rafmacalaba/datause-encoder-data huggingface dataset:
the positive TFPR effect is mainly explained by sectors like garments and fabricated metals. This contrasts with other sectors such as chemicals, where the estimated effects are negative.

# **7.2 Effects on Capital**

Table 5 reports the results for demand for capital. For the variable of interest, the results are similar to those reported in Table 4. That is, changes in digital-technology adoption, (both email and website), exporting status, and managerial experience have a positive and statistically significant effect on changes in the demand for capital. The largest effect is observed for email adoption (57 percent), followed by exporting (35 percent), and website adoption (17 percent, approximately) (Table 4, column 4). In contrast, changes in TFPR have no statistically significant effect in any specification.

Table 5: Estimates of the Digital-Technology Adoption Effects on Capital

|VariableofInterest|||WB|ES||
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
||Coef.|0.418|||0.349|
|Change in Export Status|St.Dev|(0.115)|||(0.103)|
||T-test|[3.623]|||[3.380]|
|ChiEil|Coef.||0.594||0.566|
|ange n ma<br>Adotion|St.Dev||(0.137)||(0.131)|
|p|T-test||[4.340]||[4.322]|
|ChaneinWebsite|Coef.||0.207||0.171|
|g <br>Adti|St.Dev||(0.065)||(0.062)|
|opon|T-test||[3.199]||[2.736]|
|ChiM’|Coef.|||0.009|0.008|
|ange n anagers<br>i|St.Dev|||(0.004)|(0.003)|
|experence|T-test|||[2.498]|[2.425]|
||Coef.|-0.003|-0.026|0.009|0.074|
|Change in TFPR|St.Dev|(0.076)|(0.075)|(0.076)|(0.05)|
||T-test|[0.045]|[0.346]|[0.120]|[1.488]|
|_R_<sup>2</sup>||0.254|0.266|0.252|0.265|
|_N_|||7,9|26||

Note: Table 5 presents the results from estimating equation 4.8 for the pool sample. For each of the estimated specifications, we use changes in estimated TFPR assuming the same corresponding specification as in Table 3. The estimation controls for sector, country, and time fixed effects. The “exporting status” takes value 1 if the firm sells a product in international markets; “email adoption” takes value 1 if the firm uses email to connect with clients and suppliers; “website adoption” takes value 1 if the firm has a business website; “managerial experience” takes value 1 if the firm has a manager with years of experience above the country-median. “Capital” measures the replacement value of the firm’s assets; “Exporting status” takes value 1 if the firm sells a product in international markets; “Email adoption” takes value 1 if the firm uses email to connect with clients and suppliers; “Website adoption” takes value 1 if the firm has a business website; “Manager’s experience” takes value 1 if the firm has a manager with years of experience above the country-median.

28
differences specification (6) with OLS confirms this: while having a case stuck in pre-trial for over four months is associated with an average 11.5% reduction in monthly firm revenue over the first six months following the start of the pre-trial, completing it in under four months is associated with a significantly smaller penalty (the point estimate of a 4.4% reduction for faster pre-trials is statistically insignificant; p-value of the difference _<_ 0.1; col 2, Table 8).

This pattern could, however, be explained by other factors, for example, firms whose disputes with business partners are less serious may experience smaller economic losses and the court may at the same time be able to resolve these disputes more speedily. We thus turn to our 2SLS specification. Estimating the first stage (7) reproduces the results presented in Section 5, as starting pre-trial after the decree is applied substantially increased the probability of completing it within the legislated four-month deadline (col 5, Table 8).<sup>28</sup> Despite this relatively precise first stage, estimating the reduced form impact of entering after the decree application yields a small, imprecise coefficient (col 4, Table 8). While it follows that the 2SLS estimation of the impact of finishing pre-trial in under four months produces imprecise results, we note that the point estimates are very close to the OLS coefficient (col 2, Table 8), suggesting modest bias in the OLS estimate.

We also estimate an ANCOVA specification, adapting (6) and (7) controlling for log monthly firm revenues in all six months before the beginning of the pre-trial. This specification does not allow us to estimate the impact of starting pre-trial, but instead allows us to estimate only the interaction with Fast pre-trial. We find our results are robust to this specification (Table A9). Moreover, the first stage provides some reassurance to the interpretation of our findings, as log monthly firm revenue in the six months preceding entry into court do not explain having a faster pre-trial (col 4, Table A9).

