rafmacalaba/datause-encoder-data huggingface dataset:
# Table 1. Instrumenting Coronavirus Measures

## **Restrictions on Personal Liberties**

||Stay at Home<br>(lockdowns)|Cancelling Public<br>Events|Ban on Private<br>Gatherings|Restrictions on<br>Movements|
|---|---|---|---|---|
|Daily cases|0.137***|0.364***|0.129***|0.160***|
|(thousands)<br>|(0.0187)|(0.0528)|(0.0139)|(0.0197)|
|Daily deaths|0.00304***|-0.000911|0.00247***|-0.000172|
||(0.0003)|(0.0005)|(0.0003)|(0.0002)|
|Days elapsed since|0.0166***|0.0146***|0.0136***|0.0201***|
|outbreak|(0.0010)|(0.0011)|(0.0009)|(0.0009)|
|Days elapsed since<br>|-0.0000313***<br>|-0.0000513***<br>|-0.0000155***<br>|-0.0000495***<br>|
|outbreak squared|(0.00000352)|(0.00000397)|(0.00000344)|(0.00000330)|
|Health Capacity|-0.627***|-2.552***|-5.037**|-3.440***|
|Index<br>|(0.144)|(0.194)|(0.143)|(0.152)|
|Daily cases/doctors|0.0441***|0.618***|-0.129***|0.0143|
||(0.0216)|(0.0690)|(0.0109)|(0.0172)|
|Daily cases/nurses|0.0155|0.173|-0.0462**|0.116***|
||(0.0217)|(0.158)|(0.0187)|(0.0217)|
|Temperature (min)|-0.0607***|-0.0665***|-0.0510***|-0.0599***|
||(0.0022)|(0.00286)|(0.0022)|(0.0021)|
|Democracy Index|-0.227***|-0.214***|0.108***|0.00477|
||(0.008)|(0.0132)|(0.0086)|(0.0073)|
|Demonstration|0.0661***|0.0774***|0.0621***|0.0579***|
|Effect|(0.0012)|(0.00127)|(0.0011)|(0.0009)|
|Constant|-2.710***<br>|-1.323***<br>|-3.186***<br>|-2.792***<br>|
||(0.113)|(0.0866)|(0.0878)|(0.0821)|
|_Observations_|_31,065_|_31,065_|_31,065_|_31,065_|
|_Number of_|_158_|_158_|_158_|_158_|
|_Countries_|||||
|_Proportion of total_<br>_variance due to_<br>_panel-level_<br>_variance_|_0.570_|_0.588_|_0.610_|_0.554_|
|_Pseudo R_<sup>_2_</sup>|_0.652_|_0.875_|_0.561_|_0.604_|

Note: Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01 13
**Figure 7: Capital Flows to India follow the same broad trend as flows to emerging markets starting in the early 2000s**

<!-- Start of picture text --> Total Capital Account<br>120000 4.5<br>Net Capital Inflows to Emerging Markets (% of GDP) 4<br>100000<br>3.5<br>80000 3<br>2.5<br>60000<br>2<br>40000 1.5<br>1<br>20000<br>0.5<br>0 0<br>Figure 8: Portfolio, FDI and Other inflows all accelerated during the “Surge”,<br>Portfolio and Other Flows have been rather volatile since 2009<br>50,000<br>Phase 3: Portfolio flows, and<br>other flows were<br>40,000 more volatile during<br>the sudden Stops<br>Phase 2: Rapid increase in FDI<br>30,000 as well as Portfolio and other inflows<br>20,000<br>Phase I:Steady, but modest increase<br>in FDI, portfolio and other flows<br>10,000<br>0<br>‐10,000<br>‐20,000<br>FDI portfolio Other Flows<br>Mln US $ % of GDP<br>1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14<br>mln, US $<br>1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14<br><!-- End of picture text --> Note: Other flows include NRI deposits, ECB and short term credit; portfolio flows include equity and debt flows. Capital flows refer to net capital flows. Source is RBI’s Handbook of Statistics on the Indian Economy 12
16 Toble 3. Nacroeconomic and Distributional Indicators in the Two Archetypes

