rafmacalaba/datause-encoder-data huggingface dataset:
Last but not least, measures of community-level conflict intensity are meaningful only under the assumption that individuals were actually residing in the community they are being assigned to at the time violence occurred. Since wars are often associated with population displacements, such an assumption is not innocuous. In our study of Bosnia and Herzegovina, this problem is mitigated by the fact that the LSMS survey identifies for each survey respondent the commune of residence at the time of conflict. Furthermore, the displaced likely constitute a selected sample of the overall population. Such selective migration is often hard to control for, though some researchers have made efforts in this direction. For instance, in their study of Ugandan child soldiers, Blattman and Annan (2009) attempt to correct for selective attrition by estimating attrition probabilities based on observables.

# **2.3 Establishing a Causal Link between Conflict Exposure and Mental Health**

Researchers and policy makers are interested in three main questions: (i) How large is the causal impact of war on the mental health of affected populations? (ii) How fast is recovery, if any at all?, and (iii) What are the mechanism(s) underlying recovery or the absence thereof?

Most studies in the literature rely on cross-sectional comparisons of mental health status levels between two individuals _within the same country_ , who are exposed to varying conflict intensity. By construction, this comparison cannot assess any “aggregate impact” of the conflict as a whole. This is important because even individuals who are not directly exposed to the conflict are seldom psychologically unaffected. Thus, cross-sectional comparisons allow addressing the question of the heterogeneity of war impacts across the population, but leaving unanswered the question of overall impact of the war, when the counterfactual is peace. The problem becomes even more salient when no individual has been spared by the conflict, as has been assumed by Pham et al. (2004) in the case of Rwanda.

We should note that this issue is not unique to cases where mental health is the outcome of interest. Within-country studies of the impact of conflict on health or economic development also cannot identify the aggregate effect of conflict without further (and arguably strong) assumptions.<sup>8</sup> In particular, the absence of observed heterogeneity in outcomes across areas

> 8 See Bellows and Miguel (2009), Kondylis (2009), Do (2009), and Brakman, Garretsen and Schramm (2004), Davis and Weinstein (2002), and Miguel and Roland (2006) for estimates of the impact of conflict on respectively, trust and political participation, labor market participation, health and economic growth.

8
# Table 2: **Financial Deepening Equation** - **ECM Model Dependent Variable: ALogFDC**

# 36 Annual Observations (1961-1996) used for Estimation in Columns 1 **and 2**

|**Variables**|**Column I**|**Column 2**|
|---|---|---|
|Intercept|-4.0656***|-8.0097***|
||(-2.5306)|(-3.9259)|
|logFDC,,|-0.29369***<br>(-2.5025)|-0.60247***<br>(-3.9180)|
|AlogY|0.52359**|0.42383*|
||(1.7410)|(1.4600)|
|AlogLRE|----|0.041802<br>(0.79231)|
|AlogLR|----|-0.35445<br>(-1.1770)|
|LogY,j|0.37682***<br>(2.5561)|0.38618***<br>(2.6003)|
|log LREt,|----|0.040195<br>(1.0675)|
|logLR,|----|0.21982**<br>(2.3031)|
|R<sup>2</sup>|0.26|0.44|
|DW|1.82|1.76|
|Diagnostic Tests|||
|Serial Correlationa|F (1, 31) = 0.31956 [0.576]|F (1, 27) = 0.94415 [0.340]|
|Functional Formb|F (1, 29) = 0.10931 [0.743]|F (1, 27) = 0.30739 [0.584]|
|Normalityc<br>Heteroscedasticityd|_X2(2)_=4.7105 [0.095]<br>F (1, 34) = 1.8858 [0.594]|**X2(2)**<br>=0.34358 [0.8421<br>F (1, 34) = 0.40101 [0.53 1]|
|a Langrange Multiplie<br>b Ramsey RESET test|r test of residual serial correlation<br>using the square of the fitted value|s|
|<br>c Based on a test of sk|<br>ewness and kurtosis of residuals||

d Based on the regression of squared residuals on squared fitted values

*, ** and *** indicate significance levels of 10, 5 and I percent, respectively. Figures corresponding to parameters are coefficient estimates, with related t-ratios in brackets. Results and footnotes obtained using Microfit 4.0 by Pesaran and Pesaran (1997).