> 28The point estimate appears smaller than in Section 5; this is not due to selective attrition, as we show in Section 6.3, but to specification changes imposed by having different variables across event studies.

26
- 16 -

# 4. Examoles of Residence-Country<sup>Taxation</sup>

Table 1 shows the ways dividends<sup>and branch Rrofits are taxed. As</sup> is evident, each country employs a different<sup>method, depending on the types</sup> of income, degrees of ownership and<sup>organizational forms, among others. It</sup> is therefore not possible to associate<sup>any one country with any particular</sup> rule or principle. The diversity increases<sup>as more categories of income are</sup> considered (see below).

TABLE 1 Residence Countrv Taxation<sup>of Dividends and</sup> Profits Arising from Thailand

|Source Rule|Residence<br>|Rule<br>|
|---|---|---|
|(Exemption)|Credit with<br>Deferral|Credit with<br>Accural|
|Hong Kong|Japan (S)<br>Germany (IT, PI)<br>Netherlands (IT, PI)<br>|Japan (B)<br>Germany (B)<br>|
|France (ABI)|United Kingdom (S)<br>|United Kingdom (B)|
|Germany (ABI)|France (IT, PI)<br>||
|Netherlands (ABI)|United States (S)|United States (B)<br>|
|Switzerland|Taiwan (S)<br>Singapore (S)|Taiwan (B)<br>Singapore (B)|

Sources: Price Waterhouse<sup>(1986) and Individual Tax Treaties.</sup>

|Notes:|ABI-|Active Business Income|
|---|---|---|
||B-|Branch Profits|
||CFA-|Controlled Foreign Affiliates|
||IT-|International Transport|
||PI-|Passive Income|
||TP -|Trpaty Partner|
||NTP-|Non-Treaty Partner|
||S-|Subsidiary|

In Table 2, the tax rules governing<sup>interest income in various</sup> countries are shown. Although most<sup>countries apply the residence rule with</sup> foreign tax credit, there is considerable<sup>variation arising from the</sup> definition of tax base and the<sup>limit for tax relief. Other forms of</sup>
# **_Table 17: Balance across screening participants (female)_**

||**To**|**tal**|**Co**|**ntrol**||**Movie**|||**MFB**|||**Movie + M**|**FB**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|**N**|**Mean**|**N**|**Mean**|**N**|**Mean**|**P-value**|**N**|**Mean**|**P-value**|**N**|**Mean**|**P-value**|
|Personal characteristics||||||||||||||
|Age of respondent|894|38.73|231|39.11|240|39.10|0.892|201|38.11|0.313|214|38.44|0.466|
|Married|908|0.89|233|0.89|245|0.89|0.887|205|0.89|0.887|217|0.89|0.961|
|Widowed|908|0.03|233|0.03|245|0.05|0.200|205|0.01|0.282|217|0.02|0.641|
|Single|908|0.08|233|0.08|245|0.06|0.410|205|0.09|0.680|217|0.09|0.830|
|Muslim|909|0.34|234|0.34|245|0.37|0.438|205|0.31|0.572|217|0.33|0.871|
|Christian|909|0.66|234|0.66|245|0.63|0.438|205|0.68|0.648|217|0.66|0.953|
|Can speak English|904|0.69|232|0.69|243|0.65|0.416|205|0.66|0.496|216|0.75|0.178|
|Igbo|909|0.17|234|0.15|245|0.16|0.826|205|0.21|0.128|217|0.16|0.844|
|Yoruba|909|0.78|234|0.81|245|0.80|0.696|205|0.74|**0.096***|217|0.78|0.539|
|Other ethnicitiy|909|0.05|234|0.04|245|0.04|0.704|205|0.05|0.597|217|0.06|0.403|
|_Education_||||||||||||||
|No completed school education|909|0.07|234|0.06|245|0.09|0.275|205|0.05|0.645|217|0.06|0.995|
|Primary school education|909|0.22|234|0.25|245|0.24|0.920|205|0.21|0.473|217|0.19|0.192|
|High school diploma|909|0.47|234|0.45|245|0.45|0.954|205|0.51|0.252|217|0.48|0.606|
|Diploma|909|0.12|234|0.13|245|0.10|0.398|205|0.13|0.966|217|0.12|0.775|
|Graduate school|909|0.11|234|0.11|245|0.10|0.785|205|0.09|0.623|217|0.14|0.318|
|_Household characteristics_||||||||||||||
|Household (HH) size|904|4.57|233|4.63|244|4.70|0.822|203|4.38|0.088|216|4.47|0.299|
|Number of children below 12 in HH|893|1.32|230|1.39|244|1.35|0.622|200|1.28|0.282|211|1.22|0.122|
|Number of dependents in HH|896|2.28|229|2.30|243|2.31|0.969|201|2.23|0.761|215|2.29|0.996|
|<br>Number of dependents outside the HH|851|1.36|222|1.15|225|1.32|0.264|194|1.66|**0.010****|202|1.40|0.151|
|_Business characteristics_||||||||||||||
|Months in operation|885|96.58|228|94.36|238|100.43|0.444|197|97.27|0.758|215|94.48|0.973|
|Has a savings account|908|0.55|234|0.53|245|0.58|0.215|205|0.52|0.939|217|0.58|0.262|
|Keeps written financial records|902|0.38|233|0.37|244|0.36|0.854|203|0.38|0.746|215|0.42|0.257|
|Operating inside main market|908|0.32|234|0.30|245|0.33|0.354|205|0.31|0.694|217|0.33|0.416|
|Number of employees|908|1.31|234|1.35|245|1.27|0.545|205|1.28|0.704|216|1.37|0.892|
|Business experience inyears|905|10.85|233|10.87|242|11.40|0.616|205|9.98|0.258|217|10.94|0.952|
|*** p<0.01, ** p<0.05, * p<0.1||||||||||||||

