rafmacalaba/datause-encoder-data huggingface dataset:
different way the livelihood of families relatively more involved in catfish production. One such factor is the outbreak of avian flu which hit the region in 2004. The outbreak led to large declines in the demand for poultry and to a corresponding significant increase in the demand for fish. This, however, is unlikely to be an important concern. While the initial outbreak of the avian flu in Vietnam took place in January 2004, it was an isolated episode while the epidemic only became sizeable after August 2004. Our data, instead, was collected in August 2004 with a recall period of one year, thus capturing data before the actual outbreak.

Additional evidence in support of our empirical strategy can be provided by performing a falsification experiment. If our identifying assumption holds, we should not observe any such impact for fishing households in non-Mekong regions. Based on the historical differences between North and South Vietnam pointed out before, the best candidates for this false experiment are the non-Mekong provinces of South Vietnam. We then estimate model (6) using the sample of fishing households in these areas, and using the same outcomes as in Table 5. The results, displayed in Table 6, show that in this sample, for all outcomes and for all levels of exposure, there is no evidence that the pre-AD shares are associated to the magnitude of the income change. Indeed all estimated coefficients are not only statistically not significant but also positive. We conclude that this falsification experiment helps validate our empirical strategy.

Even so, our estimates require to be further qualified. The estimates reflect the impact of the anti-dumping after allowing for different economy-wide responses to the shock. One important such response is trade deflection, that is, the shift of exports to other non-U.S. markets.<sup>20</sup> For Vietnamese catfish, trade deflection is hard to establish or to rule out, due to lack of data.<sup>21</sup> Some evidence is offered by COMEXT data on European Union imports, which indicate that imported quantities of tra and basa from Vietnam increased by 78 percent between 2002 and the first semester of 2004.<sup>22</sup> Another factor which may

> 20The empirical relevance of trade deflection in cases of anti-dumping is yet to be established. Bown and Crowley (2007a) find evidence of trade deflection in the case Japan; but Bown and Crowley (2007b) cannot find supporting evidence in the case of China.

> 21The Vietnamese government does not release export data on catfish, while publicly available data on COMTRADE is disaggregated up to the level of frozen fillets, but not specifically catfish.

> 22According to data released by the Vietnamese government, the European Union accounted for 29.6

25
sample, a 50% increase in volatility slows down productivity growth by 33% on average. And there is substantial evidence that ORCs have more volatile economies than non-ORCs (Hausman and Rigobon (2002)). Thus there is logic to a focus on volatility as an explanatory factor for Nigeria's poor growth record.

To explain high volatility in ORCs, it has been argued that countries with many interest groups competing for the resource rents are likely to overspend in good years, and under-adjust in bad years. Each interest group tries to overexploit windfall gains in an attempt to at least partially offload adjustment costs to others while fully capturing the gains from its lobbying efforts (the “voracity effect”, see Lane and Tornell (1995)). Federal states like Nigeria are thought to be especially vulnerable to what amounts to an equivalent of overgrazing the commons. An expenditure behavior that leads to overspending in good days and under-adjustment in bad days may end up with an economy with even higher volatility than is to be expected on the basis of the volatility in its revenue streams alone.

There is an alternative, or possibly complementary, explanation of high volatility in ORCs, an explanation that starts from the surprising fact that many ORCs have landed themselves in debt problems, their oil wealth notwithstanding. Borrowing may well have been restricted after the sudden reduction in net resource inflows associated with lower oil prices, because lenders fear too much of “project returns” will be diverted to the servicing of old debt, thereby undermining the credit quality of any new debt. This is the classic _<u>debt overhang</u>_ problem. _Ex post_ debt overhang problems can be resolved by renegotiating the old debt and/or creating senior classes of new debt. _Ex ante_ the possibility of debt overhang calls for stretching maturities, maintaining reserves, and generally for caution in incurring debt. Manzano and Rigobon (2001) have suggested a link between debt problems and slow growth in resource rich countries, without, however, identifying the specific channels through which this link operates.

