rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9. Correlation between the use of coping strategies and income declines**

Panel A. Household Business Definition of household enterprise

||Sold a<br>|ssets<br>|Public a<br>|ssistance<br>|Any ass<br>|istance<br>|Private a<br>|ssistance<br>|
|---|---|---|---|---|---|---|---|---|
|Sample:|FHE|NFHE|FHE|NFHE|FHE|NFHE|FHE|NFHE|
|Farm income|0.121||0.009||-0.076||-0.069||
|decline|[0.054]**||[0.020]||[0.053]||[0.080]||
|Nonfarm income||0.083||-0.012||-0.032||-0.103|
|decline||[0.063]||[0.017]||[0.061]||[0.064]|
|Between $2.15 and|0.178|0.154|-0.027|-0.037|-0.020|-0.017|-0.053|-0.062|
|$3.65|[0.134]|[0.096]|[0.052]|[0.053]|[0.050]|[0.032]|[0.077]|[0.074]|
|Between $3.65 and|-0.088|-0.053|-0.028|-0.038|0.014|0.024|0.012|0.007|
|$6.85|[0.121]|[0.075]|[0.058]|[0.056]|[0.039]|[0.017]|[0.072]|[0.063]|
|Above $6.85|-0.071|-0.049|-0.000|-0.035|0.083|0.075|0.074|0.015|
||[0.119]|[0.068]|[0.066]|[0.054]|[0.095]|[0.059]|[0.136]|[0.093]|
|Constant|-0.031|0.016|0.016|0.029|-0.025|-0.050|-0.012|0.075|
||[0.077]|[0.067]|[0.050]|[0.054]|[0.039]|[0.091]|[0.082]|[0.100]|
|Observations|218|427|378|791|378|791|447|1,130|
|R-squared|0.397|0.281|0.148|0.132|0.205|0.146|0.279|0.263|
||||ETH|ETH,|ETH|ETH,|ETH,|ETH,<br>MWI|
||ETH,|ETH,|,<br>|MWI,|,<br>|MWI,|MWI|,<br>|
|Countries|NGA|NGA|MWI,<br>NGA|NGA,<br>ZWE|MWI,<br>NGA|NGA,<br>ZWE|,NGA,<br>SLE|NGA,<br>SLE,<br>ZWE|

Panel B. Household Head Definition of a household enterprise

||Sold|Assets|Any As|sistance|Public|Assistance|Private|Assistance|
|---|---|---|---|---|---|---|---|---|
||FHE|NFHE|FHE|NFHE|FHE|NFHE|FHE|NFHE|
|Farm income|0.160||-0.045||-0.019||-0.079||
|decline|[0.099]||[0.053]||[0.020]||[0.063]||
|Nonfarm income||0.181||-0.092||-0.035||-0.154|
|decline||[0.123]||[0.060]||[0.031]||[0.091]*|
|Between $2.15|0.159|0.289|-0.020|-0.085|0.008|0.008|-0.044|-0.108|
|and $3.65|[0.108]|[0.170]*|[0.051]|[0.080]|[0.016]|[0.020]|[0.055]|[0.085]|
|Between $3.65|-0.033|0.057|0.017|-0.009|-0.008|0.003|0.008|-0.001|
|and $6.85|[0.038]|[0.059]|[0.023]|[0.028]|[0.014]|[0.016]|[0.040]|[0.041]|
|Above $6.85|-0.029|0.039|0.149|0.120|0.031|0.008|0.186|0.102|

