rafmacalaba/datause-encoder-data huggingface dataset:
using the (rather weak) statistical tests available. One must interpret this rather carefully, for even if it is hard to reject the hypothesis that commodity prices follow random walks, there are no plausible theories which suggest that these prices should follow random walks, and rather good arguments why eventually they should return to an equilibrium determined by demand and supply. Deaton and Laroque (1989) have also studied this problem, using more sophisticated methods, but the same commodity price data. Their measures of persistence are the sum of all autocorrelation coefficients (whether significant or not), with the sums being linearly declining weighted averages over the window widths of 20 or 40 years. Their results are reported in Table 5. Where the same commodity appeals in both studies the measures of persistence from both studies are given in Table 4. In some cases the agreement is close--thus either 45 or 59 percent of the price shock in bananas is persistent, similarly for rice and palm oil. For others the differences are considerable, with Deaton and Laroque's (more reliable) measures tending to be 'ower than those of Cuddington and Urzu'a, which Deaton and Laroque argue are likely on statistical grounds to be biassed upwards. If one takes the Cuddington and Urzua evidence then perhaps one-half of price shocks are persistent for many of the important export crops of developed countries. If one takes Deaton and Laroque's estimates then about one-quarter of price shocks are permanent. Even in this case, though, Table _5_ shows the high firstorder autocorrelations, so three-quarters or more of the price shock will persist for at least a year, and even after two years typically 60 14
# **<mark>REPUBLIKA SRPSKA</mark>**

# **Law on Enterprises (Official Gazette of the Republika Srpska, No. 24/98, 62/02)**

# **<u>Current Text:</u>**

# **Article 331.**

- (1) Statutory capital of a limited liability company may not be less than 5.000 Convertible Marks. (2) A ready-money founding deposit of a particular member may not be less than 500 convertible marks in dinar countervalue according to the exchange rate on the day of purchase.

Exceptionally from provisions of paragraph 2 of this article, if members of a limited liability company are entities who purchase preferred shares, in accordance with special regulations, a ready - money deposit of a particular member may be less than specified.

# **<u>Suggested Text:</u>**

# **_Article 331._**

- **_(1) Statutory capital of a limited liability company may not be less than 2.000 Convertible Marks._**

- **_(2) A ready-money founding deposit of a particular member may not be less than 100 convertible marks._**

Exceptionally from provisions of paragraph 2 of this article, if members of a limited liability company are entities who purchase preferred shares, in accordance with special regulations, a ready - money deposit of a particular member may be less than specified.

# **<mark>BRCKO DISTRICT</mark>**

Law on Enterprises (Official Gazette of the Brcko District, No. 11/01, 10/02)

# <u>Current Text:</u>

Article 343

- (1) The capital stock shall amount to at least 5.000 KM, and each founding share amounting to at least 100 KM. The least amount of capital stock and the least amount of members’ founding share shall be paid prior to the entry in the Register.

- (2) The capital stock may be funded with monetary contributions or contributions in things and rights. Contributions in things and rights shall be subject to the appropriate application of the provisions of Article 153 of this Law.

- (3) Non-monetary contributions may consist of movable and immovable property, rights, and enterprises or parts of enterprises.

- (4) Non-monetary contributions shall be delivered to the company in full prior to the notification for entry in the Register. If the value of a member's non-monetary contribution does not reach the value of his assumed founding share, the member shall pay the difference in money.

- (5) The founding shares shall be delivered to the company in such a manner as allows the management to make free use of them.

- (6) Monetary contributions shall be paid to the company's bank account.

Page 35 of 51
the fact that the average share of government budget allocated for education has decreased during the pandemic and remained below 2019 levels in 2022,<sup>5</sup> reinforces the salience of accurate understanding by policy makers.

_Figure 2: Policy makers underestimate learning poverty by large margins in EAP_ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Estimated by officials Actual estimates<br><!-- End of picture text --> Source: CGD–World Bank surveys, 2020 and 2022 and estimating interim learning poverty for Pacific Island Countries (2023). Only countries for which both data were available were included in the average (“Non-EAP”). When countries were surveyed in both waves, we use the most recent data. Solomon Islands and Vanuatu were surveyed in 2021.

