rafmacalaba/datause-encoder-data huggingface dataset:
**_Table 3: Parameter values, changes and predicted growth effects (real GDP per capita)_**

|||**2000‐2**|**015**|**early 2**|**000s**|**late 20**|**00s**|**early 2**|**010s**|
|---|---|---|---|---|---|---|---|---|---|
||parameter|_change _|_predicted_<br>_effect_|_change _|_predicted_<br>_effect_|_change _|_predicted_<br>_effect_|_change _|_predicted_<br>_effect_|
|_Persistence_|0.781|0.189|0.118|0.084|0.066|0.115|0.089|0.189|0.148|
|_Structural:_||||||||||
|Δln(schooling)|0.018|0.508|0.016|0.056|0.001|0.245|0.004|0.207|0.004|
|Δln(credit/GDP)|0.074|0.565|0.072|0.264|0.020|0.104|0.008|0.196|0.015|
|Δln(trade/GDP)|0.082|0.086|0.012|‐0.135|‐0.011|0.222|0.018|0.000|0.000|
|Δln(govt C)|‐0.262|0.142|‐0.064|‐0.082|0.021|‐0.085|0.022|0.308|‐0.081|
|Δln(tele lines)|0.141|0.346|0.084|0.627|0.088|‐0.129|‐0.018|‐0.152|‐0.021|
|Δln(institutions)|‐0.003|0.379|‐0.002|0.314|‐0.001|0.065|0.000|0.000|0.000|
|_Stabilization:_||||||||||
|Δln(inflation)|‐0.011|0.145|‐0.003|‐0.016|0.000|0.253|‐0.003|‐0.092|0.001|
|Δln(exch rate)|‐0.064|‐0.076|0.008|‐0.202|0.013|0.336|‐0.021|‐0.211|0.013|
|Δ bank crisis|‐0.040|‐0.200|0.014|‐0.200|0.008|0.000|0.000|0.000|0.000|
|_External:_||||||||||
|Δln(TOT change)|0.118|0.004|0.001|‐0.017|‐0.002|0.085|0.010|‐0.064|‐0.007|
|Δln(commodity<br>prices)|10.482|0.002|0.028|0.001|0.013|0.002|0.017|‐0.001|‐0.014|
|predicted growth|||0.312||0.216||0.126||0.057|
|**annualized**|||**1.9%**||**4.3%**||**2.5%**||**1.1%**|
|**actualgrowth**|||<br>**5.3%**||<br>**2.3%**||<br>**3.8%**||**9.9%**|

Source: Authors’ calculation, obtained by inserting Ghana’s values for the explanatory variables and using the regression coefficients of the baseline model presented. Note that results for the period 2000-2015 need not be consistent with the aggregation of the three included 5-year periods because the former uses the growth between the early 1980s and the late 1990s a lagged dependent variable in the persistence term which is not reflected in the 5-year intervals.
# 4.2. Gasoline prices and PM2.5 air pollution

Similar to the results for diesel prices, the price of gasoline appears to be negatively correlated with air quality, as a US$1 increase in price per liter is tied to a 22.2 μg/ m<sup>3</sup> decrease in PM2.5 concentrations, significant at the 10 percent level (table 6). Two of the three remaining coefficients are in line with expectation: the positively correlated time trend and the quantity of gasoline consumed. Contrary to earlier results for diesel, GDP per capita is negatively correlated with air quality. However, none of the three nonprice variables are statistically significant.

**Table 6. CCE estimation results**

||**Coefficient**|**SE**|**P-value**|**Significance**|
|---|---|---|---|---|
|θ|0.2825|4.0000|0.944||
|p|-22.1996|12.7100|0.081|*|
|q|0.1532|0.2496|0.539||
|GDP/cap|-0.0111|0.0075|0.138||

_Notes:_ θ = time trend; _p_ = fuel price (intl $ PPP) ; _q_ = quantity of fuel consumed per capita (toe) ; GDP/cap = GDP per capita (2017 US$ PPP); *** = significant at the 1% level, ** = significant at the 5% level, * = significant at the 10% level

# 4.3. Coal prices and PM2.5 air pollution

In contrast to the other two fuels, results for coal are markedly different (table 7). Contrary to what one might expect, coal prices and GDP per capita are positively correlated with PM2.5 concentrations, whereas quantity of coal consumed is negatively correlated. The only semblance to the earlier results is the positive time trend. While this may reflect unexpected behavior, none of the results are statistically significant.