22
|Social Dimensions of Adjustment and Development Project|Nadine T. Poupart|
|---|---|
|Social Dimensions of Adjustment and Development Project|Nadine T. Poupart|
|Social Dimensions of Adjustment and Development Project|Nadine T. Poupart|
|Social Dimensions of Adjustment and Development Project|Nadine T. Poupart|
|Social Dimensions of Adjustment and Development Project|Nadine T. Poupart|
|Social Dimensions of Adjustment and Development Project|Nadine T. Poupart|
|Rural Project Fund<br>||
|Rural Project Fund||
|Rural Project Fund||
|Rural Project Fund||
|Education Project(03)||
|Education Project(03)||
|Education Project(03)||
|Education Project(03)||
|Education Project(03)||
|Agricultural Rehabilitation Project||
|Agricultural Rehabilitation Project||
|Agricultural Rehabilitation Project||
|Agricultural Rehabilitation Project||
|Agricultural Rehabilitation Project||
|National Livestock Project|Jean J. Delion|
|National Livestock Project|Jean J. Delion|
|National Livestock Project|Jean J. Delion|
|National Livestock Project|Jean J. Delion|
|National Livestock Project|Jean J. Delion|
|National Livestock Project|Jean J. Delion|
|National Livestock Project|Jean J. Delion|
|National Livestock Project|Jean J. Delion|
|National Livestock Project|Jean J. Delion|
|Agricultural and Livestock Services Project|Ousmane Seck|
|Agricultural and Livestock Services Project|Ousmane Seck|
|Agricultural and Livestock Services Project|Ousmane Seck|
|Agricultural and Livestock Services Project|Ousmane Seck|
|Agricultural and Livestock Services Project|Ousmane Seck|
|Agricultural and Livestock Services Project|Ousmane Seck|
|Agricultural and Livestock Services Project|Ousmane Seck|
|Agricultural and Livestock Services Project|Ousmane Seck|
|Agricultural and Livestock Services Project|Ousmane Seck|
|HighwayMaintenance Project(02)||
|HighwayMaintenance Project(02)||
|HighwayMaintenance Project(02)||
|HighwayMaintenance Project(02)||
|Education Rehabilitation Project|Nicholas L. Bennett|
|Education Rehabilitation Project|Nicholas L. Bennett|
|Education Rehabilitation Project|Nicholas L. Bennett|
|Education Rehabilitation Project|Nicholas L. Bennett|
|Education Rehabilitation Project|Nicholas L. Bennett|
As previously mentioned, unemployed youths are not included in our analysis. As shown in table 3, this group of youths compares similarly to the employed youth in average age and schooling, as well as in the proportion of indigenous population, of people who are illiterate and of those who still attend school. The main difference between both groups is the high percentage of females and lower percentage of people who are single in the unemployed group. Although the data of ENCEL 2017 did not include information on the activities carried out by these youths, due to the low school attendance rate in this group, it is reasonable to think that females are committed to household activities and it is not possible to know if they will later be incorporated into the labor market or not.

**<u>Table 3. Characteristics of occupied and unemployed youths of ENCEL 2017</u>**

||ENCEL 2017Youths|questionnaire (n=5,101)*|
|---|---|---|
|Variables of interest|Occupied (n=3,310)|Unemployed (n=1,449)|
|Average age|25.98|25.87|
|Females (%)|38.55|89.99|
|Indigenous (%)|26.13|25.53|
|Single (%)|38.88|22.22|
|Illiterate (%)|3.08|2.42|
|Attend school|3.66|4.89|
|Average years of schooling|9.27|9.34|

* There are youths for whom it is not possible to know their condition of activity, because they did not answer the relevant questions. Source: Own elaboration from ENCEL 2017.

# C. _Design for estimating the effects of PROSPERA_

Defining a design to estimate the impacts of PROSPERA represents a methodological challenge at the present time. One possibility would be to use the experimental design established at the beginning of the program. This design only enables us to examine differences between households within a period of 18 months in which they received intervention. After 20 years, this represents 7.5% more exposure to the intervention than the original treatment group. Apart from the difficulties in estimating and correcting for the effects of this sample loss, sampling inaccuracies and the probable interaction between treatment groups during the duration of the experiment (Faulkner, 2012), it is not completely clear how the measured effects, if any, from this small variation in the period of exposure at the end of the 1990s, should be interpreted. Resorting to quasi-experimental methods to estimate the effects of PROSPERA, however, is not an easy task.

