rafmacalaba/datause-encoder-data huggingface dataset:
|VARIABLES|(1)<br>Net sep.rate|(2)<br>Net sep.rate|(3)<br>Net sep.rate|
|---|---|---|---|
|Lockdown exposure [0,1]|7.14***<br>(0.52)|5.71***<br>(0.43)|5.60***<br>(0.43)|
|Lockdown exposure X Share differentiated in t0|-2.02***<br>(0.35)|||
|Lockdown exposure X Z(Number of suppliers in t0)||3.13***<br>(0.30)||
|Lockdown exposure X Z(Import volume in t0)|||1.73***<br>(0.63)|
|Number of observations|913,822|913,822|913,822|
|R-squared|0.04|0.04|0.04|
|Firm fixed effects|Yes|Yes|Yes|
|Quarter fixed effects|Yes|Yes|Yes|

Table 1: Resilience of US importers to supply side shocks _Notes_ : Number of suppliers in t0 and import volume in t0 are normalized as Z-scores by subtracting off the sample mean and dividing by the standard deviation. Standard errors in parentheses are clustered at the firm level. *** p<0.01, ** p<0.05, * p<0.1 There is heterogeneity in responses across firms. Perhaps surprisingly, firms whose imports are more differentiated by the measure of Rauch (1999) have fewer net separations as they maintain relationships even in the presence of a supply shock. In contrast, firms with more suppliers, and those with larger overall import volumes experience greater net separations, potentially because they have a greater pool of relationships on which to draw. Khanna, Morales and Pandalai-Nayar (2022) find similar patterns of heterogeneity when studying the response of firms to supply-side shocks generated by lockdowns in Indian districts. Using firm-to-firm transactions data, similar to those in Panjiva but covering also domestic transactions, they exploit the differential exposure of firms in that state to supplier risk during the pandemic. Because the severity of lockdowns differed greatly across Indian states, suppliers located in states with more strict lockdowns experienced a larger disruption. In an event-study, they show that firms with suppliers exposed to more severe lockdowns experienced an increase in net separation rates, a similar result to Column 1 of Table 1. The intensive margin of trade as measured by the value of inputs and firm output also suffered. They also document that these disruptions were less severe 25
**Table A7.2 Twitter Reform Sentiment and Intensity and FDI Inflows into the GCC**

|Dependent Variable:|FDI Inflows (% of GDP)|
|---|---|
|Twitter Reform Intensity (t)|-0.222<br>(0.665)<br>|
|Twitter Reform Sentiment (t)|0.403<sup>**</sup>|
||(0.119)|
|Twitter Reform Intensity X Sentiment (t)|-0.894<br>(0.597)|
|Twitter Reform Intensity (t-1)|-1.479|
||(1.082)|
|Twitter Reform Sentiment (t-1)|-0.478|
||(0.543)|
|Twitter Reform Intensity X Sentiment (t-1)|0.647|
||(0.765)|
|GDP Growth (t)|0.0816|
||(0.137)|
|Inflation Rate (t)|0.437|
||(0.292)|
|Trade Openness (t)|0.0930|
||(0.0685)|
|Institutional Quality (t)|0.628|
||(3.686)|
|Country FE||
|Year FE||
|R2|0.663|
|Mean of Dependent Variable|1.753|
|Number of Countries|6|
|Observations|46|

Note: This table presents the relationship between GCC Twitter reform intensity and reform sentiment and FDI net inflows to the GCC. Coverage includes the six GCC countries between 2011 to 2019. Twitter intensity and sentiment are derived from Arabic tweets, and the measures are normalized to have unit variance. Trade openness is measured as the value of exports plus imports as a percentage of GDP. Institutional quality is the average of six measures from the World Governance Indicators database. Standard errors are clustered at the country level and papered in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 48
- (1) Communities consider that the benefits of risk reduction and climate adaptation will only be reaped in the long-term. Many farmers find it unattractive to invest in the future when HIV/AIDS has such a profound impact in day-to-day life.

- (2) It is increasingly difficult to mobilize communities because too many farmers are either attending to funerals or attending to the sick (if they themselves are not sick). The situation has resulted in organizations competing for fewer able-bodied community members to attend meetings or join community-based projects.

# _6.3.3. Impact on Finances_

The six target institutions reported that over the past decade, HIV and AIDS related attrition has negatively affected the financial capacity of the organizations (and therefore indirectly impacted on services delivery). The impact was mainly through increasing funeral-related direct expenses as well as increasing cost of absenteeism.