**Table 7. CCE estimation results**

||**Coefficient**|**SE**|**P-value**|**Significance**|
|---|---|---|---|---|
|θ|0.1018|0.9966|0.919||
|p|0.7975|1.6634|0.632||
|q|-0.3587|0.6144|0.559||
|GDP/cap|0.0016|0.0027|0.542||

_Notes:_ θ = time trend; _p_ = fuel price (intl $ PPP) ; _q_ = quantity of fuel consumed per capita (toe) ; GDP/cap = GDP per capita (2017 US$ PPP); *** = significant at the 1% level, ** = significant at the 5% level, * = significant at the 10% level There are several possible explanations why national coal consumption may be highly inelastic to price variations. For example, coal-fired power generation serves baseload electricity and is not typically adjusted in response to short-term coal price variations. Instead, it is likely to operate continuously, with price variations absorbed by operators, which are frequently state-owned enterprises. A similar rationale may apply to industrial uses of coal—for instance, in steel production and manufacturing— where producing less in response to increased input prices may not be an option, and the additional cost is passed on to consumers instead. Moreover, this analysis focuses on PM2.5 particles, but coal 11
**Table 1: Descriptive statistics on the sample [employed working age population]**

||_Male_|_Female_|_All_|
|---|---|---|---|
|Employment Rates|48.30|30.80|39.60|
|||**Employed Individuals**<br>||
|Sample size|6,536|4,445|10,981|
|Weighted sample|240,956|162,608|403,564|
|**Age Group**||||
|% 15-24|7.00|6.55|6.73|
|% 25-34|25.09|25.41|25.28|
|% 35-54|60.26|57.43|58.57|
|% 55-64|7.65|10.61|9.41|
|**Education**||||
|% Low Skill|80.43|71.27|76.74|
|% High Skill*|19.57|28.73|23.26|
|**Region**||||
|% Skopje|27.28|32.26|29.29|
|% Bitola|11.59|12.54|11.97|
|% Veles|8.16|7.89|8.05|
|% Kumanovo<br>|6.60<br>|5.49<br>|6.15<br>|
|% Ohrid|11.08|7.48|9.63|
|% Strumika|9.23|11.63|10.19|
|% Tetovo|15.15|7.30|11.98|
|% Shtip|10.91|15.42|12.73|
|**Industry**||||
|% Agriculture/mining/fishing|5.38|1.97|4.00|
|% Manufacturing|24.04|34.61|28.30|
|% Elec/Gas/Water|5.69|1.39|3.95|
|% Construction|12.73|1.81|8.33|
|% Wholesale/retail|11.11|12.60|11.71|
|% Hotels/Restaurants|3.92|3.15|3.61|
|% Transport/communication|7.92|2.58|5.77|
|% Fin & Real State Svs.|3.63|6.05|4.60|
|% Public Admin.|11.46|7.21|9.75|
|% Education/health/Social Work|9.47|25.15|15.79|
|% Other services|4.66|3.48|4.19|
|**Ownership**||||
|% Private|50.6|54.2|52.1|
|% Non private**|49.4|45.8|47.9|
|**Total**|100.0|100.0|100.0|

Source: Author’s estimates using 2006 Macedonia LFS data. * High-skill workers are defined as those with at least complete higher education (university and above). ** Includes public and semi-private companies such as utilities (gas, water, and electricity).

11
‘Uninhabited’ grid cells are removed from the estimation sample. Corresponding summary statistics are presented in Table 4 and Table 5. The size of the restricted sample is now of 427,553 pixels.

Table 3. Number of observations for the whole sample before selection

|||**sett**|**lement**||
|---|---|---|---|---|
||**Uninhabited**|**Rural**|**Urban**|**Total**|
|Total Observations|5,410,141|303,450|41,895|5,755,486|
|Percentage of total|94%|5%|1%||

our main variable of interest, nightlights, exhibits a relatively low average intensity at the pixel level. This observation is aligned with the overall low density of distribution of nightlights, as corroborated by a population density of 16 person per km2. Additionally, the average value of the urban dummy variable, which is close to 0, indicates a prevalence of rural flooded pixels over urban flooded pixels within our sample.

