rafmacalaba/datause-encoder-data huggingface dataset:
- 22 -

trades the dominating carriers have combined to establish so-called _rate agreements_ through which they collectively pledged to reduce their active tonnage on such trade routes and to abide by commonly agreed freight tariffs. These rate agreements have all the makings of a cartel. Owing to these arrangements, the container freight rates in the cross-Atlantic and Pacific trades are the only instances in the entire seatrade market where nominal increases have occurred since 1990.

Table 4: OCEAN TRANSPORT RATE INDICATORS Gyrations in Response to Changing Market Conditions

||I|1 1984|**1986**|<br>1988|1990|1 1992|
|---|---|---|---|---|---|---|
|**Container**Trades|**Trade**Routes:||||||
|(Major Conference Rates|Europe-Far East|2,31P|2,309|3,263|3,266|2,778|
|in US$/TEU)|Atlantic westbound|-|2,485|2,841|3,121|3,248|
||Pacific westbound|- -|_650_|1,157|1,498|1,629|
||Europe-Australia|4,884|5,325|6,942|7,884|6,386|
|General Cargo Trades|||||||
|(Tramp Trip Charter|Vessel Capacity:||||||
|<br>in US$/dwt/month)|12000-20000 dwt|7.4|6.0|12.2|11.9|10.0|
||20000-35000 dwt|4.9|3.7|8.4|7.9|7.4|
|Dry Bulk Trades|Vessel Canacitv:||||||
|<br>(SingleVoyage Rates|2500 dwt|18.1|19.2|35.1|32.8|30.9|
|in US$/ton of cargo)|120000 dwt|6.0|3.7|6.8|6.9|4.6|
|Tanker Trades|Vessel C'wacitv:||||||
|(Period Market|30000 dwt|6.6|6.8|7.9|11.6|9.6|
|in US$/dwt/month)|250000 dwt|0.8|1.4|1.6|2.7|1.8|

|||||a|Gener<br>nnual av|al Freig<br>erages;|ht Index<br>1972 =|<br>100|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|<br>1980|<br>1981|1982|1983|1984|1985|1986|198|7 1988|1989|1990|1991|1992|
|<br>369|<br>321|232|220|217|208|172|203|<br>261|284|261|250|228|

Source: Lloyd's _"Shipping Economist'._ Various issues. London; and Maritime Research Services. Parlin, New Jersey 34. But the vast majority of carriers in the international ocean transport industry is reeling under the effects of reduced freight earnings. What makes their lot not easier is the fact that the prices paid for second-hand vessels or tonnage sold for demolition have gyrated in ways which turned out to be unfavorable for them. When freight markets were solid and shipowners showed little predilection to sell tonnage in order to improve their cashflow situation, second-hand and demolition prices were relatively high, but in times of market decline both prices dropped (see Table 6 on page 24). Due to the long lead times in shipbuilding, which may extend over three years between ordering and delivery, the movements in vessel unit prices were less volatile. The actual cost of new ships have steadily increased since 1986 in the wake of speculative ordering which has characterized the market during most of the intervening years. The new and costly ships are being successively delivered until about 1994 and will burden their
# **E. Food consumption**

Table 8 presents average levels of food consumption per adult per month for different sub‐groups of food commodities. All values are presented in nominal terms, real terms adjusted for the spatial and seasonal differences in the cost of living, and for inter‐year price variations using the CPI and taking HBS 2011/12 and NPS 2010/11 as base periods for HBS and NPS, respectively. The number of food items is much higher in the HBS than in the NPS. The level of consumption expenditure seems to be higher on food items such as meat, milk and cheese, fruits, sugar, and coffee, tea and soft drinks in the NPS than in the HBS. Both surveys show similar trends in consumption on most items, except for bread and cereals, fish, milk, cheese and eggs, and vegetables, with the variations being much more important in the HBS than in the NPS.

