rafmacalaba/data-use-mentions-extended huggingface dataset:
with those on rainfall (deviation from the longrun average), price (deviation of the predicted from the actual price level), and incidence of conflict/crime as proxies for covariate shocks. Using this approach, various indicators of household welfare such as consumption, education, asset accumulation, and nutrition levels are used to account for the multidimensionality of the impact of shocks.

In the basic model, we estimate the following specification: ln(yij) = Xijβ+ Sijα+ γj + εij where yij is an indicator of household welfare such as consumption, food consumption score (FCS), dietary diversity, and poverty status for household i in community/state j ; Xij is the vector of household characteristics such as demographics, assets, and employment for household i in community/state j ; Sij is a vector of shocks; γj is a vector of community characteristics or fixed effects; and εij is the error term. Our main parameter of interest in this specification is α . The extent to which shocks affect the welfare of Sudanese households can be examined by assessing whether α is significantly different from zero.

While this specification allows us to estimate the effect of shocks on household welfare, it does not allow us to capture the heterogeneity in the impact of shocks across space and households—how does the 27
ICT Use, Competitive Pressures and Firm Performance in Mexico<sup>_∗_</sup> Leonardo Iacovone<sup>_†_</sup> Mariana Pereira-L ́opez<sup>_‡_</sup> Marc Schiffbauer<sup>_§_</sup> JEL Classifications: E23, F14, L25, O31, O33 Keywords: ICT, productivity, competition, Mexico

> _∗_ ACKNOWLEDGEMENTS: We thank Gerardo Durand, Gerardo L ́opez, Sim ́on S ́anchez Trinidad and Gabriel Romero from INEGI for their support with the data use in compliance with the confidentiality requirements set by the Mexican Laws. We also want to thank Martin Guzman from the Ministry of Economy for his help with Mexico’s trade data. Finally, thanks to Remi Jedwab, Bob Rijkers, Pablo Cotler, Irving Rosales, Alejandro Rodr ́ıguez and Gabriela Luna as well as the participants of the World Bank’s ABCDE Conference 2015 for their valuable comments. All errors are naturally our own.

> _†_ Senior Economist, World Bank email: liacovone@worldbank.org

> _‡_ Consultant, World Bank & Universidad Iberoamericana email:mpereiralopez@worldbank.org

> _§_ Senior Economist, World Bank email: mschiffbauer@worldbank.org
0.476|0.469|0.463|
||(0.356)|(0.353)|(0.359)|(0.363)|
|_N_|18,158|18,158|18,158|18,158|

Notes: This table reports estimates of the eects of trade agreements on FDI over the period 1990-2011. The dependent variable is the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, and directional pair xed eects. The estimates of all xed eects, including the constant, are omitted for brevity. The standard errors in all specications are clustered by country pair. The dierence between the three columns are in the set of trade agreement variables. Specically, column (1) reports the average PTA eect across all agreements in the sample. Column (2) adds the eects of DTAs. Column (3) isolates the DTAs with investment provisions. Finally, in addition to PTAs, DTAs, and DTAs with investment provisions, column (4) introduces a continuous variable for investment depth. See text for further details.

25
a country in the 25<sup>th</sup> percentile of the distribution. The impacts of the other IC indicators are of similar magnitudes. This finding shows that improvements in investment climate are important in reducing the barriers of trade and in leading to the convergence of export performance of countries.

I tested the findings which were presented in Table 6, with several alternative indicators of investment climate such as rule of law from WGI, logistic performance index (LPI) from WTI, a measure of infrastructure from Enterprise Surveys database, and a measure of property rights from index of economic freedom (IEF). The information contained in these variables are similar to the ones presented in Table 6, however they are obtained from different sources.<sup>12</sup> Rule of law captures the extent to which agents have confidence in and abide by the rules of society, the quality of contract enforcement, the police and the courts, as well as the likelihood of crime and violence. The second indicator is logistic performance index (LPI) which reflects the overall performance of a country’s logistics environment.<sup>13</sup> 10 The standard deviation in the investment climate indicators are calculated over the largest regression sample which corresponds to 205 observations. The change is applied in the direction of improvement in the investment climate indicator.