# (b) **Results of the Economic Growth Equation**

The economic growth equation has been estimated using two different models: the ECM; and the ARDL. The ECM has the advantage of having a capability to fully deal with the problem of non-stationarity; however, it is inadequate in handling high order 15
# ENDNOTES

1. The concept of informality was first introduced by the 1972 ILO Employment Mission to Kenya. For an excellent survey regarding definitions, empirical studies, and policy recommendations on the informal sector, see Lubell (1991).

2. The work by Hernando de Soto, presented in his book _The Other Path_ (1989), has contributed enormously to the understanding of the role of political institutions and legal structures in the rise of informal sectors. De Soto's analysis draws from the contributions of Douglas North (1981) and Mancur Olson (1982).

3. Readers familiar with the determinants of informal economies may want to go directly to the theoretical model.

4. See Romer (1986), Lucas (1988), Barro (1990), and Rebelo (1991).

5. Given that the codes of labor legislation are also rather extensive in developed countries, it is not surprising to find "clandestine," or informal, employment in those countries as well. However, given that the productivity of labor is higher in developed than in underdeveloped countries, the distortionary power of labor regulations in developed economies is less important. Kaltzmann (cited in De Grazia, 1982, p. 34), discussing the French experience, writes that "the main reason for clandestine employment is economic: one of the parties increases his income, the other reduces his costs. Two economic causes play a dominant role: inadequate remuneration and the burden of taxation and social security costs." De Grazia (1982, p. 35) writes that "In Italy, the phenomenon [of informal employment] is ... more blatant, if not universal. Entire neighborhoods of Naples have been transformed into secret workshops (specializing particularly in shoe- and

-32-
# **Gauging the Welfare Effects of Shocks in Rural Tanzania**

Luc Christiaensen, Vivian Hoffmann, and Alexander Sarris<sup>1</sup>

## **JEL classification** : D12, D61, I30, O12

**Key words:** Coffee price, Illness, Death, Drought, Vulnerability, Tanzania

1 The authors are Senior Economist at the World Bank, Ph.D. candidate at Cornell University, and Director of the Commodities and Trade Division at the Food and Agriculture Organization (FAO) of the United Nations, respectively. They would like to thank Donald Mmari, Lucas Katera, Donald Sango and Professor Joseph Semboja at Research on Poverty Alleviation (REPOA) in Dar es Salaam, Tanzania, who managed the survey on which this research is based, and Panos Varangis and Benno Ndulu for their continuous support of the project. Funding by the EU Trust Fund on Commodity Management and the Dutch-Japanese Poverty Reduction Strategy Trust Fund is gratefully acknowledged. The findings, interpretations, and conclusions expressed are entirely those of the authors, and they do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent, or the views of FAO and its member countries.
<!-- Start of picture text --> a Number of ;<br>BaeeeX HIunderweight342 - 4,000 chilrden<br>z° y = gf Hl 4.001 - 8,000<br>. re rn, o = 8,001 - 10,000<br>cd { MBE 10,001 - 20,000<br>A, WM 20,001 - 36,571<br>2é° ia =Hre: uml Sy ihe<br>eae, Ne<br>a ae cs ><br>z > Bay of Bengal S<br>B Ks<br>0 50 100 km "<br>88°E 89°E 90°E STE 92°E<br><!-- End of picture text --> <!-- Start of picture text --> a Number of severely<br>dy.heh}u so. Mlunderweight53 - chilrden<br>z % = we [i501 000<br>* < a Ls ae er = 2,001 - 3,000<br>ees fs ME 3,001 - 5,000<br>~ s ; HB 5,001 - 7,830<br>é| paras~ a tismay eeiS<br>anes Ia Me hh<br>v Pak aS. Li a<br>z Oo Bay of Bengal s<br>B . a,<br>0 50 100 km x<br>88°E 89°E 90°E STE 92°E<br><!-- End of picture text -->
Figure 28: Mean Adjusted R<sup>2</sup> , by Weather Product, Temperature Metric, and Model <!-- Start of picture text --> .06 .14<br>.05 .12<br>.04<br>.1<br>.03<br>.02 .08<br>* Weather Product * * Weather Product * .06<br>CPC CPC<br>ERA5 ERA5<br>MERRA-2 MERRA-2<br>* Model * * Model *<br>Weather + Weather2 + FE + Inputs Weather + Weather2 + FE + Inputs<br>Weather + Weather2 + FE Weather + Weather2 + FE<br>Weather + Weather2 Weather + Weather2<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 1 2 3 4 5 6 0 1 2 3 4 5 6<br>Specification # - sorted by model & effect size Specification # - sorted by model & effect size<br>.28<br>.26<br>.24<br>.22<br>.2<br>* Weather Product * .18<br>CPC<br>ERA5<br>MERRA-2<br>* Model *<br>Weather + Weather2 + FE + Inputs<br>Weather + Weather2 + FE<br>Weather + Weather2<br>Weather + FE + Inputs<br>Weather + FE<br>Weather 0 1 2 3 4 5 6 mean adjusted R2<br>Specification # - sorted by model & effect size 95% C.I.<br><!-- End of picture text --> _Note_ : The figure presents the mean adjusted R<sup>2</sup> , by weather product and model specification, aggregated over temperature metric, country, and outcome variable. Only one extraction method is included. Across panels, the figure includes 864 regressions, with each panel including 288 regressions. Each column includes 48 regressions, which are for each specification model and each temperature metric. The northwest panel presents results from the linear and quadratic model specifications. The northeast panel presents results from the linear and quadratic model specifications with fixed effects. The southwest panel presents results from the linear and quadratic model specifications with fixed effects and input controls.

