rafmacalaba/datause-encoder-data huggingface dataset:
frame is shorter than most other studies of livelihood diversification, some of which span decades (Michler and Josephson, 2017; Khan and Morrissey, 2023).

# **2.4 Food Insecurity**

We examine food insecurity as our primary outcome variable to measure household well-being. We use the Food Insecurity Experience Scale (FIES), which is an experience-based metric which can be used to compare prevalence rates of food insecurity across national and sub-national populations. Following the FIES standard survey model (Smith et al., 2017), respondents to the pre- and postoutbreak surveys answer eight questions aimed to capture whether the respondent or other adult households members:

1. were worried they would not have enough to eat,

2. were unable to eat healthy and nutritious food, 3. ate only a few kinds of food, 4. had to skip a meal, 5. ate less than they thought they should, 6. ran out of food, 7. were hungry but did not eat, or 8. went without eating for a whole day.

Following standard practice (Bloem and Farris, 2022), we count the number of affirmative answers to these eight questions to categorize households into mild, moderate, and severe food insecurity. Households which answered affirmatively to between one and three FIES questions are classified as experiencing mild food insecurity. Households which answered yes to between four and seven questions are classified as experiencing moderate food insecure. Households are classified as severely food insecure if they responded affirmatively to all eight questions.

FIES scores using these integer values may be limited by several factors. First, some postoutbreak rounds do not include food insecurity modules, so there are gaps in the data, just as there are with income sources. Second, there are inconsistencies in the reference period for food insecurity questions in the pre-outbreak data.<sup>6</sup> In Ethiopia and Malawi, the reference period in the pre-outbreak data is the last seven days while in the post-outbreak period it is 30 days. In Nigeria, the reference period is consistent (30 days) both pre- and post-outbreak. Finally, in Malawi, only six of the eight questions were included in the pre-outbreak survey.

> 6Online Appendix A includes a the exact wording of each question (including reference periods) in each country both before and after the pandemic began.

9
# **A Additional Figures and Tables**

<!-- Start of picture text --> 2011 2012<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>0 2 4 6 8 10<br>Vessel Size<br>Kappa(S)<br><!-- End of picture text --> Figure 15: _κ_<sup>_t_</sup> ( _·_ ) Function Note: The figure plots the _κ_<sup>_t_</sup> ( _S_ ) function with our benchmark estimation. _κ_<sup>_t_</sup> ( _S_ ) is the probability of a ship with private security as a function of the vessel size, _S_ , in year _t_ .

48
|Theory of change|0.150**|0.147**|0.165|0.131|0.145**|0.143*|
|---|---|---|---|---|---|---|
||(0.0663)|(0.0732)|(0.104)|(0.113)|(0.0661)|(0.0729)|
|Private sector<br>consultation|0.167|0.318**|0.439**|0.570**|0.142|0.293*|
||(0.132)|(0.154)|(0.203)|(0.229)|(0.132)|(0.153)|
|Degree of private<br>sector involvement|-0.385***<br>(0.137)|-0.319**<br>(0.138)|-0.832***<br>(0.216)|-0.747***<br>(0.217)|-0.381***<br>(0.137)|-0.316**<br>(0.137)|
|Degree of connectivity<br>increase|-0.00521|-0.0721|-0.0369|-0.174|-0.00835|-0.0733|
||(0.0748)|(0.0816)|(0.113)|(0.125)|(0.0745)|(0.0813)|
|Investment at border|0.0176|0.0726|-0.417|-0.533|0.0283|0.0892|
||(0.264)|(0.282)|(0.429)|(0.469)|(0.264)|(0.282)|
|Geographic scope|-0.127|-0.0888|-0.131|-0.199|-0.124|-0.0845|
||(0.0932)|(0.0994)|(0.143)|(0.150)|(0.0929)|(0.0991)|
|Openness at approval|||||||
|[(Imports +<br>|-0.0623|-0.132|1.212*|0.965|-0.0599|-0.111|
|Exports)/GDP]|(0.419)|(0.489)|(0.654)|(0.839)|(0.418)|(0.486)|
|Any DPO in past 5<br>||0.0726||1.001***||0.0578|
|years||(0.275)||(0.362)||(0.275)|
|Any DPO in past 5|||||||
|years: Industry-trade-<br>services||-0.175||-0.874||-0.135|
|||(0.343)||(0.560)||(0.342)|
|Any DPO in past 5<br>years: Public||-0.204||-0.194||-0.231|
|administration||(0.513)||(0.776)||(0.511)|
|Any DPO in past 5<br>years: Transport||-0.267<br>(0.187)||-0.138<br>(0.295)||-0.259<br>(0.187)|
|Observations|40,572|40,572|17,813|17,813|40,426|40,426|
|R-squared|0.318|0.331|0.356|0.374|0.320|0.333|
|Sample|All|All|Exclude<br>20% at the<br>nodes|Exclude<br>20% at the<br>nodes|Exclude if<br>Human<br>Settlement<br>Index > 2.5|Exclude if<br>Human<br>Settlement<br>Index > 2.5|
|Specification|No DPOs|With<br>DPOs|With DPOs|No DPOs|No DPOs|No DPOs|

