rafmacalaba/datause-encoder-data huggingface dataset:
and the log likelihood function is: This is equivalent to a least square nonlinear estimation;  has no influence on the estimation, as long as it’s a normal distribution. With the log likelihood function (10),  i and  i can be directly estimated for each individual i with the MBDC data.

Once  i is estimated for each individual, models can be constructed and estimated to analyze its determinants. One simple example is to have the following linear functional form: where x are personal specific characteristics.  s are coefficients to be estimated; e1 is a random errors.

# **7. Survey Data Analyses**

# _7.1  WTP Responses_

1,933 respondents accepted to be interviewed. 594 respondents did not give positive answers (i.e., a “not sure”, “probably not” or “definitely not” answer was selected) at the price of zero, which may reflect that the respondents do not think they would need this preventive medicine. At the other end, 205 persons did not give negative answers (i.e., definitely yes, probably yes or not sure was selected) even at the highest bid price, 15,000 Yuan, which means that the price range did not cover the whole WTP range if people answered the questions honestly.<sup>9</sup> 838 respondents have their WTP located in the price range. Two respondents have cancer so the preventive medicine is not meaningful to them. 135 respondents did not fully complete the questionnaires, and 146 respondents gave some unreasonable answers to the likelihood questions, such as giving a higher acceptance probability for a higher bid price<sup>10</sup> . Those responses would have been corrected by the enumerators if it is a traditional in-person interview. Table 1 summarizes the statistics of the responses.

# _7.2 Characteristics of the Respondents_

> 9 Almost all members in the focus group and pre-tests believed 15,000 Yuan to be more than the highest possible WTP for a medicine preventing cancer for one year. But apparently, some people may have very high WTP for such a medicine if they give honest answers.

> 10 This may make sense in reality if people believe that a higher price means a higher quality.

11
a model which incorporates a realistic transportation sector into a broader trade model<sup>8</sup> . Using this model, they estimate the magnitude of search frictions. Demirel et al. (2010) develop a simple, two-location model to study the role of matching frictions for backhauls. In a frictionless market, when there are more carriers than shippers interested in a backhaul trip, the price of backhaul trips should fall to zero. Since these prices are empirically positive, Demirel et al. (2010) argue that matching frictions must be present, and calibrate a matching model with data from Rhine shipping.

The magnitude of search frictions depends on the effectiveness of matching platforms and information technology. A series of papers have focused on Electronic Vehicle Management Systems (EVMS) adoption in North America. Conceptually, EVMS reduces communications costs and therefore makes search and matching easier. Hubbard (2003) found that U.S. trucks with EVMS drove 12.7 percent more loaded miles. Looking at Canadian trucks, Barla et al. (2010) found that while EVMS reduced empty miles on backhaul segments, empty miles on fronthaul segments increased. They argue that falling search costs may induce more search, or encourage carriers to travel empty to more profitable markets. The relationship between search costs or information technology and empty trips may not be monotonic.

In an earlier setting, Mansell (2001) finds that freight exchanges reduced empty trips in the United Kingdom by 5 percentage points, from a baseline of 28 percent empty trips. Heilmann (2020) studies the effect of the Uber Freight platform on matching jobs and reducing empty trips. He finds that deadhead miles in the United States fall by 22.6 percent, or 4 percentage points, and that utilization increases from 81.6 percent to 85.8 percent. However, the effect of matching platforms may not be monotonic. Rosaia (2020) considers the effect of competing platforms within the NYC taxi market. He finds that competing platforms can exacerbate search frictions by creating two artificially thin markets. Similarly, Fréchette et al. (2019) find that when platforms have partial penetration, they can cause the market on and off the platform to become thinner. Ghili and Kumar (2020) find that small platforms concentrate around most dense areas of supply, which creates geographic inequalities.

# **3.3 Regulation**

Regulations which restrict some carriers from taking jobs from available shippers are common, directly forcing some carriers to return empty. In an international context, cabotage restrictions protect domestic carriers by preventing foreign carriers from picking up withincountry jobs after completing an international trip (Bove et al. (2018)). For example, 1999 agreements between Cameroon, Chad, and the Central African Republic established that

> 8For a broader survey of how to estimate matching functions, or equivalently, how to detect the presence of matching frictions, see Petrongolo and Pissarides (2001) and Brancaccio et al. (2020b).

