rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 8. Difference in total monetary consumption per capita between the lean season and non-lean season waves, split by rural and urban households** _Panel A: Rural_

||Burkin|a Faso|Ni|ger|Sen|egal|All except C|had and Mali|
|---|---|---|---|---|---|---|---|---|
||No|Full|No|Full|No|Full|No|Full|
||controls|controls|controls|controls|controls|controls|controls|controls|
|Lean season<br>wave|-0.1861***|-0.1871***|-0.0866**|-0.0991***|-0.0911**|-0.0900***|-0.1211***|-0.1305***|
||(0.0444)|(0.0331)|(0.0346)|(0.0243)|(0.0406)|(0.0251)|(0.0268)|(0.0168)|
|N|3863|3802|4447|4359|3214|3194|11524|11355|
|R-squared|0.0272|0.3721|0.0081|0.3625|0.0081|0.4274|0.0123|0.4362|

# _Panel B: Urban_

||Burkin|a Faso|Nig|er|Sen|egal|All except C|had and Mali|
|---|---|---|---|---|---|---|---|---|
||No|Full|No|Full|No|Full|No|Full|
||controls|controls|controls|controls|controls|controls|controls|controls|
|Lean season<br>wave|-0.0668|0.0150|0.0553|0.0735*|-0.0917*|-0.0891***|-0.0422|-0.0238|
||(0.0782)|(0.0355)|(0.0790)|(0.0419)|(0.0544)|(0.0260)|(0.0445)|(0.0194)|
|N|3154|3134|1573|1560|3939|3913|8666|8607|
|R-squared|0.0025|0.6005|0.0018|0.5446|0.0064|0.5255|0.0011|0.5779|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors clustered at the ‘grappe’ level in parentheses. Dependent variable is the log of total monetary consumption per capita. Individual-level sampling weights applied. ‘Full controls’ specifications include controls for the gender, age, and education level of the household head, household size, the dependency ratio, household walls material, household roof material, household floor material, access to electricity, access to improved drinking water, and access to improved sanitation as well as region fixed effects. Source: EHCVM and World Bank estimates.

Within rural areas, we observe that it is crop-cultivating households where the effects of seasonality appear to be largest; non-crop-cultivating households in rural areas appear to be impacted far less. For crop-cultivating households, the difference in consumption between the lean and non-lean season wave is around 13.1 percent, statistically significant at the 1 percent level (Table 9). By contrast, there is no statistically significant difference between the two waves for rural households that do not cultivate crops. Again, using Equation 2 to test the difference between these two effects, the coefficient on the interaction term (between the crop cultivation dummy and the lean season dummy) is statistically significant at the 1 percent level, in the specification with full controls. Therefore, it is agriculture that exposes rural households to the effects of seasonality.

20
# **_World Sugar Prices_**

Figure 5 shows average annual world free market prices for raw sugar and estimated free market prices for plantation white refined sugar for the sugar years (October-September) 1970-95. A longer constant-world price series for plantation white for the period 1965-95 is illustrated in Fig. 11. In the past world free market prices have been extremely volatile, with particularly sharp peaks and collapses between 1973 and 1976 and again between 1979 and 1982. Since 1982 prices have been less volatile, however, and in real terms have been on average at about the same level as they were in the 1960s and early 1970s i.e. at about US 10 cents/pound in constant 1981 prices. In contrast to most other primary commodity prices, there was no statistically significant downward trend between 1965 and 1995. The reduced volatility since 1982 is probably in part a consequence of the much larger volume of sugar traded in the world free market than before 1975 ( Data S5) and also of the increased role of corn and other sweeteners, which have become a significant constraint to massive price increases of the kind which occurred in between 1973 and 1975, and 1979 and 1981 .

Figure 5: **Nominal world prices for raw and refined (plantation white) sugar, 1970-95** <!-- Start of picture text -->
-RAW  REFINED<br>i  I  S  .'\\.  . .<br>. ~  I  ...<br><!-- End of picture text -->

Source: See Data SI for details World prices for plantation white sugar are not published regularly, and so the series of international prices for Indian plantation white sugar shown in Figs 5 and 11 has been estimated from the free market international price of raw sugar plus an average refining margin equivalent to 90 percent of the margin between international raw sugar prices and the international prices of fully refined sugar. Taking 90 percent of the full margin follows Goldar and Gulati (1991), who used price differences existing during a few years in the 1980s when London prices for plantation white were reported. These calculations are shown in Data S1. As shown there and in Fig. 5, the 12
**_<u>________________________________________________________</u>_**

# **Annexes**

## **Annex A. World Business Environment Survey**

Regulations can be burdensome in different ways - they can impose direct costs (formal and informal payments, facilitation costs, expenditures of staff time) or indirect costs (the inefficient allocation of firm resources in response to the incentives created by regulation and regulatory enforcement). In the WBES, firms evaluated the severity of several potential regulatory constraints.

