rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: ICT firms do not import or export significantly more than other firms Average share of input and output exchanged domestically:

||||Inputs||||
|---|---|---|---|---|---|---|
||ICT|ICT<sup>_∗_</sup>|non-ICT|Manufacturing|Retail|Other<br>Industries|
|% inputs from West|66%|61%|65%|54%|64%|68%|
|Bank and Gaza|||||||
|Observations|126|60|407|87|202|118|
|_H_0 : ICT = non-ICT|(0.86)|(0.64)|||||
||||Output||||
||ICT|ICT<sup>_∗_</sup>|non-ICT|Manufacturing|Retail|Other<br>Industries|
|%<br>output<br>to<br>West|76%|70%|77%|59%|79%|80%|
|Bank and Gaza|||||||
|Observations|126|60|407|87|202|118|
|_H_0 : ICT = non-ICT|(0.75)|(0.29)|||||

_Notes:_ ICT<sup>_<u>∗</u>_</sup> excludes retailers and repairers. P-values for Wald tests reported in parentheses. _Sources:_ Authors’ survey.

as fundamentally oriented inwards. This is consistent with the conjecture that difficulties to import and export affect all firms, regardless of the industry. Given the lack of differentiation in the exposure to international markets between ICT and the rest of the economy, we abstract from these considerations in the model.<sup>28</sup>

# **6 Empirical Results**

In this section, we report empirical results that shed light on the individual mechanisms described above. We will complement the empirical strategy suggested by the model with other supporting evidence from the survey. Note that in this section we will not be able to estimate the relative importance of the different channels. This will be done in the next section, where we will compute counterfactuals, highlighting the effect of each mechanism separately.

The constraints to growth ICT firms face are the same as the constraints faced by other Palestinian firms. We asked business owners and managers their opinion on the major constraints to the growth of their businesses. Table 7 presents the results separately for ICT and for the other firms interviewed. The answers are remarkably similar whether the question was asked to an ICT firm or to other firms. A third replies that the current economic

> 28For a discussion of the role of internal transportation infrastructure on international trade, see Co ̧sar and Demir (2016).

21
<!-- Start of picture text --> FO Circles Non-FO Circles<br>2006 2008 2010 2012 2014 2006 2008 2010 2012 2014<br>Actual/authorized discharge<br>Head Tail<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Figure 3: Head and Tail DPRs by year for FO and non-FO channels system consistently under-provides surface water relative to its design parameters; i.e., the ratio of actual to authorized discharge is substantially less than one for the entire 9-year period. Second, the water shortfall is _greater_ at the tail than at the head. To understand the greater water shortfall at the tail, recall that under the quota-based _warbandi_ system each watercourse gets a share of flow into the channel determined by outlet width. Thus, if discharge measured at the head gauge of the channel is, say, 80% of authorized over the whole season, then each outlet along that channel, including the very last one where tail discharge is measured, would automatically receive 80% of its water entitlement or design flow. If, however, upstream outlets are enlarged or the canal is breached or silt is not removed in a timely manner – or, more benignly, the flow entering the channel is highly variable within – the filling cycle then relatively less water makes its way to the tail of the channel over the course of the season; for any given value of _DPR_<sup>_H_</sup> , there is a lower value of _DPR_<sup>_T_</sup> .

12
equity conversion rate and the bids will, in effect, represent the market valuation of this rate (see balance sheets below).

4. The investment brokerage, as part of the sale of the enterprise shares, will assist in the conversion process of the outstanding tax debts to equity. The conversion would take place only as part of the transaction.

