy|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Reform dummy (SARE)|0.0035|0.0049|0.011**|0.0124***|-0.0075**|-0.0075***|
||(0.0038)|(0.0034)|(0.0045)|(0.0038)|(0.0035)|(0.0021)|
|Primary education dummy||0.0498***||-0.0090*||0.0588***|
|||(0.0035)||(0.0052)||(0.0041)|
|Secondary education dummy||0.0916***||-0.0423***||0.1339***|
|||(0.0043)||(0.0089)||(0.0075)|
|High school education dummy||0.0578***||-0.1373***||0.1951***|
|||(0.0072)||(0.0111)||(0.0078)|
|University education dummy||0.2171***||-0.1586***||0.3758***|
|||(0.0061)||(0.0151)||(0.0147)|
|Female dummy||-0.3958***||-0.2653***||-0.1305***|
|||(0.0109)||(0.0133)||(0.0075)|
|Age||-0.0002***||-0.0020***||0.0018***|
|||(0.0003)||(0.0002)||(0.0003)|
|Married dummy||-0.0578***||-0.0781***||0.0203***|
|||(0.0069)||(0.0058)||(0.0033)|
|Census variables*time trend|No|Yes|No|Yes|No|Yes|
|R-squared|0.003|0.206|0.008|0.094|0.010|0.117|
|No. of observations|1,636,225|1,636,225|1,636,225|1,636,225|1,636,225|1,636,225|

_Note:_ Standard errors in parentheses (clustered at municipality level). Regressions include quarter and municipality fixed effects. Census variables are log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita from the 1999 Economic Census, converted to per capita levels using population data from the 2000 Demographic Census. Significance levels: * 10%, ** 5%, *** 1% 44
acceptable (Farmer et al., 2011). The consequences of these teacher-to-student and studentto-student socialization experiences emerge in the classroom in various ways. Some teachers offer students many opportunities to work with peers whereas others do not. Peer interactions in some classrooms are positive (characterized by positive conversation and cooperation) whereas others are negative (characterized by name calling, teasing, or bullying).

Synthesis work on peer relationships in schools points to a few key features of effective peer relationships: warmth, structure, and autonomy support. Warm peer relationships give students an opportunity to listen to each other and talk openly. These interactions provide emotional support, establish a basis for respect, and give students a sense of belonging. Although the structure provided by peers differs from that offered by teachers, peer interactions offer structure by clearly signaling to students that they can rely on each other for help and guidance, by offering models of competent academic behavior, and by establishing clear and consistent expectations about shared materials. Peer interactions can also offer autonomy support in that working to understand each other’s viewpoints, explaining the relevance of school work to one another, or the healthy negotiation that comes in group work helps students develop a sense of autonomy (Furrer, Skinner, & Pitzer, 2014).

The quality of peer interactions and student collaboration skills has been linked to academic competence in both the short and long term. In one longitudinal study, Buhs, Ladd, & Herald (2006) examined longitudinal data from kindergarten to fifth grade to examine the extent to which peer rejection early on related to later classroom engagement and achievement. Further, the work considered the extent to which being excluded by peers or peer abuse (e.g., kids saying bad things about them) was the mechanism explaining the relation between early peer rejection and later outcomes. Findings showed that a student’s own perception of peer rejection was related to subsequent poor treatment by peers toward that child, resulting in the student’s withdrawal from classroom participation and more avoidance of school. It appears that the chronic maltreatment from peers perpetuated problems and led to a downward spiral toward lower participation and achievement. Further longitudinal research links classroom peer relationships in third through fifth grade to graduation rates 10 years later. Researchers measured peers’ perception of relationships (social preference, aggression, and withdrawal) and found that students with the greatest aggressiveness were less likely to graduate from high school (Risi, Gerhardstein, & Kistner, 2003).

