rafmacalaba/datause-encoder-data huggingface dataset:
Figure 2: The proliferation of NTMs over time and development level of destinations <!-- Start of picture text --> Products Countries<br>2000 2003 2006 2009 2012 2015 2018 20 22 24 26 28 30<br>Years GDP of destionation country (log)<br>(a) Proliferation of NTMs (b) The use of NTMs by income status<br>1200 100 2<br>1000<br>80<br>800 1.5<br>600 60<br>1<br>400 40<br>Number of countries imposing an NTM<br>Count of NTMs applied by destionation (log)<br>Number of HS6−digit products attracting an NTM 200 20 .5<br><!-- End of picture text -->

# **3.2** **_Firm-level export data_**

Our source of firm-level customs transaction data for the three selected African countries is the Exporter Dynamics Database, described in Fernandes et al. (2016). The data is collected by the Trade and Integration Unit in the Development Research Group at the World Bank from the customs agencies and statistical institutes of the respective countries. For each transaction, the firm-level customs data contains unique information on the exporting firm, the country of origin, the export destination, the product classification at the HS6-digit level, the year of the transaction, and the Free on Board (FOB) value and quantity of export. As such our unit of analysis is at the firm-product-destination-year level.<sup>13</sup> We make use of firm-level customs transactions for three selected African countries due to data availability. These include Burkina Faso (2005-2012), Malawi (2006-2019), and Senegal (2000-2019). To merge the NTM panel data with the firm-level trade data at the HS6 product level, we convert the HS6 product codes in both the firm-level trade data and the NTMs data into a consolidated version, following the procedure outlined in Fernandes et al. (2016).

## **3.2.1** **_Stylized facts on firm-level exports_**

We observe a total of 16,021 unique firms throughout the sample used in the study. Of these, 1,579 firms are located in Burkina Faso, 8,015 firms are located in Malawi, and 6,428 firms are located in Senegal (Table 1). About 70% of the firms are active exporters in the manufacturing sector with the remaining 30% exporting agricultural products. The number of firms we observe increased a lot over time from 320 firms in 2000 to 2,407 firms in 2019. The same is true for the number of products exported by the firms and the number of destinations that they serve. We also observe that the number of high-income destinations has risen over time, increasing from 23 in 2000 to 63 in 2019. However, most firms only serve a few destinations or export a few products (see Figure A3). The average firm only serves three destinations and exports 29 products, with differences across origin countries and sectors. The average 13We followed the procedure outlined in Fernandes et al. (2016) to clean the firm level data.

8
91 days, in the future.<sup>13</sup> A subset of these subjects were revisited some time prior to t=61 and given the opportunity to revise the allocation between t=61 and t=91. The extent of this revision is our dependent variable. We examine correlates of this revision decision corresponding to each of the four potential determinants of revision outlined above. Surveys at the baseline and revision stages measured household wealth, income, and expenditures as well as the participants’ expectations for each of these variables.

In addition, we implemented a complementary test of hyperbolic discounting models in our experimental context: average revisions toward sooner should be larger the shorter the time lag between the revision decision and the first disbursement date (t=61). We randomized the number of days prior to t=61 when each experimental subject had to make the revision decision.<sup>14</sup> We find that initial allocations usually adhere to the law of demand; individuals allocated more income to later periods when offered higher rates of return to waiting. We interpret this to indicate that experimental subjects mostly understood the choices they were making and made them in a way that economic theory has some hope of explaining. We also find that “static” preference reversals are frequent, but only slightly more likely to be “present”-biased (as opposed to “future”-biased). This finding largely contrasts other studies using the multiple price list (MPL) method, but is consistent with Andreoni and Sprenger’s (2011) findings that UC San Diego students exhibit no present-bias on average.

Turning to revision behavior, we find that revisions are common, often substantial in size, and shift money both sooner and later. We find support for “present”-biased preferences as a driver of revisions: subjects shift more money toward sooner when: (1) their initial allocations are “present”-biased, and (2) the time lag to disbursement is shorter (when the revision decision is made six or fewer days prior to day t=61). In contrast, we find no evidence that social pressure importantly affects revision decisions. We estimate, with considerable precision, that respondents’ revisions are not much higher when one’s spouse’s sooner allocations are larger than one’s own, or when the subject lives with many other relatives in the village. Similarly, we find little evidence that shocks or financial sophistication (a proxy for mistakes in initial allocations) strongly predict revisions, though here the standard errors on our estimates are sufficiently large that we cannot reject a null hypothesis of large effects.