5. Given the layoffs likely following the IBOT, such a scheme should be supported by a compensatory program to assist the unemployed and to reduce their dependence on enterprise-based social resources. (e.g. severance pay program financed by an IFI) Suppose the market value of the firm is V, the minimum equity stake desired by the prospective buyer a, the total tax debt outstanding T, and the debt-to-equity conversion factor c, and a and c = [0,1]. The buyer pays aV, and will receive _a_ percent of shares, (1 - a)V worth of shares being left with the incumbent shareholders. The tax authority will be forced to revalue its tax "assets" downward by (1 - c)T. Finally, there remains the crucial question of the utility to the incumbent majority shareholders, since their agreement will be required before any IBOT can proceed. Since the tax authority is selling cT worth of shares to the new buyer, the residual _aV -_ cT represents a "payoff' to the insiders for accepting the dilution of their shares.

A numerical example of the balance sheet restruScturing of such transaction will illustrate: let us assume that a company, whose book value is $50 million, is sold for $30 million in a competitive tender. For simplicity, assume that its only liabilities are tax debts and shareholder equity, and that the company is 100% manager-owned.<sup>2 9</sup> The investor considers that acquiring 60% is the minimum stake required for control sufficient to carry out restructuring. Under the new share-capital structure, therefore, the outsider should own $18m and the insiders $12m. The tax debts of $45 m are converted to, e.g., shares worth $15 m (a 1:3 ratio) to be sold to the outsider and the proceeds are transferred to the tax authority. A residual of $3 m will be paid to the insiders in exchange for their acquiescence in the dilution of their shares (from 100% to 40%).

|**Before Ownership Transformation**($ millions|)||
|---|---|---|
|Assets<br>50|Tax Arrears|45|
||Insider Equity (100%)|_5_|
|Total<br>50|Total|50|

**29** Normally, an enterprise with outstanding tax debts would not have positive shareholders equity, as that equity would be written down against outstanding claims. We are trying, however, to approximate (albeit crudely) the situation in some Russian firms where unpaid tax debts continue to be listed on the liabilities side, before any write-downs against shareholders' capital. If such a write-down occurred, creditors (tax authorities) would become new owners-something we are trying to avoid. Thus we propose a mechanism that allows new investors to, in effect, buy off those debts, then convert them into equity claims.

18
events occur per week. We define a cooking event as having taken place if the recorded temperature exceeded 40 degrees Celsius. The number of peaks is calculated with the help of signal processing and analysis (O'Haver, 1997) and a cooking event is one where there is a peak above 40 degrees Celsius. The use of 40 degrees Celsius helps remove the possibility of counting peaks that are really due to normal temperature variation not related to cooking. The maximum ambient temperature recorded in the sample was 35 degrees Celsius and the average was 25 degrees.

To measure the frequency of use during a monitoring period, we simply count the number of times the stove surface temperature exceeds the critical value. The average stove use in a round is therefore total frequency/total days temperature was measured. We express this value per week, because _injera_ tends to not be cooked every day. The key outcome variable is the number of times the Mirt stoves are used per week.

Using this experimental design and data management system, the key outcome data were generated and assembled. Other data on household characteristics, demographics and cooking situations were collected using a household survey. The following section discusses the empirical methods used to analyze these data.

# **4. Variables and Empirical Methods**

There are two major steps to the analysis. Because our stove distribution was random at the household level, we begin our empirical analysis with use frequency descriptive statistics and ranksum tests of differences that exploit this randomness. Second, we evaluate the factors affecting the number of Mirt stove cooking events that occur on average per week during the four monitoring periods.

A key objective of the analysis is to test whether the 6 treatments applied affect stove use frequency. That is, we attempt to determine the effect of each treatment on stove use had the stove been distributed without that treatment. For example, we might want to test the effect of our network treatment combined with free distribution (i.e. treatment 6) compared with free distribution and no network treatment to the same household (treatment 5). Formally, we would therefore like to estimate the average effect of each treatment vis-à-vis all other treatments for the same participating households. That is, ideally, we would like to see the effect of all our 6 treatments on each randomly