Table 4. Summary statistics for habited flooded pixels (rural and urban categories) sample

||**N**|**Mean**|**SD**|**Min**|**p5**|**Median**|**p95**|**Max**|
|---|---|---|---|---|---|---|---|---|
|_PopDens_|427,553|16.99|75.32|0|0|0|89.89|1367.76|
|cf_cvgBuff05|427,553|11.74|3.33|1|5.34|12|16.49|21|
|avg_radBuff05|427,553|1.02|4.16|0|0.06|0.2|3.75|88.18|
|urban|427,553|0.12|0.32|0|0|0|1|1|

When considering the conflict-affected areas variable, the number of treated pixels – reflecting conflict-associated with each flooded grid cell - will depend on the conflict definition and buffer size considered. Table 5 provides a summary statistic of the number of conflicts and fatalities across different buffer sizes going from 10km to 100km.

Table 5. Summary statistics of variables differing by buffer size

|**Bufer size**|**Variable**|**Mean**|**SD**|**Min**|**p5**|**Median**|**p95**|**Max**|
|---|---|---|---|---|---|---|---|---|
|100km|conficts|16.71|59.46|0|0|5|45|740|
||fatalities|4.12|11.59|0|0|1|14|156|
|90km|conficts|13.98|49.77|0|0|4|38|645|
||fatalities|3.54|10.15|0|0|1|12|154|
|80km|conficts|11.56|41.84|0|0|4|31|577|
||fatalities|3.03|9.19|0|0|1|11|135|
|70km|conficts|9.25|34.58|0|0|2|26|526|
||fatalities|2.48|7.93|0|0|0|10|113|
|60km|conficts|7.15|27.23|0|0|1|25|422|
||fatalities|1.98|6.82|0|0|0|9|102|

11
5 cases; where government assistance occurs, it is disproportionately to the better off. If it comes, assistance is largely from other households. The most common coping strategy is dissaving, but borrowing is also quite common, as are—albeit to a lesser extent—selling assets and delaying plans. Dissaving is more likely and borrowing is less likely following a covariate shock like an agricultural shock. Health shocks stand out from other shocks in triggering multiple coping strategies. We next estimate consumption regressions, and find only limited evidence of households not being able to smooth consumption in the face of shocks. These results contrast with households’ responses to questions asking their own assessment of the impacts of shocks: the majority said they had to cut back consumption following a shock, and reported that it affected their welfare, with a mean score of 3.2 on a 4‐point scale ranging from 1 (not at all) to 4 (a lot). Finally, we find that household members experiencing a health shock did not recover their former subjective health following a health shock, losing, on average, 0.5 points on a 5‐point scale.

# II. **THE SETTING**

Lao PDR is one of the poorest countries in South East Asia, with an ethnically diverse and predominantly rural population.<sup>1</sup> The Central and Southern regions are mountainous in the east, but also endowed with fertile plains that can be irrigated for rice and other crops along the Mekong and other rivers; the North is mainly mountainous and rugged, with no irrigable land other than limited mountain valleys. In many parts of the country, economic integration is limited, with 30 percent of villages in the Northern and Southern parts of the country having no wet season road access (World Bank 2010).

> 1 In 2007, 71% of the population was engaged in agriculture as the primary source of employment and income (World Bank (2010)).
between 5% and 6% of households’ total spending. In other words, energy spending as share of household total spending is similar across socioeconomic groups, without marked differences across quintiles. But despite being small, these differences are still interesting. In fact, it is the households in the poorest and richest quintiles that spend a higher proportion of their budgets on energy (just over 6% of their total spending). But while the poorest spent its largest share on electricity, the richest does so on gasoline.

Large differences become evident when absolute spending is compared across quintiles of the consumption distribution. Figure 4: Per Capita Expenditures on Energy (by quintiles of consumption, in TND) shows that the richest individual spends more than 200 times per capita than a poor individual. Socioeconomic disparities in spending remain for diesel as well, but are notably reduced for electricity and LPG. In fact, spending on LPG is more uniform across all socioeconomic groups, between TND 20 and 29, and it is the fourth quintile that spends the most.

