rafmacalaba/datause-encoder-data huggingface dataset:
**TABLE 3. MARGINAL PRODUCTIVITY DIFFERENTIALS AND RELATIVE WAGES**

|_Variable_|_Productiv_<br>_Differenti_|_ity_<br>_al_|_Relative_<br>_Wage _||_Reject_<br>_i_<br>_i_<br>_o_<br>_H_<br>λ<br>φ =<br>:|
|---|---|---|---|---|---|
|_Ho: coefficient=1_||||||
|Men|2.05<br>(0.13)|*|1.52<br>(0.04)|*|*|
|Upper secondary|1.84<br>(0.13)|*|1.44<br>(0.04)|*|*|
|University or more|3.82<br>(0.28)|*|2.37<br>(0.07)|*|*|
|(3 - 10] years in the firm|1.12<br>(0.05)|*|1.19<br>(0.03)|*||
|(10 - +) years in the firm|0.76<br>(0.04)|*|0.97<br>(0.02)||*|
|In-house training|1.25<br>(0.06)|*|1.16<br>(0.03)|*||
|External formal training<br>|1.49<br>(0.08)|*|1.24<br>(0.04)|*|*|
|Adjusted R<sup>2</sup><br>Observations|0.90<br>6,866||0.96|||

_Notes:_ Calculations based on ENESTYC 2001. Standard errors are shown in parenthesis. * denotes significance at 5 percent level. The Wald statistic was used to test the equality of the estimated wage and productivity differential. Reference group: women, lower secondary or less, until three years of experience in the firm, and without any form of training.

With respect to gender, the results show that men are significantly more productive – 105 percent – than women. Likewise, their wages are 52 percent higher, but not as high as their productivity (we reject the equality of both differentials). This implies that even though men are better paid relative to women because they are more productive, their salary increase does not fully compensate for their higher productivity.

In the case of education, the results show that workers with upper secondary education are 84 percent more productive with respect to the base case (the base case being workers with lower secondary or less) and workers with higher education are 282 percent more productive with respect to the base case. However, they earn less compared to what can be justified by the productivity differentials. These workers earn 44 percent and 137 percent more respectively. On average, the workers are not able to fully appropriate the productivity gains of education through wages. This issue might point to institutional rigidities that are beyond the scope of this paper.

13
land with low nutrients, but application of fertilizers could improve the production yield. Coastal Bermudagrass is another herbaceous crop widely grown in the United States. It is a warm-season perennial crop and is currently grown on 10 to 15 million acres in the southern US for forage and hay production (Boateng et al., 2007). Although coastal Bermudagrass can grow on different soil conditions, it requires fertilization (especially Nitrogen) to achieve a good annual biomass yield (6 – 10 dry tons per acre).

# **2.2.3. Agricultural residues**

**Corn stover** is normally left in a field after corn kernel is harvested, including stalks, leaves, cobs, and husks.  Corn stover is usually produced at a rate of 1 kg (dry) per kg of corn grain or 4 dry tons per acre per year (Kim and Dale, 2004; Heaton et al., 2008).  The United States generates about 80-100 million dry tons of corn stover per year (Kadam and McMillan, 2003).

**Wheat straw** is the leftover for wheat grain production and it is generated at a rate of 1-3 tons per acre per year under intensive farming conditions (Reitz, 1976).  The annual wheat straw generation in the United States is approximately 82 million dry tons (Kadam and McMillan, 2003).  In addition to cellulose, hemicelluloses, and lignin, wheat straw also contains a significant amount of pectin and proteins (Schmidt and Bjerre, 1997).

**Rice straw** is generated from rice production all over the world.    It has similar contents of cellulose, hemicelluloses, and lignin compared to other cereal crop residues, but it has higher silica content than most other cereal straw.  The annual worldwide rice straw generation rate is about 731 million dry tons in 2007, including Africa - 20.9 million tons, Asia - 667.6 million tons, Europe - 3.9 million tons, North America - 37.2 million tons, and Oceania - 1.7 million tons.