**14 |** P a g e
15

# TABLE 3 POLAND, AUSTRIA, GERMANY AND BOLIVIA CLASSICAL HYPERINFLATIONS

|||=<br>**S**|<br>**55**<br>M|=I<br>**S**<br>**W'**||
|---|---|---|---|---|---|
|_POLAND_|1921|880852|345311.0|0.39202|126.9|
||1922|879313|530428.0|0.60323|212.0|
||1923|1119800|426000.0|0.38043|156380|
||1924|1829000|1703000.0|1.04543|n.a.|
||1925|1981593|1981884.0|1.00015|6.8/l|
|_AUSTRIA_|1919|1309|632.3|0.48308|n.a.|
||1920|1089|168.0|0.15248|n.a.|
||1921|660|197.0|0.29653|842.0|
||1922|733|118.0|0.15830|3132.2|
||1923|367|2se.6|0.70000|135.6|
|_GERMANY_|<br>1920|11266|4223.7|0.37492|257.4|
||1921|11963|5336.2|0.44e04|28.7|
||1922|9965|3580.5|0.35931|1688.3|
||_1923_|13513|1676.7|0.12408|8.7E+10|

|_BOLIVIA /2_|1980|48.30|40.50|9.70|0.83851|47|
|---|---|---|---|---|---|---|
||1981|38.90|32.00|8.30|0.82262|29|
||1982|49.40|34.10|5.00|0.69028|133|
||1983|43.30|24.60|3.40|0.56813|269|
||1984|46.00|21.00|2.20|0.45652|1281|
||1985|23.90|13.90|2.90|0.58159|11750|
||1986|22.90|19.40|4.50|0.84718|278|
||1987|24.10|16.20|6.20|0.67220|15|

_SOURCES:_ _POLAND, Sargent. AUSTRIA Dornbush & Fischer. GERMANY. Young. BOLIVIA Country Economic Memorandum._ _/i : December rate of change over the three months preceding. 12 : Total expenditures and Revenues as % GDP._
# **Table 5: Relation between expenditure growth, poverty, and inequality**

|||Dependent varia<br>|ble is the growth of pe|r capita expenditure (L|og Yt– Log Yt-1)<br>||
|---|---|---|---|---|---|---|
|||FE|||GMM||
|Exlanator variables|||||||
|py|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged log of per capita expenditure|-0.195***|-0.819***|-0.852***|-0.149***|-0.187***|-0.595***|
||(0.033)|(0.058)|(0.062)|(0.044)|(0.049)|(0.091)|
|Lagged Gini index|-0.631*|0.100|0.078|-0.581***|-0.853***|-0.960***|
||(0.362)|(0.146)|(0.156)|(0.203)|(0.161)|(0.166)|
|Lagged poverty rate|-0.170**|-0.070|-0.069|-0.055|0.055|-0.111|
||(0.073)|(0.068)|(0.071)|(0.102)|(0.100)|(0.078)|
|Lagged log of other State spending|||0.002**|||0.001|
||||(0.001)|||(0.002)|
|Lagged log of investment spending|||0.001|||0.000|
||||(0.005)|||(0.008)|
|Lagged log of population density|||-0.067|||0.046***|
||||(0.071)|||(0.014)|
|Lagged share of urban population|||-0.001|||0.003***|
||||(0.001)|||(0.001)|
|Lagged share of population with high-school diploma|||0.051|||0.060|
||||(0.283)|||(0.171)|
|Lagged share of ethnic minority population|||0.001***|||-0.000|
||||(0.000)|||(0.000)|
|Lagged share of wage income|||0.252**|||0.373***|
||||(0.095)|||(0.111)|
|Lagged share of non-farm income|||0.149|||0.238**|
||||(0.103)|||(0.096)|
|Constant|2.101***|6.704***|6.732***|1.655***|1.861***|5.082***|
||(0.369)|(0.469)|(0.496)|(0.390)|(0.422)|(0.703)|
|Year FE|No|Yes|Yes||||
|Province FE|Yes|Yes|Yes||||
|Observations|567|567|567|567|567|567|
|R2/ Chi2|0.093|0.894|0.903|2298|3926|7831|
|Number of provinces|63|63|63|63|63|63|

