rafmacalaba/datause-encoder-data huggingface dataset:
sume that the effect of liberalization is homogeneous for all firms.<sup>4</sup> Differently, some recent work from Aghion, Blundell, Griffith, Howitt, and Prantl (2004) shows that the effect of competition is non-linear and heterogenous depending on the productivity level of the firm. Only “good firms”(e.g. close to the productive frontier) are positively affected by competition as their innovative effort is enhanced in response to the increased entry threat of foreign competitors. Vice-versa “bad firms” (e.g. far from the productive frontier) are negatively affected because the intensified entry threat only reduces their expected profits as their efficiency is too low to allow them to compete successfully with foreign entrants(Aghion, Burgess, Redding, and Zilibotti 2004).

In this paper we contribute to the existing literature in two ways. On the theoretical front we develop a neo-Schumpterian model that extends previous work by Aghion, Blundell, Griffith, Howitt, and Prantl (2004) and still reaches consistent results with their model at industry-level, while generating novel results at the firm-level. These results are more in line with much of the firm-level studies pointing toward a positive impact trade liberalization on firm performance “on average”. Consequently we bring such a model to the data and test it by using plant-level data from a developing country. Our approach, differently from much of the previous literature that analyzed the effect of liberalization in a cross-country setting focusing mostly on outcomes (e.g. relationship between growth and openness), is much more micro and concentrates on a specific mechanism through which liberalization may influence firms’ responses (see literature review by Hallak and Levinsohn (2004)).

The paper is divided into five sections. In the next section we discuss the existing literature related to our study. In the third one we present a set of stylized facts and sketch the theoretical model, which generates two main predictions tested in the subsequent econometric analysis presented in section four. The last section concludes and highlights some avenues for further research.

# 2 Existing Literature

This paper is related to various strands of the literature, in particular the recent works of Aghion and Bessonova (2006) and Aghion, Burgess, Redding, and Zilibotti (2004) arguing that liberalization boosts innovative efforts, but only for those firms that are more productive while it weakens the incentive to innovate for less productive ones. These models are characterized by three main elements. First, innovation is the main driver of firm-level growth. Second, a firm innovates as long as its postinnovation profits are larger then pre-innovation ones. Third, the effect of a successful innovation is limited and a firm can only advance “one step a time” over the “productivity ladder”. This implies 4 Schor (2004) study on Brazil and more recently Lileeva and Trefler (2007) using Canadian plants data are two notable exceptions.

3
students exposed to a traditional lecture or videos (Alhalabi, 2016; Allcoat, & von Mühlenen, 2018).

**Figure 7:  % Difference in posttests between students exposed to VR vs non-VR training [Cognitive Skills]**

|Allcoat & von Mühlenen (2018)||Plant cells (VR vs Video)|
|---|---|---|
|Alhalabi (2016)||Famous inventors|
|Allcoat & von Mühlenen (2018)||Biology concepts (VR vs Video)|
|Alhalabi (2016)||Networking|
|Allcoat & von Mühlenen (2018)||Biology concepts (VR vs Text)|
|Alhalabi (2016)||Transportation|
|Akpan & Strayer (2010)||Anatomy|
|Alhalabi (2016)||Anatomy|
|**Rupasinghe et al.(2011)**||Use of boroscope (oral test)|
|**Finkelstein et al.(2005)**||Circuits (essay)|
|**Finkelstein et al.(2005)**||Circuits (test)|
|**Tatli and Ayas (2013)**||Chemistry|
|**Rupasinghe et al.(2011)**||Use of Eddy labs (oral)|
|**Tanyildizi & Orhan (2007)**||Synchronous motors|
|Farra et al. (2018)||Emergency Preparedness|
|**Hwang & Hu (2013)**||Science|
|**Yang & Heh (2007)**||Synchronous motors|
|**Yang & Heh (2007)**||Synchronous motors|
|Kockro et al. (2015)||Anatomy|
|Allcoat & von Mühlenen (2018)||Plant cells (VR vs Text)|
|**Rupasinghe et al.(2011)**||Use of Eddy labs (written)|
|**Rupasinghe et al.(2011)**||Use of boroscope (written)|
|**Tatli and Ayas (2013)**||Chemistry|
|Parong & Mayer (2018)||Biology|
|-80.0<br>-60.0<br>-40.0<br>-20.0|0.0<br>20.0<br>40.0|60.0<br>80.0<br>100.0<br>120.0|

_Note_ : studies in bold are in the field of engineering, science, and technical education. Underlined studies are in the field of health and safely. Other studies are in the field of general education. The bold vertical dotted line represents the average observed effects. Gray vertical dotted lines represent the standard deviation of the observed effects.