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20 changes. This suggests that we should observe a<sup>large fraction of the unemployed taking jobs very</sup> shortly after they exhaust benefits, and very few taking<sup>jobs just prior to exhaustion. Viewed In the context</sup> of labor supply, an increase in potential duration (at a fixed<sup>WBA)is equivalent toan increase in uneamed</sup> Income. Since we assume people value leisure, a higher<sup>potential duration reduces the supply of labor</sup> and Increases actual duraton.

Two approaches have been used to examine this<sup>question. The first, and much more</sup> widesproad, simply compares the duration of spells<sup>among a group of unemployed workers or across</sup> indivduals to the potential duration of benefits.<sup>(These studies are most convincing when dierences in</sup> potential duration arise from differences In laws across<sup>jurisdictions, or from legislated changes in</sup> potential duration.) The evidence generally supports the prediction<sup>that longer potential duraton results</sup> in longer spells, other things equal. More recently, several<sup>studies (e.g., Meyer, 1990) have examined</sup> the fractlon of unemployed workers whose speils have lasted t weeks who find jobs<sup>during week t+ 1 (the</sup> hazard rato of leaving unemployment). The hazard<sup>rate drops sharply during the last few weeks of</sup> eligibility, and rises sharply immediately after benefits are exhausted.

Taken together, the evidence suggests that Increasing potential<sup>duration does Increase</sup> the average duration of spells of unemployment. Unlike<sup>in the answer to the first question, though, no</sup> consensus Is possible about the size of the effect. All we can<sup>be sure of Is that longer potential duration</sup> increases average duration, and thus the average rate of<sup>unemployment.</sup> Here too, a more generous Ul program generates impacts counter to the goal<sup>of stabilizing employment.</sup> An Important policy question is how the impact of a 1-week<sup>increase in potential duratlon</sup> on the unemployment rate varies with existing rules on potential duration. On one<sup>level the answer is</sup> nearly clear Since the number of unemployed workers decreases steadily as duration Increases, t Is almost certain that the effect is larger If potential duration<sup>Is short.Amore subtle question is how a 1-</sup> week Increase In potential duration affects the hazard rate as<sup>potential duration Increases. No theory</sup> yields unambiguous predictions on this question, and there Is<sup>no evidence on it.</sup>
55 _So, In effect, whlle in an economic sense I see that as belng the most likely outcome, and nothing which we have sald during this meetlng changed that feellng, I thlnk the 2.5 percent outcome Is not golng to work over the 1990s._ _I see the consequences of a slowdown, of the recession, both socially and politically, for countrles which now have 10 percent unemployment; for a trade system which now Is under serious stress as contrasting to a sort of dynamic momentum in the 1960s, I see the consequences of the slowdown for the developing countries, where the debt problem has been heavy for the past seven years, where democratic regimes are under heavy pressure. So I see that the recession could have much more severe consequences than perhaps Boltho Is forecasting._ WPI/BOLTHO/SF:I
Second, the figures for public provincial banks include Buenos Aires, a case apart from the others. When Buenos Aires data are removed, the percentage of good credits in each category drops by about 5%. After removing Buenos Aires and correcting for potential overstatements, the percentages of good credits will almost certainly trail those for the earliest privatizers by an even greater amount. Therefore, it is fair to conclude that, as of 1 996, the earliest privatizers had much higher quality portfolios thani the remaining public provincial banks.

Whether the privatized banks can maintain quality is another important question. Indications are, however, positive. Of the early privatizers, nearly half have been private for at least two years -- the averages in Table 9, therefore, reflect some ability to maintain the portfolio quality inherited after purging residual assets and liabilities. In addition, the operating income and cost data presented above is encouraging. At the very least, beginning and recent privatizers can take heart that, in the cases to date, the provinces have been able to create a privatized entity whose initial portfolio quality approaches that of the top private banks.