**Table 8: Comparison of monthly food consumption by sub‐group in Tanzania Mainland**

||**2007**|**HBS**<br>**2011/12**|**Variation**<br>**%**|**2008/09**|**2010/11**|**NPS**<br>**2012/13**|**Variation**<br>**2010/11–**<br>**2012/13**|**Variation**<br>**2008/09–**<br>**2012/13**|
|---|---|---|---|---|---|---|---|---|
|**Food consumption per**<br>Food consumtion|**adult equiv**|**alent, mont**|**hly**||||**%**|**%**|
|p,<br>nominal|16,871|35,322|109%|33,499|37,854|52,620|39%|57%|
|Food consumption,<br>real|16,731|35,311|111%|34,496|38,560|53,160|38%|54%|
|Food consumption,<br>real, CPI|27,740|35,311|27%|41,290|38,560|42,276|10%|2%|
|Number of food<br>|193|171||57|57|57|||
|products<br>Food groups, monthly pe<br>|r adult equi|valent consu<br>|mption, nom|inal|||||
|Bread and cereals|6,249|13,502|116%|11,334|11,135|15,787|42%|39%|
|Meat|1,886|2,721|44%|7,056|6,913|9,501|37%|35%|
|Fish|1,303|2,403|84%|2,249|2,437|3,345|37%|49%|
|Milk, cheese and eggs|460|1,144|149%|2,805|2,952|4,738|61%|69%|
|Oils and fats|914|1,370|50%|1,358|1,602|1,814|13%|34%|
|Fruit|910|983|8%|4,063|4,313|5,619|30%|38%|
|Vegetables|3,436|7,086|106%|5,042|5,321|7,327|38%|45%|
|Sugar, jam, honey &<br>chocolate|984|1,002|2%|1,778|1,956|2,272|16%|28%|
|Food products n.e.c.|168|255|52%|262|253|255|1%|‐3%|
|Coffee, tea and cocoa|199|153|‐23%|344|355|398|12%|16%|
|Water, soft drinks,<br>juices, etc.|363|226|‐38%|2,098|2,363|3,423|45%|63%|

30
Panel A of Table 3 presents the results. We find that the establishment of private credit bureaus in a country reduces its average short-term to total debt ratio in the corporate sector by about 3% ceteris paribus, which is statistically significant at the 1% level. This result is consistent with our hypothesis that better credit information reduces the need for banks to use short-term debt as a screening device (see Section 4). Our findings also support the prediction by Flannery (1986) and Diamond (1991) that debt maturity is shorter when there are more information asymmetries.

As for macroeconomic fundamentals, lower GDP growth rate and/or higher inflation are associated with a shortening of debt maturity (consistent with the literature). Furthermore, firm characteristics appear important in explaining within country variation in corporate debt maturities over time: adding these variables in the regressions improves the R-squared (within group) by 17%. In line with our expectations, larger and/or more profitable firms obtain easier access to long-term finance. Although we only have data for publicly listed firms, which tend to be relatively large firms, we expect that the effect of better credit information in lengthening debt maturities would be even stronger for smaller unlisted firms, which generally have no credit ratings and present higher informational asymmetries.

In order to further examine the institutional determinants of corporate debt maturity structure around the world, we next turn to estimate the following panel regression: _STD/TD_ = f _(Credit Information Quality, Legal Institutions, Financial Development, Macroeconomic Environment, Firm Characteristics, Year Fixed Effects)_ where we have replaced country fixed effects with several institutional variables capturing different degrees of development in the legal framework and the financial sector across countries. We include a Common Law Origin indicator, Shareholder Rights, Creditor Rights, and a Corruption index as measures of the quality of _Legal Institutions_ . Furthermore, we use five variables to capture the degree of domestic _Financial Development_ , namely the Deposit to GDP ratio, the Private Bond to GDP ratio, the Bank Costs to Assets ratio, a Financial Center indicator and a Banking Crisis indicator.

