rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Summary of Life Satisfaction Equations - Pooled World Sample and World Regions

||World||<br>Re|<br>gions||
|---|---|---|---|---|---|
|||+ sig.|- sig.|non sig.|tot.|
|Importance of family > importance of friends (a)|-0.138<br>(13.35)**|0|13|3|16|
|Importance of family < importance of friends (a)|-0.283<br>(13.26)**|0|14|2|16|
|Importance of work > importance of leisure (b)|-0.1<br>(8.97)**|0|8|8|16|
|Importance of work < importance of leisure (b)|-0.07<br>(5.50)**|0|6|10|16|
|Importance of religion > importance of politics (c)|0.114<br>(10.51)**|10|0|6|16|
|Importance of religion < importance of politics (c)|-0.027<br>(1.97)*|2|2|12|16|
|Child independence Vs. Child obedience|-0.06<br>(5.44)**|0|9|7|16|
|Trust in people Vs. trust in institutions|0.07<br>(4.99)**|7|0|9|16|
|Justified homosexuality|-0.127<br>(8.93)**|1|10|5|16|

32
reflects a weighted average of per capita GDP where the weights reflect the “revealed comparative advantage of each country” in that particular product. Using “revealed comparative advantage” as a weight ensures that the ranking of products is not distorted by country size. The index is intended as a proxy for the level of productivity of each particular export product.

Based on these estimates, the export income for each country is calculated as a weighted average of the productivity of exports. Weights are defined as the share of each product in total exports of an individual country, as follows: Figure 5 below shows GDP per capita and export income in 2004 for all countries in the sample. As can be observed in the graph, there is a positive and strong correlation between export income and GDP per capita. The relationship does not seem to be linear but it has a shape of half a U. Furthermore, there are some interesting and important “outliers” which support the argument that high-growth countries tend to have export incomes that are larger than their GDP per capita which, in turn, suggests that these values tend to converge over time. For example, the export income for China in 2004 was $16,967 compared to a PPP-adjusted GDP per capita of only $5,419. Similarly, in 2004 India had an export income equivalent to 4.7 times its GDP per capita ($13,550 and $2,907 respectively).

# **Figure 5**

<!-- Start of picture text --> Export Income and GDP per capita for All Countries, 2004<br>40,000<br>USA<br>35,000<br>30,000 CHE<br>25,000<br>45 o line<br>20,000<br>ARG<br>15,000<br>10,000<br>5,000 CHN<br>MWI IND<br>0<br>0 5,000 10,000 15,000 20,000 25,000<br>Export Income, 2000 PPP $<br>GDP per capita, 2000 PPP $<br><!-- End of picture text --> The export income for Argentina in 2004 was US$12,440.40 compared to a GDP per capita of US$ 12,225.00. Hence Argentina is in the group of countries which have an export income almost equal to their GDP per capita. This would indicate, following the argument of HHR, that the prospects for future growth in Argentina are not as good as some other countries. Furthermore, Argentina has an export income that is lower than that of countries with a similar level of GDP, such as Lithuania and Poland (see Figure 6 below). On the other hand, some countries that have a level of export income similar to that of Argentina have lower levels of GDP per capita. This applies to Tunisia, Colombia, Venezuela and El Salvador, among others. The conclusion would appear to be that Argentine exports are not particularly productive which, according to HHR, might imply lower growth prospects.