# _III.3 Sources of macroeconomic volatility in Nigeria_

Nigeria was ranked as the third most volatile economy in terms of trade volatility out of 90 countries and the fourth in terms of (real) exchange rate volatility out of 84 countries in a recent World Bank study covering the period 1961-2000.<sup>6</sup> Large oil wealth exposes many oil producers to oil price volatility. Because volatility acts as a tax on investment in traded goods production, notably in agriculture and manufacturing, such an exposure could have an adverse impact on growth. This is further complicated

> 6 World Bank (2003), Nigeria: Policy Options for Growth and Stability, PREM3, Africa Region, Report No. 26215-NGA.

13
6 responsiveness of trade to income, but the evidence suggests that their role was less prominent.

Our work relates to two branches of economic literature. A first set of papers analyzed the changes in the long-run relationship between trade and income. Irwin (2002) documents the variation of the world trade elasticity between 1870 and 2000 providing valuable insights into the underlying factors. Freund (2009) calculates the elasticity of trade to GDP in tranquil and crisis times, and shows that the latter tends to be higher. Escaith et al. (2010) provide evidence of the increase in the world trade elasticity in the 1990s and relate this to the process of vertical specialization. Differently from these studies, our main interest is in understanding the extent to which changes in the long-run trade and income relationship can explain the global trade slowdown. We also attempt to analyze in a more systematic way some of the factors that may underpin the changing relationship between trade and GDP.

Our work also relates to the wide literature on the trade collapse of 2008-2009 (among others, Bems et al., 2010, Levchenko et al., 2010, Bussiere et al., 2013, Abiad et al., 2014).<sup>3</sup> This literature studies the causes of the sudden, severe and synchronized collapse of world trade that followed the global financial crisis in 2008-2009. The emerging consensus is that the driving factor of the collapse was the sharp contraction in aggregate demand, concentrated on trade-intensive components, and amplified by other factors, such as inventory effects and trade credit constraints (Bems et al., 2013). Our study complements this work by taking a longer-term view and investigating the structural versus cyclical determinants of the current trade slowdown. Interestingly, we document that while cyclical factors dominated the trade collapse, structural determinants are key to understanding the current slowdown.

The rest of the paper is organized as follows. In Section II, we present some stylized facts about the trade slowdown. In Section III, we estimate the elasticity of trade with respect to

> 3 Early contributions to this literature were collected in Baldwin (2009). Bems et al. (2013) have a recent survey.
TABLE A23: Impact of leaders’ exposure on intermediary outcomes, by closest leader’s predicted commitment

||(1)<br>Nutrition|(2)<br>Stimulation|(3)<br>Health|(4)<br>Micronutrients|(5)<br>Proteins|(6)<br>Attitudes|(7)<br>Hygiene|
|---|---|---|---|---|---|---|---|
|ITT Leader|-0.08|0.06|0.03|-0.07|-0.01|0.04|-0.04|
||(0.15)|(0.09)|(0.10)|(0.12)|(0.14)|(0.10)|(0.11)|
|ITT leader X low commitment|-0.13|-0.07|-0.06|-0.20|-0.31<sup>_∗_</sup>|-0.02|-0.21|
||(0.16)|(0.15)|(0.15)|(0.16)|(0.18)|(0.15)|(0.15)|
|ITT leader X medium commitment|-0.18|-0.16|-0.21|-0.17|-0.36<sup>_∗_</sup>|-0.14|-0.14|
||(0.20)|(0.15)|(0.13)|(0.16)|(0.21)|(0.15)|(0.15)|
|ITT leader X high commitment|0.03|0.01|0.08|0.12|-0.27|-0.21|0.04|
||(0.20)|(0.14)|(0.14)|(0.17)|(0.19)|(0.16)|(0.15)|
|ITT leader X very high commitment|-0.01|0.00|0.03|-0.18|-0.16|0.15|-0.03|
||(0.22)|(0.15)|(0.13)|(0.16)|(0.19)|(0.16)|(0.18)|
|Observations|2062|2062|2073|2059|2062|1970|2068|
|ITT leader + ITT leader X low committed = 0|0.01|0.89|0.72|0.01|0.045|0.84|0.04|
|ITT leader + ITT leader X medium committed = 0|0.08|0.34|0.05|0.02|0.03|0.41|0.13|
|ITT leader + ITT leader X high committed = 0|0.64|0.55|0.20|0.71|0.04|0.23|0.95|
|ITT leader + ITT leader X veryhigh committed = 0|0.63|0.64|0.42|0.02|0.19|0.13|0.65|