Figure 3: Household Expenditure on Energy (% of total household expenditures by quintiles of consumption) Figure 4: Per Capita Expenditures on Energy (by quintiles of consumption, in TND) <!-- Start of picture text --> 7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>Quintile Quintile Quintile Quintile Quintile Total<br>1 2 3 4 5<br>Gasoline Diesel LPG Electricity<br><!-- End of picture text --> <!-- Start of picture text --> Quintile 3<br><!-- End of picture text --> _Source:_ WB staff calculations using SUBSIM

# **4.2. Socioeconomic Distribution of Energy Subsidy Benefits**

The distribution of monetized benefits accruing from subsidies is the result of a number of considerations, such as the degree of price progressivity, the pro-poor nature of the price structure, the share of subsidized price on final price, and the composition and total consumption by socioeconomic group. Unsurprisingly, energy subsidies are concentrated on LPG and electricity subsidies. Moreover, the concentration of benefits from these two sources is true for all consumption quintiles (Table 7, left panel). This implies that all of the poor, the bottom 40%, and richer individuals obtain most of their energy subsidies from LPG and electricity. In relative terms, they represent 53.3% and 40.4% of total subsidies, shares that for the poorest quintile slightly increase to 54.5% and 42.2% of their total energy subsidies, respectively. For the rest of the quintiles, subsidies from both sources also capture the lion’s share of total benefits. However, the distribution of total benefits by socioeconomic group also shows that energy subsidies— either universal or targeted—are pro-rich. The poorest quintile captured the lowest share of all 14
Peters, Dominique (2010), “Price Competitiveness in Central and Eastern Europe – a case study for transition economies” IMK Study No 1/2010, Hans-Böckler-Stiftung Institut für Makroökonomie und Konjunkturforschung (IMK), Düsseldorf.

Rodrik, Dani (2008), “The Real Exchange Rate and Economic Growth”, _Brookings Papers on Economic Activity_ , Fall, pp365-412.

Turner, Antony G. and Stephen S. Golub (1997), “Towards a system of Multilateral Unit Labor Cost-Based Competitiveness Indicators for Advanced, Developing and Transition Countries”, Working Paper WP/97/151, International Monetary Fund.

25
Carleton, T. A., and S. M. Hsiang. 2016. “Social and Economic Impacts of Climate.” _Science_ 353 (6304): aad9837.

Carter, M. R., P. D. Little, T. Mogues, and W. Negatu. 2007. “Poverty Traps and Natural Disasters in Ethiopia and Honduras.” _World Development_ 35 (5): 835–56.

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Dang, H.-A. H., S. Hallegatte, and T.-A. Trinh. 2023. “Does Global Warming Worsen Poverty and Inequality? An Updated Review.” _SSRN Electronic Journal_ .

Deaton, A. 2007. “Height, Health, and Development.” _Proceedings of the National Academy of Sciences_ 104 (33): 13232–37.

Deuchert, E., and C. Felfe. 2015. “The Tempest: Short-and Long-Term Consequences of a Natural Disaster for Children’s Development.” _European Economic Review_ 80: 280–94.

Duque, V., M. Rosales-Rueda, and F. Sanchez. 2018. “How Do Early-Life Shocks Interact with Subsequent Human-Capital Investments? Evidence from Administrative Data.” In _IZA World of Labor Conference_ .

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Hallegatte, S., A. Vogt-Schilb, J. Rozenberg, M. Bangalore, and C. Beaudet. 2020. “From Poverty to Disaster and Back: A Review of the Literature.” _Economics of Disasters and Climate Change_ 4 (1): 223–47.