Throughout the past 20 years, the program has adapted not only to the demands for continuous improvement of its design and operational processes, but also to different emergencies and changing political climates it has experienced. Throughout this period of time, there have been numerous changes, including the creation and elimination of program components, as well as 20
<!-- Start of picture text --> % of new suppliers that were Significant actual & potential negative<br>screened using labor practices impacts for labor practices in the supply<br>criteria (G4‐LA14) chain and actions taken (G4‐LA15)<br>SME 21.9%<br>SME 16.9%<br>Large 38.8% Large 29.2%<br>MNE 41.8% MNE 32.9%<br>All 37.9% All 29.0%<br>Grievances about labor practices filed,<br>addressed, and resolved through formal<br>grievance mechanisms (G4‐LA16)<br>SME 34.2%<br>Large 36.7%<br>MNE 42.6%<br>All 38.2%<br><!-- End of picture text --> Labor‐related information with regard to wages, health and safety practices, and employment of local managers is provided less often across all firms, and a lower percentage of MNEs discloses information in these cases compared to large firms. The gender wage gap (LA13) is only reported by 42% of MNEs. The share of MNEs reporting health and safety topics covered in formal agreements with trade unions (LA8) is even lower (29.8%), while MNEs are more likely to reveal the share of local managers in their workforce (39%). The percentage of large firms reporting on these indicators is higher.

Similarly, labor practices of suppliers are disclosed less often across firms, but MNEs report slightly more. While multinationals tend to disclose less information on questions related to labor practices of suppliers with regard to supplier screening (LA14) (42%), negative impacts (LA15) (33%), and grievances filed, addressed, and resolved (LA16) (43%), large firms and SMEs both report even less.

The regression results shown in Table 14 indicate that MNEs are more likely to report the number of grievances about labor practices filed, addressed, and resolved (LA16). Figure 5 confirms that the significant difference in reporting between MNEs and non‐MNEs found seems to be driven by lower reporting of both SMEs and large firms relative to MNEs. By contrast, MNEs are less likely to report on health and safety topics covered in formal agreements with trade unions (LA8). Figure 5 reveals that this is driven by higher reporting of large firms (40.9%) compared to SMEs (21%) and multinationals (29.8%).

We do not find a statistical difference in reporting patterns between MNEs and non‐multinationals for other labor indicators. The underlying reason is that large firms and MNEs show very similar reporting patterns across most indicators, while SMEs tend to underreport (see Figure 3), so the real difference lies 20
Although the share of renewables is relatively small in total installed capacity, they are the largest in terms of new capacity addition. For example, from 2010 to 2020, the capacity addition of solar power is the largest (675 GW) followed by the wind power (552 GW) (Figure 2c). Solar and wind together accounted for almost half of the total capacity added during the 2010-2020 period. Due to the stringent climate change targets, particularly if the net-zero path is followed, the share of renewables (solar, wind, hydro, and other renewable energy technologies (RETs)) would be much higher by 2030.

Investments in renewables are expected to accelerate due to the current debate on the netzero target (i.e., a goal to have net-zero GHG emissions globally) by the middle of this century. The net-zero target is consistent with the 1.5 °C targets -- limiting the increase in the global mean surface temperature of the earth’s surface to 1.5 °C above the pre-industrial level--. To stay in the path of meeting this target, the IEA estimates that the world needs 630 GW of solar PV and 390 GW of wind to be added annually by 2030 (IEA, 2021a). It also projects that two‐thirds of the total energy supply should be met from wind, solar, bioenergy, geothermal, and hydro energy by 2050 to realize the net-zero target.

**Figure 1. Investments (Billion US$) in non-fossil fuel-based technologies and their share (%) in total energy investment (2015-2020)**

Source: IEA (2020a) There is a sharp rising trend in the number of corporations joining the 100% renewable energy consumption (RE100) group, which brings together organizations that have set a target to 5
<!-- Start of picture text --> 2,500<br>£ _. 2,000<br>ao ¢<br>2<br>a<br>=<br>«<br>2 2% & 1,500 ©<br>os<br>-— 7<br>3 = 1,000<br>a + 9 %<br>co ¢<br>= 500<br>=<br>.<br>0<br>0 20 40 60 80 100<br>ESE index<br><!-- End of picture text -->
13

## **IV.** Assessing the Gains from Regulatory Reform

While information on the economic impacts of regulation is limited, there is a fairly comprehensive database in the United States and in some other countries that provides a good indication of the scope for regulatory reform. Moreover, several countries are in the process of developing useful information that would help streamline the regulatory process (see Box 2). Here, we first examine the potential for improving social regulation and then examine the potential gains from reforming economic regulation.

