rafmacalaba/datause-encoder-data huggingface dataset:
that, particularly self-employment, may be desirable. Being one's own boss is valued in the industrialized world, and there is no reason to think that developing country workers value their independence less. There is support in the extensive interviews ofMonterrey workers undertaken by Balan, et al. (1973), and increasingly elsewhere, for a "life cycle" pattern where workers enter into salaried work, accumulate knowledge, capital, and contacts, and then quit to open their own informal businesses. The low formal sector productivity and firm specific capital reduce the opportunity cost of such moves and should lead to an increased share of self-employment in total work.

# **VL Trade Reform and Sub-Contracting:**

In phase II, contract work is not obviously a disadvantaged sector. Figure 3 suggests that contract earnings follow the same increase relative to formal salaried wages shown by the selfemployed. Mobility between the formal and contract sectors is heightened during the upturn of phase II. It is possible that contracting exists partially as a Pareto improving way to avoid the inefficiencies in the labor legislation (or duplication ofbenefits coverage) discussed above, rather than to avoid the legislation per se, and that efficient workers may do better on a piece rate basis in good times than working in a factory."9 However, the data after 1991 may suggest a less sanguine interpretation of increased outsourcing to reduce labor costs in the face of increased global competition. Figure 1 reveals a secular decline in the contribution offormal salaried work, from over 38% to 35% of the interviewed 19 Roberts'(1989) Guadalajara interview **s** ugges **t** hat given the very weak unions and low wages, informalization is not primarily a strategy for reducing remuneration and worker control over production: 'Market uncertainty **and** the large number of income opportunities in the city mean that it is usefiu for _both_ employees and employers to **have** flexibility in allocating labor."(italics added, p. 48).

18
# **6 Summary and Conclusions**

## **6.1 Summary of Results**

Using newly collected survey data on direct supplier-multinational linkages in Chile, Ghana, Kenya, Lesotho, Mozambique, Swaziland, and Vietnam, this paper evaluated whether foreign investors differ from domestic producers in terms of their overall performance, linkages with the local economy, and supplier assistance which all influence the firms’ potential to generate productivity spillovers. Besides apparel, the firms in our sample cover two natural resourcesintensive industries, namely agribusiness and mining. We found that foreign investors outperform domestic producers in terms of sales, firm size, productivity, exporting behavior, and direct export share. While this would imply a higher knowledge and productivity spillover potential compared to domestic firms, foreign investors have fewer linkages with the local economy in terms of using domestic inputs and workers. However, the findings also show that certain service inputs, namely technical services and transport, security, cleaning, catering, and other services, show a higher potential for linkages. There is also some evidence that foreign firms offer less assistance to local suppliers. Fewer linkages and supplier assistance both can limit the positive impact from FDI.

In a next step, we studied the relationship between foreign investor characteristics and the FDI spillover potential. In sum, we found that foreign investor characteristics matter for FDI linkages and supplier assistance, but the size and direction of the relationship depends on the measure of FDI spillover potential we used. For example, a multinational’s presence in the host country is negatively associated with the share of domestically sourced inputs if the firm has been in the country for at least 20 years, but positively related with the percentage of domestic workers. Other foreign firm characteristics, on the other hand, show a less ambiguous picture. Market-seeking FDI, for example, shows a positive relationship with the share of sales to the host country as well as the probability of supplier assistance. And suppliers with the largest investor from SSA are associated with a larger share of sales to the local market and a higher likelihood of supplier assistance. Suppliers with the largest investor from Asia also sell a significantly larger share of output to the local market, but offer significantly less assistance to their domestic suppliers.

The second part of this paper first examined the role of supplier firms’ absorptive capacities for FDI linkages. These firms supply to multinationals in agribusiness, mining, and apparel, but produce a variety of inputs across the value chain. The results indicated that several supplier characteristics 31
quota allocations, and indeed the total milds quota remained substantially lower than the 47.7% obtained in 1980-81 in the aftermath of the Brazilian frost. The other milds producers, supported by the USA, but opposed by Brazil, the African robusta producers and the EU, argued for quota reallocation to be embodied in any new agreement.