11 The formula applied is as follows:  _Export it_ (75)   _Export it_ (25)   8 *  _Indicatorit_  1 * (log( _MATTRI it_  1(75))  (log( _MATTRI it_  1(25))) . 12 The rule of law is an exception. Although it is also collected through WGI, the variables used in constructing rule of law are different than the ones used for regulatory quality. I have included this indicator as it has been used in several studies such as Dollar and Kraay (2003) and Freund and Bolaky (2008) as a proxy for institutional quality and
experiences varied from case to case, but that, on the whole, transitional unemployment was quite small. In a survey of more than fifty studies of the adjustment costs of trade liberalization in the manufacturing sector—mostly industrialized economies—Matusz and Tarr (1999) argue that the adjustment costs associated with transitional unemployment are not high and that unemployment durations generally quite short. Indeed, in some cases employment appears to increase more or less instantly—as, for example, Harrison and Revenga (1998) report for Costa Rica, Peru and Uruguay. In their (non-random) sample, developing countries tended to display increasing employment after trade reform, while former centrally-planned countries in transition to a market economy showed the opposite. The attribution problem is huge for the latter countries, however, for so much else was going on. Most studies of trade and employment refer to manufacturing employment, with little indication of whether their results generalize to agriculture or services, or indeed anywhere outside the formal sector. This is a major shortcoming, at least as much conceptual as practical.

Particularly in poor economies it makes no sense to equate meaningful work with formal employment. Most employment is informal, even in manufacturing, and even formal jobs offer little by way of effective social protection or improved safety provisions. Firms and/or workers may consciously prefer informality (Maloney 2004), especially if doing so has tax or regulatory advantages, including remaining below the sights of corrupt officialdom. There is a concern that trade liberalization is associated with great informality. This is disputed—see below—but even where it is true one needs to go a great deal further before one can conclude that liberalization has reduced the overall welfare emanating from work.

A further mystery is whether those laid off following trade liberalization are disproportionately poor. In developed countries, Kletzer (2004) suggests ‘yes,’ but for developing countries we are far from sure. Enterprise surveys report the responses of firms to trade liberalization, but typically give little information on the characteristics of
scheduled, it is difficult to be cost competitive with road. **Low rail costs are achieved by the loss of service flexibility** **_._**

**Road costs** are mainly governed by variable costs rather than fixed costs, typically in the ratio 60:40. Both the cost and infrastructure aspects of road are more flexible than rail. Each truck is allowed a relatively small payload (max of 56 tons in South Africa) and operates as an independent unit. In order to reduce variable unit costs, it is important for trucks to pick up paying return loads, and trucks will often reposition and wait for a return load. Road transport is therefore a flexible and reliable door‐to‐door service, but often more expensive than a similar rail service. It has been shown that many customers are willing to pay more for predictability and faster door‐to‐door services.

Therefore, road provides a **flexible** system able to carry **small loads independently** , while rail operates from terminals and requires enough freight volume to make up a train.

# <u>Bulk rail prices are internationally competitive, but general freight is not</u>

South African tariffs compare relatively well with international prices. A recent survey of logistics professionals found that rail fees and charges are cheaper in South Africa compared to Australia, Canada, India and Russia (World Bank, 2014a). This is mainly due to highly competitive prices for the bulk coal and iron ore lines. Transnet offers customers on bulk export lines better prices because the long‐term nature of the contracts leads to lower fixed costs (through higher asset utilization) and provide a steady source of revenue which is important to issue debt in capital markets. General freight tariffs are substantially higher than those of the bulk coal and iron ore export lines.<sup>9</sup> The OECD estimated that freight tariffs per tonne/km at PPP exchange rates in 2005 were
<!-- Start of picture text --> 0.3<br>—US data differences<br>> --DSGE differences<br>*B 0.25<br>Cc<br>ne}®<br>wy 02<br>+82.0.15 aati os aoe<br>3<br>No.1<br>o<br>e)E<br>S 0.05<br>0<br>1283216 8 6 4 3 2<br>Frequency (quarters)<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>—US data levels<br>> —-DSGE levels<br>B25<br>Cc<br>ne}®<br>= 2<br>+821.5<br>3<br>No<br>o<br>o)<br>E<br>$05<br>\<br>6 ZS<br>12832 16 8 6 4 3 2<br>Frequency (quarters)<br><!-- End of picture text -->
**Figure 5.** Population-adjusted incidence of public programs by type of household. Uruguay, 2017. **Panel A.** Direct taxes and transfers

<!-- Start of picture text --> 250%<br>200%<br>150%<br>100%<br>50% Direct Taxes<br>0% AFAM-PE<br>Non-Contributive pensions<br>-50%<br>Food and Other direct transfers<br>-100%<br>-150%<br>Adult Only Only Only maleOnly male Other Other<br>couple - female female adults - adults - childless types of<br>children adults - adults - children no families<br>children no children<br>children<br>Panel B. Education<br>350%<br>300%<br>250%<br>200%<br>150%<br>100% Child Care<br>Preschool and Primary<br>50%<br>Secondary<br>0%<br>Tertiary<br>-50%<br>-100%<br>-150%<br>Adult Only Only Only male Only male Other Other<br>couple - female female adults - adults - no childless types of<br>children adults - adults - no children children families<br>children children<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology Notes: The shares of the population are as follows: Adult couple - children (29%), Only female adults – children (5%), Only female adults – no children (2%), Only male adults – children (2%), Only male adults – no children (3%), Other childless (36%), Other types of families (22%
# **_5 Findings_**

# _5.1 Sample Characteristics_

The characteristics of the sample are described in Table 1 along with tests for the equality of means across rounds. The rural communes in the sample have a poverty rate in 2004 (based on poverty mapping) between 27 percent and 32 percent. This rate, which is the forcing variable, is 29.6 percent on average for all the communes. For the INDH-treated communes, the average is 30.9 percent, compared to 28.6 percent for the control communes. The mean difference between the areas is significant and since the poverty rate estimate is based on the 2004 poverty map, it remains the same for all three waves.