It is more difficult to draw strong conclusions regarding portfolio composition. To some extent, the composition may depend upon the resource endowments of the province in which the bank resides. It is difficult, therefore, to argue that the industrial composition of the private bank portfolios is inherently superior to that of the other banks, and that we should expect privatized provincial. banks to create similar portfolios over time. We might, however, expect less credit to be allocated to the government once political incentives have been replaced by purely economic ones. The data indicate that the remaining public provincial banks have the highest share of their portfolios concentrated in government services (17.6%), followed by beginning privatizers (14.8%). Recent and early privatizers allocate a little more than 10% of their portfolios to government services, while private banks devote a little less than 5% of theirs 22
value placed on reductions in risk of death. Hedonic studies of automobile prices (Atkinson and Halvorsen, 1990; Dreyfus and Viscusi, 1995; Andersson, 2005) decompose automobile price into the price of various vehicle characteristics, including the probability of a fatal accident. The marginal cost of a risk reduction should equal the value of the reduction to the purchaser at the margin. Studies involving bicycle helmets and car seats (Jenkins et al., 2001; Blomquist, Miller and Levy, 1996) are based on the assumption that, for the marginal buyer, the value of the risk reduction achieved equals the cost of buying it. This allows the researcher to infer the value of safety from purchases of such safety equipment.

Studies have also attempted to infer the value of safety from seatbelt usage (Blomquist, 1979; Blomquist, 1991) and vehicle speeds (Ghosh, Lees and Seal, 1975; Ashenfelter and Greenstone, 2002). To accomplish the former, the time cost of using a seatbelt (assumed equal at the margin to the benefits of using the belt) must be monetized. Likewise, the time saving associated with faster speeds must be monetized to infer the rate at which people are trading money for higher risk of death at faster speeds.

Revealed preference studies are difficult to implement in a developing country context. The data required to implement an hedonic pricing study of the automobile market would be difficult to obtain in India. Even if such data existed, they would apply to a small segment of the population. (Only 13% of households in Delhi own cars.) More importantly, revealed preference studies have a serious drawback even in a developed country setting. These studies measure the risk reductions associated with safety equipment or safer vehicles by the _objective_ risk reductions achieved. The studies implicitly assume that consumers’ risk perceptions match objective risks—that consumers think they are buying the risk reduction that is measured by objective

- 4 -
Table A.14: Father’s labor supply: Using median dowry to proxy for expected dowry

|**Dependent Variable:**|**F**|**ather’s days worke**|**d in a year**|
|---|---|---|---|
||All<br>(1)|Above poverty line<br>(2)|Below poverty line<br>(3)|
|_Firstborn girl_ * _Post_* _Expected dowry_|5.83***|7.01***|4.75|
||[1.75]|[1.89]|[4.58]|
|_Expected dowry_ * _Post_|2.37|1.80|2.03|
||[3.23]|[4.49]|[3.98]|
|_Firstborn girl_ * _Post_|-11.12**|-13.09**|-13.90|
||[4.62]|[5.55]|[9.76]|
|N|71,282|36,703|29,395|

NOTES: This table reports the coefficients corresponding to specification (4). The sample is restricted to households where all children have the same mother. Each column corresponds to a different regression. The dependent variable is the number of days worked each year. _Firstborn girl_ indicates that the firstborn child of the household is female. Post indicates that the year of labor is later than the first child’s year of birth. Expected dowry (in INR 10,000) for a female (male) child is defined as the _median_ net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.

57
<!-- Start of picture text --> for the Gini index at<br><!-- End of picture text --> <!-- Start of picture text --> level<br><!-- End of picture text --> Figure 2: Lowess Curve for the Gini index at 2-digit level <!-- Start of picture text --> Lowess curve for Gini of employment, value added and exports against GDP per capita (PPP) at 2-digit level<br>Employment Value-added Exports<br>] - I<br>||I<br>|| I<br>||I<br>|| |<br>n | mn | n |<br>||I<br>|| I<br>||I<br>|| I<br>oo | = | a<br>_= || _= || _= Il<br>°°°<br>||<br>~;||~,||~‘||<br>|| I<br>||I<br>|| l<br>wo | ‘2 | * |<br>I<br>|| |<br>||I<br>wn |wn | wn l<br>o © 6 cs) oo S © % © Oo<br>SS Ss ssSs FP SsSs SF Ps sss<br>GOP per capita, PPP (constant 19385) GDP per capita, PPP (constant 1935) GDP per capita, PPP (constant 1985)<br><!-- End of picture text -->