More relevant to trade facilitation, _Customs Procedures and Trade Regulations_ imposed serious constraints on more than half of all firms in LAC and South Asia, 49 percent of firms in Africa, and 44 percent of firms in the MENA region (Batra, Kaufmann and Stone, 2003). More than one-third of firms in the developing East Asia and the CEE regions considered this as a serious constraint. Small enterprises were significantly less constrained by customs and trade regulations in comparison with large and medium-size firms. The average waiting time for goods to be processed through ports and customs varied substantially, ranging from roughly 2 days in OECD nations, East Asia, and China (a firm’s median wait was generally less than half a day), to more than 9 days in Central and Eastern Europe (a median of 5 days), 11 days in South Asia (a median of 7 days), and more than 16 days in Africa (a median of 10 days). These estimates exclude outliers, or processes that take more than 90 days.

## **Percentage of Firms That Rated Regulatory Constraints on Firms as “Major” or “Moderate”**

|Constraints|CEE|LAC|CIS|Africa|<sup>East Asia</sup><br>Developing|<sup>OECD</sup>|South<br>Asia|MENA|East Asia<br>NIC/China|
|---|---|---|---|---|---|---|---|---|---|
|High Taxes|89.8|82.9|82.6|76.2|74.5|72.3|71.6|53.6|39.3|
|Tax Administration<br>and Regulation|<br>74.5|62.5|65.2|59.3|54.6|59.0|52.2|50.8|20.9|
|Customs/Trade<br>Regulation|36.3|55.9|32.0|48.6|36.0|28.0|58.9|44.3|20.3|
|Labor Regulation|18.2|52.5|33.7|38.0|35.2|47.7|60.0|32.6|27.4|
|Business<br>Registration|38.3|55.3|23.4|25.5|27.7|32.4|40.2|25.9|21.6|
|Environment|26.4|37.2|28.1|21.7|29.4|37.8|43.7|24.1|17.1|
|Foreign<br>Exchange/Currency|29.8|33.1|20.9|36.1|35.2|19.1|40.5|26.1|17.8|
|Fire/Safety<br>regulation|22.1|28.5|21.6|15.9|29.4|29.8|27.4|21.6|12.2|

_Source : Batra, Kaufmann and Stone (2003)_ 42 __________________________________________________________________________________________________________
from 6% of the informal sector workforce in 1989 to 21% by 2010 and may account for the depressed loan shares in the informal sector over our study period.

Table 1d shows the breakout of the unorganized sector by one-person plants vis-à-vis larger unorganized plants.<sup>18</sup> Panel A shows the raw observation count of one-person and larger plants, while Panel B applies sampling weights to these observation counts. The latter panel suggests that the share of one-person plants in the unorganized sector has increased steadily from 32% in 1994 to 57% in 2010. Panel C shows the share of plants reporting access to financial loans without applying sampling weights, while Panel D uses sample weights. The share of one-person plants reporting access to financial loans is considerably lower than larger plants, and we see this gap widen with the recent explosion in one-person firms. The sample shift towards one-person firms plays an important role in the financial trends observed for the unorganized sector.

Table 1b reports some descriptive statistics for misallocation indices for Indian districts. Given the background of Table 1a, we focus our empirical analysis on the misallocation of financial loans in the organized sector only. While it is certainly feasible to estimate misallocation for loans across the informal sector as well, we hesitate to do so given the small fraction of unorganized businesses reporting loans – which would translate into the majority of observations having zero shares for this input. It seems prudent to instead start with the organized sector and its well-populated and mostly non-zero financial loan data, given our understanding of the misallocation metrics in this setting, and then expand outward in future work.

Panel A of Table 1b shows the average misallocation metrics using our baseline computation of misallocation at the district-industry level but aggregated to the district level using the shares in the concerned factor. Panel B shows descriptive statistics using the misallocation metric calculated from LP-Sivadasan style TFP but without the industry aggregation step, while Panel C uses OLS TFP to compute the misallocation index. When interpreting Table 1b, it is important to recall that higher numbers indicate greater misallocation.

There are two points of Table 1b that are worth highlighting. First, access to external finance in the organized sector started with lower misallocation in 1989 vis-à-vis land and buildings and

> 18 In most analyses we assign a 0.5 value for part-time employees when calculating total employment of a plant. For this exercise, we assign a plant with one employee, whether full time or part time, as a one-person firm.