89
global chains’ presence and the TFP of firms in the food supplying industries is verified across firm sizes, as the interactive term remains positive and significant at the 1 percent level. The estimated coefficient becomes smaller as the size of firms considered falls. This finding implies that the presence of global chains benefits large food suppliers the most and has a smaller impact on smaller firms. For suppliers with more than 25 employees, presence of foreign chains on average can lead to 19 percent increase in their TFP while for firms employing fewer than 5 people the chains’ presence would only lead to 12 percent increase in their TFP. Similarly, a doubling in chain stores will lead to a 14.2 percent increase in TFP among suppliers with more than 25 employees but only an 8.5 percent increase among firms with fewer than 5 people. In our data, half of the firms are smaller than 5 employees and only less than a quarter of firms have more than 25 workers.

These results are intuitive in that as large retail chains tend to source large volumes they are more likely to work with larger suppliers. Thus it is not surprising that larger manufacturers are the major suppliers to global chains and hence, benefit most from their expansion. Small firms produce less for foreign chains, because they do not have the technology and financial support to meet the quality or quantity requirements set by the chains. Note, however, that it may be in the interest of retail chains to keep some small suppliers as a way of increasing price pressure on the larger producers (see the example of Wal-Mart in Mexico in Javorcik et al. 2006).

# **7. Conclusions**

This study uses Romanian firm-level data to examine the link between the entry of global retail chains and developments in the supplying sectors. The econometric results lead us to the following conclusions. First, the expansion of global retail chains leads to a significant increase in the total factor productivity in the supplying industries. For instance, their presence in a region increases TFP of firms in the supplying industries by 15.2 percent and doubling the number of chains will lead to a 10.8 percent increase in TFP. However, their presence benefits larger firms the most and has a smaller impact on small enterprises. This conclusion is robust to several extensions and specifications, including the instrumental variable approach.

The results indicate that opening of the retail sector to FDI may stimulate faster productivity growth in upstream manufacturing in the context of transition and developing 22
# **Appendix 1: Tongan migrants compared to Tongan natives and other developing country migrants**

(Characteristics of 18 to 45 year olds)

||Tongan<br>PAC<br>migrants|Tongans<br>surveyed<br>in Tonga|Stock<br>All Devel<br>migrants in t|New arrivals<br>oping country<br>he United States|Stock<br>All Develo<br>migrants|New arrivals<br>ping country<br>in Canada|Mexicans<br>in the U.S.<br>new arrivals|Mexicans<br>in<br>Mexico|
|---|---|---|---|---|---|---|---|---|
|Age: Mean|31.1|30.5|31.8|27.7|33.0|31.7|26.2|29.8|
|(std dev)|(8.2)|(7.8)|(7.6)|(7.2)|(7.6)|(6.9)|(6.8)|(7.9)|
|Proportion female|0.50|0.52|0.47|0.44|0.52|0.52|0.36|0.53|
|Proportion married|0.68|0.55|0.59|0.48|0.59|0.71|0.47|0.66|
|Years of education: Mean|11.7|10.5|12.8|11.0|14.1|15.1|8.7|7.9|
|(std dev)|(3.4)|(3.7)|(2.9)|(4.4)|(3.4)|(3.3)|(4.1)|(4.3)|
|Proportion workingforpay|0.58|0.37|0.63|0.55|0.68|0.54|0.60|0.59|

# Notes:

new arrivals are classified as those arriving in the past 2 years. Source: Tongans from Pacific-Island New Zealand Migration Survey Migrants in the United States are from 5% public use sample of the 2000 Census, obtained through the Integrated Public Use Microdata Series (IPUMS) website (Ruggles et al. 2004).