|Simulation||AFm||AF||kI||LAm|
|---|---|---|---|---|---|---|---|---|
|Period|2|7|2|7|2|7|2|7|
|GDP|103.0|120.0|103.0|117.0|104.0|129.0|100.0|107.0|
|Export valueb|21.6|23.6|23.8|<br>28.9|17.1|<br>21.5|19.7|21.2|
|Investment/GDPe|21.5|20.5|18.9|<br>15.5|<br>21.4|23.6|13.8|<br>14.8|
|Inflation<br>rate (CPI)|16.8|16.9|20.7|18.1|35.4|30.0|100.3|<br>30.0|
|Devaluation<br>rate|15.0|15.0|27.0|17.6|30.0|30,2|108.0|<br>31.4|
|Interest<br>rate|20.1|21.2|20.3|23.4|35.9|34.1|49.0|42.1|
|Overall<br>deficit/GDP|4.0|6.2|5.2|9.0|6.2|6.7|8.2|17.4|
|Primary<br>deficit/GDPd|3.1|4.6|3.2|5.2|3.9|3.0|4.4|5.8|
|Debt/exportse|113.0|239.0|103.0|<br>189.0|<br>124.0|224.0|107.0|<br>289.0|
|Debt service/exports|4.5|9.3|8.3|15.1|11.3|21.0|17.2|46.2|
|Domestic debt/GDP|-|-|-|-|3.4|4.0|5.8|7.0|
|Domestic<br>Debt service/<br>goverrunent revenue|-|-|-||7.1|9.0|4.7|18.0|
|Householdforeign<br>assets/<br>exports|-|-|||34.4|_42.0|48.2|57.2|
|Theil index'|66.4|58.6|61.5|50.8|37.1|35.0|33.4|43.2|
|Poverty<br>gap<sup>9</sup>|2.68|<br>2.71|<br>2.77|<br>2.71|<br>0.84|<br>OA.3|<br>0.91|<br>1.49|
|Poverty<br>Rate|30.4|31.1|29.9|29.5|11.8|10.5|12.1|19.5|

Notes: AFB: Africa no-shock; AFS: Africa, shock; LAB: Latin America, no shock; LAS: Latin America, shock

- a. Period I u 100.

- b. In foreign currency units.

- c. In real terms.

- d. Does not include interest payments on the public debt.

- e. Public and private external debt.

- f. A decrease in the value of the index indicate less income inequality.

- g. The poverty gap indicates the percentage of income that must be redistributed to bring alt those below the poverty line to the poverty line.
# **4.  Using Gravity Estimates to Compare Domestic Trade Cost Indicators**

This section provides an illustrative assessment of the relative importance of trade costs using gravity model estimates. The gravity model predicts that the volume of trade between two countries is proportional to their income and inversely related to the distance between them. In addition to these core variables, gravity equations can contain other variables influencing trade, including institutional characteristics or trade policy variables. Estimates from gravity models have been used in a wide variety of applications due to the ease with which one can infer the impact of a change in an explanatory variable on trade flows. Indeed, much of the research reviewed in the previous section centers on exploring the impact of trade costs in a gravity context. One difficulty in the research on Africa in regard to trade costs is the limited data available for the region.

**Table 3 Trade-Cost Indicators**

|**Indicators**|**Units**|**Source**|**Coverage**|
|---|---|---|---|
|OTRI and TTRI|Ad-valorem<br>equivalent|Kee et al. (2008)<br>and WB Global<br>Monitoring<br>Report2008|104 countries,<br>including 22 SSA<br>countries|
|Number of days to<br>export/import|Days|Doing Business<br>(2008)|188 countries,<br>including 47 SSA<br>countries|
|Costs associated with<br>export/import<br>procedures|US$ per 20-foot<br>standard container|Doing Business<br>(2008)|Idem|
|Documents necessary<br>to export/import|Number of<br>documents|Doing Business<br>(2008)|Idem|
|Logistic Performance<br>Index|Aggregate index<br>(range: 5-1)|World Bank<br>(2007), LPI|150 countries,<br>including 39 SSA<br>countries|

Among data surveyed in the previous section, however, three sets of indicators have reasonably good coverage in Africa. These include the trade restrictiveness indices (TRI) estimated by Kee et al. (2008), the trading-across-the-border indicators reported by Doing Business, and the Logistics Performance Index (LPI). Table 3 describes their main attributes.

In order to make the Doing Business trading-across-the-border indicators (time, number of documents, and costs of import and export procedures) comparable across countries, several assumptions are made about the shipped products and the container that contains them. Indeed, products considered in the data are not hazardous, do not require refrigeration, and do not require any special phytosanitary or environmental safety certification. In addition, the products are shipped by ocean in a dry cargo, full container load of 20 feet. The costs do not include unofficial payments such as bribes that may be involved with trading goods. Although shipping some products involves conditions that 24
declining trend, in particular among the EU candidate and EU neighborhood countries, and in both of these groups a decline in national savings ensued.