30
<mark>Essers, D., K. Megersa, and M. Sanfilippo (2021). The productivity gaps of female-owned firms: Evidence from Ethiopian census data.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>, 69(2): 645683.</mark> <mark>Fairlie, R., and F. M. Fossen (2022). The early impacts of the Covid-19 pandemic on business sales.</mark> _<mark>Small Business Economics</mark>_ <mark>58: 1853–1864.</mark> <mark>Fernández-Cerezo, A., B. Gonzalez, M. I. Peinado, and E. Moral-Benito (2022). Firm-level heterogeneity in the impact of the COVID-19 pandemic.</mark> _<mark>Applied Economics</mark>_ <mark>DOI: 10.1080/00036846.2022.2133894</mark> Firpo, S., N. Fortin, and T. Lemieux (2007). Decomposing wage distributions using recentered influence function regressions. Working paper, University of British Columbia.

——— (2009). Unconditional quantile regressions. _Econometrica_ , 77(3): 953–73.

<mark>Forsythe, E., L. B. Kahn, F. Lange, and D. Wiczer (2022). Where have all the workers gone? Recalls, retirements, and reallocation in the COVID recovery.</mark> _<mark>Labour Economics</mark>_ <mark>, 78(October): 102251.</mark> Gertler, M., and S. Gilchrist (1994). Monetary policy, business cycles, and the behavior of small manufacturing firms. _The Quarterly Journal of Economics_ , 109(2): 309–340.

Gu, X., S. Ying, W. Zhang, and Y. Tao (2020). How do firms respond to COVID-19? First evidence from Suzhou, China. _Emerging Markets Finance and Trade_ , 56(10): 2181-2197.

<mark>Guerrero-Amezaga, M. E., J. E. Humphries, C. A. Neilson, N. Shimberg, and G. Ulyssea (2022). Small firms and the pandemic: Evidence from Latin America.</mark> _<mark>Journal of Development Economics</mark>_ <mark>, 155(March): 102775.</mark> <mark>Herskovic, B., B. Kelly, H. Lustig, and S. Van Nieuwerburgh (2020). Firm volatility in granular networks.</mark> _<mark>Journal of Political Economy</mark>_ <mark>, 128(11): 4097-4162.</mark> <mark>Hu, S. and Y. Zhang (2021). COVID-19 pandemic and firm performance: Cross-country evidence.</mark> _<mark>International Review of Economics & Finance</mark>_ <mark>, 74 (July): 365-372.</mark> https://doi.org/10.1016/j.iref.2021.03.016 ILO (2019). Small matters: Global evidence on the contribution to employment by the selfemployed, micro-enterprises and SMEs. International Labor Organization (ILO). <u>https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/--publ/documents/publication/wcms_723282.pdf</u> ILO and GIZ (2013). Is small still beautiful? Literature review of recent empirical evidence on the contribution of SMEs to employment creation (Geneva and Bonn). International Labor Organization (ILO) and Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).