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# Policy Research Working Paper Series

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPSI 04|7C6ted'lvoire: Private Sector<br>Dynamics and Constraints|Enrique Rueda-Sabate<br>Andrew Stone|r November<br>1992|P. Infante<br>37642|
|WPS104|8 Targete.<br>and Indicators in World<br>Bank Population<br>Projects|George Baldwin|November<br>1992|0. Nadora<br>31091|
|WPS104|9 Money Demand and Seignorage.<br>Maximizing<br>Inflation|William Easterly<br>Paolo Mauro<br>Klaus Schmidt-Hebbel|November<br>1992|R. Martin<br>31448|
|WPS105|0 Marginal Income Tax Rates and<br>Economic<br>Growth in Developing<br>Countries|William Easterly<br>Sergio Rebelo|November<br>1992|R. Martin<br>31448|
|WPS105|1 The Legal Framework<br>for Private<br>Sector Activity in the Czech and<br>Slovak Federal Republic|Cheryl W. Gray|November<br>1992|M. Berg<br>36969|
|WPS1<br>052|A Reappraisal<br>ofHow Oral<br>Rehydration<br>Therapy Affected Mort<br>in Egypt|Hoda Rashad<br>aiity|November<br>1992|0. Nadora<br>31091|
|WPS105|3 Development<br>of the Zimbabwe<br>Family Planning<br>Program|Alex F. Zinanga|December<br>1992|0. Nadora<br>31091|
|WPS105|4 Distributional<br>Impact of Cash and<br>In-Kind Social Transfers in Eastem<br>Europe and Russia|Branko Milanovic|December<br>1992|S. Moussa<br>39019|
|WPS105|5 Wealth, Weather Risk, and the<br>Composition<br>and Profitability<br>of<br>Agricultural Investments|Mark R. Rosenzweig<br>Hans P. Binswanger|December<br>1992|C. Banton<br>34783|
|WPS1<br>056<br>|Earnings and Education<br>in Latin<br>America: Assessing Priorities for<br>Schooling Investments|George Psacharopoulo<br>Ying Chu Ng|s December<br>1992|L. Longo<br>39244|
|WPS1057<br>|Measuring<br>the Incomes of Economi<br>of the Former Soviet Union|es Socio-Economic<br>Data<br>Division<br>International<br>Economics<br>Department|December<br>1992|E. Zamora<br>33706|
|WPS1058<br> <br>|The Pricing of Country Funds and<br>Their Role in Capital Mobilization<br>for<br>Emerging Economies|Ishac Diwan<br>Vihang Errunza<br>Lemma W. Senbet|December<br>1992|R. Vo<br>31047|
|WPS1059<br>i|Political Economy of Policy Reform<br>n Turkey in the 1980s|Ziya6nis<br>Steven B. Webb|December<br>1992|S. Gustafson<br>37856|
|(7)<br>Smoke or<br>chew<br>tobacco|-0.005**<br>(0.002)<br>-0.002<br>(0.002)<br>-0.001<br>(0.002)<br>0.000<br>(0.002)<br>4,421<br>0.155<br>Yes<br>Yes<br>Yes<br>383<br>0.195|e-in-dierence<br>t is age 16 to<br> and month of|
|---|---|---|
|(6)<br>Number of<br>marriages<br>n<br>or unions|-0.020<br>(0.020)<br>0.032*<br>(0.018)<br>0.024<br>(0.020)<br>0.002<br>(0.021)<br>4,421<br>0.108<br>Yes<br>Yes<br>Yes<br>383<br>19.89|ased on a dierenc<br> Comparison cohor<br>tor for high caste|
|(5)<br>Age at<br>rst<br>cohabitatio|0.001<br>(0.002)<br>0.000<br>(0.001)<br>0.000<br>(0.001)<br>0.000<br>(0.001)<br>4,420<br>0.039<br>Yes<br>Yes<br>Yes<br>383<br>1.059|are reported b<br>art of the war. <br>clude an indica|
|(4)<br>Age at<br>rst<br>birth|-0.030*<br>(0.017)<br>0.016<br>(0.016)<br>0.009<br>(0.020)<br>-0.009<br>(0.023)<br>4,420<br>0.146<br>Yes<br>Yes<br>Yes<br>383<br>17.50|ed estimates<br>ge at the st<br> controls in|
|(3)<br>Age at rst<br>sexual<br>intercourse|-0.026<br>(0.019)<br>0.018<br>(0.015)<br>0.012<br>(0.019)<br>-0.028<br>(0.025)<br>4,421<br>0.144<br>Yes<br>Yes<br>Yes<br>383<br>17.56|ent on the treat<br> table refer to a<br>ebo test. Other|
|(2)<br>Number<br>of sex<br><br>partners|-0.003<br>(0.003)<br>0.001<br>(0.001)<br>-0.003<br>(0.003)<br>0.000<br>(0.001)<br>4,421<br>-0.013<br>Yes<br>Yes<br>Yes<br>383<br>1.065|) level. Treatm<br>All ages in the <br>erves as a plac|
|e||%<br> <br> s|
|(1)<br>Ever<br>use<br>contraceptiv|-0.001<br>(0.002)<br>-0.001<br>(0.001)<br>-0.002<br>(0.002)<br>0.001<br>(0.003)<br>4,421<br>0.110<br>Yes<br>Yes<br>Yes<br>383<br>0.872|he 1% (5%) (10<br> at village level.<br>son group that|
||Age 22 to 29 _×_ Months of war<br>Age 9 to 15 _×_ Months of war<br>Age 4 to 8 _×_ Months of war<br>Age0 to 3 _×_ Months of war<br>Observations<br>Adjusted R-squared<br>Birth year and month xed eects<br>Regional trends and other controls<br>Village xed eects<br>Number of clusters<br>Control mean (Age 16 to 21)|Note: *** (**) (*) indicates signicance at t<br>specication. Standard errors are clustered <br>21 and cohort 22 to 29 is a second compari<br>interview xed-eects.|