_Note:_ Low, medium, high and very high commitment indicates whether leader is predicted to be in the 2, 3, 4, or top quintile of leaders with highest participation in the quizzes. All outcome variables are standardized using the mean and standard deviation of the control household. The first 6 columns show ITT estimates on indices aggregating information regarding early childhood investments, as reported by the caregiver. Column 7 aggregates information regarding hygiene of the child, as observed by the enumerator. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1, _∗∗_ p<0.05, _∗∗∗_ p<0.01 71
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Table 7: Impacts on Fishery Income by Quarters

||Income<br>Total<br>(main job)<br>income|Income<br>(main job)|Total<br>income|
|---|---|---|---|
||(1)<br>(2)|(3)|(4)|
|**[Panel A] Geogra**|**phic control group**|||
|treat _×_ [Q2-2016]|-0.456***<br>-0.463***|-0.524***|-0.534***|
||(0.144)<br>(0.145)|(0.111)|(0.114)|
|treat _×_ [Q3-2016]|-0.252**<br>-0.252**|-0.383***|-0.382***|
||(0.109)<br>(0.110)|(0.091)|(0.093)|
|treat _×_ [Q4-2016]|-0.047<br>-0.063|-0.312***|-0.299***|
||(0.101)<br>(0.097)|(0.109)|(0.105)|
|Observations|2,246<br>2,246|1,762|1,762|
|R-squared|0.356<br>0.355|0.064|0.064|
|**[Panel B] Indust**|**ry control group**|||
|treat _×_ [Q2-2016]|-0.631***<br>-0.628***|-0.630***|-0.653***|
||(0.081)<br>(0.078)|(0.067)|(0.067)|
|treat _×_ [Q3-2016]|-0.311***<br>-0.307***|-0.459***|-0.449***|
||(0.069)<br>(0.066)|(0.064)|(0.063)|
|treat _×_ [Q4-2016]|0.009<br>0.009|-0.319***|-0.316***|
||(0.078)<br>(0.075)|(0.065)|(0.065)|
|Observations|7,991<br>7,991|4,486|4,486|
|R-squared|0.315<br>0.299|0.046|0.048|
|Month FE|Yes<br>Yes|Yes|Yes|
|Individual FE|No<br>No|Yes|Yes|
|Standard errors in|parentheses, *** _p <_0_._01,|** _p <_0_._05,|* _p <_0_._1|

Note: this table shows the impact of Formosa on fishery income (monthly;’000 VND; log-transformed) for each subsequent quarters after the start of Formosa incident in April 2016. “Treat” indicates saltwater fishers living in Ha Tinh, Quang Binh, Quang Tri, and Hue. Geographic control group (Panel A) consists of all saltwater fishers living in provinces south of Phu Yen (i.e. distant from Formosa region). Industry control group (Panel B) consists of workers in unaffected industries (i.e. manufacturing, construction, and retail) living in the four Formosa-affected provinces. Columns 1-2 report results using a sample consisting of all individuals identified as saltwater fishery workers (for Panel A) and workers in the control industries (for Panel B) before May-2016. Columns 3-4 report results using a sub-sample restricting to only individuals who were surveyed twice – before and after May-2016. Standard errors are clustered at the district level.