10
ASIA AND OCEANIA REGION

## 1. Estimates and Projections

|**Year**|**Population**|De.<br>**ratio**|**Period**|Growth<br>rate|**C4**|CDR|TFR|'I<br>**NaLes**|00<br>**Ftes**|**INN**|**x1o00**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**198S**|2576202|63.3|1985-89|1.72|26.0|8.7|3.18|63.4|65.5|67|88|
|**1990**|2807781|58.8|1990-94|1.57|24.0|8.2|2.89|64.8|67.1|58|75|
|**1995**|3037537|57.6|1995-99|1.39|21.7|7.8|2.63|66.2|68.8|50|63|
|2000|3255429|55.7|2000-04|1.23|19.7|7.4|2.47|67.7|70.5|42|53|
|**2005**|3461433|52.2|2005-09|1.08|18.2|7.3|2.33|68.8|71.8|37|47|
|2010|3653986|49.0|2010-14|0.97|17.1|7.3|2.21|69.9|73.1|31|39|
|2015|3835854|47.3|2014-19|0.90|16.4|7.4|2.15|71.0|74.5|26|32|
|2020|4012503|47.7|2020-24|0.83|15.8|7.5|2.13|72.2|76.0|20|25|
|2025|4182831|48.8|2025-49|0.54|14.0|8.7|2.09|75.1|79.5|11|14|
|2050|4787708|58.6|2050-74|0.22|12.7|10.6|2.07|78.3|83.4|5|6|
|2075|5055299|66.3|2075-99|<br>0.11|12.2|11.1|2.06|80.2|85.9|3|4|
|2100|5199264|69.8|2100-24|0.07|11.9|11.2|2.06|81.6|87.9|2|3|
|2125<br>2150|5295063<br>5342583|72.3<br>73.5|2125-49|0.04|11.8|11.4|2.06|82.1|88.7|2|3|

# 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>65-89<br>80-84<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>SXX<D U ~~~30-34 12z EU<br>25-29<br>20-24<br>15-19<br>SX{>X4 gEgE%l 1 10-14 RU S|<br>ERRZ9Z gUn 1 ~~~5-9| 22 %E!<br>0-4<br>10 8 6 4 2 02 4 6 8 10 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> LAC Ci3 ) AFQICA (10 01)<br>OM C23 It)<br>ASIA (53 21)<br><!-- End of picture text --> <!-- Start of picture text --> LAC C12 5%) AF41A (18 1)<br>EM(21 88)<br><!-- End of picture text --> 26
an alternative both to ITC for procuring soybean and to soy farmers for selling their produce. By targeting soy farmers, the immediate benefit to the private company of this intervention was the improvement in procurement efficiency of soybeans resulting from the creation of a direct marketing channel. According to Kumar (2004), “ITC calculated that it saved Rs. 13.3 million in transaction costs or almost 2 percent of the total value of the produce in the first season of procurement through this intervention.” This feature makes the intervention self-sustaining because it is profitable for both the private company and farmers to implement and use it respectively.

# **V. Alternative Explanations**

A legitimate concern is that the increase in price observed post intervention may actually reflect an increase in the overall quality of soybean over time. As mentioned earlier, there is an unobservable aspect to soybean quality that refers to the oil and protein content of the seed, and there is an observable aspect of quality that refers to the presence of moisture, dirt and damaged seeds in the sample of produce offered for sale. An increase in either one or both aspects of quality will tend to bias my results. Unfortunately, I do not observe quality contingent prices of crops. The MP Marketing Board does not publish monthly prices with quality grading. Given this shortcoming, I provide two pieces of suggestive evidence that an overall improvement in quality is not what is driving my results.

Kiosks provide information on farming techniques and weather forecasts which could lead to an improvement in unobservable aspects of quality. Panel C in Table 6 provides estimates of the effect of the intervention on the annual yield of crops. While this is circumstantial evidence, it seems plausible that improvements in technique that led to increased quality would also improve yield. I find no evidence of a significant increase in annual district level yield of soy.

A related concern is that perhaps, farmers clean up their produce more after the introduction of kiosks, which would increase the observable aspects of quality leading to an increase in price. This is a valid concern, because daily buying of soybean in local markets and hubs is based on observable features of quality. The Government of India 24
> where _HighEDj_ is a dummy variable for "High External Dependence" that takes value of one if sectorj is among the top-50% of most financially dependent sectors of all sectors considered by Raian and Zingales (1998), and zero otherwise Thiq **sefip** provides a robustness che-k that controls for UiLeasuremenet error in **uie** extermai dependence ratio-of each sector estimated by Rajan and Zingales (1998). In other words, we may expect that the most financially dependent sectors show a different growth pattern in well-developed countries on average, but there may not necessarily be a different effect for the most financially dependent sector and the second most financially dependent

> sector. Similar to model (2), we also estimate the following model:

> . **_iK_ fTT** **_VA_ A L. tif** **_Li_** j **= Ct** **_fl_** ~ 17'VLj t . _Pi plSJHAR,_ **_f (Di_ 0 1** . **U** _+_ X **_-lT_ 2 *** **_T_** T T. _Hh_ **r** **_mn_ T.** **_+._** (4)

# IV. Data

The industry data is from Rajan and Zingales (1998). We use their measure of financial depe.n.denre **bh** sector based on US frn.-level data. Financial or external dendence is **colculat** as the fraction oI capital expenditures not financed with cash flow from operations. The sectors considered by Rajan and Zingales (1998) are a mix of three-digit and four-digit ISIC (International Standard of Industrial Classification) level industries. Rather than use the mix of four-digit level sectoral breakdowns for some industries and three-digit level sectorail breakdowns for other industries in Rajan and Zingales (1998) is somewhat arbitrary, we use external dependence ratios for sectors on a t.hree-digT4T*' **_SIC_** lee only. **WA f kotv.ntfo *e=ciSrulateI** t w **1he** h **_ed aveage_** exte.aldependence figure for the four-digit !SIC sectors on a tnree-digit ISIC level. For the sectors that are 9
<mark>Appendix Figure 1 focuses on business ownership and shows a sharp decline from 48% of the sample owning a business in February to only 30% in June. By October, business ownership among the original sample is still only 39%. Disentangling these business ownership changes for existing and new businesses, the figure shows that only 45% of businesses that existed at baseline in January remain open in June, a substantial decline in business activity. By October, this proportion only reaches 55%, evidencing a persistent decline among previous business owners. The frequency of business startups is also quite low, at less than 10% in June and August compared to over 50% in the pre-pandemic period. Combined, these findings indicate the difficult business environment immediately following the pandemic.</mark> <mark>Appendix Table 1 investigates the main reasons for business closure and finds that by round 1 of the survey (i.e. June 2020) and among businesses that shut down, 67% closed because of worries about COVID-19, 46% due to lack of demand, 23% due to business losses, and 20% due to lockdowns and curfews. Inability to acquire inputs (15%), access transport (15%), and obtain capital (12%) were other reported reasons for business closures.</mark> Business performance also suffers, with Appendix Figure 2 showing immediate and substantial declines in both sales and profits in the months after the crisis hit. Specifically, sales decline by 75% from February to June, with only marginal improvements by October. Similarly, profits decline by 78% in the immediate aftermath and correspondingly show minimal recovery over the next few months.

Finally, Appendix Figure 3 illustrates impacts on perceptions, confidence, and life satisfaction. Nearly 80% of survey respondents report a negative change in income in round 1 of the survey. Furthermore, more than 40% lack confidence in maintaining their living standards, and less than 20% report being satisfied with life. There are some improvements in subsequent months, but the overall levels still remain quite low. Nearly 20% of respondents even report further negative changes to income over the declines in June. Combined, these findings show that the effects of the COVID-19 crisis were hard hitting and immediate on both business and individual outcomes.

# **<mark>5. Estimation and Results</mark>**

## **<mark>5.1. Estimation Strategy</mark>**

We estimate the causal impact of grants and BDS with two rounds of pre-COVID data (baseline and February recall) and three rounds of post-COVID data, using the following ANCOVA specification: 11
# **I. Introduction**

A large and growing strand of the recent trade literature - both theoretical and empirical - focuses on firms and how they export. Since the seminal work of Eaton and Kortum (2002) and Melitz (2003), a number of papers have developed models to understand the micro foundations of export growth.<sup>5</sup> On the empirical front, issues related to exporter behavior and dynamics in general have been heavily studied.<sup>6</sup> These studies have in common the use of micro datasets on export transactions within firms from a specific country or in a region. While the conclusions from many of the studies should in principle be comparable across countries, to date there has not been a comprehensive effort to produce analytical work covering an extensive set of countries, particularly developing countries. It would be of interest to see if for example, the empirical findings identified in this growing literature generalize across countries in different regions and at different levels of development, and whether cross-country evidence could provide new insights about how firms export and expand.