> As a robustness check on the regression in Panel A, we employ alternative proxies for _Credit Information Quality_ , namely the coverage of public and private credit registries as well as a measure of the quality of accounting standards.

Results are shown in Panel B of Table 3. We find that firms in countries with better credit information have a lower ratio of short-term to total debt on average. This suggests that 20
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38
OCHA 2019a), with evolving geographic concentrations. Initially, much of the violence was focused on the eastern border of the territory held by Houthi forces. However, over time, the violence extended to the southern border, and then, eventually, to both the northern border with Saudi Arabia and the portion of Houthi-held territory that borders the Arabian Sea (e.g., Tandon and Vishwanath 2020). Furthermore, there were reports of Houthi forces targeting civilians and political opponents, and there was also an additional violent secessionist movement taking control of territory from the IRG (e.g., BBC 2019; OCHA 2020).

As the violence has evolved, official records suggest new households continued to become displaced at roughly an equal rate to the number of households that returned, leaving the total share of the population that was displaced at any point during the time period under analysis similar to the share displaced after the conflict initially escalated (e.g., OCHA 2019a). Importantly, violence was a pivotal factor in the decision to become displaced. Violence was escalating in districts from which these newly displaced households migrated more so than for households that did not become displaced (e.g., D’Souza et al. 2022); and in interviews with displaced households during the period under analysis, the majority of households reported that violence was the cause of their displacement (e.g., TFPM 2019).

However, in addition to the escalation in violence, the conflict has had economic impacts that might have also forced displacement. First, coalition naval forces have significantly restricted imports in ports that supply regions in the north where Houthi forces have a strong presence. These blockages necessitated the delivery of imports from ports farther away via trucks, which is one of the factors that has led to significantly higher food prices in the country (e.g., ACAPS 2020; etc.).<sup>5</sup> Although it is difficult to precisely gauge the intensity of this disruption to supply chains, reports have suggested that immediately following the escalation of the conflict, imports in Al Hudaydah- the port that supplies nearly the entirety of the north- dropped to 40 percent of pre-conflict

> 5Importantly, domestic food production is unable to replace imported food made more expensive through higher transportation costs given that the country imported approximately 90 percent of its food prior to the conflict (e.g., WFP 2012).

6
rather than investing in the country. The savings-liquidity effects are compounded by the uncertainty effects in discouraging domestic private investment.

**Table 2: Impact of capital flight on Investment (2-step GMM estimates)**

||_Total domestic_<br>_investment_|_Private_<br>_investment_<br>_Priv_|_ate investment_|_Public investment_|
|---|---|---|---|---|
|Lagged investment|0.651|0.620|0.520|0.638|
||(0.00)|(0.00)|(0.00)|(0.00)|
|KF|-0.007|-0.026|-0.021|0.0006|
||(0.00)|(0.00)|(0.00)|(0.79)|
|GDP growth|0.123|0.022|0.021|0.064|
||(0.00)|(0.00)|(0.01)|(0.00)|
|Public investment|||0.014||
||||(0.46)||
|Sargan test|37.9|27.94|29.57|30.91|
||(1.00)|(1.00)|(1.00)|(1.00)|
|2<sup>nd</sup>order|-1.13|0.51|-0.03|-0.63|
|autocorrelation|(0.26)|(0.61)|(0.98)|(0.53)|
|Observations|1109|717|684|705|

Notes: Regressions in levels and numbers in parentheses are p-values.

We then test econometrically whether and to what extent an increase in domestic savings leads to an increase in domestic investment in this sample of countries. As discussed above, one of the ways in which capital flight depresses investment is by reducing domestic savings. This result is useful in examining potential gains from flight capital repatriation. Our approach is to assume that repatriated capital flight will augment the recipient African country’s domestic savings, which will stimulate domestic investment.

To gage this effect of flight capital repatriation on investment, we estimate equations for gross domestic investment, public investment and private investment where investment depends on domestic savings, controlling for GDP (the accelerator effect). The results in Table 3 show that an increase in savings does lead to an increase in private, public, and total domestic investment. As expected, the effect on private investment is larger than that on public investment. According to these results, a 1 percent increase in savings leads to a 0.109 percent increase in domestic investment.