37
The charts below basically replicate figures 1A and 1B in Fajnzylber et al. (2011). The main difference is that the authors fitted a cubic polynomial, while I fit a local linear regression. Notice, as in Fajnzylber et al., young firms (0-3 months of age) are dropped from the analysis.<sup>7</sup>

> 7 Using the code of Monteiro and Assunção (2012,) I get a similar figure as shown in the appendix.

6
# **3 Empirical implementation**

The core of our empirical analysis is the estimation of (10) using a cross-country time-series dataset. However, the equation involves the quasi-innovations to the present value of income growth, which are not observed. We proceed on two stages. First, we construct the innovations based on an estimated time-series model of income growth. In the second stage, we use these innovations to estimate (10).<sup>16</sup> In this section we first discuss the econometric issues surrounding the estimation of the parameters of the risk-sharing model, and then we turn to those related to the construction of the quasi-innovations required for such task. Finally, we briefly summarize our data sources.

## **3.1 Implementing the risk-sharing model**

Estimation of (10) in a cross-country setting poses a challenging problem because each country’s consumption path depends in nonlinear fashion on the risk-sharing parameters of all countries. To make this explicit in what follows, it is useful to rewrite (10) as where ui,t is potentially heteroskedastic and possibly correlated across countries and over time.

To express the empirical model in more compact form, we introduce the following notation. Let ∆ **ci** = (∆ci,1, ..., ∆ci,T )<sup>_′_</sup> and **ε** i = (εi,1, ..., εi,T )<sup>_′_</sup> denote the (column) vectors of consumption growth and quasi-innovations to the present discounted value of income growth of country i, where T denotes the number of time series observations per country, and define the T _×_ N matrix **Z** = ( **ε** 1, ..., **ε** N). In addition, let **λ** = (λ1, ..., λN)<sup>_′_</sup> denote the vector of risk-sharing parameters for all countries, and define the N _×_ N diagonal matrices **H** and **W0** which have along their main diagonal the population of each country Hj and its per capita GDP share ωj,0, respectively, with all off-diagonal elements equal to zero. After some straightforward manipulations, the T observations of (11) corresponding to the i-th

> 16In principle, one could combine both steps into a single one, and estimate jointly the parameters of the income process and the risk-sharing model. The estimation problem would be nonlinear due to the presence of the permanent income innovations, even if the risk-sharing model were linear in its parameters. However, the risk-sharing model is itself heavily nonlinear, and this would greatly add to the complexity of the joint estimation approach. For this reason, we proceed in two stages, as described in the text.

12
# **Table 2: Plot-Level Characteristics** **<u>by Gender of Plot Manager</u>**