> 13 These dates fell within Malawi's annual hungry season, just prior to the annual harvest, when food stocks are at their lowest and consumption smoothing is a paramount concern.

> 14 The revisit date was drawn randomly from a uniform distribution of 2-16 days prior to day t=61.

5
Table 3 reports urban poverty rates and poverty deficits before the price shock and changes in poverty headcount and the poverty deficits after the price shock in the twenty most severely affected countries. The increase in urban poverty rate (or headcount, i.e., the proportion of the population below the poverty line), averages 4 percentage points, and ranges from 1 to 6 percentage points. The cost (estimated as the change in the poverty deficit) reaches an average 0.5 percentage points of GDP, ranging from 0.1 to 2.8 percent. It is noteworthy that over 90 percent of the additional monetary cost stems from the loss in real income of those who were already poor before the price shock. Conversely, the additional monetary cost accounted for by the increase in the number of poor is relatively modest. This is due to the fact that the poverty gap among the new poor is much smaller than that of the old poor after the crisis.

**Table 3. Urban** **<mark>p</mark> overt** **<mark>y</mark> at US$1 a da** **<mark>y</mark> : Countries at risk**

||Initial sit<br>(200|uation<br>5)|2<br>Δin|008 Central Sce|nario<br>Due to|Due to|
|---|---|---|---|---|---|---|
|Country|Poverty<br>Headcount|Poverty<br>Deficit|Poverty<br>Headcount|Estimated<br>Cost|old<br>poor|new<br>poor|
|Nigeria|54.6|10.0|6.1|2.8|2.6|0.2|
|Nicaragua|36.5|5.1|3.7|1.5|1.4|0.1|
|Haiti|43.5|6.4|3.5|1.2|1.2|0.0|
|Madagascar|35.8|2.6|4.0|0.8|0.8|0.0|
|Uganda|42.0|2.6|5.4|0.8|0.7|0.0|
|Cambodia|25.1|1.1|5.8|0.5|0.4|0.1|
|Benin|17.1|0.8|5.8|0.4|0.4|0.1|
|Tanzania|21.9|0.9|4.2|0.4|0.3|0.1|
|Ghana|10.0|0.4|3.1|0.2|0.2|0.0|
|El Salvador|9.6|0.6|1.4|0.2|0.2|0.0|
|Mauritania|7.0|0.2|1.8|0.2|0.1|0.1|
|Côte d'Ivoire|8.6|0.3|2.4|0.2|0.1|0.1|
|Bangladesh|8.7|0.2|5.0|0.1|0.1|0.0|
|Ethiopia|15.2|0.2|5.4|0.1|0.1|0.0|
|India|5.7|0.1|3.3|0.1|0.1|0.0|
|Burkina Faso|8.6|0.2|4.3|0.1|0.1|0.0|
|Bolivia|4.8|0.2|1.2|0.1|0.1|0.0|
|Pakistan|3.0|0.0|3.2|0.1|0.1|0.0|
|Mali|3.4|0.1|2.3|0.1|0.1|0.0|
|Burundi|12.5|0.2|2.1|0.1|0.1|0.0|

*Notes: (1) Authors’ own calculations using data from the GIDD; (2) the poverty deficit is expressed as a proportion of the sum of total household incomes; (3) the estimated cost is defined as the difference between the initial PD and the PD after the price shock; (4) households surveys have been standardized to year 2005 using information from national accounts; see Annex I for methodological details and Annex II for a complete listing of countries with results.

The results show that initial conditions matter tremendously in the determination of poverty impact, reflecting different initial urban / rural population distributions, poverty levels, gaps and income distributions. Nigeria’s 2005 GDP per capita might for instance
|Panel B: Hazard Regressions, Impact of So|phistication on Att<br>(1)|ention to Transaction Costs in US<br>(2)|
|---|---|---|
|AdjIlliq|0.988**|0.984**|
||0.020|0.011|
|Age|0.993***|0.994***|
||<.0001|<.0001|
|Married Dummy|0.879***|0.873***|
||<.0001|<.0001|
|Male Dummy|1.046***|1.045***|
||0.001|0.005|
|Retirement Acct Dummy|0.840***|0.862***|
||<.0001|<.0001|
|Retired Dummy|1.379***|1.401***|
||<.0001|<.0001|
|AdjIlliq × Age|1.000***|1.000***|
||<.0001|<.0001|
|AdjIlliq × Married Dummy|1.003*|1.006***|
||0.053|0.010|
|AdjIlliq × Male Dummy|0.993**|0.992**|
||0.021|0.043|
|AdjIlliq × Retirement Acct Dummy|0.999|0.997|
||0.441|0.251|
|AdjIlliq × Retired Dummy|0.990***|0.988***|
||<.0001|<.0001|
|Sophistication|1.265***|1.272***|
||<.0001|<.0001|
|AdjIlliq × Sophistication|0.995***|0.994***|
||<.0001|<.0001|
|Stock Controls|Yes|Yes|
|Household stratification|No|Yes|
|Calendar month dummies|Yes|Yes|
|Calendar year dummies|Yes|Yes|
|Number of Observations|156,350|156,350|
|Wald test|<.0001|<.0001|