Why is the disconnect between official estimates and estimated learning poverty levels by international organizations such as the World Bank so high in EAP? Some uncertainty in the officials' estimates of learning poverty is understandable for the following reasons: (i) the concept of a globally defined and agreed-upon “minimum proficiency level” against which students can be judged to be proficient is recent and still a work-in-progress; (ii) learning poverty levels are themselves uncertain for several reasons. Computations of learning poverty are based on sampled-based standardized tests which may not be perfectly comparable despite efforts to equate them, while sampling may lead to biases, especially for large and diverse countries such as Indonesia and the Philippines. For all participating countries in EAP, the SEA-PLM assessment appears more difficult at specific levels of competency than some of the other assessments used in the learning poverty estimates, even though in theory this is accounted for in the equating exercise; (iii) due to limited participation in international assessments, there are limited data; SEA-PLM is a new assessment and for Lao PDR (with the largest gap in our data between estimated and actual learning poverty at 73 percent) it is the only assessment that provides internationally comparable data showing how many of their children are in “learning poverty”; (iv) estimations of specific data by officials are frequently inexact based on other surveys, including data collected for the Global Education Policy Dashboard (World Bank, 2023), where public officials in Jordan, Peru, Niger, and Rwanda for example often misestimated average classroom size and level of teacher absenteeism by 10 to 20

> 5 - EFW, 2022 (https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416 <u>0200022022/related/EFW-2022-Jul1.pdf ).</u>

5
**Table 8: FPM Transfers, group size and teachers’ education**

|||Reduc|ed form<br>|||I|V<br>|||
|---|---|---|---|---|---|---|---|---|---|
||||eac|ers w|||eac|ers w|s|
||Grou|p size|<br>higher e|<br>ducation|Grou|p size|<br>higher e|<br>ducation|.|
|Panel A: Municipal supply||||||||||
|Thresholds 1-7|0.061<br>[0.041]|0.019<br>[0.048]|-0.001<br>[0.001]|-0.004***<br>[0.001]|0.075<br>[0.051]|0.025<br>[0.057]|-0.001<br>[0.001]|-0.005***<br>[0.001]|20990|
|Thresholds 1-4|0.108**<br>[0.050]|0.015<br>[0.063]|-0.002*<br>[0.001]|-0.004***<br>[0.001]|0.144**<br>[0.066]|0.021<br>[0.082]|-0.003*<br>[0.002]|-0.005***<br>[0.002]|18207|
|Panel B: Private supply||||||||||
|Thresholds 1-7|0.010<br>[0.036]|0.044<br>[0.029]|0.000<br>[0.002]|-0.002<br>[0.002]|0.012<br>[0.045]|0.057<br>[0.035]|0.001<br>[0.002]|-0.002<br>[0.002]|13471|
|Thresholds 1-4|0.043<br>[0.049]|-0.002<br>[0.002]|-0.001<br>[0.002]|-0.002<br>[0.002]|0.058<br>[0.066]|0.103*<br>[0.053]|-0.001<br>[0.003]|-0.003<br>[0.003]|10840|
|State fixed-effects|Yes|No|Yes|No|Yes|No|Yes|No||
|Municipality fixed-effects|No|Yes|No|Yes|No|Yes|No|Yes||

_Notes_ : Reduced form regressions relate the relevant outcome to theoretical transfers. In the IV regressions, theoretical transfers serve as instrument for actual transfers. All regressions include a three order population polynomial and year fixed effects. Robust standard errors clustered at the municipal level in brackets. *, **, *** represent significance at the 10%, 5% and 1% level, respectively.
Figure A.8: Examples of sites with and without oil production infrastructure (a) Site without infrastructure (b) Site with infrastructure This figure an example of a site in ISIS territory without oil production infrastructure and an example of a site in Daesh territory with infrastructure. Panel A marks coordinates (40.561188, 35.029762) over image from Digital Globe on 28 June 2012. Panel B marks coordinates (40.56361, 35.115139) over the same image.

30
# MACROECONOMIC EFFECTS OF

# TERMS-OF-TRADE SHOCKS: THE CASE OF OIL-EXPORTING COUNTRIES.

## Nikola SPATAFORA & Andrew WARNER

## **I.** Introduction

Most development economists agree that variations in world prices are an imnportant source of risk and instability for developing economies. Developing counaies derive about half their export earnings from primary commodities, whose world prices are extremely volatile. Nor is such volatility purely short-term: mauch data suggest that commodity prices undergo long periods of rise and decline. To provide one example of the magnitudes involved, the World Bank's index of non-oil commodity prices has exhibited a trend decline of about 1.5% per annum since 1948, cumulating to a 50% decline over 45 years. Over the next decade, the World Bank is forecasting that this trend will reverse and there will be a rise of about 0.7% per annum. What is the impact of these long-run movements in the external terms of trade on economic .growth and development?