Migrants in Canada are from the 2.7% public use file on individuals from the 2001 Census. Mexicans in Mexico are from the 10.6% public use sample of the 2000 Mexican Census, obtained through the IPUMS-International website (Sobek et al. 2002).

40
World Bank (2021). _World Development Indicators_ . Washington D.C.: The World Bank.

- Yan, Z. & Zong, L. (2020). _Spatial Prediction of House Prices in Beijing Using Machine Learning Algorithm_ . Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence, pp. 64-71.

- Yoshino, N. & Helble, M. (2016). _The Housing Challenge in Emerging Asia_ : _Options and Solutions._ Tokyo: Asian Development Bank Institute.

- Yilmazer, S. & Kocaman, S. (2020). A Mass Appraisal Assessment Study Using Machine Learning Based on Multiple Regression and Random Forest _. Land Use Policy_ 99 104889.

- Yoo, S., Im, J., & Wagner, J. E. (2012). Variable Selection for Hedonic Model Using Machine Learning Approaches: A case study in Onondaga County, NY. _Landscape and Urban Planning_ , 107(3), 293306.

- Zhang, B., Li, W., Lownes, N., and Zhang, C. (2021). Estimating the Impacts of Proximity to Public Transportation on Residential Property Values: An Empirical Analysis for Hartford and Stamford Areas, Connecticut _. International Journal of Geo Information_ 10(44), 1-11.

- Zhong, H. and Li, W. (2016). Rail Transit Investment and Property Values: An Old Tale Retold. _Transportation Policy_ 51, 33-48.

38
COVID-19 on GVCs in the Philippines. All non-intermediate goods are classified as Other Products.<sup>7</sup> Table 2 reveals that roughly 60% of exports in the Philippines were intermediate goods before and during the lockdown. This indicates that the Philippines largely participates in GVC trade through forward linkages.<sup>8</sup> Components/Devices were the Philippines’ highest exports before and during the lockdown (over 41%). They were followed by Electronic Data Processing, Machinery and Transport Equipment, Ignition wiring sets, and Consumer Electronics respectively.

The highest share of imports was within the category of Other Products before and under the lockdown,<sup>9</sup> suggesting low backward GVC participation by the Philippines.<sup>10</sup> Machinery and Transport Equipment dominated intermediate imports albeit with a significant drop during the lockdown. The column on difference indicates that exports of most products contracted upon imposition of lockdowns apart from Components/Devices and Consumer Electronics. Components/Devices exports increased by 2.6 percentage points while exports of Consumer Electronics rose by 0.18 percentage points. Exports of Other Products had the greatest decline (1.44 percentage points) when lockdowns were introduced. The share of imports for most products increased during the lockdown except Consumer Electronics (0.13 percentage points), and Machinery and Transport Equipment (4.61 percentage points). Components/Devices imports increased by 2.63 percentage points during the lockdown. It was also the only product whose total trade (exports and imports) increased during the lockdown.

China; Japan; the United States; Hong Kong SAR, China; the Republic of Korea; Singapore; Thailand; Indonesia; Taiwan, China; and Malaysia were the top ten export and

> 7Table A1 displays shares of top five Other Products and pre and during the lockdown in the Philippines.

> 8Forward GVC participation means a country mainly exports intermediate commodities that are used in other countries’ exports. Backward linkages is when a country’s exports are mainly composed of imported intermediate inputs. Latest data from OECD TiVA confirms relatively higher forward GVC participation of the Philippines.

> 9Specifically other manufactures, special transactions, chemicals, processed food and beverages and petroleum products as per Table A1.

> 10Latest data from OECD TiVA confirms relatively low backward GVC participation of the Philippines.