Other studies (Allcoat, & von Mühlenen, 2018; Hwang, & Hu, 2013) indicate that students who receive VR training perform just as well as students that use textbooks to complement traditional lectures. Allcoat and von Mühlenen (2018) and Hwang and Hu (2013) find that students exposed to VR training memorize better the parts of a plant cell and display better skills to calculate area and volume of 3D figures than students exposed to similar content but whose lessons were imparted using more traditional learning methods, such as videos and lectures. Parong & Mayer (2018) find that students exposed to slideshows perform better than students exposed to VR training on subjects such as biology. Finally, Kockro et al., 2015 found no significant difference 22
# **2.4 Quantities Sold and Expenditures**

Data on expenditures are obtained in two ways. First, we use the total nominal monthly sales for each store constructed from the retail scanner data. In particular, we construct a measure for quantities using real sales, which is defined as the nominal sales divided by the store price index. Second, we use the Nielsen Consumer Panel Dataset.

## **2.4.1 Nielsen Consumer Panel**

The Nielsen Consumer Panel Dataset represents a longitudinal panel of approximately 40,000-60,000 US households who continually provide information to Nielsen about their households and what products they buy as well as when and where they make purchases. Panelists use in-home scanners to record all their purchases, from any outlet, intended for personal, in-home use. Products include all Nielsen-tracked categories of food and non-food items, across all retail outlets in the US Nielsen samples all states and major markets. Panelists are geographically dispersed and demographically balanced. Each panelist is assigned a projection factor, which enables purchases to be projectable to the entire US.

For each period, we calculate the total expenditures of each household on SNAP-eligible and SNAP-ineligible products. We classify goods as SNAP-eligible based on guidelines published by the FNS. The average SNAP-eligible-goods-expenditure share across households is close to 60%. The advantage of using this measure as opposed to the nominal sales for each store is that demographic information for the household can be observed instead of county-level demographics for the store, such that we can use information on household income and household size to classify households as SNAP-eligible.<sup>25</sup> We also construct several measures of shopping intensities among households, following Stroebel and Vavra (2019). For each good purchased, the household records whether the good is purchased with coupons and if it is on sale. The barcode of the good is also scanned so the brand of the good is observed. We use three measures of shopping intensity: (1) the share of expenditures using coupons (coupon share), (2) the share of expenditures on goods that are on sale (deal share), and (3) the share of expenditures on generic store brands (store-brand share).<sup>26</sup>

> 25We classify households as SNAP-eligible if their household incomes are 130% of the poverty line or below during both the event periods in 2008-2009. Since household income is given in brackets with a 2-year lag, we take the mid-point of each bracket. Results are nearly identical using lagged or current incomes.

> 26To construct these variables in a way that controls for changing composition of the consumer basket, we follow HS by using the difference between actual and predicted shares for each household, predicting the shares using the average across all households consuming the same product group in the same time period.

14
# **References**

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- Duranton, Gilles, and Diego Puga, “Micro-Foundations of Urban Agglomeration Economies”, in Vernon Henderson and Jacques François Thisse (eds.) Handbook of Regional and Urban Economics, Volume 4 (Amsterdam: North-Holland, 2004), 2063–2117.

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- Falck, Oliver, Christina Guenther, Stephan Heblich, and William Kerr, “From Russia with Love: The Impact of Relocated Firms on Incumbent Survival”, Journal of Economic Geography 13:3 (2013), 419-449.

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- Fernandes, Ana, and Gunjan Sharma, “Together We Stand? Agglomeration in Indian Manufacturing”, World Bank Policy Working Paper 6062 (2012).