The average household size rose from 6.0 in 2008 to 6.5 in 2011, and then fell to 5.8 in 2013. Treated communes had higher household sizes in all rounds, with significant differences in 2011 and 2013. This result is driven in part by more children (individuals under 18 years of age) per household in the target communes, with significant differences in 2011 and 2013. The number of adults is also higher in treatment communes in all rounds, and increasingly so, with significant differences in 2013. Due to the differences in household size, we present all our outcomes in per capita terms. The age of the head of the household tends to rise slightly over time, with a significantly lower figure in INDH communes in 2011 and 2013. Women-headed households represent 6–9 percent of the sample and there is no difference by treatment status. In terms of the marital status of the head of the household, almost 9 out of 10 are married, with no significant differences by treatment. Nearly 68 percent of heads of household in the sample are illiterate. There are a few significant differences between the treated and the nontreated communes with regard to educational level of the head of the household, specifically a slightly greater share of heads with less than complete primary in control regions.
# **6. Conclusions**

Using unique pension fund data from Chile, this paper advances our understanding of herding behavior and, more generally, the investment practices of institutional investors. In particular, the paper exploits the richness of the data to test for and characterize herding behavior among pension funds across different asset classes and levels of the industry. In doing so, it sheds important light on the underpinnings of herding, and on the consequences that the emergence of institutional investors might have for capital market development.

The paper shows that pension funds herd significantly in their investment decisions. In particular, herding is more pronounced on instruments that are more opaque, suggesting that pension funds copy each other in their investment decision as a way to overcome informational problems. Thus, herding is more prevalent in corporate and financial institutions bonds, followed by equity and government bonds. This ranking of herding across asset classes is robust across the different levels of disaggregation we analyze (PFA level, PFA-fund level, and so forth). These findings are consistent with the view that asset characteristics matter for herding, and highlight an important shortcoming of the existing literature on herding in that it typically focuses on a particular asset class. The large prevalence of bonds in the portfolios of many institutional investors and the differences we document on the presence of herding in corporate bonds relative to equity suggest that existing evidence solely based on equity may lead to incorrect conclusions about the magnitude and potential consequences of herding. For example, the recent US experience with correlated exposure of institutional investors on mortgage-backed securities indicates that the potential destabilizing consequences of herding may be large.

Our results also shed some light on the relation between competition and herding. We find that herding is more intense when comparing similar types of funds across PFAs than when 29
independent from each other, following Here, _C_ is the number of locations in which the population is divided and _Nc_ is the number of households in location _c_ , for _c_ = 1 _, . . . , C_ . Finally, _β_ is the _K ×_ 1 vector of coefficients.

Under the original ELL methodology, the locations indexed with _c_ are supposed to be the clusters, or primary sampling units (PSUs), of the sampling design and do not necessarily correspond to the level at which the estimates need to be produced. In fact, clusters are typically nested within the areas of interest (e.g. census tracks). Presenting estimates at a higher aggregation level than the clusters (for which random effects are included in the model) may not be appropriate in cases of considerable betweenarea variability, and may underestimate the estimator’s standard errors (Das and Chambers, 2017). A way to alleviate this is to include covariates that explain sufficiently well the between-area heterogeneity in the model ( _ibid_ ). In this regard, ELL (2002) suggests the inclusion of area-level covariates as a way to improve precision, because they can explain the between-area variation in welfare and hence might reduce the contribution of area-specific residuals. Marhuenda et al. (2017) recommend putting the location effects at the same aggregation level where estimation is desired. According to this, in this paper we consider that the clusters _c_ are equal to the areas where estimation is desired.

A key feature of the implemented ELL approach is that it considers heteroscedasticity and fits a preliminary model for that, called the alpha model. Very little research has gone into this aspect of the method. In most applications, the alpha model usually has a small adjusted _R_<sup>2</sup> (which may not reach 0.05) yet tends to play a considerable role in the obtained point estimates, especially for parameters beyond the poverty rate such as the Gini index, Theil index, Poverty Gap, etc. Heteroscedasticity
resolution of 15-arc seconds – approximately 500m x 500m tiles near the equator. For this paper, the raw data are aggregated to the district level, on a quarterly basis. Nighttime light is measured in Nanowatts/cm<sup>2</sup> /steradian.