_Note:_ Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Six design and implementation characteristics are tested to see whether they could significantly affect economic activity around the project corridor—quality of the theory of change, private sector consultation, private sector involvement, extent of increased connectivity, investment at the border, and geographic scope. The results are relatively consistent across tables 2 and 3 (columns 1 and 2) for four of the six 15
impacts and the welfare gains from trade. For comparison purposes, our import weighted average elasticity is 3.9, similar to the trade elasticity estimate of Simonovska and Waugh (2014) of 4.

The results also indicate heterogeneity of elasticities across trading partners, with a median elasticity of -20 and standard deviation of 70. In fact, relative to the rest of the exporting countries, the import demand for UK products is found to be less elastic. Column (1) of Table 3 formally tests the hypothesis that all partner countries have the same elasticity for each HS 6-digit product and the hypothesis was rejected with high t-statistics. On average, the elasticity of the UK is 30 percent lower than that of the other exporting countries for the same HS 6-digit product. This differential is statistically and economically significant, which highlights the importance of estimating bilateral trade elasticities, rather than imposing the same elasticities across all trading partners. UK-EU relatively small trade elasticities are consistent with Novy (2013) and Chen and Novy (2022) who find that bilateral trade is less sensitive to trade costs if the exporting country provides a larger share of the destination country’s imports.<sup>20</sup> In addition, the inherent market power of the EU as it pertains to UK’s products could also lead to lower bilateral demand elasticities.<sup>21</sup>

> 20As a robustness check for the results of substantially lower elasticities for the UK we run a series of gravity regressions excluding one exporting country at a time. Results show that excluding the UK from the regression significantly increases the magnitude of the coefficient on distance, implying that the UK faces a lower elasticity in the EU than other exporting countries. Please refer to Section A6 of the Appendix for details.

> 21Soderbery (2021) takes a similar general approach as this paper, except his focus is on heterogeneous pass-through rates across countries and how trade impacts are shaped by importer market power. Please also refer to Juarez (2023) for evidence relating importer market power to low pass-through elasticity, based on evidence from Colombia.

14
percent in 1989, and 6.9 percent in 1990, for a cumulative drop of approximately 30 percent.<sup>13</sup> Inflation skyrocketed during the crisis, reaching almost 7,500 percent in 1990. Wages and household consumption collapsed. While there are no comparable data for the country as a whole, per capita consumption in Lima, as measured by household surveys, fell by approximately 50 percent between 1985 and 1990 (Glewwe and Hall, 1994). The collapse in real wages was even more dramatic—estimates for Lima suggest that real wages fell by 80 percent between 1987 and 1990 (Saavedra, 1998; Saavedra and Maruyama, 1998).

Schady (2004) uses comparable, nationally-representative Living Standard Measurement Study (LSMS) household surveys for 1985/86, 1991 and 1997 to analyze the impact of this recession on schooling outcomes. Schady first shows that the probability of being enrolled in school was similar before, during, and after the crisis—if anything, enrollment rates in 1997 are lower than during the crisis year of 1991; the magnitude of the difference is small, approximately 1 to 2 percentage points, although it is statistically significant. More striking are the differences in the probability that children work, which is much lower during the crisis than before or after. For older school-aged children, ages 12-17, this probability is between 16 and 22 percentage points lower in 1991 than in 1985/86 or 1997.

Schady (2004) also analyzes the impact of the crisis on the number of years of schooling attained. Table 2, reproduced from his paper, shows that children who were school-aged during the entire crisis period have completed, on average, about 0.25 _more_ years of schooling at any given age than those who were not school-aged during the crisis (upper panel). Schooling is completed in whole years, rather than fractions, so a more intuitive description of this result is that every fourth child who was fully exposed to the crisis has completed one more year of schooling than a comparable child who was not exposed. A disaggregation of these results to account for differences by the number of years of exposure to the crisis suggests that the positive effects of the crisis on school attainment levels are only apparent for children with “high” levels of exposure—that is, children who were school-aged for at least three of the five crisis years (lower panel). Schady suggests that the increase in school attainment during the recession may have partly been a result of the decrease in child labor, which freed up time that children could expend in more “effort” at school.