We think that the world has provided a natural experiment over the past 25 years that can be used to analyze this issue. Oil-exporting developing countries experienced a major rise in the world price of their main export between 1973 and 1981, followed by a smaller, but still substantial, decline between 1981 and 1989. Nominal oil prices rose from $2.70 in 1973 to $34.31 in 1981, and then fell back to $16.31 in 1989; deflating this by US producer prices, real oil prices rose from an index of 1.0 in 1973 to 5.83 in 1981 and 2.42 in 1989. From a research standpoint, we think that this episode represents a fortunate opportunity to better understand the impact of terms-of-trade shocks, for the following reasons.

First, it seems important to distinguish between permanent and tansitory, or anticipated and unanticipated changes in the terms of trade, and we believe that oil prices come closer than any other data to measuring an unanticipated permanent change. The 1973 rise was not anticipated several years before, and it was quickly and widely perceived as a permanent feature of the economic landscape. The long decline
constant across similar types of firms in each community and has the additional advantage of mitigating endogeneity bias by smoothing the data (Escribano and Guasch, 2005).

The first thing to note is that the coefficients on capital and labour are very similar for large urban manufacturing firms, small urban manufacturing firms and rural manufacturing firms. However, total factor productivity varies substantially and significantly across different types of firms. Firms in rural areas are clearly the least productive. Furthermore, firms located in rural towns are substantially more productive than firms located in other rural areas. Taken together, our results suggest that there are large differences in productivity across rural and urban areas, but that the underlying technologies adopted by different types of firms do not differ radically, at least not in the manufacturing sector. Moreover, the evidence for increasing returns to scale is weak at best.<sup>17</sup> The characteristics of the manager matter, but not a lot. Overall, the educational attainment of the manager is not strongly correlated with productivity, though we find evidence of a concave relationship between productivity and the educational attainment of the manager in small urban firms, and evidence of a convex relationship in rural towns. However, the variation in the educational attainment of the manager within subsamples is limited except in the case of small urban microenterprises, which means the impact of education on productivity cannot be estimated very precisely. Our results also suggest that there is a negative productivity premium associated with female management and that this premium is most negative in rural areas.

To what extent might these findings be driven by endogeneity? As discussed in section 4.1.1, Loening et al. (2008) have demonstrated that endogeneity is unlikely to be a major issue for the rural data. For the urban data, we have information on expenditure on rental capital and the wage bill, which we use to compute Solow shares. For the largest

> 17 When we run a pooled regression small urban firms and large urban firms, the coefficient estimates on capital and labour sum to 1.06, yet are not statistically significantly different from 1. Results are omitted to conserve space but available from the authors upon request.

-16-
still constitute less than 15 percent of all parts production by KAICA affiliates (Table 3).<sup>1</sup> In contrast, close to 60 percent of Korea's assembled vehicles are exported. Roughly one-third of parts exports go to aftermarket sales for Korean vehicles overseas, roughly one-third to aftermarket sales for other car assemblers, and the remainder for assembly overseas.

**Table 3: Destination of Korean Parts Production (Billion Won)**

|**Year**|**OEM**|**Domestic**<br>**Market**|**After**<br>**Exports**|**Total**||||
|---|---|---|---|---|---|---|---|
|2001|20,658.5|1,342.8|2,034.9|24,036.2<br>billions)|(c.|$US|20|
|2002|23,762.2|1,544.5|2,089.0|27,395.7<br>billions)|(c.|$US|23|
|2003|26060.2|1824.2|4235|32,119.4<br>billions)|(c.|$US|27|

# <u>Source: http://www.kaica.or.kr/eng/industry/overview.html</u>

The performance of the assemblers since the financial crisis has been much better than many observers predicted during the dark days of 1997-98. Production has returned to precrisis levels. New foreign owners for Samsung and Daewoo provide access to their parents' worldwide distribution networks, and the possibility for Korean-sourced vehicles to be rebadged and sold under affiliates' names in foreign markets (which might otherwise have been averse to cars easily identified as manufactured in Korea: GM is selling Daewoo-sourced vehicles in the US and Europe under the Chevrolet badge). They also facilitate access to the most recent technologies of first tier suppliers.