Taken together, the research shows that teachers play an important role in facilitating positive versus negative peer social interactions in their classrooms, often acting as “the 40
committed to the program, with each applicant allocated around 86’000 Euros. In line with the principles of the ACP-EU partnership agreement singed in Cotonou in 2000, the PIP aims to contribute to the development of the ACP’s private sector and to promote regional integration.” As for the case study itself, the brochure explains that “[w]hen Myner Exports began working with the Pesticides Initiative Program, or PIP in 2002, it was exporting about 300 tonnes of French beans, snow peas, passion fruit, and sugar snaps a year to the European Union. Today, the company exports some 900 tonnes a year. ” Quoting this particular case study in their assessment of the EU’s TRA, te Velde et al. (2006) noted that “in an ideal world, one would compare this supported company with a similar one that was not supported” (p. 21). This is precisely what we set up to do in the present paper, although using Senegal instead of Kenya as our sample of study.

We chose Senegal because the data we had access to provided a unique combination. Through a World Bank project run by Denisse Pierola and Paul Brenton at the World Bank, we obtained raw files from Senegal’s customs with export data at the transaction level for every year between 2000 and 2008. The data include the exporter’s ID, the product code, the country of destination, and the export value for over 500 HS8 fresh fruit and vegetable (FFV) products. We were able to merge the customs’ dataset with industrial-survey data and a list of PIP beneficiaries provided by the EU Commission.<sup>4</sup> This unique combination made it possible to construct a treatment group of firms that got assistance and a control group of firms that did not, and this for a sample period that ran from before the program to its end.<sup>5</sup> Using this rich data set, we used a wide array of approaches to estimate the effect of the PIP on firm-level exports of treated products (FFV). We ran DID regressions of the value of exports, by product _×_ firm _×_ destination, on control variables as well as a dummy variable marking ‘treatment’ by the PIP. In order to deal with selection issues, we combined the DID approach with propensity-score matching, although the small size of the sample and the systematic differences in size between treatment- and control-group firms considerably reduced the power of

> 4Thanks to Morag Webb for providing us with the data.

> 5Although we had similar customs data from a number of other countries, none covered years _before_ the start of the PIP. After merging, firm IDs have been deleted from the dataset for confidentiality reasons.

5
**14** **<u>│</u>**

At present, there is still no official source of information for the number of foreign multinationals in developing countries that can be used to verify the representativeness for the entire sample. The information available is compiled by the OECD in its Activities of Multinational Enterprises (AMNE) database. It contains the official data published by National Statistical Offices and Central Banks on activities of foreign multinationals. The coverage of AMNE data has been good for the United States and several European countries, none of which has a publicly available World Bank Enterprise Survey.

Recent analytical work at the OECD, however, has produced estimates of the share of foreign multinationals in total output for many more economies by combining information from two official sources: the AMNE analytical statistics and the Inter-Country Input-Output (ICIO) tables (Cadestin et al., 2018). This new data set highlights the contribution of domestic firms and foreign multinationals, as well as their foreign affiliates, to global trade and production. It essentially brings to enterprise analysis the same conceptual shift introduced by Trade in Value-Added statistics (TiVA) in trade analysis. Nineteen of the countries in the new data set have a World Bank Enterprise Survey, 14 of which we use in this study – BRICS and a number of economies in Eastern Europe, conducted in their majority around the same year, 2014 – offering a unique opportunity to assess the representativeness of foreign multinationals.

The share of foreign multinationals in aggregate output calculated by the World Bank Enterprise Surveys and the OECD are largely aligned (see Fig. 1), with a 45-degree line indicating equal shares from the two sources. The Pearson correlation coefficient, which measures the strength of association between two continuous variables (ranging from -1 to +1) reaches 0.86, statistically significant at the 1% level, indicating a high degree of correlation. However, this exercise only accounts for a limited number of countries in the sample. It also considers only a single dimension – output – with many structural elements in the two statistics, such as the selective sectoral scope of the WBES and year mismatch, justifying systematic bias in some countries over other ones. Rather than validating the sample of foreign multinationals in WBES as a whole, the alignment serves primarily not to reject the WBES as a valuable source of information for foreign multinationals based on the information we have available. The true numbers and weight of multinationals in these countries remains to be evaluated in the future.