The case of emerging Europe also appears to be consistent with what economic theory would predict: capital flows to high-growth countries (Figure 3; left chart). Hence, the allocation puzzle—that, paradoxically, capital flows to low-growth countries instead of high-growth countries—does not appear true in Europe either. These figures also suggest that the underlying strength of the link between foreign savings and growth might vary across groups. Still, an increase in current account deficits (i.e., capital inflows that happen to be absorbed) and an increase in growth are common among two of these country groups in emerging Europe (Figure 3; right chart). The opposite is the case, however, among emerging markets outside Europe—that is, high growth in incomes occurs jointly with an increase in current account surpluses (i.e., the allocation puzzle holds).

**Figure 3. Current Account Balances (% GDP) and Growth in Emerging Economies**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>-5<br>-40 -20 0 20 40 -40 -20 0 20 40<br>Current Account Balance, % of GDP<br>Non-European EME Emerging Europe EU10 EU Candidates EU Neighborhood<br><!-- End of picture text --> Source: Authors’ calculations based on World Bank and IMF data (2011). Note: Period 1997-2008.

In terms of the composition, capital flows take the form of FDI, portfolio, and banking (or other) flows. Clearly, FDI in emerging Europe played a key role; it is particularly large across all three groups of emerging European countries and is positively correlated with growth (Figure 4). This is even more evident in the period immediately preceding the crisis. But the most distinctive feature is the large role played by banking flows, which comprise both wholesale funding and financial FDI. The latter relates to the relationship between parent banks and their subsidiaries. Indeed, no other region appears to experience such a degree of ―ownership-driven‖ capital integration. Financial FDI may well combine the best aspects of alternative financing sources; namely, the risk-sharing features of FDI proper and the lower costs (relative to FDI proper) of debt-financing. But here too there are differences within emerging Europe. For example, Estonia relied almost fully on funding from Nordic banks, but in Latvia this was the case only among the subsidiaries of these banks. In turn, however, these banks account for a much smaller share of the assets of the banking system in the period immediately preceding Latvia’s crisis; 60 percent in Latvia compared to 90 percent in Estonia. It is worth noting that Nordic banks have maintained their exposures in the region, but those domestic banks that relied on wholesale funding sources and non-resident deposits were particularly vulnerable. Parex Banka suffices as an example for those familiar with Latvia’s plight in late 2008.

6
30 In 1981 and 1982, the net resource transfer to the fifteen Baker countries amounted to an annual average inflow of $8.9 billion."6 In the first three years after the debt crisis, the resource transfer averaged an annual outflow of $38.4 billion. During the three years of the Baker Plan (1986-88), the outward transfer of resources from these countries averaged $28.6 billion annually. Despite the evidence that domestic destabilization rather than outward transfer of resources has been the primary cause of recent stagnant growth in some major debtor countries, it is time for a more determined international effort to increase net capital flows to the debtor countries.

With domestic stabilization and forward growth, excess capacity would eventually be exhausted, and higher resource inflows can contribute to future growth even though stagnation in Latin America in 1988 was not primarily attributable to external debt or outward resource transfers.

Complete elimination of the outward transfer would be too ambitious over the next three to five years, as it would mean a sufficiently more rapid buildup in debt that progress in debt indicators could be halted prematurely. However, a reasonable goal would be to cut the outward transfer of resources in half, to some $15 billion annually over the next three years. This objective 16 Estimated as the differenca between the current account deficit (assumed equal to capital inflow, abstracting from reserves change) and net payments of interest and profits. Calculated from International Monetary Fund, World Economic Outlook. October 1988 (Washington: IMF, 1988), p. 104.
excluded regions makes losses more likely (due to greater loss of varieties from excluded regions).

# **IV. Overview of the Tanzanian Service Sectors**

In this section, we summarize the key institutional and policy issues in telecommunications, banking, insurance and transportation. This discussion builds on and updates several papers written in 2008 on the Tanzanian business services sectors, namely Telecommunications Management Group (2007), Carruthers (2008), Thorburn (2008), Costain (2008) and Jensen and Tarr (2008). Details on the regulatory environment in these sectors are available in Mircheva (2008). A listing of the barriers in engineering services is provided in Appendix D.