22
While most firms pay reasonable rates, a few pay extremely high interest. For the large majority interest rates vary from less than 5 percent per year and to 50 percent per year, and the distribution of firms along this range is quite uniform. However around 8 percent of firms pay over 50 percent interest and about 4 percent pay over 100 percent. These rates are always charged by informal lenders ( _prestamistas_ ), who typically charge daily interest rates. Not surprisingly, there is a negative relationship between firm size and the interest rate, although among the approximately 10 percent of firms in the EMES sample which pay interest rates in excess of 100 percent, the largest share corresponds to firms with 11-50 employees.

# **2.5 Informality**

The EMES survey captures seven distinct but related dimensions of informality. These include four sets of mandatory regulations: three dichotomous variables that take a value of 1 if a firm has a tax identification number (RUC _)_ , a municipal license, and requests receipts on all its purchases, and a continuous variable which measures the percentage of the firm’s employees registered with the Ecuadorian Institute of Social Security (IESS). The survey also records the percentage of firm’s employees with written contracts; although this is not a legal requirement as verbal contracts are recognized on an equal basis, the use of written contracts is more indicative of firms which comply with a broader set of regulations, offer more worker protections, and in particular is highly correlated with affiliating employees to IESS.<sup>6</sup> Furthermore, the EMES considers two additional dimensions which are not mandatory for most firms but which reflect having reached a higher stage of development: being incorporated through a public notary and registered in the official registry ( _registro mercantil_ ).

**Figure 5: Formality profile of firms**

<!-- Start of picture text --> by type of regulation (percent of firms in compliance) by sector of economic activity (4-criteria index)<br>Municipal<br>license Construction<br>100 80<br>80 Food vendors 60 Textiles<br>60 40<br>40 20<br>Other<br>20 Restaurants & 0 manufacturin<br>hotels<br>Social Taxpayer g<br>0<br>Security number<br>Auto repair Grocery retail<br>[1-5] employees<br>Transport<br>[6-10] employees<br>[11-50] employees<br>Receipts Principal factor index Average<br>Total<br>Source: EMES<br><!-- End of picture text --> In addition to analyzing the performance of firms along each of these dimensions individually, the paper also combines them in an index which captures the level of firm compliance along the entire spectrum of formality. We construct two indices: a “narrow” index which only includes the four mandatory 6 According to the Article 11(a) of the Ecuadorian Labor Code, employment contracts can be “explicit or implicit, and, first and foremost, written or verbal.” Furthermore, Article 12 specifies that “a contract is considered explicit when an employer and a worker agree on the conditions, whether in words or in writing.” 7
(573 firms in Kenya, 289 firms in Tanzania, and 366 in Uganda) with 5 or more employees. The Enterprise Surveys were conducted in all three countries using the same sampling methodology – stratified random sampling – along with a common questionnaire. Three levels of stratification were employed for each country: firm-size, sector of activity, and location within the country. Weights are used to ensure that the sample is representative of the non-agricultural, non-mining private sector of the economy.<sup>5</sup> Before exploring the relationship between mobile money use and investment, we provide some basic statistics to explore why firms have or have not adopted mobile money, and what types of firms tend to adopt mobile money for transactions. The latter has already been explored in depth (Gosavi, 2015) and thus we do not delve into details in the analysis. So why has mobile money been adopted by firms? Through the Enterprise Surveys, managers and owners of firms in Kenya, Tanzania, and Uganda provided responses as to why they have or have not adopted mobile money. Despite some heterogeneity in the ranking, the most cited reasons for mobile money adoption across the three countries are reducing costs and risks of transactions and satisfying costumers’ requests (Table 1). Concerning the reasons for non-adoption, the majority of non-adopter firms in Kenya cited large payments that were difficult to undertake using mobile money. In Tanzania and Uganda the most frequently cited reason is non-adoption by customers or suppliers.

What types of firms tend to adopt mobile money? In table 2 we present comparisons of averages between firms that use mobile money for transactions and those that do not. As expected, firms that use cell phones for operations are more likely to adopt mobile money; while larger and older firms are less likely to adopt mobile money for transactions. Manufacturing firms and firms that are part of a large company are also less likely to use mobile money, while a higher percentage of firms tend to adopt mobile money if they are in the capital or main business city. Firms that use mobile money tend to have higher labor productivity than firms that do not, although this difference is not statistically significant. In summary, mobile money adopting firms tend to be smaller, younger, concentrated in the service sectors, and are located in main business or capital cities.