The determination of the "other milds" producers to hold out for a quota redistribution may have been reinforced by a World Bank study circulating around this time which argued that, although the 3rd and 4th ICoAs significantly stabilized both prices and producer revenues, they typically did not give rise to higher revenues for countries other than Brazil and Colombia. and that aggregate revenues would recover from the ending of controls after less than a decade (Akiyama and Varangis, 1989, 1990).'7 In fact, prices fell by 40% in the two years following the suspension of quotas (coffee year 1990-91 compared with 1987-88, the last full year of control), and remained at around that level for four years. Furthermore, the fall in mild arabica prices was not dramatically less than that in robusta prices (35% and 48% respectively over the same period).

The situation was complicated by the lack of consensus in Brazil in favor of market intervention. The Brazilian industry was controlled by the Instituto Brasileiro do Cafe (the IBC), and it was widely considered within Brazil that it was the IBC itself, rather than the coffee growers, which was the principal beneficiary of coffee stabilization. Indeed, the coffee exporters tended to favor a free market, while the roasters (Brazil is the second largest coffee consuming country) were at best ambivalent. Collor became President in April 1990, and one of his first moves was to abolish the IBC. A major difficulty in attempting to negotiate a new ICoA was that for much of 1990-91 Brazil had no clear coffee policy. Brazil had always been 17 These results are a consequence of export quotas being met in part by increased stockholding, which has the effect of subsequently reducing prices, together with the tendency for producers other than Brazil and Colombia to profitably dispose of excess production on nonquota markets. The results differ from those reported by Herrmann (1988) and Palm and Vogelvang (1991) who saw producers in general as benefiting from the ICoA controls. Akiyama and Varangis (1989) is dated February 1989 and was in circulation by the summer of 1989.

21
systematically more likely to implement Basel III. We discuss the sample selection issues and then introduce our baseline framework. Given that our panel contains only two periods, we take the first difference of our dependent variable and regress it on the pre-treatment values of the explanatory variables.

Therefore, our baseline panel specification in its differenced form is as follows: Δa2f_sme�,�,� denotes the measure of access to finance constraints for firm _f_ in country _c_ . The difference, Δ, is taken between the values before and after the treatment—the exact years are specified in table A.1, in the appendix. B3A�0/#�� represents our variable capturing the effect of Basel III implementation, with β assessing the estimated effect of Basel III on SME access to finance. The symbol # captures the gestation lag at the time of the availability of the Enterprise Survey data after Basel III implementation and is distributed between (0,1] . X�,�,�� controls for firm-level and Z�,�� for bank and/or banking system characteristics before the treatment. W�,�� captures the country-level variables (such as the initial GDP per capita or contract enforcement levels). ES_gap� is a variable controlling for the time between the spells of the Enterprise Surveys across countries. This could be important for trending dependent variables, such as progressing financial inclusion. λIC�,�� represents initial conditions, that is, the level of access to finance constraints prior to Basel III implementation (in the initial period). ε�,� is a residual.

For simplicity, we present the above equation in linear form, which is accurate in case we were to estimate it using ordinary least squares. However, because of the limited dependent variable (access to finance as a constraint), the equation translates accordingly to a nonlinear form and is estimated using the ordered logit model. The standard errors are clustered at the country level, and the fixed effects disappear because of differencing.

We also examine whether there is a varied impact of Basel III on firm access to finance depending on firm characteristics. Young and small firms, which are typically subject to greater informational opacity than older and bigger firms, may experience the effect of the treatment in a differentiated 24
_Figure 13: Energy poverty indicators based on consensual approaches and outcome-based metrics_ _Figure 14: Overlap of monetary and energy poverty based on consensual approaches_ <!-- Start of picture text --> Apartment in multi-<br>Apartment in multi-<br>46.5 household building<br>household building<br>Leakage (roof, wall, floor, Leakages (children)<br>foundation, window) - 12.3<br>children<br>Leakages<br>Leakage (roof, wall, floor,<br>11.0<br>foundation, window)<br>Arrears on utility bills<br>Arrears on utility bill 22.2 Inability to keep home<br>adequately warm<br>Inability to keep home 0% 20% 40% 60% 80% 100%<br>27.5<br>adequately warm<br>Energy and monetary poor Only monetary poor<br>0.0 10.020.030.040.050.0 Only energy poor Neither nor<br><!-- End of picture text --> _Notes: Monetary poverty is defined via the at-risk-of-poverty measure using income measures reported in the EUSILC. Income refers to the year 2019._ _Source: Own estimates based on EU-SILC (2020)._