# **Telecommunications**

The government has implemented significant reforms in the sector since the reform program began in 1993. But even as late as the year 2000, there were only 284,000 telephone subscribers in the country, of which 174,000 were fixed line subscribers. . By 2009, fixed line subscription had not changed significantly as there were only 157,000 fixed line subscribers in Tanzania for a population of almost 38 million. But, mobile telephony has dramatically changed things--there were about 17.5 million telephone subscribers in Tanzania who were receiving service at very competitive prices from seven mobile telephone service providers. 10

> 10 For the data see the website of the Tanzania Communications Regulatory Authority at <u>http://www.tcra.go.tz/publications/telecom.htm,</u>

17
- Self-employment: refer to primary job in past three months (from question 2122 of chapter 2, Job Characteristics). Includes self-employed not employing others and employers. Exclude agriculture, forestry and fishing using the activity category from question 2107 (“What is the economic activity of your main job during the last three months?”).

- Wage worker: as before, but including respondents classified as waged, monetary or in-kind workers in question 2122.

- Agricultural worker: Individuals working in agriculture, forestry or fishing according to question 2107.

- Income: sum of basic wage, supplementary payment, bonus, incentives, overtime, profits, and other earnings as detailed in question 4104 of chapter 4 (Earnings). Add average daily wage (x 20) for irregular workers (question 4109).

## First follow-up <u>(2006) and second follow-up (2012):</u>

Both follow-up surveys have similar structure. In what follows we refer to questions in the second follow-up questionnaire.

- Self-employment: refer to primary job in past three months (from question 5134 of chapter 5). Includes self-employed not employing others and employers. Exclude agriculture, forestry and fishing using the activity category from question 5103 (“What is the economic activity of the enterprise you work in or the job you do if not in an enterprise?”).

- Migration: the survey records each respondent’s governorate (variable gov ~~Y~~ Y), qism (constructed as gov ~~Y~~ Y + qism ~~Y~~ Y), and sheyakha (gov ~~Y~~ Y + qism ~~Y~~ Y + shyakha ~~Y~~ Y). We code governorate migration, qism migration, and sheyakha migration if a respondent experiences a move across relevant geographic units between two survey waves.

Notes: ELMPS tracks international migrants but it is not possible to directly link them to the baseline survey. Consequently, we decided against including international migrants in the analysis as we could not measure their occupation type at baseline.

# **B.3 India Human Development Survey (IHDS)**

Baseline (2005): 49
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Overhead costs and net interest margins – two negative indicators of banking system efficiency - are at comparable levels as in other South Asian and East Asian countries and significantly lower than the average for low-income countries. Overhead costs are banks’ total operating costs relative to total assets while net interest margins are net interest revenue (i.e. interest revenue from loans minus interest costs from deposits) relative to total earning assets.

Comparing access to and use of banking services in Bangladesh with other countries in the region and an average for low-income countries suggests that banking penetration is adequate for the level of financial and economic development. Here, we use a new database on access to and use of banking services, compiled from regulatory entities and public information for the years 2003/2004 and discussed in Beck, Demirguc-Kunt and Martinez Peria (2005). Bangladesh has 47 bank branches per 1,000 square kilometers and four branches per 100,000 people. Figure 4 shows that geographic branch penetration is significantly higher than in comparator countries - mostly due to the high population density – while demographic branch penetration is at similar or higher levels than in Pakistan, Nepal and the average low-income country, but significantly below the level observed for India, Indonesia and Sri Lanka. ATM penetration is significantly lower, with less than one ATM per 1,000 square kilometers and one million people. Figure 5 shows that ATM penetration – both in geographic and in demographic terms – is significantly lower than in any comparator country with the exception of Nepal, which suggests limited use of technology in banking sector outreach. Figure 6 shows that the number of loan and deposit accounts per capita – a positive indicator of the use of banking services – is above the level in Pakistan and the average low-income country – while Figure 7 shows that the loan-income and deposit-income ratios – the average loan or deposit account size relative to GDP per capita and thus negative indicators of outreach – are below the levels in Pakistan and the average low8
# **1 Introduction**

Firms in developing nations face extreme weather including natural disasters, extreme heat, and drought. If climate change increases the likelihood and severity of these events, then firms have an increasing incentive to invest in various adaptation strategies. These strategies include – where a firm locates, how it configures its supply chains, and how much it invests in the physical workplace to harden it to withstand anticipated shocks. Each of these strategies feature costs that the firm or the nation will bear.