None of the organizations store records indicating actual funeral expenses over the last ten years, yet the problem is pervasive. For example, Red Cross reported increasing numbers of funerals within the organization, resulting in overspending on budget lines for funeral costs hence using funding originally allocated for disaster management work. It should be noted that the policies on funerals in Malawi usually state that when an employee dies, the employer shall provide a coffin and a maximum of three vehicles. The actual support from institutions, however, tends to extend to direct dependants of the employee, for cultural reasons.

The 2002 UNDP report calculated the cost of absenteeism on the basis of the estimated 816 months of absenteeism due to HIV/AIDS-related morbidity and mortality in 2000 alone. Based on an average monthly salary of 3,000 Malawi Kwachas (MK), this translated into an estimated opportunity cost of almost MK 2.5 million in 2000 – a substantial amount for Malawian standards. Further, the study estimated the opportunity cost related to absenteeism due to funeral attendance (see Table 7).

27
# **Appendix B:  Components of Aggregate Governance Indicators in 2006**

# **Table B1:  Voice and Accountability**

|**Code**|**Concept Measured**|
|---|---|
|**Repre**|**sentative Sources**|
|**EIU**|Orderly transfers|
||Vested interests|
||Accountability of Public Officials|
||Human Rights|
||Freedom of association|
|**FRH**|_Civil liberties_: Freedom of speech, of assembly and demonstration, of religion, equal opportunity, of excessive governmental intervention|
||_Political Rights_: free and fair elections, representative legislative, free vote, political parties, no dominant group, respect for minorities|
|**FRP**|Freedom of the Press|
|**GCS**|Newspapers can publish stories of their choosing without fear of censorship or retaliation|
||When deciding upon policies and contracts, Government officials favor well-connected firms|
||Effectiveness of national Parliament/Congress as a law making and oversight institution<br>Link between donations and policy|
||Passive voice|
|**GWP**|Confidence in honesty of elections|
|**HUM**|Travel: domestic and foreign travel restrictions|
||Freedom of political participation|
||_Imprisonments:_Are there any imprisoned people because of their ethnicity, race, or their political, religious beliefs?<br>Government censorship|
|**PRS**|_Military in Politics_ The military are not elected by anyone, so their participation in government, either direct or indirect, reduces accountability<br>and therefore represents a risk. The threat of military intervention might lead as well to an anticipated potentially inefficient change in policy<br>or even in government.|
||_Democratic Accountability._<br>Quantifies how responsive government is to its people, on the basis that the less response there is the more<br>likely is that the government will fall, peacefully or violently. It includes not only if free and fair elections are in place, but also how likely is the|
||government to remain in power.|
|**RSF**|Press Freedom Index|
|**WMO**|_Institutional permanence:_An assessment of how mature and well-established the political system is.|
||_Representativeness_:How well the population and organized interests can make their voices heard in the political system|

# **Non-representative Sources**

**AEO** Hardening of the regime **AFR** Elections are free and fair **BTI** Stateness Political Participation Institutional Stability Political and Social Integration **CCR** Civil Liberties Accountability and public voice **GII** Civil Society Organizations Media Public Access to Information Voting & Citizen Participation Election Integrity Political Financing **IFD** Policy and legal framework for rural organizations Dialogue between government and rural organizations **LBO** Satisfaction with democracy Trust in Parliament **MSI** Media Sustainability Index **OBI** Open Budget Index **WCY** Transparency of Government policy

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Table 4: Likelihood of Choosing Job A given alternate retirement savings coverage offers/ earnings combinations in Job B, total and by Gender, Age Group _(Dep Var = 1 if Job A preferred to Job B, else 0. Job A has standard/ current EPF pensions coverage features and earnings equal to respondents’ current income)_