# **Box** 2 **Regulatory Reform in Mexico**

The government of Mexico is now implementing a far-reaching program to carefully examine the country's regulatory structure at the federal, state, and local levels. The aims of the Agreement for the Deregulation of Business Activity include streamlining federal regulation, reducing corruption by codifying regulation, and helping to promote more efficient and effective regulation. The program, while new, has enjoyed some early successes. Recent legislation simplifies administrative procedures, requires a quicker administrative response time, and reduces paperwork for foreign investors. In addition, a series of legal reforms aims to simplify court proceedings and reduce the costs of commercial lending. As a result of these reforms, Mexico City's Superior Court reports that the number of civil trials filed decreased by 24% from 1995 to 1996. Agency-by-agency rule simplification and elimination is also proceeding swiftly. For example, the approval time for a business requiring health, safety, and environmental controls to begin operation has been reduced from an average of over 200 working days to a maximum of 21 working days. Finally, a complete inventory of federal rules in effect are available on the internet. Making such information more easily accessible should help to reduce corruption and compliance costs.

Source: Secretaria De Comercio Y Fomento Industrial (1996)

## **_Social Regulation_**

In the area of social regulation, it is essential to examine the likely impact of individual regulations. Hahn (1996) has compiled the most comprehensive analysis of the benefits and costs of recent regulation based on studies by government agencies. He surveyed over ninety Regulatory Impact Analyses (RIAs) for environmental, health, and safety rules from 1990 to mid-1995 and found that there is considerable variation in the type and quality of analysis agencies perform for individual rules. Benefit analyses were often incomplete, and in less than 20 percent of the rules did agencies show that quantified monetary benefits would exceed quantified costs.

To make the analysis consistent across different programs and regulations, he converted dollar estimates to 1994 dollars, and introduced a common discount rate as well as
a variety of criteria have been used to allocate infrastructure concessions, such as the minimum tariff, minimum duration of concession, minimum subsidy, highest payment to the government, and largest investment value criterion (e.g., Kerf, 1998). While 60 percent of concessions awarded based on the lowest tariff criterion were renegotiated, 11 percent of contracts granted on a highest-payment basis underwent renegotiation (Guasch, 2004).

It remains inconclusive whether the impact of adopting more than one criterion on the incidence of renegotiation. In Guasch’s (2004) regressions, the coefficient associated with multi-criteria auctions is almost always insignificant. The results may or may not be reasonable given the expected positive and negative impacts as mentioned above. The current paper readdresses this question by performing an endogenous selection model with data on concession auctions in the road and railway sectors. It cannot be overemphasized that the adoption of multidimensional award criteria is an endogenous variable, which is affected by not only project characteristics but also governance and social circumstances, such as prevalent poverty, inequality and unemployment. This point is not controlled for in Guasch (2004).

# **3. PPP  IN TRANSPORT INFRASTRUCTURE AND AUCTION DESIGN**

Since the 1990s the private sector has invested 180 billion U.S. dollars in transport infrastructure (airports, railroads, roads and seaports) of developing countries (Figure 1). The total number of private participation transactions recorded reaches about 1,000 by 2006. About 30 billion U.S. dollars flowed in these sectors for 2006 alone. By region, Latin America is the leading region, having 40 percent of total transactions. By (sub)sector, the majority are road projects, followed by the railroad sector. In terms of the amount of investment, the road and railroad sectors amount to 47 percent and 20 percent of private participation experiences in developing countries, respectively.<sup>2</sup>

> 2 In terms of the number of transactions, the road-sector is still dominant, having 48 percent. However, the second largest area is seaports (30 percent). The railroad-sector private involvement accounts for 10 percent.