Source: Authors’ calculations The Gini index follows a U-shaped pattern: Countries first diversify then they start specializing at around PPP GDP per capita of 20,000 (constant 1985 US dollars). This result is in line with the literature on diversification and economic development (Imbs and Wacziarg, 2003; Klinger and Lederman, 2004 and 2006; and Cadot et al.,2011). Imbs and Wacziarg (2003) find a turning point at PPP GDP per capita 8,675 (constant 1985 US dollars) and they note that their turning point occurs around the level of income for Ireland in 1992. Since we use data on PPP GDP per capita in constant 2017 US$, the value of the turning point in our estimation is not directly comparable. We thus convert PPP GDP per capita (constant 2017 US dollars) into constant 1985 dollars to compare with Imbs and Wacziarg (2003) using the U.S GDP implicit price deflator provided by the U.S Bureau of Economic Analysis who use GDP per capita in constant 1985$. Our turning point corresponds roughly to Ireland’s income level in 2000. Countries to the left of the turning point include Ghana, <!-- Start of picture text --> to Ireland’s income level in 2000.<br><!-- End of picture text --> <!-- Start of picture text --> Countries to the left of the turning point<br><!-- End of picture text --> <!-- Start of picture text --> Ghana,<br><!-- End of picture text --> 12
Figure 16. Labor Costs per unit value added <!-- Start of picture text --> 0.53<br>0.39<br>2008 2014<br>Source: Authors' calculation based on data from Enterprise Surveys<br>.5<br>.4<br>.3<br>.2<br>Costs of labor per VA<br>.1<br>0<br><!-- End of picture text -->

# III. Determinants of firms’ productivity

This section explores the determinants of productivity of Nigerian firms with data from three waves of Nigeria Enterprise Surveys (ES) as described in the previous section. The analysis uses three different measures of productivity: labor productivity per worker, value added per worker, and total factor productivity (TFP). Labor productivity is measured as the real annual sales divided by the number of fulltime equivalent workers by considering the temporary workers.<sup>5</sup> Value added is obtained by excluding the costs of raw materials and energy from the annual sales. Value added per worker is obtained by dividing the difference by the number of fulltime equivalent workers.

Finally, we use the TFP concept as an alternative measure of productivity at the firm level. TFP has the advantage to account for the technology of production and the level of capital. We derive the TFP from a Cobb-Douglas production function with the following technology ����� = � � ����������. ����� refers to the gross output of firm � in sector � in country � during the previous fiscal year with � and � denoting capital and labor, respectively. Using the natural logarithm, the production could be specified as follows: ����� = �+ ������ + ������ + ����� + �����. ����� is the natural logarithm of output at the end of the previous fiscal year; ���� and ���� represent the natural logarithm of the net book value of capital and the total permanent full-time employees (labor) at the end of the previous fiscal year. ����� captures unobservable productivity shocks; and ����� is an independent and identically distributed shock, which does not affect firm decision. Estimating the TFP at the firm level is challenging because of the potential correlation between productivity shocks and inputs. Firms facing a positive productivity shock could respond by using higher levels

> 5 We are grateful to Josual Wimpey from the Enterprise Surveys for the suggestion and help in the construction of full time equivalent workers.

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Chapagain, A.K., A.Y. Hoekstra, H.H.G. Savenije, R. Gautam (2006) “The Water Footprint of Cotton Consumption: An Assessment of the Impact of Worldwide Consumption of Cotton Products on the Water Resources in the Cotton Producing Countries”, _Ecological Economics_ , 60(1): 186-203 Cosbey, A. (2008) “Border Carbon Adjustment”, Paper presented to the ‘Trade and Climate Change Seminar’, June 18-20<sup>th</sup> 2008, Copenhagen.

De Cendra, J. (2006) “Can Emissions Trading Schemes be Coupled with Border Tax Adjustments? An Analysis Vis-à-vis WTO Law”, _Review of European Community and International Environmental Law_ , 15 (2): 131-145.

Delgado J. (2007) “Why Europe is Not Carbon Competitive”, _Bruegel Policy Brief_ , Issue 2007/05 (www.bruegel.org).

Demaret, P. and R. Stewardson (1994) “BTA Under GATT: Trade, Environment and the Future”, Washington, D.C.

Dimaramam, B.V. (ed.) (2006) _Global Trade, Assistance, and Production: The GTAP 6 Data Base. Center for Global Trade Analysis_ , Purdue University.