41
transport (Figure 1A). The range of estimates is based on several key factors: the ambitiousness of the goals, the type and cost of the technologies used to achieve the goals, and assumptions about population growth and urbanization. Of these, the report finds that ambitiousness of the goals (in terms of access and quality) and spending efficiency are the most important determinants of costs.<sup>6</sup> The costing analytics used in _Beyond the Gap_ go well beyond those in straightforward accounting exercises in other studies; they also explicitly model policy choices. Rozenberg and Fay (2019) use 14 unique models—a mix of costing models and partial or general equilibrium models—to arrive at the range of cost estimates. In this aspect alone, the report goes beyond the benchmarking and accounting exercises underlying many previous SDG costing studies. Rozenberg and Fay (2019) also explicitly model the outcomes of dozens of policy choices. Ultimately, the report identifies a mix of policies that would allow low- and middle-income countries to achieve key targets identified by the SDGs (universal access to water, sanitation, and electricity; improved food security; better flood protection; and eventual full decarbonization) and restrict spending to 4.5 percent of low- and middle-income countries’ GDP per year.

**Figure 1. Spending needs**

# **A. Spending needs in low- and middle income B. Spending needs, by region countries**

<!-- Start of picture text --> Percent of GDP, 2015-30<br>10 Electricity<br>Transport<br>8 Water and sanitation<br>Flood protection<br>6 Irrigation<br>4<br>2<br>0<br>Preferred scenario Maximum spending<br>scenario<br><!-- End of picture text --> <!-- Start of picture text --> Percent of regional GDP, 2015-30<br>10<br>Capital Maintenance<br>8<br>6<br>4<br>2<br>0<br>SSA SAR MENA EAP LAC<br><!-- End of picture text --> Source: Rozenberg and Fay (2019), World Bank.

A. Bars show average annual aggregate spending needs during 2015-30. “Preferred scenario” is constructed assuming ambitious goals and high spending efficiency, and “maximum spending scenario” assuming ambitious goals and low spending efficiency. Country sample includes low- and middle-income countries.

B. Bars show average annual spending needs during 2015-30. Estimates are generated using policy assumptions that cap investment needs at 4.5 percent of LMICs’ GDP per year (i.e., the “preferred scenario” in panel A). SSA=SubSaharan Africa, SAR=South Asia, MENA=Middle East and North Africa, EAP=East Asia and Pacific, LAC=Latin America and the Caribbean.

> 6 For example, one of the targets under SDG7 is to “ensure universal access to affordable, reliable and modern energy services by 2030.” This description leaves open to interpretation the _ambitiousness_ of energy consumption. For SubSaharan Africa, the cost of providing basic electrification—enough to power a few light bulbs and charge a mobile phone, for example—is estimated to be about 0.9 percent of GDP per year, on average, over 2015-30. The cost of providing electrification sufficient to power lighting plus household appliances is estimated to be 1.1-1.2 percent of GDP.