**Overlapping the different consensual measures confirms findings from the previous literature on households experiencing different dimensions of energy poverty.** For example, only half of households that report arrears on their utility bills also report an inability to keep their home warm (EU-SILC, 2020). Similar observations can be made when overlapping households that report leakages with those unable to keep their home warm (EU-SILC, 2020). These results show that various population segments encounter distinct aspects of energy poverty. Efficient targeting strategies must consider which part of the population is affected by what type of energy poverty.

# 3.3. Multidimensional Measures

**Given the multidimensional nature of energy poverty, one approach would involve using a comprehensive array of indicators to construct a thorough understanding of energy.** Among the wide range of multidimensional measures identified in the literature, we construct a multidimensional energy poverty index following the methodology developed by Bouzarovski and Tirado Herrero (2017) and Rodriguez-Alvarez et al. (2021). The advantage of this index is its simplicity and the fact that all the dimensions can be measured with one data source, the EUSILC, and, therefore, can easily be constructed and tracked over time. Tirado Herrero (2017) emphasizes that the suggested index operates under the assumption that relying solely on consensual measures (like self-reported inability to maintain warmth) may not adequately grasp the intricate nature of energy poverty. They suggest integrating indicators describing the population's housing and financial circumstances to comprehensively understand them.

The multidimensional energy poverty index is constructed as a composite index using three indicators from the EU-SILC and weighting them accordingly as follows: E E E P I Where: PPE PPEE IIEEIIEEII= (0.5 ∗IIEEI IIPPEE+ 0.25 ∗AAE EEIIEEAA+ 0.25 ∗LLEEIILLIIE AA) ∗100 27
As the sampling frames for the NSSO surveys are based on the decennial population census, we use population census data to approximate the actual number of women in the labor force across rounds. Since the census and NSSO survey years do not match, we first project the population figures for NSSO survey years from the census for compatibility. Population figures from the 1991, 2001 and 2011 censuses for various categories, including rural males, rural females, urban males and urban females are interpolated for the years viz. 1993, 1994, 1999, 2000, 2004, 2005, 2007, 2008, 2009, 2010, and 2012 using compound annual growth rates. Next, we take a simple average of the two years that correspond to each NSSO survey period viz., 1993-94; 1999-00; 2004-05; 2007-08; 2009-10 and 2011-12. The projected population figures are given in Table 3.

**Table 3: Trend in Female Labor Force between 1993-94 and 2011-12**

||Total female<br>|population<br>|Female p<br>above<br>|opulation<br>15 years<br>|FL<br>|FP (Based on UPS<br>|S)<br>|
|---|---|---|---|---|---|---|---|
|Round|Rural|Urban|Rural|Urban|Rural|Urban|Total FLFP|
|1993-94|317,673,031|110,054,315|202,964,444|73,677,754|99,518,541|17,432,031|116,950,573|
|1999-00|351,946,619|129,962,760|224,652,419|89,170,854|103,383,673|18,704,086|122,087,759|
|2004-05|375,871,197|150,098,317|246,782,858|107,059,771|121,870,827|26,147,004|148,017,831|
|2007-08|389,035,480|163,874,365|262,370,352|120,148,883|111,853,912|23,526,797|135,380,709|
|2009-10|398,066,826|173,753,871|274,142,334|129,531,026|103,722,684|25,164,653|128,887,337|
|2011-12|404,974,104|181,536,553|281,815,810|135,890,458|100,942,252|27,915,420|128,857,672|

Source: Authors’ own estimate based on NSSO and census data as noted below. Note: As described earlier, we extrapolated census data from 1991, 2001 and 2011 in order to project the total female population above 15 years of age for 1993-94, 1999-00, 2004-05, 2007-08, 2009-10 and 2011-112, across rural and urban locations. We then use the FLFP rate as calculated from NSSO data for the same years to determine the total FLFP.