21
- 27 -

Table 5: Granger causality (two ways) between institutions and markets

||||Mc||||||**VT**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||CS|**PF**|**U**|**NL**|TOT||CS|PF|**U**|**NL**|TOT|
|**PRT**|1|1||1|3|**DEU**<br>|1||1|1|3|
|**CHL**|1|1|||2|**NLD**|1|1|1||3|
|**NOR**|<br>1|||1|2|**THA**<br>|||1|1|2|
|**THA**||||1|1|**AUS**||1|||1|
|**ZAF**|||1||1|**AUT**<br>||1|||1|
|**AUS**|||||0|**CHL**||1|||1|
|**AUT**|||||0|**PRT**||||1|1|
|**BEL**|||||0|**SWE**||||1|1|
|**CAN**|||||0|**BEL**|||||o|
|**DEU**|||||0|**CAN**|||||**0**|
|**ESP**|||||0|**ESP**|||||**0**|
|**FIN**|||||0|**FIN**|||||0|
|**GBR**|||||0|**GBR**|||||**0**|
|**KOR**|||||0|**KOR**|||||**0**|
|**MYS**|||||**0**|**MYS**|||||**0**|
|**NLD**|||||0|**NOR**|||||0|
|**SGP**|||||0|**SGP**|||||0|
|**SWE**|||||0|**USA**|||||0|
|**USA**|||||**0**|**ZAF**|||||**0**|
|TOT|3|2|1|3||TOT|2|4|3|4||

Notes: See Table 12 in Appendix 2 for the list of countries.

Table 6: No Granger causality between institutions and markets

||||MC||||||VT|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**CS**|**PF**|**U**|**NL**|**TOT**|<br>|CS|**PF**|**U**|**NL**|**TOT**|
|**USA**|1|1|1|1|4|**ESP**<br>|1|1|1|1|4|
|**AUS**|1|1|1||3|**USA**<br>|1|1|1|1|4|
|**MYS**|1|1|1||3|**BEL**<br>|1||1|1|3|
|**SGP**|1|1|1||3|**CAN**<br>|1|1||1|3|
|**AUT**|1|1|||2|**AUS**|1||1||2|
|**CHL**<br>|||1|1|2|**iMYS**<br>|1|1|||2|
|**ESP**<br>|||1|1|2|**PRT**<br>|1|1|||2|
|**GBR**<br>||1||1|2|**AUT**<br>||||1|1|
|**KOR**<br>|<br>1<br>||1||2|**CHL**<br>|||1||1|
|**SWE**<br>|<br>1||1||2|**DEU**<br>||1|||1|
|**CAN**||||1|1|**KOR**<br>||||1|1|
|**DEU**<br>||1|||1|**SGP**<br>||1|||1|
|**FIN**||||1|1|**THA**||1|||1|
|**ZAF**||||1|1|**ZAF**||1|||1|
|**BEL**|||||0|**FIN**<br>|||||0<br>|
|**NLD**|||||0|**GBR**|||||0|
|**NOR**|||||**0**|**NLD**|||||**0**<br>|
|**PRT**|||||**0**|**NOR**|||||**0**|
|**THA**|||||0|**SWE**<br>|||||0|
|**TOT**|7|7|8|7||**TOT**|7|9|5|6||

Notes: See Table 12 in Appendix 2 for the list of countries.

There are other facts that help interpret some of our results. For example, the absence of causality in either direction in Malaysia and Singapore could be explained by the contractual savings regime in these countries as well as financial sector policies.
_Table 1.4: Wage Premium (%) for the DTS, General Average HS Grade - OLS Estimate on_ _Latest Salary_