6
We also posit a significant role for the competitive environment. In markets with limited competition, we would expect profit-maximizing firms to offer more limited services at higher prices. The spatial reflection of this limitation, relative to more competitive environments, would be fewer cell phone towers, concentrated in areas with larger populations and lower installation and maintenance costs.

# **4.2  Data**

To test the model, we use the best available spatially-disaggregated data sets from various public sources, including the International Centre for Tropical Agriculture (CIAT), the LandScan, the Center for International Earth Science Information Network (CIESIN), the Digital Chart of the World (DCW) and the World Bank. The data on cell phone tower locations have been provided by the GSM Association in GIS polygon files for the period 1999: Q1 – 2006: Q3. We overlay the data on a rectangular grid of evenly-space points, with a separation of 2.5 arc-minutes covering Sub-Saharan Africa. This generates 1,138,835 points, which we plot in Figure 1. Table 3 summarizes our data sources; Table 4 provides basic descriptive statistics for model variables, which exhibit great variation across grid squares: Elevations vary from sea level to 5,800 meters; slope steepness from 1.2 to 99.0; distance to a main road from 0 to 1,160 km; travel hours to a major city from 0 to 102; and proximate population from 0 to 578,000. Our policy variable is an index of the competitive environment in 2004, provided by the World Bank’s Country Policy and Institutional Assessment (CPIA) database. Globally, the CPIA competition index varies from 1 to 6. Among our sample countries in Sub-Saharan Africa, it varies from 2.0 to 4.5, with a mean value of 3.2.

Table 5 provides a quarterly time series of percentage land area covered by GSM cell towers. Overall, GSM service area coverage expanded from 2.7% in the first quarter of 1999 to 16.7% in 7
# **Table 2: Example Terms of Rainfall Insurance Contracts**

This table presents data on the terms of 2004 rainfall insurance contracts linked to castor in Mahbubnagar. 1<sup>st</sup> trigger level refers to the level of rainfall above which the phase payout is zero. Payout per mm deficient rain lists the amount paid for each mm below the 1<sup>st</sup> trigger level, until the 2<sup>nd</sup> trigger is reached. Below the 2<sup>nd</sup> trigger level, the policy pays the maximum lump sum payout listed.

||Premium<br>per acre||1<sup>st</sup><br>trigger<br>level|Payout<br>per mm<br>deficient<br>rain|2<sup>nd</sup><br>trigger<br>level|Maximu<br>m lump<br>sum<br>payout|Actual<br>rain|Actual<br>payout<br>s per<br>acre|
|---|---|---|---|---|---|---|---|---|
|Mandal|Rs|Phase|mm.|Rs|mm.|Rs|mm.|Rs|
|||_1_|60|10|25|1500|94.2|-|
|Atmakur|250|_2_|100|15|5|2000|90.0|150|
|||_3_|75|15|30|2500|184.0|-|
|||_1_|60|10|20|1500|31.0|290|
|Mahbubnagar|150|_2_|100|15|50|2000|96.0|60|
|||_3_|75|15|50|2500|171.0|-|
|||_1_|60|10|20|1500|12.0|1500|
|Narayanpet|200|_2_|100|15|40|2000|84.0|240|
|||_3_|75|15|50|2500|177.0|-|

Note: Phase 1: June 10 - July 14, phase 2: July 15 - August 28, phase 3: August 29 - October 12.

# 3.3 _BASIX Distribution and Marketing_

BASIX has extensive local distribution networks, since it also provides microfinance loans to households in villages where the insurance product is marketed. Moreover, since defaults on microcredit loans in rural areas tend to be associated with deficient rainfall, BASIX has clear incentives to market rainfall insurance, in particular to their own clients.

The insurance product was piloted in 2003 in two villages in Mahaboobnagar, and expanded to 43 pilot villages in Mahaboobnagar and Ananthapur in 2004. BASIX used four criteria to determine whether a village was suitable for insurance marketing in 2004: (i) the presence of exisiting BASIX customers to ensure some degree of trust in the institution; (ii) 200-300 of acres of groundnut and/or castor crops; (iii) a reasonable number of small and medium sized farms with 2- 10 acres of land; and (iv) a village location within 20km of the nearest rainfall reference station, to 8
26 2/ SOURCE: Official announcement October 3, 1984 and Budget statement June 1986, Government of Tanzania. Official prices announced on October 3, 1984 were unchanged as of April 1986 for gasol'ne, kerosene and diesel. Hence, it is assumed that no significant retail price changes occurred between October 1984 and April 1986. 1985 prices converted to $US at $17 TSH1 = $1 (Oct. '85 Avg); July 1986 prices at 42 Tsh = $1. Prices quoted for diesel are for "diesel gas oil" in Tanzania. 1985 LPG and heavy fuel oil prices are wholesale.