Several factors need to be considered in selecting an appropriate lignocellulosic feedstock for bioethanol production, including ethanol yield, local availability, environmental impact, competition for arable land against food and feed production, and transportation of the biomass. The average ethanol yields and annual field production rates of common lignocellulosic feedstocks are listed in Table 2 (the data are calculated based on the yields from USDA National Agricultural Statistics Service, 2008). According to the data in Table 2, herbaceous biomass, especially switchgrass and _Miscanthus_ , has much higher annual ethanol yield per acre of land 13
In a previous paper that focused on the analysis of firm-led renegotiation (Guasch, Laffont and Straub, 2003, referred to hereinafter as GLS), we analyzed a Latin American data set from the World Bank, with 307 concession projects in the sectors of transport and water, in five countries (Argentina, Brazil, Chile, Colombia and Mexico) between 1989 and 2000. We showed that more than half of these projects were renegotiated (162 out of 307) and very fast, on average 3.5 year after the signing of the contract. The paper focused on firm-led renegotiation and built a model of renegotiation that included contract characteristics, regulatory and institutional environment, as well as external shocks. The empirical analysis there confirmed most of the predictions of the model and yielded interesting policy implications, especially with respect to the importance of having a regulatory body in place at the time of awarding a concession and of choosing the appropriate regulatory regime. We showed that the mode of regulation matters and that price caps, which have been the dominant choice of policy makers in Latin America, suffer from a great fragility to shocks and trigger significant renegotiation. As a consequence, there is a growing pragmatic tendency to advocate the abandonment of price cap regulation, a synonym for the higher risk of renegotiation and higher cost of capital, and the return to an hybrid type of regulation, including some elements of rate of return (see for example Estache, Guasch and Trujillo, 2003).

The present paper complements GLS by looking at government-led renegotiations. Private sector participation has often been accompanied by sector restructuring prior to the transfer and by the implementation of a legal and regulatory framework. This is meant to protect investors from arbitrary and politically motivated intervention from the government, to protect users from the abuse of the monopoly or dominant position of the new private operators, and also to protect the competitive new entrants from a dominant incumbent operator. Quite often, the required and necessary investments are highly specific “sunk” type costs that cannot easily be recouped if the economic or political atmosphere deteriorates or if the operator were to discontinue operations. This may tempt governments to behave opportunistically and take ex post regulatory actions that expropriate the available quasi-rents.

Typical scenarios are a government or a mayor in the case of water concessions (mayors have exclusive jurisdiction on water operations) deciding in a unilateral fashion to cut tariffs or not to honor agreed tariff increases during a re-election campaign to secure popular support. Another not uncommon scenario is a new administration deciding not to honor the tariffs increase 3
# I. OVERVIEW

# A. Housing and The Current Macro-Economic Environment

The last twenty five years have seen Ghana decline from one of the richest cou. :ries in Sub-Saharan Africa to one in which the infrastructure and capital stock, including ho;'sing, are in very poor condition. During the period 1965 to 1986, real per capita gross national product (GNP) declined at an average annua' rate of 1.7 percent per year. Figure 1.1 presents data for _levels_ from _1_ recent years; Figure 1.2 presents the corresponding _changes. ,_ Recently, however, there have been signs of an upturn. An ambitious package of reforms has been instigated by the Provisional National Defence Council Government of Flt-Lt. Jerry Rawlings. The structural adjustment program includes a devaluation and eventual market determination of the exchange rate - for the Cedi (from C2.75 US$1 to C90 in 1986, when we collected the household data, and C350 by March 1989) and other policy changes including agricultural price reforms ar& reductions in government staff levels. As a result, real GNP increased by 5 percent in both 1985 and 1986 (Figures 1.1 and 1.2).

Figure 1.1: Real GNP Per Capita Figure 1.2: Change in Real GNP Per Capita <!-- Start of picture text --> '700 0<br>-4'700 _ ........ .... I....<br>$490~~ ~ .. ..... ... ~ ~ ~~ ..... .... ... . . .<br>| GNP Pe, . . , . .,/-Pefcenl<br>80 72 74 76 78 88 82 84 86 88<br><!-- End of picture text -->

<!-- Start of picture text --> 110%<br>10 '<br>IaI coange<br>70 72 74 76 78 80 82 84 86<br><!-- End of picture text --> Concern remains that housing cclocitions have deteriorated over the past decade; improvement in these conditions is required to ensure that the bent'fits of adjustment are widely shared. As the adjustment in Ghana's economy takes hold, demand for housing can be expected to increase at least as fast as real incomes. One important task for the government is to ensure that its actions do not impede or constrain potential suppliers of housing. The purpose of this pdper is to examine the constraint on the housing sector imposed by rent control in the secona largest city of Ghana, Kumasi. This concentration on rent control is not intended to imply that it is the only, or most important, constraint.

- ;./ National accounts and other aggregate data are from the World Bank's BESD database, updated by written reports. These data are in turn obtained by the Bank from the Government of Ghana.
# **4.4 Shift-share approaches**

A very popular research design in trade and migration studies involves using “shift-share” instruments (also known as “Bartik” instruments, after the first such study, Bartik (1991).) In shift-share designs, the researcher analyzes the impact of multiple shocks, each of which affects some units of observation more than others. The “share” measures the degree to which a unit is exposed to a particular shock, and the “shift” refers to the size of a particular shock. This approach has been widely used in economic research on migration.