Current developments in the domestic and global industries present new challenges for

> 1 The values recorded for exports in these KAICA data do not match those in the graphs presented earlier in this paper primarily because tire manufacturers are not members of KAICA (tires constitute close to a third of Korea's autopart exports); nor are some other auto component manufacturers.

33
**Figure 8: Global losses due to flooding**

<!-- Start of picture text --> 50000000<br>45000000<br>40000000<br>35000000<br>30000000<br>25000000<br>20000000<br>15000000<br>10000000<br>5000000<br>0<br>Losses 10 year moving median<br><!-- End of picture text --> Future expectations of loss from flooding generally predict further growth in impacts. For example, a foresight report based on IPCC climate predictions estimated that the increasing risk of urban flooding in the UK alone might cost from 1 to10 billion pounds a year by 2080 (Office of Science and Technology 2003; Evans et al. 2008).

The reduction of flood losses could potentially be tackled quite dramatically in the short to medium term via reduction of exposure and vulnerability of populations to flood risk. Analysis of economic and insured losses have been carried out extensively by Pielke (2000; 2006), Munich Re (2001; 2003; 2004a; 2004b; 2005; 2011), Barthel and Neumayer (2010), Crompton and McAneney 2008, Swiss Re (2010) and others. These studies demonstrate that the attribution of increased losses across exposure, vulnerability and hazard is currently weighted towards exposure and vulnerability rather than hazard.

# **1.7 Are other losses increasing?**

**Other losses, which are harder to quantify and are less well-represented in statistics, include the effects on health and stresses on local health services.** Stagnant water, the lack of safe drinking water and damaged sanitation systems afflict flood-affected populations, especially children less than five years of age. The incidence of acute diarrhea, malaria, respiratory infections and skin diseases is high. As an example, the floods in Dhaka in 2007 triggered the largest number of patients ever to attend the already overstretched International Centre for Diarrheal Disease Research Bangladesh Health Centre in Dhaka City (ALNAP 2008). There are also long term impacts which are generally not seen immediately after flooding. Studies such as Ahern et.al. (2005) and Few and Matthies (2006) reveal that floods have long-term, “hidden” effects, in the form of stress and trauma during and after the flood event. Increased flooding

13
governance structures were also found to insulate firms during the financial crisis (Minichilli et al., 2016). The one study to examine the management-crisis response nexus found that managerial ability helped dampen the negative effect of crude oil price uncertainty on firm performance in the United States between 1983 and 2016 (Phan et al., 2020).

The pandemic has prompted a new wave of studies quantifying effects on firms, primarily using surveys. Baker et al. (2020) show that COVID-19 increased economic uncertainty. Revenue loss, mass layoffs, closure, and liquidity have been impacted for firms in China (Dai et al., 2020), the U.S., and Europe (Bartik et al., 2020; Humphries et al., 2020; Abigail Adams-Prassl, Teodora Boneva, Marta Golin, 2020; Fairlie, 2020). While many firms confronted multiple effects at once, depressed demand is most often reported and has become a more prominent concern where pandemic disruption persists (Dai et al., 2020; Balleer et al., 2020). Firms in the United States show increases in cash holdings in March 2020 as COVID19 fears mounted (Acharya and Steffen, 2020), while revenue loss has led to financial distress among SMEs in hard-hit locations (Bartik et al., 2020; Zhang). Among these studies, there are very few systematic surveys of firm responses across sectors and countries. Beck et al. (2020) show evidence from 500 firm survey responses across 10 emerging countries that firms responded to the pandemic by reducing investment than by reducing payrolls, while surveys of firms operating on e-commerce platforms reveal sales and employment suffered among SMEs (Facebook and World Bank, 2020). These are not unexpected findings, given that the pandemic has been strongly associated with increased uncertainty among firms (Altig et al., 2020). Data collected by the World Bank from over 100,000 firms in 51 primarily lowand middle-income countries using short COVID-19 Business Pulse Surveys (COV-BPS), reveal that firms focused post-COVID-19 labor adjustments along the intensive margin, by increasing furloughs and reducing working hours, with only a small fraction laying off workers (Apedo-Amah et al., 2020). This study further underscores that the shock has exacerbated financial constraints most among small firms. While the long-run effects of the shock have yet to be measured, Barrero et al. (2020) use survey evidence to argue that the pandemic is in 4
# **<u>Sanitation Status</u>**