> 5 Certain sectors are not covered including: agriculture, mining, and some service sectors such as education, health, and financial intermediaries. More information on the Enterprise Surveys global methodology is available on the website http://www.enterprisesurveys.org/.

7
<!-- Start of picture text --> L-<br>+, NG we MAURITANIA<br>: -<br>gs co<br>4a < a Se ro Ve<br>SamarCarOe<br>Roa LK SE Cs<br>Ao hes<br><< OS om<br><!-- End of picture text --> <!-- Start of picture text --> Type & Condition<br>Good Fair Poor Unknown<br>— eee Paved<br>Seems momen sesee “pestUnpave<br><!-- End of picture text --> <!-- Start of picture text --> ~~<br>ie Y BUH e<br>= *a, unefs<br>ee a oe i<br><!-- End of picture text --> <!-- Start of picture text --> 4 ICT - Intemational Gateways<br>Ge Fined Transmission Meteor<br>4 ICT-GSM Coverage<br><!-- End of picture text --> <!-- Start of picture text --> Pe<br>i<br>(’ r<br>—ee : MAURITANIA<br>eo s ‘ pie Sapa y ’ Fi<br>sf<br>= san. Gs |<br>* omer wet<br><!-- End of picture text -->

<!-- Start of picture text --> POWER<br>Power Plants | Type and Capacity (MW)<br>HYDRO THERMAL OTHER<br>fe)0 fo}0 Ou0 <<br>@@ 0»<br>Power Lines (KV)<br>—= Medium lm High<br><!-- End of picture text -->

<!-- Start of picture text --> ~~<br>Fi . 6 MALI<br>GUINEA-BISSAU omen<br>SZ<br><!-- End of picture text --> <!-- Start of picture text --> Current Irrigation [i Area)<br>21% Qo @ao.<br>[i bas<br><!-- End of picture text -->
# 3.2 Individual Data

I also use the 2016 Nepal Demographic Health Survey (NDHS 2016) in the analysis. The survey was implemented by New ERA under the aegis of the Ministry of Health of Nepal and was funded by the United States Agency for International Development (USAID). The data collection took place between June 19, 2016 and January 31, 2017.

The NDHS 2016 is a nationally representative survey of the female population ages 15 to 49. The sampling frame for the survey was based on the updated version of the 2011 Nepal Population Census. After the implementation of the 2015 constitution, based on the population several VDCs and Municipalities within districts were merged to form rural development areas and urban areas. The old Village Development Committees (VDC) in the rural and enumeration areas (EAs) in urban places essentially form primary sampling units (PSU) for the 2016 NDHS. In the nal sample, 383 clusters or PSUs were selected with probability proportional to their population size (see Ministry of Health, 2017). Figure A.3 illustrates the coverage of the survey. All 75 districts except Manang and Mustang were sampled; however, these two districts also had zero casualties during the conict and are excluded from the baseline analysis. As illustrated in Figure 3 (months of war) and Figure A.4 (casualty count), conict intensity varied signicantly across the 383 NDHS villages (clusters).

Within the selected DHS clusters, 30 randomly selected households were interviewed, and all women aged 15 to 49 who were permanent residents or visitors who stayed in the household the night before were eligible for the interview. A sub-sample of about half of the households were selected for biomarker information. All children aged 0 to 59 months and women 15 to 49 years in these households were administered the anthropometry, hemoglobin, and blood pressure measurements. I limit the analytical sample to those that are born before or were in utero during the start of the conict in February 1996 so that they are old enough to be sampled in the ever-married NDHS 2016 women sample and have children that can be incorporated in the second-generation analysis. The nal analytical sample size is 4,421 women ages 20 to 49 at the time of the survey (0 to 29 at the start of the conict) and their 2,168 under age 5 children.

Table 2 summarizes women's health outcomes and their exposure to conict and demographic characteristics. I divide women into two cohorts: ages 0 to 15 (treatment) and 16 to 29 (control) at the time of the start of the conict. While the women in the former cohort would have still been in 11
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