> 13 Per capita GDP was essentially flat in 1991 and 1992, before beginning a recovery in 1993. Nevertheless, the crisis was so severe that it was not until 2005 that per capita GDP in Peru reached the pre-crisis, 1987 level—despite generally positive growth rates from 1993 onwards.

12
context of other countries for which value‐added trade data are available. The horizontal axis measures direct value‐added exports of services and the vertical axis measures indirect domestic value‐added exports of services (the value‐added exports of services embodied in exports of goods). Lines representing the median shares divide the countries into groups occupying four quadrants.

For financial services, Figure 15 shows that there is heterogeneity across developing East Asian countries:

- In the bottom left quadrant is Indonesia. The low competitiveness of financial services is reflected in the low share of direct exports and the low level of embodied exports—which could reflect the low financial services intensity of goods production, the reliance primarily on imported financial services, or both.

- In the top left quadrant are China, Vietnam and Thailand, which are not yet sufficiently competitive to export financial services directly, but which do export a significant share indirectly. That goods sectors in these countries rely significantly on domestically produced financial services could be because financial services in these countries have reached an intermediate level of development at which they can compete in the domestic market but not yet internationally. It could also be that restrictions on cross‐border imports in these countries oblige goods producers to use domestically produced services.

- In the top right quadrant is Malaysia, whose more developed financial services sector exports both directly and indirectly.

- In the bottom right quadrant are Cambodia and less clearly, the Philippines, along with such “mature deindustrializers” as Britain and the United States, where the domestic tangible industries have shrunk in importance and financial services are mostly exported directly.  The Philippines is too similar to Vietnam to be treated as qualitatively different, and Cambodia’s presence may merit further exploration.

31
f Brick weight is 2.03 kg in Kathmandu Valley and 2.76 kg outside of Kathmandu Valley. g Weight of each solid brick is 2.5 kg and the hollow brick is 2.0 kg taken from http://gorkhachineseitta.com/. h Based on a rapid survey of 22 brick kilns across the country. In general, the bricks in the Tarai region are about 1.25 to 1.5 times larger than the bricks in Kathmandu Valley.

A2.2 Intensity-Related Data Compilation [ ] rrh eecc jj jj CC (tt) CCeeii CC (tt) There is a wide variation in thermal specific energy consumption (SEC) within and across different brick kiln technologies. Even with the same kiln technology, many factors influence thermal SEC for firing clay bricks, including the combination of external and internal fuels used (e.g., coal, sawdust, rice husk, briquette, pellet, coal ash, carbonaceous industrial wastes etc.), the quality of the fuels used (e.g., fuels with different calorific values), the type and weight of desired clay-fired bricks (e.g., solid, perforated or hollow bricks) and the kiln’s overall heat loss and the air leakage management. For example, in Pakistani brick kilns, additives such as agricultural residue, sawdust, _gutka_ , poultry waste, and rubber, are used with coal that ranges from low 2.5% to as high as 100% (Ahmad et al., 2022).

One of the main advantages of a ZZK is its lower SEC as compared to that of FCBTK. On average, in comparison to conventional FCBTK, the ZZKs in India require about 20% less energy (Maithel et al., 2013). Likewise, the VSBK consumes about 35% less fuel as compared to FCBTK because of better combustion and heat recovery, and low heat losses (Maithel et al., 2014).

**Table A2.4** : Compilation of Specific Energy Consumption (SEC) of different brick kilns in South Asia (MJ/kg of clayfired brick)