||(1)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|
|VARIABLES|DTS|DTS|DTS|DTS|DTS|DTS|
|Years of schooling|0.062**|0.045|0.052|0.057|0.049|0.049|
||(2.331)|(0.799)|(1.164)|(1.173)|(0.872)|(1.106)|
|Standardized HS grade|0.029|0.027|−0.013|0.027|0.029|−0.006|
||(0.667)|(0.542)|(−0.291)|(0.663)|(0.616)|(−0.120)|
|Work experience|0.007|0.008|0.018|0.011|0.012|0.022**|
||(0.604)|(0.702)|(1.679)|(1.064)|(1.199)|(2.699)|
|Work experience (squared)|−0.000|−0.000|−0.000*|−0.000|−0.000|−0.000**|
||(−0.960)|(−0.782)|(−1.933)|(−1.235)|(−1.288)|(−3.254)|
|Female|0.022|−0.113|0.008|−0.114|−0.124|−0.022|
||(0.223)|(−1.281)|(0.076)|(−1.281)|(−1.817)|(−0.223)|
|Mother's educational level|0.001<br>|0.001<br>|0.001<br>|0.001<br>|0.001<br>|0.001<br>|
||(1.030)|(1.547)|(1.268)|(1.849)|(1.532)|(1.257)|
|DTS implementation scheme dummy|||||||
|(1=Block release)||0.104||||0.060|
|||(1.734)||||(1.566)|
|Given training plan from TVI (1=yes)||0.019||||0.049|
|||(0.257)||||(0.803)|
|Weeks spent for in-school learning|||0.003**|||0.004**|
||||(3.391)|||(3.119)|
|Weeks spent for in-company training|||−0.002|||−0.002|
||||(−1.235)|||(−1.440)|
|Number of hours per week for in-company|||||||
|training|||−0.011|||−0.005|
||||(−0.447)|||(−0.232)|
|Number of hours per week for in-company|||||||
|<br>training (squared)|||0.000|||0.000|
||||(0.234)|||(0.021)|
|How to learn work process - from|||||||
|supervisors||||0.127||0.092|
|||||(1.168)||(0.900)|
|How to learn work process - from peers||||−0.013||−0.078|
|||||(−0.128)||(−0.853)|
|How to learn work process - by yourself||||−0.200||−0.193|
|||||(−1.833)||(−1.728)|
|How to learn work process - from all||||0.042<br>||0.001<br>|
|||||(0.462)||(0.014)|
|Reported to TVI during in-company|||||||
|training (1=yes)|||||0.009|−0.035|
||||||(0.181)|(−0.846)|
|Times visited TVI teacher during in-|||||||
|company training (1=yes)|||||−0.004|−0.003|
||||||(−1.463)|(−1.655)|
|Constant|8.226***|8.612***|8.899***|8.277***|<br>8.634***|<br>8.696***|
||(23.766)|(12.073)|(18.858)|(13.949)|(13.266)|(15.532)|
|Observations|397|192|192|206|189|189|
|R-squared|0.070|0.096|0.171|0.113|0.100|0.198|
|Enrollment Year dummy|YES|YES|YES|YES|YES|YES|
|School dummy|YES|YES|YES|YES|YES|YES|

_Source: DTS Tracking Survey, 2015, World Bank. World Bank calculations. Note: Robust t-statistics in parentheses. * p<0.1; ** p<0.05; *** p<0.01._ 35
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<mark>that “... the culmination in the world production of petroleum is expected to occur by about the end of the present [20</mark><sup>th</sup> <mark>] century. In the United States the culmination in the production of crude oil is expected to occur before 1970, and that of natural gas before 1980.” Similar assessments were made in food commodities. Brown (1995), for example, conjectured that when China begins importing food, there may not be enough grain in the world to meet that demand, which will inevitably lead to high prices of agricultural commodities.</mark> <mark>Among numerous critiques of the “Club of Rome” views and the “peak oil” hy‐ pothesis, perhaps the sharpest came from</mark> Goeller and Weinberg (1978, p. 4) who argued that technology and substitutability effectively transform non‐renewable to renewable resources: “With three notable exceptions—phosphorus, a few trace elements needed in agriculture, and energy producing fossil fuels ( _CHx_ )—society can subsist on inexhaustible or near‐inexhaustible minerals with relatively little loss of living standards.”

# _A1.2. Renewed scarcity concerns_

Following the post‐2000 commodity price boom, resource scarcity concerns reemerged, this time with a focus on the demand pressures coming from EMDEs <mark>—especially China and India, which together account for nearly 40 percent of the world’s population.</mark> Rogers (2004, p. 3), for example, argued that “A new bull market is under way, and it is in com‐ modities—the ‘raw materials,’ ‘natural resources,’ ‘hard assets,’ and ‘real things’ that are the essentials of not just your life but the lives of everyone in the world.” Similarly Heap (2005), noted that China’s high rates of urbanization, industrialization, and capital for‐ mation set the stage for a super cycle, similar to the ones experienced during the late 1800s and early 1900s (driven mainly by growth in the United States) and the years fol‐ lowing the Second World War (prompted by reconstruction in Europe and the Japanese expansion.) <mark>Several papers focused on super cycles, especially for metals (Radetzki 2006; Cuddington and Jarrett 2008; Erten and Ocampo 2013).</mark> <mark>Concerns were heightened about energy during 2011‐13 when oil prices averaged above $100/bbl. References to the “peak oil” hypothesis intensified as oil prices peaked.</mark><sup>**10**</sup> <mark>Income growth in China and India was cited as the main factor behind the price increases of food commodities, especially following the 2007‐08 food price spike. Indeed, the June 2009 issue of the</mark> _<mark>National Geographic</mark>_ <mark>noted that “... as countries like China and</mark>