42
Table 2-3 Summary Statistics for Subsidized and non-Subsidized Spillover Variables (calculated based on income tax)

|||||Sub<br>|sidized<br>|||||Non-Sub<br>|sidized<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Hor|zontal|Bac|kward|For|ward|Hori|zontal|Back|ward|For|ward|
||Number||||||||||Std||Std|
|Year|<br>of Obs|Mean|Std. Dev.|Mean|Std. Dev.|Mean|Std. Dev.|Mean|Std. Dev.|Mean|.<br>Dev.|Mean|.<br>Dev.|
|1998|95,879|0.076|0.060|0.022|0.015|0.024|0.047|0.112|0.068|0.033|0.018|0.038|0.050|
|1999|103,945|0.083|0.067|0.025|0.018|0.027|0.056|0.125|0.069|0.040|0.020|0.046|0.064|
|2000|102,465|0.096|0.072|0.029|0.020|0.033|0.070|0.130|0.070|0.041|0.021|0.049|0.068|
|2001|114,461|0.102|0.075|0.031|0.020|0.034|0.057|0.130|0.067|0.039|0.021|0.052|0.083|
|2002|122,218|0.107|0.080|0.035|0.025|0.041|0.091|0.128|0.066|0.037|0.018|0.047|0.059|
|2003|138,158|0.110|0.078|0.034|0.023|0.042|0.083|0.131|0.069|0.039|0.021|0.053|0.080|
|2004|202,551|0.132|0.090|0.041|0.027|0.054|0.110|0.129|0.063|0.038|0.020|0.051|0.070|
|2005|194,120|0.132|0.096|0.041|0.028|0.055|0.110|0.131|0.064|0.039|0.021|0.058|0.092|
|2006|216,924|0.138|0.094|0.043|0.028|0.057|0.101|0.126|0.061|0.039|0.020|0.057|0.097|
|2007|254,905|0.138|0.089|0.044|0.026|0.062|0.111|0.119|0.061|0.036|0.021|0.054|0.086|

36
governance indicators (Kau¤man, Kraay and Mastruzzi, 2005) (henceforth KKM) and the Docquier and Marfouk (2006) migration data set in their cross-sectional analysis. Focusing on skilled migration, they examine the impact of the brain drain on sending country’s institutional development and ...nd a positive e¤ect on “political” institutions (i.e., on “political stability”and “voice and accountability”) but a negative e¤ect on “economic”institutions at home (i.e., on “government e¤ectiveness”, “regulatory quality”, “rule of law”, and “control of corruption”). However, their results su¤er from the limits of a cross-sectional analysis<sup>9</sup> and, as they themselves acknowledge, from the weakness of their instrumentation strategy (they instrument skilled emigration rates using countries’ geographical characteristics). In this paper we look instead at migration in general, focus on democratic institutions, and consider dynamic-panel regressions. We ...nd that the emigration rate and the level of human capital both positively a¤ect democracy and economic freedom at home. This implies that unskilled migration has a positive impact while skilled migration has an ambiguous impact on institutional quality. Using the point estimates from our regressions, we simulate the marginal e¤ect of skilled emigration on institutional quality. In general the simulations con...rm the ambiguous e¤ect of high-skill emigration. It is only when the incentive e¤ects of emigration on human capital formation are taken into account that a signi...cant institutional gain obtains for some countries in the short run, and for many countries in the long-run.

# 2 Empirical analysis

## 2.1 General Considerations

Empirical investigation of the e¤ect of emigration on institutions in a cross-section or a panel setting raises a di¢ cult trade-o¤. In a cross-sectional dimension, it is possible to use better data both for migration and institutional quality. In particular, for migration, it is possible to use the Docquier and Marfouk (2006) data set, which considers international migration by educational attainment. This data set describes the emigration of skilled workers to the OECD for 195 source countries in 1990 and 2000. For institutional quality, the World Bank Governance data by Kaufmann, Kray and

> 9The KKM (2005) data set starts in the late 1990s and is therefore not long enough to allow for panel data analysis. Similarly, the Docquier and Marfouk (2006) dataset o¤ers estimates of emigration rates by skill levels for 1990 and 2000 only.

6
# **1 Introduction**

The debate on the Dominican Republic - Central American Free Trade Agreement (DR-CAFTA) between the U.S., five Central American countries (Costa Rica, Guatemala, Honduras, Nicaragua, and El Salvador), and the Dominican Republic has been heated by the usual arguments surrounding recent trade deals. A seemingly persuasive argument against these deals is that although new jobs in Central America may be generated, this may be done at the expense of American jobs and to the detriment of local workers hired in jobs that do not comply with minimum labor standards.<sup>1</sup> The persuasiveness of this argument comes from its partiality; the argument considers only the distributional effects of trade reforms and the fact that these reforms create winners and losers, with the poor being most likely to be the latter. Similarly, the pro-trade-deals assumptions that free trade indisputably favors growth and that growth trickles down to the poor are partial and deserve scrutiny. A careful assessment of whether trade reform can be beneficial to poor people and what can be done in the short-term to correct potential anti-poor effects is needed to settle the debate, but it is also a difficult task.