To fill this gap, we gather exporter-level customs information from 38 developing and 7 developed countries around the world to build the _“Exporter Dynamics Database”_ (henceforth referred to as the Database), containing comparable measures of exporter, product and market dynamics and which is available to researchers and policy-makers worldwide. The Database contains measures at different levels of disaggregation: a) country-year, b) country-year-product (HS 2-digit, HS 4-digit, or HS 6-digit) and c) country-year-destination mostly for the period between 2003 and 2010 (with longer time series for some countries). The Database covers

> 5 See Bernard, Eaton, Jensen, and Kortum (2003), Bernard, Redding, and Schott (2007), Das, Robert, and Tybout (2007), Chaney (2008), Arkolakis (2010), Bernard, Redding, and Schott (2011), Eaton, Kortum and Sotelo (2012) and Redding (2011) for a review.

> 6 See Eaton, Kortum, and Kramarz (2008) for France; Eaton, Eslava, Kugler, and Tybout (2008) for Colombia; Amador and Opromolla (2008) for Portugal; Iacovone and Javorcik (2008) for Mexico; Andersson, Lööf, and Johansson (2008) for Sweden, Freund and Pierola (2010) for Peru; Manova and Zhang (2012) for China, Masso and Vahter (2011) for Estonia, De Lucio, Mínguez-Fuentes, Minondo, and Requena-Silvente (2011) for Spain, Ekholm, Moxnes, and Ulltveit-Moe (2012) for Norway, Fabling and Sanderson (2012) for New Zealand, among others. See Bernard, Jensen, Redding, and Schott (2007, 2011) for reviews of the literature.

2
# **Appendix 2: Constructing the Lerner Index**<sup>**20**</sup>

The Lerner Index is defined as the difference between banking output prices and marginal costs (relative to prices). It is calculated as: where P is the price of outputs and MC is the marginal cost.

Price is calculated as the total gross revenue of the bank divided by the total assets. We compute the marginal costs by taking the derivative with respect to total assets from the following empirical specification of the translog cost function: ln(Cit) = α0i + β0ln(Qit) + β10.5[ln(Qit)]<sup>2</sup> + α1ln(W1it)+ α2ln(W2it) + α3ln(W3it)+ β2ln(Qit)*ln(W1it) + β3ln(Qit)*ln(W2it) + β4ln(Qit)*ln(W3it) + α4ln(W1it)*ln(W2it) + α5ln(W1it)*ln(W3it) + α6ln(W2it)*ln(W3it) + α70.5[ln(W1it)]<sup>2</sup> + α80.5[ln(W2it)]<sup>2</sup> + α90.5[ln(W3it)]<sup>2</sup> + α10 ln(Equity)it+ α11ln(Net Loans)it + Fi + Yt + eit where Cit is total operating plus financial costs for bank i in time period t, Q is total assets, W1 is the ratio of interest expenses to total deposits, W2 is the ratio of personnel expenses to total assets, W3 is the ratio of other operating and administrative expenses to total assets, Equity is the ratio of firm equity to total assets, Net Loans is the ratio of net loans to total assets, Fi are firm fixed effects and Yt are the full set of year dummies. A constrained fixed effects regression with time dummies is estimated under restrictions of symmetry and homogeneity of degree one in the price of inputs.

We use bank-level data from Bankscope to calculate the Lerner Index. Only banks classified as commercial, cooperative, Islamic, savings, and bank holding companies are considered in the analysis.

> 20 We follow the literature on the Lerner index mentioned in footnote 11.

33
**Figure 3c: Net producers, net consumers, and autarky households for corn**

<!-- Start of picture text --> 0.8<br>0.7<br>0.6 Autarky<br>0.5<br>Net Consumer<br>0.4<br>0.3<br>Net Producer<br>0.2<br>0.1<br>0<br>10.5 11.5 12.5 13.5 14.5 15.5 16.5<br>-0.1<br>Log of per eq adult expenditure<br>Net Producer Net Consumer Autarky<br>Fraction<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.