_Formal economic activity_ Formal sector activity is proxied through the number of active formal firms. Admittedly formal firms have different sizes, and the ones for which data are available are likely to be among the largest ones. YY<sup>FF</sup> However, previous research suggests that larger formal firms are more likely to be impacted by conflict (Blumenstock et al. 2018). It follows that the observed change in the number of active formal firms overestimates the actual change in the overall economic activity. The net effect of these size and impact biases is unclear. In the absence of more information, it is assumed here that the relative change in formal economic activity is proportional to the relative change in the number of formal firms for which data are available. yy<sup>FF</sup> The number of active formal firms is computed from a list of organizations that signed up for plans with multiple phones belonging to the same account. Public and non-profit organizations are excluded from this list, whereas multiple accounts from the same organization are consolidated into one. This results in a sample of 2,300 private firms covering 125,000 subscribers. Whether a firm is active is inferred from call detail records from all the phones registered under the corresponding corporate subscriber account. Call records contain the identity of the parties involved in the communication, as well as the timing of the communication and the location of the antenna where the call was initiated. The raw data were provided by a major mobile phone operator in Afghanistan and processed by Blumenstock et al. (2018). For this paper, the data were made available at the district level, on a monthly basis, for the period from April 2013 to April 2016. Formal firms
|TableA8:First-stage—|Refugeeintensity a|ndreceptivity|
|---|---|---|
||(1)|(2)|
|Receptivity|0.833***<br>(0.078)|0.839***<br>(0.078)|
|Round FEs|YES|YES|
|Fixed Effects (FEs)|HH|HH|
|Controls|YES|YES|
|R-squared|0.977|0.977|
|First-stage F|114.30|114.50|
|# of EAs|433|433|
|N|12937|13522|

_Notes:_ This table provides the first-stage results. The dependent variable is logarithm of refugee intensity and the instrument is logarithm of receptivity. Household sample weights from ESS2 applied. Column (1) is the first-stage result for consumption and consumption poverty regressions, while column (2) is the first-stage result for wealth and wealth poverty regressions. Both firststages control for household FEs, round FEs, and 12 time-varying control variables. The coefficient estimates for the 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

58
/L electricit ) y ) . Multiplying the DBI by the inverse of the labor share,<br>which we take from the Penn World Table version 9.0, we get the level of the cost of entry in units of labor:<br>DB L = ( start +( Y/Lelectricit ) y ) ∗ WY ∗L = ( start + electricitW y ) . Expressed in this fashion, the Doing Business’s<br>cost of entry is comparable to feτ E in the model. Thus, to isolate the Doing Business counterpart of<br>τ E , we divide DB L by the calibrated value of the technological component of the cost of entry, fe (i.e.,<br>τDB E = DB fe L ). The vertical axis of the figure plots log �1 + τDB E �, the de jure proxy of the entry barrier,<br>against log �1 + τ E � in the horizontal axis, the model-based entry.<br>2.0<br>1.5<br>1.0<br>0.5<br>log(1+Doing Business)<br>0.0<br>−0.5<br><!-- End of picture text --> Figure 5 provides information on the degree of correlation and the relative

> 31See the note to Figure 5 for a description of this conversion.

37
unless sample sizes are proportional to the size of each stratum.<sup>6</sup> With stratification the probability of selection of each unit is, in general, not the same. Consequently, individual observations are weighted by the inverse of their probability of selection which is equivalent to the weighted average of the estimates for each stratum, with weights equal to the population shares of each stratum.

I classify the data available in this dataset into four groups of variables that are relevant to my estimation procedure. The first group consists of data needed in the estimation of the productivity of each firm. This includes output data given by total sales and inputs (labor, capital, and materials). Labor input is defined as the total labor cost, which includes wages, salaries, bonuses, etc. Capital input is given by the net book value of machinery, vehicles, and equipment. Cost of materials is defined as the cost of raw materials and intermediate goods used in production. With these data it is possible to estimate the value added of the firm and other measures of productivity. It is also possible to identify which firms are exporters as there is information on the proportion of total sales that are sold in foreign markets.

The optimal way to measure each firm’s productivity would be to use methods such as those introduced by Olley and Pakes (1996) and Levinsohn and Petrin (2003) to estimate total factor productivity (TFP); however, these procedures cannot be applied as there is no time dimension in the data. As an alternative, I approximate TFP using Head

> 6 All regression that follow in this study use the correct weights given by the sampling methodology. The dataset is described as a svyset (i.e., survey set) in Stata. Firms missing weight information as well as those that their data was collected using a simple random methodology where given a weight equal to one.

9