There are various channels through which trade liberalization affects the poor as discussed in conceptual terms by McCulloch, Winters and Cirera (2001), although empirical evidence is rather thin, disparate and piecemeal. In this study, a numerical simulation model – a computable general equilibrium (CGE) model – in conjunction with a non-behavioral micro-simulation module based on household survey data for Nicaragua<sup>2</sup> is used to estimate _ex-ante_ the effects of a DR-CAFTA-like trade shock on poverty. The CGE model has the advantage of being a counterfactual analysis tool that can generate price effects which are directly and unequivocally linked to a trade reform. The changes in relative factor prices (particularly between labor and capital remunerations, and between skilled and unskilled labor wages) and relative goods prices (such as between food and non-food items) are then linked to the household survey to generate income distribution effects. This methodology does not maintain full

> 1 See Elizabeth Becker’s CAFTA article on the New York Times article, April 6th, 2004.

> 2 For similar modeling frameworks see Ravallion and Chen (2004), Nicita and Olarreaga (2004), Bussolo, Lay, and van der Mensbrugghe (2005), Bussolo and Medvedev (2005), Hertel _et al_ . (2004), Ianchovichina _et al_ . (2001).

2
experience, accumulated knowledge, and better access to finance due to their past record of successes. Thus, older firms have a higher propensity to engage in R&D activity. However, the opposite claim that propensity to invest in R&D is lower for the older firms is also made. One reason for this is that as per the “product life cycle” model of Klepper (1996), there are fewer technological opportunities available to the relatively older firms. Another reason is that younger firms are likely to have better access to high-skilled workers because of the dynamic and vibrant environment in which they operate (Baron et al. 2002, Hannan et al. 1996, Pallegrino 2018). This increases their propensity to invest in R&D. Further, older firms tend to suffer from inertia and organizational rigidity which may make them less capable of dealing with uncertain nature of demand for innovative products and services (Nelson and Winter 1982). In contrast, younger firms typically operate in an environment characterized by high level of uncertainty and therefore are more inclined to take risks and explore new ideas and technologies (Audretsch 1995, Coad et al. 2016a, b).

Exporting is another factor found to be positively correlated with the propensity to spend on R&D and innovate. One reason for this is that compared to non-exporters, exporters operate in more dynamic and competitive markets. Hence, they are forced to innovate to remain competitive. Another reason is that exporting markets offer better learning and imitation opportunities in terms of new technologies. Additionally, export markets can increase innovation by increasing market size, thus spreading the cost of innovation over a larger market (for example, Grossman and Helpman 1991, Bustos 2011, Aghion et al. 2018, and Lileeva and Trefler 2010). However, it is not clear whether exporting causes firms to innovate more or that it is the innovating firms that selfselect into exporting (see for example, literature survey by Harris and Moffat 2011). For instance, in a recent study, using Slovenian microdata over 1996 to 2002, Damijan et al. (2010) test for 16
# **Figure 7. Change in world prices (%) relative to the baseline in 2020**

<!-- Start of picture text --> Other services<br>Transport services<br>Construction<br>Gas distribution<br>Electricity<br>Other manufacturing<br>Chemicals<br>Refined oil<br>Diesel<br>Gasoline<br>Other mining<br>Natural gas<br>Crude oil<br>Coal<br>Forestry<br>Processed food<br>Biodiesel<br>Ethanol<br>Livestock<br>Oilseeds<br>Other cereal grains<br>Corn<br>Wheat<br>Vegetables, fruit<br>Sugar (cane & beet)<br>Paddy rice<br>‐4.0 ‐2.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0<br>AT ET<br><!-- End of picture text --> <!-- Start of picture text --> 2.0<br>AT<br><!-- End of picture text --> 33