**Figure 4a: Income per capita (net sales) from rice production**

<!-- Start of picture text --> 50000<br>0<br>10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5<br>-50000<br>Rural<br>-100000<br>National<br>-150000<br>Urban<br>-200000<br>-250000<br>Log of per eq adult expenditure<br>National Urban Rural<br>Net Sales<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.
# **1. Introduction**

A large majority of micro, small, and medium-sized firms throughout the developing world operate in the informal sector (La Porta and Shleifer, 2014a). This is certainly the case in Benin, where the national statistics agency has estimated that the informal sector represents up to 70 percent of GDP and 95 percent of employment (INSAE, 2009). A high level of informality is often seen to be costly for governments (who lose out on tax revenues and information on the firm sector), formal firms (who may suffer from unfair competition), and for the informal firms themselves (who may not be able to access bank financing, public contracts, or government programs, may face corruption or intimidation from tax inspectors, and as a result have low productivity) (e.g. Levy 2008; Farrell, 2004; Perry et al, 2007; La Porta and Shleifer, 2014b). In response, many countries have implemented business entry regulation reforms in order to reduce informality, spurred by the work of De Soto (1989) and the _Doing Business_ project of the World Bank (World Bank, 2016).

However, a review of the existing evidence suggests that easing entry regulations and providing information on the formalization process has only a very limited impact on the formalization of existing informal firms (Bruhn and McKenzie, 2014). The largest impacts have come from reforms which make it easier and cheaper to register for a status not directly linked to tax registration. In Peru, Alcãzar et al. (2010) find that offering a subsidy for the cost of obtaining a municipal license led to 10 to 12 percent of informal businesses obtaining it, while in Malawi, Campos et al. (2015) find that offering assistance with registering in a company registry had a large impact on business registration, with 75 percent of those offered assistance obtaining a business registration certificate. In contrast, impacts have been much smaller when registration for taxes is involved. Providing information and removing the upfront cost of registration had no effect on tax registration in randomized experiments in Sri Lanka (de Mel et al. 2013), Bangladesh (de Giorgi and Rahman, 2013), Brazil (Andrade et al, 2013), Malawi (Campos et al, 2015), or Colombia (Galiani et al, 2015). One interpretation of this evidence is that burdensome regulations are not the main reason these firms are informal, but instead they are rationally choosing to be informal because the benefits of formalizing are low for them compared to the tax and other costs (Maloney, 2004).

The limited success of these studies in getting firms to formalize has meant there have been few opportunities to actually measure what the benefits to informal firms of tax registration actually are. Some evidence is available from Sri Lanka, where de Mel et al. (2013) paid firms to 2
_Intertemporal Adjustment and Fiscal Policy in a Fixed Exchange Rate Regime_ Therefore the _REER_ equation is written as follows: where:

- _rus_ , _t_ is the real U.S. interest rate (the Fed Funds rate minus a three-month centered

moving average of the rate of growth of the U.S. consumer price index).

- δ is the capital depreciation rate.<sup>16</sup>

- _a.yt / kt_ is the marginal productivity of capital ( _a_ is the exponent of the Cobb-Douglas function).

According to exports equation (41), the second term of the right side of equation (39) is the value of the REER required to maintain exports at the same level in the next period.<sup>17</sup> The estimated equation for the rate of growth of capital is very close to the theoretical equation: where _txt_ is the exogenous tax rate.

Finally, the form of the production function is assumed to be: Since parameter ( _a_ ) is estimated from equation (39), the index of exogenous efficient labor units (gt) can be computed directly by solving (44) for _gt_ : Thus the production function is used to simulate _yt_ at each period of time.

The set of equations (41), (42), and (43) is estimated using the nonlinear least squares system. Estimation results are presented in table 3.1. All coefficients have the expected sign and are very precisely estimated, using robust standard errors calculation. The elasticity of exports

> 16 The capital stock time series is computed by adding gross investments data and using an initial value given in the Argentina Central Bank database. We used a quarterly depreciation  rate δ =0.01375 in order to obtain the same profile as  the annual series given in the database. 17 log( _X t_ + 1) = _x_ 0 + _x_ 1 log _REERt_ + _x_ 2 log( _WDt_ ) + _SVt_ = log( _X t_ ) . Solving for REER t yields the expression in

brackets.

23