Our calculation from NSSO survey data across rounds suggests that FLFP rates for the age group of 15 years and above had declined from 42.6 percent in 1993-94 to 31.2 percent in 2011-12 (Table 2). The decline had been continuous during the last two decades, except for the year 2004-05, when it rose to 42.7 percent. While the FLFP rate had initially been higher in rural areas, the decline in the FLFP rate was also more severe in rural areas. As Table 2 suggests, rural areas experienced a drop of 13.2 percentage points during the period, corresponding to a drop of 3.1 7
signicant estimate on _INVij,t_ , which suggests that, on average, the PTAs with investment provisions in our sample have led to a 34.33% (std.err 14.535) increase in FDI between their members. This result complements the ndings from Laget et al. (2021), who use rm level data for the period 2003-2015 and obtain positive estimates of the eects of provisions related to `intellectual property rights' and `visa and asylum', which vary between 32 and 50 percent, but do not nd signicant eects of investment provisions on FDI.

Finally, in column (4) of Table 2 we use the DTA database to construct a continuous variable ( _INV_ DEPTHij,t_ ), which counts the number of investment provisions within each of the DTAs in our sample. The number of investment provisions across the DTAs in our sample varies between 7 and 41. The estimates from column (4) do not reveal a signicant impact of the increase in the depth (number of provisions) on FDI. In fact, and pushing inference to the limit, our estimates suggest that the impact of additional provisions is actually negative. A possible interpretation of this result is that more investment provisions make the agreements more dicult to comply with. Despite the fact that our estimate on _INV_ DEPTHij,t_ is insignicant, we use it in combination with the positive estimates on _INVij,t_ to construct a continuous FDI response to the impact of DTAs. The resulting eects are all positive and vary between 16.4% (std.err. 19.06) and 66.41% (std.err. 32.99), depending on the number of investment provisions.<sup>19</sup> We conclude the econometric analysis with two additional sets of experiments that further capitalize on the richness of the DCRTA data set to shed light on the links between DTA provisions and FDI.<sup>20</sup> First, we complement the analysis of Laget et al. (2021), who study the impact of several alternative PTA disciplines/provisions on FDI, by investigating the eects of all provision types from DCRTA on aggregate FDI. To this end, we rely on the dummy-variable specication from column (3) of Table 2 by sequentially replacing the

> 19 ˆ Specically, to obtain these bounds, we used the expression (exp(ˆ _βINVij,t_ + _βINV_  DEP T Hij,t × Nmin,max_ ) _−_ 1) _×_ 100 , where _β_<sup>ˆ</sup> _INVij,t_ and _β_<sup>ˆ</sup> _INV_  DEP T Hij,t_ are the corresponding estimates from column (4) of Table 2), and _Nmin,max_ denotes the minimum (7) and the maximum (42) number of investment provisions in our sample.

> 20 We are very grateful to Nadia Rocha and Vanessa Alviarez for suggesting to investigate the eects of additional provisions and alternative measures of the depth of DTAs.

27
Table 5: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Sector Heat Sensitivity

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||High|Low|High|Low|High|Low|
|Log(Revenue)|-0.429|-0.125|-0.106|0.061|0.154|0.21|
||(0.101)|(0.104)|(0.065)|(0.06)|(0.097)|(0.092)|
|Log(Rev. per worker)|-0.249|-0.065|-0.047|0.046|0.116|0.136|
||(0.062)|(0.057)|(0.038)|(0.033)|(0.056)|(0.049)|
|Invest (binary)|-0.004|-0.002|-0.001|-0.005|0.001|-0.007|
||(0.002)|(0.003)|(0.002)|(0.002)|(0.004)|(0.004)|
|Log(Investment)|-0.328|0.058|-0.044|-0.077|0.184|-0.186|
||(0.186)|(0.159)|(0.104)|(0.086)|(0.152)|(0.118)|
|Log(Avg. wage)|-0.145|-0.037|-0.024|0.005|0.074|0.038|
||(0.045)|(0.053)|(0.029)|(0.028)|(0.049)|(0.042)|
|Log(Capacity Util)|0.06|-0.025|0.037|-0.02|0.018|-0.016|
||(0.027)|(0.022)|(0.018)|(0.012)|(0.029)|(0.018)|
|Log(Energy Intensity)|0.15|-0.025|0.116|-0.047|0.089|-0.065|
||(0.072)|(0.067)|(0.049)|(0.046)|(0.077)|(0.072)|
|Innovation|-0.003|-0.043|-0.004|-0.018|-0.005|0.002|
||(0.016)|(0.018)|(0.009)|(0.01)|(0.016)|(0.016)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and whether a firm is in a heat-sensitive industry. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model for firms not in heat sensitive sectors and columns labeled “high” evaluate the estimated model for firms in heat sensitive sectors within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