|Source<sup>a</sup>|CK|DDK|FCBTK|ZZK ND|ZZK ID|VSBK|HK|TK|Quality<sup>b</sup>|
|---|---|---|---|---|---|---|---|---|---|
|1<sup>c</sup>|1.58|-|1.30|1.19|-|-|-|-|P|
|2<sup>d</sup>|2.10|2.97|1.30|1.06|1.03|0.80|1.36|1.40|S [3]|
|3|2.90|-|1.30|1.30|1.10|0.80|1.20|1.20|S (V)|
|4|2.00-4.00|2.00-4.00|1.00-1.50|1.0-1.2|-|0.80-1.50|2.00-4.00|1.5-2.00|S (V)|
|5<sup>d,e</sup>|-|-|0.99-1.19|-|0.60-1.06|-|-|-|P|
|6<sup>f</sup>|2.00|-|1.30|1.13|1.18|-|-|1.60|S [16]|
|7<sup>g</sup>|-|-|1.12-1.21|-|-|-|1.95-2.12|-|P|
|8<sup>d,h</sup>|-|-|3.21|1.90|-|-|-|-|P|
|9<sup>i</sup>|2.91|-|1.12|-|-|0.84|-|-|P|
|10<sup>d,j</sup>|1.33|1.90|1.17|1.07|1.14|-|-|-|P|
|11<sup>d,k</sup>|-|-|-|1.10|-|-|-|-|P|
|12<sup>l</sup>|1.38-1.92|2.80-3.14|0.95-1.82|0.91-1.15|-|0.90|1.21-1.52|-|U|
|13|2.10|2.97|1.30|1.06|1.03|0.80|1.20-1.36|1.40|S [16]|
|14<sup>d</sup>|2.36|-|1.25|1.16|0.92|0.83|1.50|-|S (V)|
|15|1.50-2.00|2.50-3.00|1.00-1.40|1.0-1.2|-|0.75-1.00|-|-|S (U)|
|16<sup>m</sup>|2.91|-|1.22|1.06|-|0.95|-|1.46|P|
|17<sup>d</sup>|-|-|1.16|0.92|-|0.83|-|-|S|

46
27 Figure 5: The evolution of urban composition in Tanzania <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50% Other urban areas<br>40% Regional Capitals<br>30%<br>Dar es Salaam<br>20%<br>10%<br>0%<br>1957 1967 1978 1988 2002 2012<br>Census Year<br>Share of Urban Population<br><!-- End of picture text --> Source: Wenban‐Smith and Ambroz (2014).
Table 11: Distribution of deaths by age and year

|Year|2006|2007|2008|2009|total|
|---|---|---|---|---|---|
|Age-group||||||
|0 – 15|2|4|2|6|14|
|16 – 45|5|2|4|4|15|
|46 +|3|5|6|10|24|
|Total|10|11|12|20|53|

Table 12: Distribution of deaths by age and cause, 2006-2009

|Cause|malaria|pneu/TB|fever/fu|childbirth|other|total|
|---|---|---|---|---|---|---|
|Age-group|||||||
|0 – 15|4|3|2|4|1|14|
|16 – 45|4|3|0|4|4|15|
|46 +|7|5|6|0|6|24|
|Total|15|11|8|8|11|53|

weather roads in July 2009 contributed to both rows, in virtue of having moved from one category to the other over the period 2006 – 2009. The null that the patterns are the same cannot be rejected (Fisher’s exact test, _p_ = 0 _._ 227).

All of the 53 deaths appeared to have involved acute causes, so that timely treatment was of the essence. In 49 of these cases, the respondents gave answers to the question, would the deceased have survived had there been more timely treatment? The respondents’ answers ran as follows: in 2 cases, no; in 38 cases, yes; and in 9, don’t know. In no fewer than 19 cases, no medical treatment was sought at all; and it was asserted that in 13 of these, the person would have survived with timely treatment. The same was asserted for no fewer than 25 of the 30 individuals who did get treated. Thirty-five deaths occurred in villages not then served by a road; in 29 of these, it was asserted that timely treatment would have made all the difference. Of the 14 deaths in villages so served, the corresponding number was 9. There is no great difference here.

30
-5-

number of banks had been stable between 38-42 in the 1970's.

Prior to deregulation, interest rates on both deposits and credits were fixed by the government at low levels. vlith accelerating inflation in the 1970's, real interest rates on depcsits were largely negative (see Figure 1). Another set of players that proved especially important in the events after the deregulation were the brokerage houses. Most brokerage houses were establishsd around the year 1979, when industrial corporations started to issue bonds.4 Some "bankless" industrial groups, unable to enter the banking business, formed their own brokerage houses.5 The rapid development of the-admittedly small--bond market also encouraged the establishment of independent brokerage houses.<sup>6</sup> Deregulation in the banking sector was started by two important steps undertaken in July, 1980, whereby: a) Legal restrictions on deposit and loan interest rates were removed, and, b) banks were allowed to issue negotiable certificates of deposit (CDs).7 Following Artun (1983), the events that followed these deregulatory steps and culminated in the financial crisis of 1982 can be summarized in two stages:8 4Artun (1983, pp. 70, 77) argues that bond issues resulted from new financing requirements due to the impact of devaluations of 1978-79 on corporations.