Fischer, C. and A. Fox (2009) “Comparing Policies to Combat Emissions Leakage: Border Tax Adjustments Versus Rebates”, RFF Discussion Paper DP 09-02, Resources for the Future, Washington, D.C.

Goh, G. (2004) “The World Trade Organization, Kyoto and Energy Tax Adjustments at the Border”, _Journal of World Trade_ , 38 (3): 395-423.

Grossman, G. (1980) “Border Tax Adjustments: Do They Distort Trade?” _Journal of International Economics_ , 10: 117-128.

Guan, D. and K. Hubacek (2007) “Assessment of Regional Trade and Virtual Water Flows in China”, _Ecological Economics_ , 61(1): 159-170.

Helm, D., R. Smale and J. Phillips (2007) “Too Good to be True? The UK’s Climate Change Record”, Ismer, R. and K. Neuhoff (2007) “Border Tax Adjustment: A Feasible Way to Support Stringent Emission Trading”, _European Journal of Law and Economics_ , 24: 137-164.

Lockwood, B. and J. Whalley (2008). “Carbon Motivated Border Tax Adjustments: Old Wine in Green Bottles?” NBER Working Paper 14025, National Bureau of Economic Research, Cambridge MA.

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# **3.2 OIA and BIA of public spending on education**

An OIA and BIA of public spending on education were conducted in all six sample countries: Côte d’Ivoire, Liberia, Zambia, Paraguay, Tajikistan, and Thailand. As indicated in section 2, the analysis requires: (i) unitary public benefits per beneficiary from data on total public spending and number of enrolled students; (ii) beneficiaries’ contributions toward education, based on the reported costs incurred in education from household surveys; and (iii) allocation of those unitary benefits and contributions across a distribution of welfare (proxied by income, consumption, or wealth), in the case of traditional BIA, and a distribution of opportunity—specifically, access/use probability to public education—in the case of OIA. To the extent possible, which varies from country to country, the information on spending is disaggregated by type of education (preschool, primary, or secondary) and region (by the relevant subnational administration: region, department, or province). Table 2 provides a summary description of the sources of information and level of disaggregation for each country.

**Table 2: Information Sources** **<u>and Level of Disaggregation of Public Spending</u>**

|**Country**|**Survey**|**Years**|**Fiscal sources**|**Year(s) of**<br>**fiscal data**|**Level of disaggregation**<br>**of fiscal data**|
|---|---|---|---|---|---|
|Côte<br>d’Ivoire|Household Living<br>Standards Survey|2008|World Bank (2008)|2007|Primary and secondary<br>level|
|Liberia|Core Welfare<br>Indicators<br>Questionnaire|2007|World Bank (2010)|2007/2008|Primary and secondary<br>level|
|Paraguay|Permanent<br>Household Survey|2003<br>to<br>2010|Ministry of Education<br>Ministry of Health<br>Ministry of Finance<br>Preliminary results of BOOST in Paraguay|2004<br>2009|Education:<br>Regions<br>Preschool/primary/<br>secondary<br>Health:<br>Regions<br>Type of center|
|Tajikistan|Tajikistan Living<br>Standards Survey|2003<br>2007<br>2009|Preliminary results of BOOST in Tajikistan|2009|Regions|
|Thailand|Household Socio-<br>Economic Survey|2008|Local Administrative Organization Survey<br>Ministry of Education<br>Comptroller General’s Department<br>ONESQA(2010)|2008|Regions<br>Primary/secondary|
|Zambia|Living Conditions<br>MonitoringSurvey|2010|Ministry of Finance and National Planning<br>Ministryof Education Statistical Bulletins|2009|Primary/secondary<br>Provinces|

_Source_ : Author’s compilation. _Note_ : Data for enrollment come from the household surveys in the case of Côte d’Ivoire and Tajikistan.

The average public unitary benefits to children enrolled in public school vary in this country sample from US$6.9 in Liberia (for primary education) to US$531 in Paraguay (for secondary education). As expected, differences are also large in terms of the unitary benefits per student across levels of education. For example, in Liberia the ratio of the unitary benefits on secondary education over the unitary benefit on primary education exceeds 12. In contrast, it is only 2.3 for Paraguay. For countries 13