> **10** In May 2008, for example, “peak oil” appeared 100 times in the news—the crude oil price averaged $123/bbl that month (in January 2016, it appeared only 3 times and the crude oil price averaged $30/bbl). The average monthly appearance of “peak oil” dropped from 42 times during 2008‐11 to 3 times during 2017‐18 (based on Google Trends, “worldwide”, “all categories”, “news search”).

_— 33 —_
a financially viable electricity sector. Nonetheless, with our joint model of households and the distribution utility, we can undertake counterfactual analyses using di↵erent prices, to explore the relationship between regulated tari↵s, connection charges, and access rates. Furthermore, despite the reforms, electricity access rates in Uganda are low, although increasing. We can use our model to explore the other demand-side and supply-side factors that have constrained connection rates.

The primary data source for our analysis is the Uganda National Panel Survey, a household panel that began in 2009–10. We use data from the first three waves of this survey: 2009–10, 2010–11, and 2011–12. The survey has a complete section on household energy use. We use the geographical coordinates of the enumeration area to match households to several additional datasets, including the locations of the distribution and transmission lines and a map of solar radiation. Monthly price data for energy commodities is from the Uganda Bureau of Statistics and (for electricity) the Electricity Regulatory Authority. Distribution utility financial data are from Umeme.

Kerosene is the dominant energy source used for lighting in Uganda. Our demand analysis includes both the demand for kerosene and the demand for electricity, as well as the choice of the type of fuel. Higher incomes increase the demand for both kerosene and electricity, conditional on using that fuel. However, higher income households are more likely to choose either electricity or solar, and less likely to choose kerosene. Increasing reliability or decreasing the distance to the grid both increase the probability of obtaining an electricity connection. Higher kerosene prices also shift households towards electricity.

Our primary motivation for estimating the demand model is to analyze the incentives faced by the distribution utility. For a utility charging a high regulated price, sufficient to cover variable costs, each additional connected household will make a positive contribution to profits. These profits create an incentive for the utility to reduce its connection charge so that more households wish to connect. The utility trades o↵the lost revenue from a lower upfront connection charge, against the higher future profits from having more customers. This description matches the current setting in Uganda.

An alternative scenario is that the utility charges a low regulated price that is insufficient to cover variable costs. This lower price means that each connected customer has a higher electricity consumption. However, the utility loses money on each customer that connects, creating an incentive to set a high upfront connection charge to discourage new connections. This analysis describes the electricity sector for several countries in Sub-Saharan Africa and explains the observation of low connection rates combined with high connection charges.

4
Experts were asked to specify only the non-carbon values of rainforest preservation, as the political process will likely identify carbon values separately.<sup>14</sup> We recognize that such a separation may be hard to achieve, and several of the experts pointed to this as being the most difficult aspect our stylized survey instrument.

Experts were told to assume that, if the program were enacted in their country, payments would in general be collected as an annual national tax on all households. Later in their questionnaire, experts were asked which of two specific payment vehicles, an income tax or an increase in a utility bill, would be preferable to use. They were also asked whether they thought the payment vehicle would bias WTP estimates in the population surveys upwards or downwards.<sup>15</sup> The preferred payment mechanism varied across experts. Many, in particular those from the United States, indicated that a tax payment vehicle would bias answers in the downward direction.

# **2.3 Other Questions**

The Delphi expert questionnaires contained several other questions with various aims: to inform on background variables in estimating WTP functions; as background for understanding other features of the experts and their credibility; as a way to control for whether answers from experts were “real” or could rather be distorted by misunderstandings or lack of knowledge; and to gather information that could be useful in design of the population surveys that we aim to do later. Among issues questioned were:

- Perceived difficulty in answering the questions, whether the overall exercise was viewed as meaningful, and whether experts viewed their own answers as reliable.

- Experts’ own background in SP research, in terms of work time allocated to such activities, knowledge of the SP literature, numbers of CV and CE studies conducted and papers published and reviewed, and benefit transfer studies carried out and refereed.

-

-

-

- Familiarity with specific key journals covering environmental economics.

- Highest educational degree.

- Gender.

> 14 The issue here is similar to that encountered with assessing air pollution policy options in the United States where health effects are not quantified in economic terms while other impacts are quantified in economic terms. 15 These questions were not posed to the European sample.

9