For example, one might be interested in the impact of immigration flows into a particular location (say, U.S. cities) from different countries around the world. The shift-share instrument _Bl,t_ would be as follows: where _zl,j,t−_ 1 (the “share”) is the initial share of immigrants from migration-source country _j_ in location _l_ (as a share of all immigrants to the U.S. from country _j_ in the initial year _t −_ 1), and _mj,t_ (the “shift”) is the normalized change in immigration from country _j_ into the U.S. as a whole. _Bl,t_ is then the predicted inflow of migrants to destination _l_ in time _t_ : the weighted average of the national inflow from each country (the shift), where the weights (the shares) are the initial fractions of migrants across destinations _l_ from each country of origin _j_ .

There have been a number of advances in the econometrics of shift-share research designs in recent years. The recent work has made clear how to clearly justify the exogeneity of the shift-share instrument. In addition, there has been important work clarifying how to calculate correct standard errors in shift-share designs. Researchers will need to make a case why either the shifts and/or the shares can be considered exogenous (uncorrelated with the error term), and then the effective sample size that determines standard errors will depend on how many independent shifts or shares there are. We next discuss some of these issues, while acknowledging that this is a still fluid and developing literature, with consensus best practice still evolving.

## **4.4.1 Justifying exogeneity of the shift-share instrument**

In many studies, the shift-share variable is calculated analogously to equation 8 above, in which the shifter is an aggregate flow (in this case, aggregate immigration inflows from origin country _j_ to the entire U.S.). The canonical Bartik (1991) study is an example. Other examples include Cortes (2008), who studies the impact of low-skilled immigration to the U.S. on the prices of goods and services provided by such immigrants; Gonzalez and Ortega (2013) who study the impact of immigration to Spain on housing prices; and Glitz 25
Table 4: Results from the log-earnings equation. Regression coefficients for the dummy variable identifying internal migrants who moved for economic reasons.

|||Wo<br>|men<br>|||M<br>|en<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Internal migration|0.365***|0.432***|0.224***|0.228***|0.344***|0.390***|0.216***|0.212***|
||(0.0594)|(0.0596)|(0.0602)|(0.0555)|(0.0358)|(0.0358)|(0.0358)|(0.0362)|
|Sociodemographics|||YES|YES|||YES|YES|
|Fixed effects for|||||||||
|the states of origin||YES|YES|YES||YES|YES|YES|
|Self-selection bias||||YES||||YES|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 The income gains from the migration decision are calculated as follows:

# **The results suggest that men who migrated benefit more than women from the migration decision.**

**However, when migration occurs for economic reasons, women benefit as much as men and even more.** Table 5 shows the earning gains for all migrants versus stayers and migrants who moved for economic reasons versus stayers. The labor income gain for all men who migrated is 14.5 percent, five years after the migration choice, while for all women who migrated is only 9.5 percent (five percentage points lower). Nonetheless, labor income gains from internal migration due to economic reasons are higher for men and women, but especially for women. Indeed, women and men who moved internally for economic reasons earn around 25 percent more than those who did not move internally (earnings gains are 26 percent for women and 24 percent for men). These findings show that women benefit from internal economic-related migration as much as men do and even more.

Table 5: Earning gains for women and men who migrated internally due to economic reasons

||**Income**<br>|**gap=**exp(βm)<br>|
|---|---|---|
||All migrants versus stayers|Migrants who moved for economic|
|||reasons versus stayers|
|**Women**|1.095|1.256|
|**Men**|1.145|1.236|

35
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE the full impact of under-pricing was not felt on the company’s balance sheet until 2009, at which point the net financial position went into the red (figure 13)

## **Figure 13. Hidden costs**

BPC’s economic inefficiencies, driven by underpricing (hidden costs as percentage of revenues, 2004-2009) <!-- Start of picture text --> 25 2.0<br>20 1.5<br>15<br>1.0<br>10<br>0.5<br>5<br>0 0.0<br>-5 2004 2005 2006 2007 2008 2009 -0.5<br>Losses Underpricing<br>Collection Inefficiencies Net financial position per kWh<br>US cents per kWh<br>Percentage of the revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009) and Briceño-Garmendia and Shkaratan (2010). _Note:_ [*] Projection. Opex = operating expenditure; kWh = kilowatt-hour.

Since 2009, Botswana has made bold efforts to improve the power sector. In tandem with Morupule, the country has implemented important tariff adjustments: 30 percent in 2010, another 30 percent increase in June 2011 with a third increase scheduled for 2012. These decisions are temporarily correcting the misalignment tariffs-costs and set BPC is a very strong path to solid financial viability. Yet, for a permanent solution, particularly after the debt service of Morupule kicks in, Botswana would need to adopt a new electricity tariff policy and ensure its implementation through an independent regulator.