10
28 banks be the same for each country or should, for each<sup>country, a number of</sup> banks with the highest exposure to that country be selected? Since our theoretical model does not incorporate differences among large banks,<sup>there is</sup> not a clear theoretical argument for choosing one strategy over the<sup>other.</sup> The nature of the available data, however, only permits us to<sup>look at a</sup> constant set of large banks. One may argue, in any case,<sup>that choosing the</sup> same set of the largest money center banks, independently<sup>of particular</sup> country exposure, is the more sensible strategy. The large money<sup>center banks</sup> have been unfailingly the most important players in making these<sup>loans and</sup> most likely are the banks with the greatest volume of business, in addition<sup>to</sup> debt, with the debtor countries. In comparison with the smaller<sup>banks, the</sup> large banks have greater exposure overall to all foreign borrowers. This also leads us to believe that they are the ones that would be more likely to lobby for the imposition of penalties on defaulting countries.

The exposure data that we use to compute debt concentration is compiled by the Federal Rese-ve Board. In this data banks are categorized as<sup>the</sup> largest nine, the next largest 15, and remaining U.S. banks. Largeness,<sup>in</sup> this data, is measured by t1 asset size of the banks. Accordingly,<sup>one</sup> measure that can be created using this data is the exposure<sup>of the largest</sup> nine U.S. banks to a country scaled by the exposure of the U.S.<sup>banks that are</sup> not in the largest 24 banks to that country (from here on, variable<sup>name is</sup> conl). For the countries in our sample we present the mean and<sup>the standard</sup> deviation for this variable on a country basis in Table 2.15 The total sample 15Conl was computed as 174 and 30 for Zambia and Malawi-respectively. These countries were excluded from the analysis not only because these numbers are extraordinarily high relative to the sample mean, but also they show very high volatility between quarters suggesting deficiency in the data.
<!-- Start of picture text --> 1,400<br>1,200<br>1,000<br>800<br><=<br>= 600<br>Oo<br>400<br>200<br>- ~~ — — = = =<br>2010 2011 2012 2013 2014 2015<br>-200<br>MExports-Ecuador MExports- Venezuela Imports - Ecuador<br><!-- End of picture text -->
1 point drop for females. The transition from self-employment to employment gives much greater rewards to females (0.4 points more) than to males (0.1), which could be interpreted as a more risk-loving behavior of men.

# **4.2 The Social Norm Effect of Unemployment**

The literature on happiness highlights the important role of comparisons in individuals’ well-being. It is income relative to others that matters or to oneself in the past. A slightly different comparison mechanism is at play when it comes to labor market status, but comparisons are still present. The previous section shows that the transition from employment to unemployment is causing a drop in SWB. Furthermore, there is no habituation effect : the unemployed feel on average significantly worse than those in employment, even after controlling for other factors<sup>11</sup> . Aware of this pattern, a relevant question arises. How does other’s employment affects one’s well-being ? Are those losing a job also comparing themselves to others in unemployment? If yes, in what ways ? Finally, do these comparisons affect their job search behavior ?

## **4.2.1 Theory : a binary choice model with externalities**

As in Akerlof’s (1980) social norm model, we can add others’ behavior and beliefs in the utility function. Whereas previous well-being estimations were only accounting for personal unemployment ( _Ue_ ) and were of the form _Wi_ = _W_ ( _Ue, X_ ), we are now interested in a utility function including a norm, beliefs and reputation effects, such that

## _Wi_ = _W_ ( _G, R, A, d_<sup>_c_</sup> _, ε_ )

Agent’s utility _Wi_ is dependent on their private consumption _G_ , their reputation _R_ , their belief in the norm _d_<sup>_c_</sup> , obedience to the norm _A_ (1,0), and personal tastes _ε_ . Let us suppose that reputation depends itself on the proportion of believers _μ_ and one’s own actions _A_ , such that _R_ = _R_ ( _μ, A_ ). If everyone believes in the norm ( _μ_ = 1) and agent _i_ does not follow it (A=0), he suffers from reputation effects. As less people believe in the norm, the reputation effect from not following it is reduced and this in turn pushes more people not to obede. As Akerlof explains, if there are no reputation effects, the only possible equilibria are derived from the traditional utility function with tastes _ε_ and consumption _G_ . However, if deviation from the norm is costly in terms of reputation, we may have a stable equilibrium in which the norm is self-sustained and agents follow it. It is a simple example of a binary choice model with externalities, in which two equilibria are sustainable.

> 11Clark (2006) finds that unhappiness does not decrease with unemployment length using the GSOEP and the ECHP, but has mixed results using the BHPS

13