The indicators reported in this study correspond to weighted averages across establishments using sampling weights w� of the Enterprise Surveys that are based on the frame provided by official sources, as well as their differences across groups in absolute and/or relative terms. Every data point x� is calculated over the total of n firms i in each country for the national average, the subsample of foreign multinationals defined using a 10 percent threshold and then the subsample using a 50 percent threshold.
56%, 44% and 4.7% respectively for the insurance model and 53%, 47% and 4.5% respectively for the liquidity model.

> The overall goodness of fit is again captured by the likelihood ratio test (chi-square test), and suggests both specifications are statistically significant at 1%. The points of comparison emerge clearly as we examine the results on individual variables.

(1) Gross liabilities: Gross liabilities are highly robust, statistically significant predictors of crises, whether log-levels or change in log-levels are used.

(2) Capital flight: Capital flight also should be a predictor of crises according to this model, and is **in** line with our use of gross liabilities. The results are mixed. While the variable enters **with** the correct sign, **it** is not statistically significant.

(3) Net foreign assets: Netting out assets reduces the equations predictive power, and the net variable is not significant. Net liabilities are defined as gross liabilities minus gross assets, in order to be consistent across countries. An alternative would be to use external debt figures (the most comprehensive data sources being the OECD, lenders figures and the World Bank, debtors figures) but these are constructed using quite a different methodology and include all kinds of concessional and strategic lending and borrowing.

(4) Fundamentals: Once again, the control variables that are meant to represent the fundamentals are robust and significant: log of GDP, gross external liabilities, capital account restrictions and index of political violence.

# 6 **Conclusions:**

In this paper, the objective was to demonstrate how different crises that are apparently "observationally equivalent", can be distinguished with the help of data on gross foreign liabilities. This means that different transmission mechanisms that are difficult to disentangle based on movements in the typical predictors, can be clearly identified with the help of 15
widespread use of English as an international language **.”**<sup>**12**</sup> Globalization has most often been analyzed at the country level. Sources of data and analysis judging country level globalization as a dynamic concept are rare. Some authors use rankings of countries on a number of dimensions of globalization, such as the work of A.T. Kearney (Foreign Policy, 2002) who use a composite index of globalization. This index builds on indicators of trade, finance, personal contact, and information exchange. Using the Kearney data base, we investigate the issue of country level globalization and its impact on city performance, with cities judged on the ability to sustain a track record of services to their citizens.

For globalization at the city level, we utilize data from the GaWC research (Taylor, Walker, 2000), which has city level data constructed from a superposition of networks of accountancy, advertising, banking/finance, insurance, law, and management consultancy firms—the business services indicators of Friedman (1986). This data base demonstrates the “unevenness of globalization” when one looks at the concentration of cities by region for example. We refer to this as the Taylor database. With such data, we can pursue the question of why some cities are left behind in globalization.

# **4 Determinants of City Performance: Key Hypothesis Tests and Simple Model Specification**

Because of the paucity of comparable data on cities worldwide, we need to be nimble on the model specification we use. We search for a specification that allows us to capture the dynamics of globalization, governance and performance of cities, as well as define specific hypotheses to be tested.

## **4.1 A Simple Framework**

We specifically consider the role that governance plays at different levels of globalization of cities. The schematic chart below allows us to pursue this analysis empirically. We distinguish between three dimensions; two of which are ‘inputs’ (quality of governance and extent of city globalization, in the vertical and horizontal axis, respectively), and one an outcome (service delivery/city performance, in the ‘virtual’ third dimension in the chart, illustrated in the chart by the 45 degree diagonal line). With such a schematic chart we define the key testable hypotheses on the effects of globalization and governance on city level performance.

> 12 <u>http://www.progressiveliving.org</u>

11