The estimated link between investment and saving as measured by the coefficient of 0.109 may seem smaller than expected. However, in this sample of countries, there are many factors that may explain this small elasticity of investment to savings. First, the weak link between investment and savings is a result of the inefficiencies of the financial systems in these countries that are unable to channel savings into productive investment. In particular, African financial systems are unable to perform their role of maturity transformation, creating a mismatch between the demand of long-term financing and the supply of savings which are mostly kept in the form of liquid and semi-liquid assets. Second, public investment, which is a large fraction of domestic investment is financed partly by aid and therefore not very sensitive to domestic saving.

**14**
schools did not improve scores in an English prociency test. Using observational data from rural primary schools of Thailand, teachers' exposure to in-service training has been shown not to predict instructional quality or student achievement in Thai language, math, social and natural studies, character development and work orientation tests (Raudenbush et al., 1993). However, others nd that teacher training enhances students' performance in these subjects. For instance, Angrist and Lavy (2001) documented that in-service training has had a signicant impact on students' achievement in math and reading in non-religious elementary schools in Jerusalem, whereas the impact on the achievement of students in religious schools was inconclusive. Similarly, Harris and Sass (2011) and Lai et al. (2011) found that teachers' qualications and on the job training improve student outcomes. These results from previous studies are consistent with the ndings of this studyextra teacher training, on top of books, weakly improves test score in some subjects. However, when training is provided along with appropriate books, it strongly improves student outcomes.

After all, the circumstances under which training becomes eective could be diverse. Among other factors, whether the teachers have the necessary teaching aids to implement any pedagogical technique they acquire from training could be crucial. Especially in countries where essential education inputs may be missing, in-service teacher training could render ineective. In fact, as we have documented above, when training is combined with book provision, test scores in most subjects improve substantially.

Table 4: Impact of extra teacher training, on top of books, on test score

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Peabody|Math|Listening|Reading|Writing|Total Score|
|ATE|0.243|-0.0563|-0.0491|-0.229|0.321<sup>_∗_</sup>|0.229|
||(0.38)|(0.84)|(0.72)|(0.48)|(0.08)|(0.88)|
|_N_|2968|2968|2968|2968|2968|2968|

b coecients; p in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05 , _p <_ 0 _._ 01 , _p <_ 0 _._ 001 Note: The matching variables are characteristics of the student, household, teacher and school. For the full list of covariates see Table A.1 in the annex.

20
**<u>Table 3. Customs Clearance Times in 2010 for IHC and Non-IHC Firms</u>**

||Distr<br>|ibution of de<br>|claration-l<br>|evel custom<br>|s clearance ti<br>|mes in 2010<br>|in hours<br>||
|---|---|---|---|---|---|---|---|---|
||Mean|25<sup>th</sup><br>percentile|Median|75<sup>th</sup><br>percentile|90<sup>th</sup><br>percentile|Standard<br>deviation|Coefficient<br>of<br>variation|Number of<br>import<br>declarations|
|IHC firms by the|||||||||
|end of 2013|6.66|0.40|0.84|1.72|3.64|45.46|6.83|23,516|
|Non-IHC firms by|||||||||
|the end of 2013|36.16|0.57|1.41|5.03|70.42|440.57|12.18|622,264|
|All firms|35.09|0.56|1.37|4.66|69.35|432.59|12.33|645,780|

Source: Authors’ calculations based on Serbian customs data.

Table 4 shows the distribution of monthly inspection rates in 2010 for firms that did and did not adopt the IHC program by the end of 2013. The mean inspection rate for IHC firms is lower than for non-IHC firms, and IHC inspection rates are much lower in the upper half of the distribution. For inspections it is not the case that the distribution for IHC firms is strictly below that of non-IHC firms. Many non-IHC firms are substantially smaller than IHC firms, and so many would import few shipments in a given month. Thus it is possible that even with relatively higher frequency of inspections for non-IHC firms there could be months in which those firms’ relatively few shipments were not stopped for inspection. This explains the relatively large share of firmmonth combinations with an inspection rate of zero among non-IHC firms.