||**Gen**|**der**||**Age **||**Total**|
|---|---|---|---|---|---|---|
||Male|Female|Age < 30|Age 30 - 39|Age >= 40||
|No retirement savings fund or pension, 5%<br>higher income (B1)|0.739***|0.760***|0.769***|0.763***|0.714***|0.749***|
||[0.0375]|[0.0390]|[0.0480]|[0.0434]|[0.0496]|[0.0270]|
|No retirement savings fund or pension, 10%<br>higher income (B2)|0.755***|0.739***|0.810***|0.716***|0.730***|0.748***|
||[0.0356]|[0.0411]|[0.0444]|[0.0435]|[0.0519]|[0.0269]|
|Monthly pension upon retirement based on<br>contribution, 5% lower income (B3)|0.601***|0.689***|0.717***|0.613***|0.585***|0.640***|
||[0.0404]|[0.0426]|[0.0472]|[0.0508]|[0.0547]|[0.0294]|
|Monthly pension upon retirement based on<br>contribution, 10% lower income (B4)|0.630***<br>[0.0417]|0.626***<br>[0.0453]|0.695***<br>[0.0475]|0.603***<br>[0.0620]|0.576***<br>[0.0518]|0.628***<br>[0.0306]|
|N|568|470|344|362|332|1038|
|_Standard errors in brackets_<br>_* p<0.05, ** p<0.01, *** p<0.001_|||||||

We see that when offered the prospect of no retirement savings fund/ pension, the majority (over 74%) of respondents regardless of gender and age, and of the level of earnings increase offered (5% or 10%) prefer Job A with the current features of the EPF-managed retirement fund. However, when offered the prospect of a monthly pension (in Jobs B3 and B4), we see a small decline in the share of respondents who prefer job A, suggesting that a monthly pension may be more attractive to some gig workers compared to a lumpsum amount. Younger workers (in their twenties) appear to have a slightly stronger preference for a retirement fund that makes a lumpsum payment to a monthly pension, compared to older workers Table 5a). We find no differences in these patterns of job preference between freelancers with or without private retirement savings or EPF coverage currently (Table 5b).

14
While outlined indicators are presented in context of the DIa variable, which describes discrepancies in import reporting, they can be applied to any of the six variables discussed above, including the general DI distribution outlined in Figure 1. This report uses indicators related to DIa , DIb , DIc variables. Thus, we supply country level indicators that can describe both import and export reporting practices of a country and product level indicators that describe the quality of reporting on products.

# 4. Data description

This analysis relies on UNSD’s Comtrade annual database, the largest and most widely used resource in empirical investigations of international trade. It covers all trade transactions recorded in the Harmonized System (HS) and Standard International Trade Classification (SITC) product nomenclatures. See Appendix 6 for details on the database tables. The Annual Discrepancy Index and Trade Reporting Indicators below currently provides discrepancy measurement in HS Nomenclature, while SITC was used for nomenclature comparison. The Annual Discrepancy and Reporting index data can be downloaded from the World Bank Data Catalog using the links below. See Appendix 6 for details on the database tables.

- <u>Discrepancy Index Annual for H5 nomenclature</u>

- <u>Discrepancy Index Annual for H4 nomenclature.</u>

- <u>Discrepancy Index Annual for H3 nomenclature</u>

- <u>Discrepancy Index Annual for H2 nomenclature</u>

- <u>Discrepancy Index Annual for H1 nomenclature</u>

- <u>Discrepancy Index Annual for H0 nomenclature</u>

- <u>Trade reporting Indicators all nomenclatures</u>

- <u>Trade reporting Indicators by commodity all nomenclatures</u>

Country and product level trade reporting indicators provide coverage for quality of reported trade across the various versions of the Harmonized System nomenclatures (H0 to H5). The number of countries included in our database for each year and nomenclature version is detailed in Appendix tables 3 through 14. We provide both import and export reporting indicators for each country and product. The quality of reported trade for countries and products is evaluated at the 6-digit product groups in the HS nomenclature.

20
model describes the best trade in manufactured products. It is poor at explaining trade in natural resources such as oil or agricultural products, since natural resource flows are determined by the exporters' endowments rather than GDP. However, due to data limitations, we have been unable to derive reliable estimates for manufactured exports.

Based on Table 6, we may conclude that all three South Caucasus countries tend to export less than predicted by the gravity model. The overall ratio of potential to actual exports range from 1.5 for Azerbaijan to 2.4 for Armenia to 6.9 for Georgia. (These ratios would, in fact, be even higher since current estimates do not take into account trade in agricultural products, energy, and other non-manufactured goods.) The low export volumes can be partly explained by the physical restrictions on the movement of goods. However, a more important reason for weak export performance is the dearth of internationally competitive goods produced in the region outside of natural resources.

Geographically, the South Caucasus countries tend to trade sufficiently with politically friendly neighbors, the CIS countries and undertrade with the EU, the USA, and hostile neighbors. Neighboring markets are less competitive and have weaker quality requirements for imports than sophisticated European and American markets. Past traditions of regional trade also facilitate short-haul trade flows.