46
Figure 3: 91-day treasury bill rate, 2000–14 <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Source: Data from Bank of Ghana.The interest burden of this increased and high public debt stock was considerable. In 2015 interest payments alone amounted to GH ₵ 9.5 billion (compared with a total debt stock of GH ₵ 9.5 billion in 2008). Interest payments as a percentage of GDP increased from 2.8 per cent in 2008 to 7.1 per cent in 2015 (Figure 4).

Figure 4: Interest payments as percentage of GDP, 2000–15 <!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Note: * Figure for 2015 is projected.

Source: Data from Ministry of Finance and Economic Planning.

This high interest burden, combined with a rising wage bill, left Ghana’s government with very little money for other critical areas. To put this in perspective, the government of Ghana’s 2015 15
which probably explains the multiple failures of many initiatives in this area. Only a couple of importers may be on the side of reform.

# **2. An Analytical Demand Model – Some Theoretical Considerations**

Long dwell times (which account for a large share of containers in terminals) are one of the key issues that needs to be addressed (probably across the continent) and are related mostly to factors under the control of shippers. This confirms one of the initial hypotheses of this analysis, which is that the behaviors and strategies of shippers have an impact on dwell times in ports. Demand by importers for port dwell time beyond the time required to complete port operations and transactions seems to be related mainly to inventory management and the ―business model‖ used (including the extent of informal practices).

Due to the fact that the demand from importers seems to explain a large part of long dwell cargo, in this section we present theoretical foundations explaining current demand issues in SSA and then present some empirical analysis, based mainly on firm surveys.

The model examines cost minimization strategies and profit maximization strategies. Coupled with various market structures, it seeks to explain why behaviors that are perceived as irrational, such as leaving cargo in the port are the best option for an importer. It describes the economic foundations of rational decisions with regard to container storage in port terminals and off-dock container yards (ODCYs).

Storage operations can be defined as a subcomponent of an international logistics pathway that starts with loading containers in the supplier’s facilities and ends with unloading them in the customer’s facilities. We define the logistics pathway as ―a sequential set of logistics operations, warehousing, depot operations, port operations, trucking, and freight forwarding, which deal with the end-to-end movement of freight‖ (Magala and Sammons, 2008). In addition, we focus on containerized trade only, specifically containerized trade through international ports.

When deciding to import a certain quantity of containerized cargo, shippers have to choose either directly or indirectly (through contracted shipping and freight forwarding agents or logistics providers) what logistics pathway to use. This is an informed supply chain decision that is generally based on a combination of rational criteria such as cost, delivery time, frequency, and risk as well as some behavioral patterns (for example, repeat-buyer behaviors).

Our objective is to model how shippers make rational decisions about logistics pathways and, more specifically, what are the drivers of demand for storage in port terminals or ODCYs. Figure 2 presents the set of players involved.

4