**<u>Table 4.  Monthly Inspection Rates in 2010 for IHC and Non-IHC firms</u>**

|||Distribution<br>|of firm-mo|nth inspection<br>|rates in 2010<br>|||
|---|---|---|---|---|---|---|---|
||Mean|25<sup>th</sup><br>percentile|Median|75<sup>th</sup><br>percentile|90<sup>th</sup><br>percentile|Standard<br>deviation|Number of<br>firm-month<br>combinations|
|IHC firm by end of 2013|0.08|0.04|0.07|0.11|0.17|0.08|381|
|Not IHC firm by end of||||||||
|2013|0.20|0|0|0.27|1|0.34|110220|
|All firms|0.20|0|0|0.26|1|0.34|110601|

Source: Authors’ calculations based on Serbian customs data.

Table 5 shows summary statistics on the annual import values in 2010 for firms that do and do not adopt the IHC program by the end of 2013. IHC firms are, on average, much larger importers: the mean annual import value for an IHC firm is 27 times higher than for non-IHC firms. But Serbia’s largest importing firm is not an IHC firm and the dispersion in annual import values – as measured by range of the distribution – is actually much wider for non-IHC firms. This 27
Table 4: SVARMA(AICc) results at PM2 _._ 5 _>_ 10, _R_<sup>ˆ2</sup> = 0 _._ 732, 39 estimated parameters on ( _N −_ max( _p, q_ ) _× T_ ) _×_ 3 = 2160 data points with 213 fixed demeaning components. _AICc_ = _−_ 4153 _._ 838.

||Pollution<br>(pol)|Bottom Expenditures<br>(bot)|Expenditures<br>(exp)|
|---|---|---|---|
|_φ polt−_1|-0.097**<br>|-0.150***<br>|-0.085**<br>|
||(-2.503)|(-2.803)|(-2.367)|
|_φ polt−_2||-0.073**<br>|-0.049<br>|
|||(-2.048)|(-1.482)|
|_φ polt−_3|0.041*<br>|-0.045<br>||
||(1.773)|(-1.446)||
|_φ bott−_1|0.092**<br>(2.446)|||
|_φ bott−_2||-0.271***|-0.200***|
|||(-4.573)|(-3.518)|
|_φ bott−_3|||-0.093**<br>|
||||(-2.326)|
|_φ expt−_1|||-0.324***<br>(-5.421)|
|_φ expt−_2||0.133***<br>(2.995)||
|_φ expt−_3||0.052*<br>||
|||(1.964)||
|_M polt−_1|-0.362***|0.198***||
||(-6.212)|(3.19)||
|_M polt−_2|-0.105**<br>(-2.33)|||
|_M polt−_3|0.120***<br>(2.96)|||
|_M bott−_1|-0.146***|-0.447***||
||(-3.43)|(-12.057)||
|_M bott−_2||0.194***|0.260***|
|||(2.864)|(3.825)|
|_M bott−_3||-0.216***<br>||
|||(-4.225)||
|_M expt−_1|||-0.106<br>|
||||(-1.496)|
|_M expt−_2||-0.139***|-0.400***|
|||(-2.911)|(-8.092)|
|_M expt−_3|||-0.159***|
||||(-3.934)|
|_ρ_|0.833***|0.123|0.162|
||(24.272)|(1.374)|(1.46)|
|_b_|0.118|0.151|0.208|
|_σ_|0.906|0.080|0.101|
|_ν_|2.004|7.313|4.703|
|p white-noise|1.000|0.187|0.864|

_Note: ∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01 _Constant omitted, t-statistics in parenthesis for the SARMA components._ xviii
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