We survey the emerging microeconomics literature focused on firm adaptation to climate change. We explore the role of firm heterogeneity, weather dynamics and expectations about these dynamics, and government policy that together determine how different firms are affected by weather shocks. Climate change is costly for firms if it lowers their profits. Firms will anticipate this ”treatment effect” caused by climate change and will take proactive steps to offset this damage if the costs of adapting are lower than the expected present discounted value of benefits of making these offsetting investments. If firms can successfully adapt to rising weather risks, then this will raise overall productivity and help developing countries lower their poverty rates despite the harsher conditions these nations face going forward (Hallegatte, 2016; Hallegatte and Rozenberg, 2017).

Our survey melds several strands of literature. For example, the economic geography literature has emphasized the costs of “bad geography” for productivity and economic development Gallup et al. (1999). The weather risks that a firm faces are location specific. Many developing nations are located in regions that face greater _natural disaster_ risks such as typhoons and are exposed to _extreme_ heat and precipitation (Bakkensen and Barrage, 2018; Hsiang and Jina, 2014). The _risks and uncertainty_ posed by extreme weather to firms in the developing world are amplified by _weak governance_ capacity (Acemoglu and Robinson, 2013; Porta et al., 2008).

One important implication of weak governance is unreliable local infrastructure. If roads flood and if the electricity grid breaks down, then these firms will be even less productive as supply chains are immediately disrupted by shocks (Allcott et al., 2016; Chong et al., 2014). Developing countries have limited resources - both human and financial - to undertake investments in fixing such disruptions. We explore cases where private and public resilience _policies and investments_ are substitutes and cases where they are complements.

Figure 1 illustrates the key themes explored in our survey on firms in the context of climate change adaptation.

1
# **Table 5 Export Growth Decomposition: Brazil (first quarter 2009 to third quarter 2011)**

||**Export**<br>**growth**<br>**(%)**|**Export**<br>**market**<br>**share**<br>|**Performance**<br>**(export growth**<br>**without**<br>|**Pull factors (sp**<br>**composition**<br>**whic**|**ecialization,**<br>**effects), of**<br>**h:**|**Push fact**<br>**growth with**|**ors (export ma**<br>**out compositio**<br>**which:**|**rket share**<br>**n effects), of**|
|---|---|---|---|---|---|---|---|---|
|**G-3**||**change (%)**|**composition**<br>**effects) (%)**|**Geo-graphical**<br>**(%)**|**Sectoral (%)**|**Overall**<br>**(value) (%)**|**Price**<br>**component**<br>**(%)**|**Volume**<br>**component**<br>**(%)**|
|**European**<br>**Union**<br>**(EU27)**|6.9|-2.9|9.0|-1.6|-0.5|-0.6|0.0|-0.6|
|**of which Euro Area**<br>**(EA17)**|5.8|-4.1|7.4|-1.3|-0.3|-2.2|-0.4|-1.7|
|**Japan**|7.4|-2.4|4.5|3.6|-0.6|-5.1|2.4|-7.2|
|**United States**|6.5|-3.3|5.1|1.2|0.2|-4.3|-0.8|-3.7|
|**BRICS**|||||||||
|**Brazil**|13.2|3.4|11.2|0.8|1.2|1.1|0.6|0.4|
|**Russian Federation**|18.4|8.6|16.0|-0.8|3.3|5.2|0.9|4.3|
|**India**|16.3|6.5|13.9|-1.3|3.8|3.3|0.5|2.8|
|**China**|11.8|2.0|15.9|-0.2|-3.9|6.1|-0.7|6.9|
|**South Africa**|14.4|4.6|13.0|2.2|-0.8|2.6|-0.9|3.5|
|**MIST**|||||||||
|**Mexico**|12.3|2.5|15.5|-3.0|-0.2|5.6|-0.3|6.0|
|**Indonesia**|17.1|7.3|14.9|1.0|1.2|4.4|1.8|2.6|
|**Korea, Rep. of**|15.7|5.9|12.8|4.3|-1.4|2.4|0.2|2.2|
|**Turkey**|8.7|-1.1|12.5|-1.4|-2.4|2.8|-1.9|4.8|
|Source:Authors’calculat<br>Note: Figures are in aver|ion.<br>age annual|growth.|||||||

19