JQ/ SOURCE: 1985 prices from Swaziland Energ Assessment, Yellow Cover, World Bank. July 1986 prices from personal communicatin with USAID regional economist for Southern Atrica. Prices quoted for diesel are for "bus rate" diesel fuel as almost all diesel pumps in the country are set at teh bus rate. Industrial diesel 1985 price is for "mid-duty" diesel fuel. November 1985 prices converted to $US at 2.7E = $1; July 1986 prices at 2.2 E = $1.

11/ SOURCE: September 1985 prices from Latin America and the Caribbean, Countr-y Programs Department, World Bank. August 1986 prices form Shell OIl, Haiti (new prices effective May 1986). Diesel prices are for "gasoil" in Haiti. All prices converted at G5 = $1.

i2/ SOURCE: August 1986 prices have not changed (in nominal colones/liter) since 1983, as per SDE Costa Rica. August 1986 prices converted to $US at 56.3 CRC = $1; November 1985 prices at CRC 52.8 = $1.

13 SOURCE: Central Bank of Brazil, July 1986 Bulletin. Prices in Cr$/liter for gasoline and diesel oil, and Cr$/kg for fuel oil, assumed to be residual with density of .94 kg/1. October 1985 prices converted to $US at 8.56 Cr$ = $1; May 1986 at 13.84 Cr$ = $1. A price freeze on petroleum products has been in effect since early 1986, but a new 28% tax has been imposed on gasoline on 25 July 1986. This tax is not reflected in the table.

14/ SOURCE: All prices from de Lucia and Associates, converted to $US at 2B$ = $1 for each period.

_15/_ SOURCE: July 1985 local prices from Table 2.2, Ecuador Energy Assessment, December 1985, World Bank. 22 July 1986 local orices quoted in Office memorandum of 31 July 1986 of EGYD2, World Bank. Local prices converted to $US at official rates for July 1985: 67.175 SUCRES = $1; and July 1986 = 109 SUCRES = $1 (before devaluation).
level GCR port efficiency scores, indexed from one to seven (7 is the highest score).<sup>39</sup> To illustrate how their analysis may be applied to predict trade flows, the authors developed a country-specific maritime cost index (driven to a large degree by WEF GCR port efficiency) and used it in the standard bilateral gravity model of trade. They concluded that increasing maritime transport costs from the 25th to the 75th percentile would decrease bilateral trade by 22 percent.

# **4.4.3 Transport Costs as Measures of Port Efficiency**

Blonigen and Wilson (2008) expanded upon Clark et al. (2004) by estimating port-specific efficiency rates. Whereas Clark et al. (2004) utilizes _country_ level measures of port efficiency, which are based on survey results that are unsuitable for longitudinal analysis, Blonigen and Wilson (2008) used timevarying port fixed effects to capture an _individual_ port’s contribution to maritime shipping costs. Using US import data sourced from the National Data Center of the Army Corps of Engineers, they produced estimates for the top fifty (50) American ports and top one hundred (100) foreign ports (by import value from the US perspective). Foreign port efficiency measurements are next aggregated into a country-level estimate and then are used in a standard gravity trade model. Without controlling for country-level fixed effects, the elasticity of bilateral trade with respect to port efficiency is 1.27. Since port efficiency estimates are time-varying, country fixed effects can be included. Thus, controlling for unobserved country-specific confounds reduces the bilateral trade port efficiency elasticity to 0.32. Based on these, moving a country from the 25th to 75th percentile of port efficiency increases bilateral trade by a more modest 5 percent.

Feenstra and Ma (2014) used this approach to measure trade facilitation. They used port infrastructure to analyze the extensive margin—or product variety—on both import and export trade. A 10 percent increase in port efficiency increase export variety by 1.5-3.4 percent but only increases the intensive margin by 0.2-1 percent; this result is broadly consistent with a literature, best exemplified by Bergin and Lin (2008), which documents how and why intensive and extensive margins respond 39The scores included a country’s overall level/quality of infrastructure, cargo handling restrictions, mandatory port services, and severity of the organized crime. These four factors can explain more than 77 percent of WEF GCR variation.

39
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