||**Total**|**Exclusively**<br>**Male**<br>**Managed**|**Exclusively**<br>**Female**<br>**Managed**||
|---|---|---|---|---|
|**Yield**|||||
|Value of Output per Acre (USD)|144|158|129|***|
|**Plot Characteristics**|||||
|Area of Plot in Acres|0.69|0.85|0.54|***|
|Good Land Quality|61.4%|62.3%|60.6%||
|<br>Fair Land Quality|32.3%|32.1%|32.4%||
|<br>Poor Land Quality|6.2%|5.5%|6.9%|**|
|<br>Irrigated|1.9%|2.6%|1.3%|***|
|<br>Rain-Fed|95.9%|95.0%|96.7%|***|
|Swamp/Wetland|2.2%|2.3%|2.0%||
|Hilly|9.9%|7.9%|11.9%|***|
|Flat|45.4%|47.8%|43.0%|***|
|Gently sloped|38.2%|38.9%|37.6%||
|Steep|3.2%|2.8%|3.6%|*|
|<br>Valley|3.1%|2.5%|3.8%|***|
|**Distance from Homestead to Plot**|||||
|Less than 15 Min|65.0%|66.4%|63.5%|**|
|15 Min - 30 Min|16.3%|15.5%|17.1%|*|
|30 Min - 60 Min|11.9%|10.9%|12.9%|***|
|1 Hour - 2 Hours|5.7%|5.8%|5.5%||
|Over 2 Hours|1.2%|1.3%|1.0%||
|**Tenure & Investment**|||||
|Land Owned by the Household|82.2%|83.2%|81.1%|**|
|Erosion Control/Water Harvesting Facility is Located|31.7%|32.1%|31.3%||
|<br>on the Plot|||||
|**Labor Inputs**|||||
|Any Family Labor Used on Plot|96.1%|96.2%|96.0%||
|Family Labor Days per Acre|130|121|140|**|
|<br>Any Hired Labor Used on Plot|26.9%|30.5%|23.3%|***|
|Hired Labor Days per Acre|8.2|9.3|7.1|***|
|**Non-Labor Inputs**|||||
|<br>Improved Seed Applied|6.7%|9.2%|4.2%|***|
|Purchased Seed Applied|25.3%|29.2%|21.3%|***|
|<br>Value of Seed per Acre (USD)|3.98|4.27|3.69||
|<br>Any Manure Applied|4.1%|4.7%|3.5%|***|
|<br>Kilograms of Manure per Acre|40|35|45||
|<br>Any Chemical Fertilizer Applied|1.2%|2.1%|0.4%|***|
|<br>Value of Fertilizer per Acre (USD)|0.64|1.17|0.12|***|
|<br>Any Crop Protection Chemicals Applied (pesticides,|4.9%|7.0%|2.8%|***|
|fungicides, etc.)|||||
|<br>Value ofCropProtectionChemicalsper Acre(USD)|0.79|1.19|0.39|***|
|Number of Observations(Plots)|6999|3500|3499||

16
**Table 4** : Effects of the PSI-PMI Program on Student Learning Outcomes: Using the Researcher Designed Exam Score

||Outcome<br>|variable: Stand<br>|ardized mathe<br>|matics score<br>|
|---|---|---|---|---|
|Variable|Within-sch<br>|ool control<br>|Between-sch<br>|ool control<br>|
||gro|up|grou|p|
||[1]|[2]|[3]|[4]|
|PSI-PMI program|0.634**|0.544***|0.848***|0.655***|
||(0.247)|(0.150)|(0.306)|(0.207)|
|Student-level covariates|NO|YES|NO|YES|
|School-level covariates|NO|YES|NO|YES|
|Average mathematics score for control|38.78|38.78|33.37|33.37|
|Standard deviation of mathematics score for<br>control|21.91|21.91|18.51|18.51|
|Observations|875|875|1,044|1,044|
|R-squared|0.065|0.349|0.122|0.24|

_Note:_ The results are based on equation 1. The outcome variable is the standardized mathematics score on the researcher designed exam. Robust standard errors clustered at the school level are in parentheses. PSI-PMI = Progressive Science Initiative and Progressive Mathematics Initiative.*** p < 0.01, ** p < 0.05, * p < 0.1 _School covariates_ variables include: presence of electricity in school, good desks in school, school has a library, a computer lab, running water in the school, student enrollment, student-teacher ratio, percentage of qualified teachers, percentage of female teachers, experience of the school principal, age of school principal, administration experience of school principal, level of education of school principal, school principal is female, share of students with high performance in mathematics, English, and science on the WASSCE. _Student-level covariates_ variables include the following: percentage of students not repeating any grade, percentage of students earned credit and above on GABECE mathematics, percentage of students earned credit and above on GABECE English, percent of students earned credit and above on general science, all at baseline. And socioeconomic characteristics of students—percent of students in households that has a quality roof, a wall, television, car, access to grid electricity, parents have some education.

We later estimated equation (1) using the official WASSCE scores from 2018. The results are presented in Table 5. The result from the preferred specification is strikingly similar to the pre-analysis using the survey and researcher-administered test (0.59 versus 0.54 as reported in Table 4). The estimates from the between-school control are positive and similar in magnitude, although they are not statistically significant. The lack of statistical significance, in this case, is likely due to the small sample size offering limited statistical power. The estimated effects from the preferred specification correspond to a real significant gain in learning improvement that is equivalent to an increase of about 9 percentage points from a low base of 20.18 in the control group.