While regional trade may be boosted by conflict settlements, massive exports to the developed nations would not happen without a major industrial restructuring.

With higher income levels, trade potential would indeed be higher. Table 7 presents potential export volumes under the World Bank projections of the GDP levels and country populations in 2002. Under this scenario, the ratio of potential to current trade would increase to 1.8 for Azerbaijan, 4.1 for Armenia, and 8.0 for Georgia. The largest potential gains would be for exports to the EU and the USA. In case of Azerbaijan, a number of regional partners might see a reduction in exports, redirected to other destinations.

The above analysis suggests the following magnitudes of missing exports (the lower bound represents exports flows under the GDP levels in 1996; the higher bound uses the projected 2002 GDP levels): Armenia to Azerbaijan - US$3.2-6.7 million, Armenia to Turkey - US$35.6-65.7 million, Azerbaijan to Armenia - US$3.2-4.6 million.

A number of case studies show that export opportunities can be associated with the opening of borders. The World Bank study on the prospects for Armenia exports<sup>5</sup> indicated the advantage of products with high value-to-weight ratio, in order to overcome the geographic factor. However, if regional borders open, the weight restriction may be relaxed. For instance, there may be a good market for Armenia's building materials

> 5 Hurwitz (1995).

23
yield and alkaloid content of coca crops and the efficiency of clandestine cocaine producing laboratories. In 2004, however, UNODC started a series of field studies to complement its crop monitoring system by estimating coca leaf yields per hectare, average weight loss for sundried and oven-dried leaves, and conversion rates from coca leaf to cocaine, among others (see the Coca Cultivation Surveys for the three producer countries produced by UNODC in 2005 and 2006.) The implementation of these surveys, however, is often hampered by the social tensions prevailing in the coca producing regions and by the farmers’ reluctance to collaborate with the interviewers. Despite the difficulties in carrying out these studies, their implementation is of the greatest importance, not only to better understand coca and its derivatives’ markets, but also to be able to evaluate the efficiency of antidrug programs and monitor changes in each link of the cocaine production process.<sup>17</sup> Not surprisingly, according to UNODC the main trend of potential cocaine production is very similar to that described above for coca cultivation, that is, a relatively stable total potential production of cocaine from 1990 to 1999 (at about 850 metric tons) and then decreasing between 2000 to 2003. By 2003 cocaine production had reached a minimum level of about 800 metric tons due, almost completely, to the large decrease in potential production in Colombia (see Figure 6 (a)). For 2004 and 2005, however, new estimates of coca leaf yields per hectare obtained by UNODC and the Colombian government point to worrisome results, namely, that

> 17 For instance, in 2004 the media reported the discovery, in the Sierra Nevada (in the northern part of Colombia), of a new coca variety which supposedly had higher cocaine content, a higher level of purity, and was also resistant to glyphosate. This new variety was seen as the response of drug traffickers to the intensive aerial spraying efforts by the Colombian government, with strong financial and technical support from the US (see McDermott, 2004). However, the Transnational Institute (TNI) has questioned the validity of this report arguing that “A few scientific facts provide grounds for questioning the credibility of this report about the cocaine alkaloid content of the coca leaf...The report’s claim that the plant is resistant to glyphosate is equally ambiguous” (see TNI, 2004).

13
Though initially intended to be a temporary measure, these policy responses soon became a prominent fixture that resulted in a development strategy geared towards industrial import substitution with lesser emphasis on the agricultural and export sectors.

# **_Import Substitution_**

The enactment of the highly protective 1957 tariff code reinforced the government’s import substitution policy by providing incentives to domestic producers of final consumer goods. High tariff rates were imposed on non-essential consumer goods while low rates were applied to essential producer inputs. This created a strong bias against agriculture and exports. An analysis of effective protection rates (EPR) by sector and commodity (Power and Sicat 1971; Tan 1979) revealed that the highest EPRs from the 1950s to 1970s were granted to import substituting consumer industries; in contrast agriculture and primary (mining) products, which accounted for two-thirds of exports during the period, were characterized by the lowest EPRs. The weighted average EPR provided to the manufacturing sector was 44 percent in 1974 compared to a much lower 9 percent protection for agriculture and mining. In spite of the passage of the revised 1973 tariff code, which was primarily aimed at decreasing tariff dispersion, a large disparity in tariff levels persisted, especially by South East Asian standards.