# Information and communication technologies

## **Achievements**

Botswana has made enormous strides in providing access to information and communication technologies (ICTs), especially mobile communications. In just over a decade, the number of mobile subscriptions went from zero to exceeding the country’s population by the end of 2010. While the real level of access is lower than these numbers suggest, accounting for duplicate and inactive subscriptions and the trend towards dual-SIM card phones,<sup>6</sup> Botswana’s mobile penetration of 118 per 100 people in 2010 was the highest in Sub-Saharan Africa (table 12).

Botswana’s high mobile penetration is partly due to the early introduction of competition in 1998, when two operators were licensed, Mascom and Orange. This duopoly continued for a decade before a third mobile operator, a subsidiary of the incumbent fixed-line operator Botswana Telecommunications

> 6 According to one survey carried out between 2007 and 2008, 59.5 percent of Botswana’s population 16 years and older had a mobile phone. This corresponds to some 46 percent of total mobile subscriptions in 2008, giving some idea of the high number of duplicate and inactive subscriptions. Applying the same ratio to the 2009 mobile subscriptions results in an estimate of 87 percent of those aged 16 and older with a mobile phone. The survey data are published in Gillwald and Stork (2008).

27
reached a level of development required to catalyze the development of private investment in infrastructure.

Some empirical studies have investigated the determinants of private investment in developing countries, but most of them consider private flows to the economy as a whole and not to specific infrastructure sectors. Moreover, to our knowledge, very few empirical analyses have investigated the attractiveness of a country’s overall economic development level or financial development level to private investors in developing countries.

Pargal (2003) examines the effects of the regulatory framework on private investment in infrastructure in nine Latin American countries from 1980 to 1998 and finds that the investment regime’s liberalization and the existence of independent regulatory agencies are the most significant institutional determinant of private investment. Ouattara (2004) investigates the long-run determinants of private investment in Senegal from 1970 to 2000 and reaches the conclusion that public investment, GDP per capita, and foreign aid positively influence private investment. In contrast, credit to the private sector and terms of trade surprisingly tend to hinder private investment in Senegal. Likewise, Zerfu (2001) finds that GDP, its growth rate, and public investment in infrastructure significantly foster private investment in Ethiopia while lack of macroeconomic stability tend to negatively affect investment.

Examining the determinants of infrastructure private investment in 61 developing countries over the period 1970-2003, Kinda (2008) also finds a significant positive effect of economic growth, physical infrastructure, and level of development of the financial sector, in particular, credit granted to the private sector by the banking sector. He also finds out, as in previous studies, that private investment is negatively influenced by macroeconomic and political instability. For the case of Ghana during the period 1970-1992, Asante (2000) finds that public investment, lagged private investment, and the growth of real credit to the private sector are key determinants of private investment. However, the author finds that the growth rate of GDP negatively influences private investment and so does macroeconomic and political instability.

# **3. The data**

active in the project finance market are China, India, Malaysia, South Africa, and Thailand.

7
We account for the proportion of the firm’s sales made directly abroad (Exports) and the proportion of the firm owned by foreign entities (Foreign ownership). Both the variables are prepandemic and taken from the ES. Studies have found that exporters (see Demir and Javorcik 2020) and firms with foreign ownership (see Liu et al. 2021, Gu et al. 2020) may be more at risk from the pandemic. This could be because exporters and foreign-affiliated firms tend to rely more on global supply chains, which have undergone tremendous shocks during the pandemic. Lack of proper international law means that an unexpected adverse economic shock, such as COVID-19, may increase the risks of non-payment or non-delivery of prepaid goods across borders.

Another important issue is the support provided by the government to help private firms deal with the pandemic. Cirera et al. (2021) find that policies such as credit and cash transfers appear to be helping firms address liquidity constraints, while receiving wage subsidies seems to be associated with a lower probability of firing workers. Guerrero-Amezaga et al. (2022) find that across eight Latin American countries, government support led to improved outcomes at the firm level. Interestingly, both of these studies find that the smallest firms benefit less from government programs. Thus, we account for a dummy variable equal to 1 if the firm received any national or local government support in response to the pandemic and 0 otherwise (Government support). The data source for the variable is COV-ES.

A high tax burden squeezes profits and drains the firm of much-needed cash reserves. As a result, high taxes may adversely affect firm performance during the pandemic. Our countryindustry fixed effects capture the tax burden that is common to all firms within a country or country-industry cell, but not the difference between SMEs and large firms. We account for such effects using a dummy variable equal to 1 if the firm in the pre-pandemic period reported the tax rates as a major or very severe obstacle to its current operations and 0 if it reported them as a no 15