In Table 5, columns 5 to 8, we use a different outcome measure, which is a dummy variable indicating whether a student received a satisfactory score on both English and 20
Table 8: Productivity and Technology Sophistication

||||ln(Sa|les per wo|rker)|||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|_Kj_|0.234<sup>_∗∗∗_</sup>|0.266<sup>_∗∗∗_</sup>|0.223<sup>_∗∗∗_</sup>|0.300<sup>_∗∗∗_</sup>|0.304<sup>_∗∗∗_</sup>|0.233<sup>_∗∗∗_</sup>|0.231<sup>_∗∗∗_</sup>|
||(0.007)|(0.007)|(0.007)|(0.009)|(0.009)|(0.007)|(0.007)|
|_Hj_|0.191<sup>_∗∗∗_</sup>|0.585<sup>_∗∗∗_</sup>|0.150<sup>_∗∗∗_</sup>|0.084<sup>_∗_</sup>|0.109<sup>_∗∗_</sup>|0.189<sup>_∗∗∗_</sup>|0.202<sup>_∗∗∗_</sup>|
||(0.040)|(0.042)|(0.040)|(0.048)|(0.049)|(0.040)|(0.040)|
|_Sj_|0.493<sup>_∗∗∗_</sup>|0.631<sup>_∗∗∗_</sup>|0.504<sup>_∗∗∗_</sup>|0.527<sup>_∗∗∗_</sup>|0.530<sup>_∗∗∗_</sup>|0.460<sup>_∗∗∗_</sup>|0.422<sup>_∗∗∗_</sup>|
||(0.019)|(0.020)|(0.019)|(0.024)|(0.024)|(0.020)|(0.022)|
|Markup|||||0.189<sup>_∗∗∗_</sup><br>(0.052)|||
|Management (Z-Score)||||||0.062<sup>_∗∗∗_</sup><br>(0.011)|-0.003<br>(0.018)|
|_Sj_ * D(High Management)|||||||0.068<sup>_∗∗∗_</sup><br>(0.015)|
|Constant|6.118<sup>_∗∗∗_</sup>|5.951<sup>_∗∗∗_</sup>|7.948<sup>_∗∗∗_</sup>|5.484<sup>_∗∗∗_</sup>|5.208<sup>_∗∗∗_</sup>|6.206<sup>_∗∗∗_</sup>|6.217<sup>_∗∗∗_</sup>|
||(0.175)|(0.147)|(0.310)|(0.203)|(0.217)|(0.175)|(0.175)|
|N|13046|13046|13046|8553|8553|13046|13046|
|R-squared|0.407|0.234|0.435|0.341|0.342|0.409|0.410|
|Sector FE|Yes|Yes||Yes|Yes|Yes|Yes|
|Country FE|Yes|No||Yes|Yes|Yes|Yes|
|Sector x Country FE|||Yes|||||

Notes : Estimates of specification (9). D(High Management) is a dummy that takes the value 1 if the management z-score of the establishment is above the median, and 0 otherwise. Markup is the gross markup (1+markup%)for the main product or service produced in this establishment. Columns (4) and (5) are calculated only for the sample where the markup data is collected. All regressions estimated using establishment-level sampling weights. *, ** and *** denote 10%, 5% and 1% significance respectively.

54
**Figure 9: SACU countries with slower aging have faster investment growth, which pushes down rents**

# **A. Capital Stock**

Cumulative change in capital stock 2015-50 (percent) <!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Botswana Namibia South Africa Lesotho and<br>Swaziland<br>Capital rental rates<br>Lesotho and<br>Botswana Namibia South Africa Swaziland<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>Baseline Faster productivity growth<br>Education convergence Mobilized savings<br>ER convergence ER convergence + gender parity<br>All interventions<br><!-- End of picture text -->

# **B. Capital rental rates**

Cumulative change 2015-50 (percent) Source: Authors’ simulation results 20