5
TABLE 4: CORRELATION BETWEEN PSYCHOLOGICAL CONSTRUCTS AND ECONOMIC SUCCESS

||Personal<br>Initiative<br>(1-5)|Error<br>management<br>(1-5)|Self Efficacy<br>(1-5)|Entrepreneurial<br>locus of control<br>(1-5)|Entrepreneurial<br>Identity<br>(1-5)|Attitude to Risk<br>(1-8 where a<br>higher score is<br>less risk averse)|Entrepreneurial<br>Activity Planning<br>(1-3)|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Success: Business Profits above<br>|0.0993***|0.137***|0.202***|0.303***|0.155***|0.0496|0.135***|
|median at baseline (Yes=1; No=0)|(0.0263)|(0.0267)|(0.0278)|(0.0354)|(0.0386)|(0.103)|(0.0198)|
|Mean for low profit businesses at|4.342|4.210|4.049|3.556|4.236|4.196|2.076|
|baseline PI/BSED study|(0.0708)|(0.0719)|(0.0750)|(0.0954)|(0.104)|(0.278)|(0.0535)|
|Observations|2001|2001|2001|2001|2001|2001|2001|

_Notes:_

* significant at 10% level ** significant at 5% level *** significant at 1% level

(1) Controls include: household asset wealth, secondary school educational attainment, received WEDP loan at baseline, age of owner, and marital status at baseline.

(2) OLS regression analysis includes all entrepreneurs who are part of the PI/BSED study sample and outcomes are measured at baseline.

# **4.3 IMPACT RESULTS**

The following section presents the intention-to-treat estimates of the trainings on a number of business performance, psychological and business practices outcomes.

# **4.3.1 Impact on firm closure and business performance**

Table 5 presents the impact of the DOT (panel A) and PI/BSED (panel B) trainings on business performance. In the DOT sample, 25% of the sample had closed their business operations two years after the training and we find no impact of DOT training on survival rates of firms. The closure rate was commensurate with reports of increased taxation law enforcement in 2017 that saw a number of businesses shutting down. We find a positive impact of the DOT training on profits with average monthly profits 30% higher for the treatment group versus the control group. Data are from the two follow-up survey rounds and Table 5 includes the average impact for the one- and two-years post-training. As can be seen in the table, we cannot reject the hypothesis that the impact of the DOT training on monthly profits is equal in the two follow-up rounds, one year and two years post the training, but the profit impacts do seem to attenuate over
2009), but it has a number of innovative aspects. First, unlike most NEG applications, including applications to China (see, for example, Au and Henderson, 2006; Moreno-Monroy, 2008; Bosker _et al_ , 2010; Hering and Poncet, 2010a, 2010b), it allows sectoral disaggregation; in our case, we disaggregate the regions of analysis into urban and rural sectors. Second, this paper is one of the first to use the full set of structural equations from an NEG model for the explicit purpose of applied policy evaluation (for a similar application to the reform of China’s _Hukou_ system see Bosker _et al_ , 2010). Third, while most empirical NEG applications use a simplified measure of transport costs – such as straight line distance – we utilize detailed GIS-derived data on a country's road network to derive travel time estimates that can be shown to relate closely with transport costs (see Combes and Lafourcade, 2005; Bröcker, 2002). Our travel time estimates are taken from a detailed digital representation of China's road network with and without the NEN.<sup>5</sup> Building on earlier work by Fujita _et al_ . (1999, chp. 7) and Fingleton (2005b, 2007), we develop a two-sector, multi-region, NEG model of the Chinese economy based on 331 prefectural level regions (prefectures for brevity) spread over the entire territory of China.<sup>6</sup> Prefectures are typically large and varied, so that each contains both an urban and a rural economy. The urban-rural distinction in China is fundamental to understanding the Chinese economy and this distinction is reflected in our impact measures, which are aggregate real income together with real urban wages and real rural wages. As noted above, an important variable in the set of equations leading to the short-run equilibrium of the NEG model is the concept of "market access" or "market potential." A prefecture with strong market access will, typically, possess pecuniary externalities which will tend to enhance wage levels. Fundamentally, market access is shaped by transport costs. We compute the changes in estimated travel times between each pair of prefectures as the basis for calculating transport cost, and, therefore, market access, changes.

NEN impact evaluation is constrained by necessary trade-offs between geographic specificity and data availability. Since consistent time series data for our impact measures and other relevant variables are not available for a geographically comprehensive sample of prefectures, and because the NEN can be expected to have had substantial spatial general equilibrium effects which might otherwise be difficult to capture, we adopt a counterfactual approach.

> 5 The data set and the construction of our GIS road networks data are discussed in more detail in Appendix A. 6 Our analysis focuses only on mainland China. The only province not included is Tibet. This is because of data constraints.

4
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