|_Improved Sanitation, Shared_|‐0.098|‐0.096|‐0.090*|‐0.092*|‐0.118**|‐0.118**|‐0.088|‐0.091|‐0.079|‐0.080|
|---|---|---|---|---|---|---|---|---|---|---|
||(0.086)|(0.086)|(0.053)|(0.053)|(0.056)|(0.056)|(0.065)|(0.064)|(0.050)|(0.050)|
|_Improved Sanitation, Private_|‐0.159**|‐0.158**|‐0.099*|‐0.096*|‐0.113**|‐0.109**|‐0.073|‐0.067|‐0.112**|‐0.109**|
||(0.077)|(0.077)|(0.051)|(0.051)|(0.055)|(0.055)|(0.064)|(0.063)|(0.049)|(0.049)|
|_(baseline: Unimproved Sanitation)_|||||||||||
|Log Household Size|‐0.138|‐0.137|‐0.067|‐0.073|‐0.040|‐0.045|‐0.038|‐0.051|‐0.035|‐0.041|
||(0.101)|(0.101)|(0.057)|(0.057)|(0.060)|(0.060)|(0.071)|(0.070)|(0.054)|(0.054)|
|Household in Urban Areas (=1)|‐0.125|‐0.128|‐0.018|‐0.023|‐0.005|‐0.011|0.022|0.013|‐0.012|‐0.015|
||(0.095)|(0.095)|(0.056)|(0.056)|(0.058)|(0.058)|(0.066)|(0.066)|(0.053)|(0.053)|
|**Wealth Index**|||||||||||
|_Second Quintile_|0.005|0.007|‐0.041|‐0.046|‐0.055|‐0.060|‐0.077|‐0.085|‐0.011|‐0.014|
||(0.078)|(0.078)|(0.053)|(0.053)|(0.056)|(0.056)|(0.066)|(0.066)|(0.050)|(0.050)|
|_Third Quintile_|‐0.041|‐0.041|‐0.005|‐0.008|‐0.011|‐0.017|0.033|0.029|0.009|0.006|
||(0.084)|(0.083)|(0.055)|(0.055)|(0.059)|(0.059)|(0.068)|(0.067)|(0.052)|(0.052)|
|_Fourth Quintile_|‐0.018|‐0.021|‐0.045|‐0.049|‐0.032|‐0.038|‐0.040|‐0.053|‐0.034|‐0.037|
||(0.090)|(0.090)|(0.061)|(0.061)|(0.065)|(0.064)|(0.075)|(0.074)|(0.058)|(0.058)|
|_Richest Quintile_|0.205|0.199|‐0.155*|‐0.159*|‐0.169*|‐0.175**|‐0.278***|‐0.287***|‐0.129|‐0.133*|
||(0.136)|(0.135)|(0.082)|(0.082)|(0.087)|(0.087)|(0.093)|(0.091)|(0.079)|(0.079)|
|_(baseline: First Quintile)_|||||||||||
|HH's Floor is made of Earth, Dung (=1)|0.262***|0.260***|0.141**|0.139**|0.162***|0.158***|0.114*|0.107|0.124**|0.122**|
||(0.089)|(0.089)|(0.060)|(0.060)|(0.060)|(0.060)|(0.068)|(0.067)|(0.056)|(0.056)|
|Functioning Handwashing Station (=1)|‐0.020|‐0.018|‐0.028|‐0.032|‐0.005|‐0.011|‐0.010|‐0.019|‐0.029|‐0.033|
||(0.061)|(0.061)|(0.039)|(0.039)|(0.042)|(0.042)|(0.050)|(0.050)|(0.037)|(0.037)|
|Unsafe Disposal of Child Feces (=1)|‐0.052|‐0.049|0.002|‐0.003|0.020|0.013|0.063|0.062|0.027|0.025|
||(0.064)|(0.064)|(0.054)|(0.054)|(0.060)|(0.061)|(0.085)|(0.085)|(0.050)|(0.050)|
|**Observations**|234|234|742|742|667|667|508|508|816|816|
|**Pseudo R2**|0.181|0.182|0.0902|0.0932|0.0870|0.0904|0.0789|0.0898|0.0810|0.0839|
|**Model VIF**|‐|‐|‐|‐|‐|‐|‐|‐|3.03|3.03|
|**Goodness of Fit Test**|3.34|3.94|6.71|8.19|6.07|5.69|2.49|5.67|5.42|9.32|

Standard Errors are Robust Clustered at Household Level

*** p<0.01, ** p<0.05, * p<0.1

14