# **_Export Taxes on Agriculture_**

Agricultural export taxes ranging from 4 to 10 percent were introduced following the 1970 devaluation to stabilize the BOP position. Initially intended to be temporary, the agricultural export tax ended up being incorporated into the 1973 tariff and customs code as a major source of government revenue. The world commodity prices boom in 1974 prompted the imposition of an additional export tax to enhance government revenue. Not surprisingly, this worsened the bias against agriculture, resulting in additional resource reallocation from 6
55 difficult to estimate and it makes sense to allocate most or all of the current refugees back to their origin country.

For most countries in Africa the level of net international migration is so small that it will have little effect on population estimates and projections. The United Nation's 1987 Monitoring report estimates that 3/4 of the 31 African countries for which data were available had foreign-born populations that made up less than 4.1 percent of the total population, although these estimates were not based on very recent data in many cases (United Nations, 1988). The exceptions were Ghana (6.6 percent), Libya (8.8 percent), Gambia (11.1 percent), and Cote d'Ivoire (21.3 percent). The median sex ratio of the foreign-born population in 24 countries in Africa was 110, so there was only a slight preponderance of males in the migration flows. For several countries, the sex ratios of the foreign-born population were less than 100 (Burkina Faso, Central African Republic, Comoros, Malawi, and Swaziland). On the other hand, the sex ratios were greater than 150 in Gambia, Liberia, Libya, Reunion, and South Africa.

The previous World Bank and United Nations estimates differ dramatically for only a small number of countries in Africa and most of the differences may be attributed to the way in which refugees are allocated. A few relevant considerations for selected countries are highlighted below.

Table 22. Refugees in Africa in Need of Protection and/or Assistance, 1987

|Algeria|167,000|Malawi|420,000|
|---|---|---|---|
|Angola|92,000|Morocco|800|
|Benin|3,700|Mozambique|500|
|Botswana|5,200|Nigeria|4,800|
|Burkina Faso|180|Rwanda|19,000|
|Burundi|76,000|Senegal|5,600|
|Cameroon|7,300|Sierra Leone|200|
|Central African Republic|<br>5,100|Somalia|430,000|
|Congo|1,200|South Africa|180,000|
|Cote d'Ivoire|600|Sudan|817,000|
|Djibouti|13,500|Swaziland|67,000|
|Egypt|1,080|Tanzania|266,000|
|Ethiopia|220,000|Togo|1,700|
|Gabon|100|Tunisia|200|
|Ghana<br>.|.<br>140|Uganda|120,400|
|Kenya|9,000|Zaire|338,000|
|Lesotho|2,000|Zambia|149,000|
|Liberia|110|Zimbabwe|150,500|

Note: Different sources vary substantially for the estimates for Algeria, Burundi, Lesotho, Somalia, South Africa, and Swaziland. Source: U.S. Committee for Refugees (1988:30-31).
In some of my specifications, to control for _Zis_ , I include the wage bill for each sector and location with a census-tract fixed effect to capture _qi_ . Then, I estimate the following equation using the Economic Censuses in 1999, the omitted category is the formal sector, where _γi_ is the census-tract fixed effect, _γI_ is an informal sector dummy variable, and _γ_ is a constant term. From the optimal number of firms, we have that _γI_ = ln( _σI FI_ ), and _γ_ = ln _β_<sup> ̃</sup> + ln( _σF FF_ ).

Table C8 reports the results for this estimation for different specifications. I run the previous equation, including state and municipality fixed effects, and in the even columns, I control for the wage bill. I obtained that on average, the value of _β ≈_ 0 _._ 7. I also find that the entry fixed cost for a firm into the informal sector is approximately 0.15, and into the formal sector is 1.1. This means that the fixed cost to enter into the formal sector is more than five times the one to enter into the informal sector. This result is consistent with the fact that the average size in terms of workers of informal firms is lower, but that there are more informal firms in the economy.

# **C.3 Algorithm**

In this section, I explain the main algorithm to solve for the general equilibrium model. The system of equations is described in section 4. The sub-index _t_ represents simulation. The algorithm is based on Alvarez and Lucas (2007) and it is a contraction mapping. It is as follows:

1. Guess an initial vector of wages _⃗w_<sup>0</sup> , and number of residents in each location _⃗L_<sup>0</sup> .

2. Given a vector _⃗w_<sup>_t_</sup> and _⃗L_<sup>_t_</sup> , compute the following equations:

- Labor supply equations:

- Average income

- Commercial floor space prices

15