40
administered to fill institutional data that was missing from the prior surveys.

# _3.1 Variable Definitions and Descriptive Statistics_

The current study draws on data from 3 of the 8 surveyed counties, Ning, Yueqin, and Shaoxin, in Zhejiang province and two counties, Nancheng and Anfu, in Jiangxi province. These counties were selected for analysis because rice is the staple crop in all of them. Complete data were available from both periods for 398 households in these rice producing counties.

Table 1 presents descriptive statistics for the key variables to be utilized in the econometric analysis. The average labor intensity of rice production rose by about 30% between the two survey periods. The intensity of investment (measured as the input of labor time into construction of wells, irrigation and drainage ditches, ponds, application of organic fertilizers, land flattening, soil improvement etc.) rose only slightly over the period, though as the standard deviations show it became much more variable over time.

The impact of property rights upon these two key allocation variables is of course the primary focus of this study. As described in Liu _et al._ (1998), distinct property rights regimes have evolved at the local level in the nearly two decades since the creation (or at least formal recognition) of the household responsibility system. To construct village and time specific measures of transfer rights and tenure security, the principle component method of factor analysis was used to create a one dimensional measure (a factor score) of both security and transfer rights from sets of multiple indicator variables.'<sup>8</sup> The factor score variables are scaled as standard normal variables, with higher values indicating greater security and less 26
# **3.4 Placebo Tests and Applicant Characteristics**

We use data from In-Tech application forms to examine whether applicants on each side of the cutoff have similar characteristics. Table 1 shows averages of these characteristics for funded and non-funded proposals, as well as RD estimates for the difference in these characteristics at the cutoff, using the different bandwidths discussed above.

The averages show that the characteristics of funded and non-funded projects are very similar. About 25% of projects applied only for the research phase (as opposed to research and implementation), which lowers the average project duration across all projects to 32 months instead of the 36 months that are the standard for research plus implementation. The majority (54%) had two consortium partners, with 33% having three partners and only 13% having four or more partners. In slightly less than half of the consortia the leader was an enterprise, as opposed to an R&D institute, scientific unit or a public university. About 26% of leaders were based in Warsaw and they had on average 73 to 99 research employees and close to 140 technical and administrative staff. The first partner had about 140 research employees on average. Close to 80 percent of project managers had experience abroad.

Leaders had received on average 50 million PLN (US$14 million) in research funds from the government budget for science during the last three years before the In-Tech application. The requested grant size for the In-Tech project was about 2.5 million PLN (US$714,000) on average. For about half of the projects, the application forms stated that they plan to develop a new product or process as opposed to improving an existing product or process. About 14% of projects had the aim of improving a business process. Finally, 44% projects had plans to sell their innovation on the export market.

The RD coefficients in Table 1 show a statistically significant difference in project characteristics at the cutoff for only two variables. First, the specification with bandwidth 8 suggests that project leaders above the cutoff are more likely to be based in Warsaw. Second, both the specifications with bandwidth 8 and with the local linear optimal bandwidth suggest that projects above the cutoff are more likely to develop a new product or process vs. improve an existing product or process. However, both of these characteristics do not show a statistically significant difference in the specification with the local mean optimal bandwidth. Also, since we are looking at differences for 16 characteristics, we can expect a couple of them to be statistically significant due to chance. To 12