**Table 4: Attention to Transaction Costs and Trading Ability**

45
Corrections and Supplementa. Data. Since the publication of the report, data have been updated to account for new information including revisions of GDP in the IMF International Financial Statistics Yearbook. In Annex Table 1, these data are italicized to denote an update or correction. In the cases where only the ratio was provided by the source, no total spending figure is listed in Annex Table 1. For the most part, these changes are minor, but a few major revisions were made including changes for the following countries: The Netherlands, Switzerland, France, Greece, Italy, Luxembourg, Belgium, Ukraine and Benin. All spending on invalidity pensions had not been included in the original estimates for France, Italy, Greece, Luxembourg, the Netherlands and Belgium.

Since these data were used in the figures mentioned above, the regression results will be affected by these revisions. The corrected version of the regression results presented in Figures 1.8 and 1.9 are presented in Table 2 below. The results are not significantly different from those presented in the book due to the relatively small modifications to the data and the robustness of the results. The cross-section sample data used for the regressions is presented in Annex Appendix 2.

**Annex Table 2 Corrected Regression Results for Determinants of Pension Spending Share of GDP**

||Intercept|Pop6O+|Pop60+2|YCAP90|YCAP9O|2AdjustedR2|
|---|---|---|---|---|---|---|
|(1)|-0.35404|0.01791|0.023095|0.000248|-0.000000012|<br>0.88|
||(-0.44)|(0.1)|(3.2)|(1.9)|(-2.1)||
|(2)|0.27528|0.10921|0.02202||-|0.86|
||(0.3)|(0.6)|(2.9)||||
|(3)|-2.6426<br>(7.4)|0.6429<br>(22.3)|-|--||0.85|
|(4)|-2.65096|0.64743|-|0.000006||0.84|
||(6.1)|(13.6)||(0.1)|||
|_(5)_|0.46519|-|-|0.00054|0.000000003|<br>0.53|
||(0.9)|||(9.9)|(1.2)||
|(6)|0.68239<br>(1.4)|-|-|0.00543<br>(10. 1)|-|0.52|

Number of observations = 92 t-statistics in parentheses Dependent variable is public pension spending as share of GDP from Annex Appendix Table I POP60+ = Percentage of population over age 60 in 1990 YCAP90 = Per capita income in 1990 from Table 30 of 1992 "World Development Report." 15
between re-classification and migration. Individuals were classified as poor or non-poor based on the per capita household expenditure of their households in the respective years.

When the employment data are re-classified in this way, the share of the workforce employed in low-productivity agriculture (poor) dropped by 1.55 percentage points between 1993 and 2002. Moreover, the change in the share of the workforce classified as urban and non-poor actually declines by 0.91 percentage points (instead of increasing by 9.88 percentage points in the official “published” data).

**Figure 2: Changes in Employment Shares, 1993 to 2002**

<!-- Start of picture text --> Urban<br><!-- End of picture text --> <!-- Start of picture text --> Rural<br>-0.41<br>Non<br>-0.91 Poor<br>Non<br>Poor -2.41<br>-0.59<br>Ag<br>+6.70<br>Ag<br>Non - Agriculture Poor<br>-1.55<br>Poor -1.55<br>+0.72<br>Agriculture<br>Non - Ag<br><!-- End of picture text --> _Note:_ The size of each rectangle is about proportionate to the numbers of workers employed in each category in 1993. There were 78.5 million workers employed in 1993 and 86.9 million in 2002. _Source_ : Susenas 1993 and 2002.