5 Examples are Meban of Transturk Holding, Eczacibasi Yatirim of Eczacibasi Holding and Oyak Yatirim of Oyak Holding.

6 One has to make a distinction between the brokerage houses discussed in the text, which traded, at least in the initial stages of the process, in securities of industrial firms and banks, and, other unorganized money market institutions that collected funds solely against Rersonal cheques and IOUs. The latter type of institutions (dubbed "market bankers" in Turkey) mushroomed following the deregulation and were the actors of another crisis that unfolded at the end of 1981. These institutions and their evolution will not be addressed in this study.

7 For a more comprehensive overview of financial reform, see Akyuz (1988).

8 The following summary is primarily based on accounts given in Colasan (1983, 1984a, 1984b), Ulagay (1987), and Artun (1983, 1985).
<!-- Start of picture text --> st<br>w<br>c oN<br>€<br>fas]<br>o<br>><br>5<br>[o)<br>=<br>To]<br>wo<br>oe aA SS<br>- — a __ LO<br>~OS<br>1996 1998 2000 2002 2004 2006 2008 #2010 2012 #«2014<br>—*— Comp Prim —®— Com JH —i— ComSH —#— Univ —*— Total<br><!-- End of picture text -->
# **DETECTING ILLEGAL TRADE PRACTICES BY ANALYZING DISCREPANCIES IN FOREST PRODUCTS TRADE STATISTICS: AN APPLICATION TO EUROPE, WITH A FOCUS ON ROMANIA**

## **Abstract**

Discrepancies in bilateral trade statistics for forest products have recently attracted attention as potential indicators of illegal trade practices. For example, if exporters understate quantities in order to evade export taxes or quotas, then one might expect reported exports to be less than reported imports. Discrepancies in trade statistics can exist for reasons that have nothing to do with illegal activities, however, such as measurement error and shipment lags. Any attempt to infer evidence of illegal activities from statistical discrepancies must control for these other explanations.

The mean discrepancy for sawnwood exported by Romania during 1982-97 was significantly different from zero for coniferous sawnwood but not for nonconiferous sawnwood. Yet, the sign of the discrepancy for coniferous sawnwood—reported exports tended to be greater than reported imports—implies that illegal trade activities were more likely occurring in Romania’s trading partners than in Romania.

An econometric analysis of bilateral trade statistics for Romania and other European countries found evidence that measurement error, shipment lags, and intentional underreporting all play a role in explaining discrepancies for both types of sawnwood. The econometric model is not sufficiently reliable, however, for estimating the portion that was due solely to illegal activities or determining whether those activities occurred primarily in Romania or in it trading partners. Moreover, given that it is based on observed discrepancies in bilateral trade statistics, it fails to detect illegal trade activities that occur simultaneously in both importing and exporting countries. For these reasons, econometric methods appear unlikely to be of practical use in revealing illegal trade activities in the Romanian forest sector.
**Exhibit 15: Employment and Taxes by Status**

|**2008 ‐ 2012**|**Regi**|**stered Companies: Post‐2008 and**<br>**Active as of 2012**<br>(a)|**All Tax**|**payers (memo)**<br>(b)|
|---|---|---|---|---|
|**Tax Base**|CAGR|Contribution to Tax<br>Base<br>Contribution<br>to Total|CAGR|Contribution to<br>Total|
||(i)|(ii)<br>(iii)|(iv)|(v)|
|**CIT**|0.67|0.46<br>0.03|0.07|0.05|
|**PIT**|0.84|0.07<br>0.00|0.91|0.01|
|**PAYE**|1.44|0.11<br>0.06|0.67|0.51|
|**VAT**|1.25|0.34<br>0.09|0.49|0.25|
|**Duties**|1.38|0.39<br>0.07|0.26|0.18|
|**Employment**|1.16|0.16|0.25|1.00|

_<u>Note:</u>_ CAGR = Compound Annual Growth Rate; CIT = corporate income taxes; PAYE = pay as you earn taxes; PIT = personal income tax; VAT = value‐added tax.

Panel (a) of Exhibit 22 refers to companies registered after 2008 and active as of 2012 (dubbed for simplicity as “Relevant”). It shows:

- (i) Compound Annual Growth Rate (CAGR) in tax declarations (by tax base) and employment from 2008 to 2012;

- (ii) Changes in declarations by relevant companies in  a given tax base relative to change in declarations by all taxpayers in the same tax base; and

- (iii) Change in declarations by relevant companies in a given tax base relative to change in declarations by all taxpayers in all tax bases combined.

Panel (b) of Exhibit 22 refers to all taxpayers. It shows:

- (i) Compound Annual Growth Rate (CAGR) in tax declarations (by tax base) and employment from 2008 to 2012; and

- (ii) Change in declarations by all taxpayers in a given tax base relative to change in declarations by all taxpayers in all tax bases combined.

21