Moreover, the reclassified data suggest that the growth of the rural non-farm sector has been an important route out of poverty with an increase of 6.7 percent in the share of workers who are non-poor and working outside of agriculture in rural areas. Thus, the role of rural non-farm sector in poverty reduction is hidden in the official data 12
**<u>Table 2. The district fixed-effect RDD-DD regressions of employment of individuals</u>**

||||Dependen<br>|t variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|Currently<br>working<br>(yes=1, no=0)|Self-<br>employed<br>farm work<br>(yes=1, no=0)|Self-<br>employed<br>non-farm<br>work (yes=1,<br>no=0)|Having wage<br>job (yes=1,<br>no=0)|Log of<br>monthly wage<br>(wage<br>workers)|Log of<br>monthly wage<br>(all workers)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program * Post-program period|-0.0277|-0.1144**|0.0538***|0.0330|0.1285|0.5545*|
||(0.0198)|(0.0554)|(0.0151)|(0.0512)|(0.2694)|(0.2845)|
|(Poverty rate – 50) * Post-program|-0.0024|0.0070|-0.0056***|-0.0038|-0.0059|-0.0338|
|period|(0.0024)|(0.0057)|(0.0018)|(0.0043)|(0.0335)|(0.0269)|
|Program * (Poverty rate – 50) * Post-|0.0047*|-0.0026|0.0046**|0.0026|0.0041|-0.0002|
|<br>program period|(0.0025)|(0.0062)|(0.0018)|(0.0049)|(0.0348)|(0.0300)|
|Age|0.0387***|0.0243***|0.0040***|0.0104***|0.0444***|0.1132***|
||(0.0014)|(0.0013)|(0.0005)|(0.0006)|(0.0060)|(0.0066)|
|Age squared|-0.0005***|-0.0003***|-0.0000***|-0.0001***|-0.0006***|-0.0015***|
||(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0001)|
|Male|0.0136***<br>(0.0046)|-0.0653***<br>(0.0072)|-0.0076**<br>(0.0030)|0.0865***<br>(0.0051)|0.0910***<br>(0.0197)|0.9974***<br>(0.0567)|
|Ethnic minorities (yes=1, Kinh=0)|0.0523***|0.1805***|-0.0990***|-0.0292*|-0.3732***|-0.2248|
||(0.0130)|(0.0265)|(0.0109)|(0.0168)|(0.0511)|(0.1580)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1697***|0.2242***|0.0402***|-0.0947***|6.8517***|-0.6763***|
||(0.0330)|(0.0343)|(0.0101)|(0.0190)|(0.1321)|(0.1886)|
|Observations|128,446|128,446|128,446|128,446|27,510|128,446|
|R-squared|0.289|0.190|0.066|0.174|0.203|0.154|
|The mean value of the dependent<br>variables before the program|0.854|0.734|0.040|0.081|7.483|1.394|

Note: This table reports district fixed-effect RDD-DD regressions of employment of individuals using individual-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

41
rise in trucking costs. The lower probability of securing the next trip from the destination may have led to an increase in cost in the current route. Drivers needed to be compensated for higher health risks.

To establish the average increase in freight rate during the lockdown and post-lockdown periods, we revert back to trip level data from JDL trucks. We define two dummies to capture the national lockdown ("lockdown") and post-lockdown ("post") periods. We run a before-after (single difference) regression of the log of freight rate on the post dummy which takes the value of unity if the date is after June 30, 2020, and zero otherwise.<sup>11</sup> This regression includes the log of the distance between OD pair and origin and destination fixed effects. Column 1 of Table 3 reports the estimates from this simple regression excluding the lockdown period from the sample. The coefficient of post dummy indicates a nearly 32 percent increase in freight rate during this period. To capture the heterogeneity with respect to distance, the regression in column 2 introduces the interaction of post with the log of distance. The interaction term has a positive sign indicating a larger increase in freight rate for longer distance trips. The regressions so far excluded the lockdown period. In column 3, we include the lockdown period (March 24- June 30, 2020) in the sample and the lockdown dummy in the regression. Consistent with Figure 3, the estimate indicates about a 39 percent increase in freight rate during the lockdown period (column 3). When interaction with distance is introduced (column 4), the estimate implies a doubling of the average freight rate for shorter trips and a lower increase for longer distance trips during the lockdown period. The results for postlockdown in column 4 are similar to that in column 2.

The regression results suggest a heterogeneous increase in freight rates between lockdown and postlockdown periods, and with respect to the distance between OD pairs. With such heterogeneity, the policymakers may want to know how such heterogeneous increases in freight rates may affect inter-regional trade. To answer this question, we examine two aspects of the trade: (i) intensive margins of trade focusing on how shipment flow will be affected among OD pairs already trading with each other; (ii)

> 11 Though the national lockdown was lifted in early June 2020, many of the restrictions including restrictions on mobility were still in place until the end of June. Those restrictions were removed by June 30, 2020. We take June 30th as the end date of the lockdown.

12
below). In Malaysia, for historical reasons, several established foreign service suppliers hold majority or full equity ownership and their larger foreign equity holdings have been grandfathered. Finally, Vietnam currently does not permit majority foreign ownership in the three sectors shown in Table 5. However, Vietnam is in the final stages of negotiating accession to the WTO and, if past accession agreements are any guide, it may well commit to majority or full foreign ownership in at least some of these sectors.

The criteria for rules of origin simulated are based on those found in the Hong Kong-Mainland CEPA (see previous section), plus an ownership and control criterion. In particular, the simulations adopted the following criteria:<sup>14</sup>

- (A) Incorporation under domestic laws

- (B) Substantial business operations, as measured by:

- (B1)  Possession of an operating license

- (B2)  Minimum capitalization requirement (for financial services)

- (B3)  Minimum number of years of establishment (3-5 years)

- (B4)  Owning or renting of premises

- (C) Domestic ownership and control, as measured by more than 50 percent of equity holdings

- (D) Share of domestic employees greater than 50 percent

As an illustration, Table 3 presents the simulation results for the Philippine’s banking sector. The results in this case are relatively straightforward. All but two of the non-ASEAN banks in the Philippines are branches of foreign-incorporated banks. Thus, only two foreign banks would meet the domestic incorporation criterion. The substantial business operations criteria are all met, as they mirror operational requirements under domestic laws and all banks have been operational for at least 5 years. None of the non-ASEAN banks would meet the domestic ownership and control requirement, but all of them employ 50 percent or more Filipino residents.

Overall, three main findings emerge from the five country case studies.

<u>First, the single most relevant rule of origin criterion is domestic ownership and control. In other</u> words, when allowed, non-ASEAN service providers prefer to enter foreign markets through majority or fully owned affiliates, rather than entering into minority joint ventures with local companies. Thus, if a company entered an ASEAN country for the sole purpose of obtaining preferential access to another market and was forced into a minority joint venture, it would most likely depart from its preferred way of doing business and incur certain transaction costs.

> 14 Depending on data availability and the characteristics of the different sectors, the studies sometimes had to adapt the ‘substantial business operations’ criterion.

13
<!-- Start of picture text --> NO TRANSPARENCY TRANSPARENCY<br>1.5<br>><br>='1.0<br>Cc<br>oO<br>a<br>0.5<br>0.0<br>0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00<br>Fraction of Group Money Embezzled<br>Dp RANDom [| ELECTED<br><!-- End of picture text -->

<!-- Start of picture text --> NO TRANSPARENCY TRANSPARENCY<br>2.0<br>1.5<br>2<br>@ 1.0<br>oO<br>a<br>0.5<br>0.0<br>0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00<br>Fraction of Group Money Embezzled<br>Dp RANDom [| ELECTED<br><!-- End of picture text -->
4

# 1.  The Data Set

Data for this study come from the 2005/06 Ghana Living Standards Survey (GLSS 5), a nationally-representative survey of 8,000 households carried out by the Ghana Statistical Service (GSS). This survey, administered from September 2005 to September 2006, contains detailed information on all aspects of living conditions in Ghana, including income, expenditure, health, education, savings, and credit. As part of this survey, a supplemental migration and remittances module was administered to a nationally representative sub-sample of 4,000 households.<sup>2</sup> This paper uses the data from the migration and remittances sub-sample of 4,000 households. In carrying out the analysis we dropped 59 households because of missing data, which resulted in a sample of 3,941 households.

Since the focus here is on remittances, it is important to clarify at the outset how these income transfers are measured and defined. Data on remittances includes transfers received in three forms: (1) money (cash); (2) food; and (3) non-food goods.<sup>3</sup> While most remittances (about 75 percent) come in the form of money (cash), including food and non-food goods is important because it leads to a more accurate measure of the total flow of remittances to households in Ghana. In this study each household that is classified as receiving remittances – either internal (from Ghana) or international (from African or other countries) -- is assumed to receive exactly the amount reported in the survey. Households which report having migrants but do not report receiving remittances are classified as non-remittance receiving households. Using this definition distinguishes our work from much of the previous empirical literature on migration and household behavior by focusing on the origin of income flows rather than presence or absence of a migrant in the household. This approach seems sensible for two