rafmacalaba/datause-encoder-data huggingface dataset:
of output shifts them left on the economies-of-scale curve (Harrison 1994; Aitken and Harrison 1999; Crespo and Fontoura 2007). It is also more likely in the short term that foreign firms bid away high quality labor from domestic firms by offering higher wages and benefits (labor turnover effect), resulting in a potentially negative spillover effect (Sinani and Meyer 2004; Hoekman and Javorcik 2006; Crespo and Fontoura 2007).

Both effects can reverse in the medium to long term, if domestic firms become more productive due to increased competition and start to absorb skilled workers from multinationals. Given that our data sample only allows us to control for short-term effects, the negative coefficients on the foreign output share variable are not surprising. Moreover, they are in line with other studies rejecting the existence of positive intra-industry FDI spillovers (Havranek and Irsova 2011) or studies that find negative intra-industry spillovers (e.g. Aitken and Harrison 1999; Djankov and Hoekman 2000; Konings 2001, among others).<sup>18</sup> A sector’s average share of foreign ownership in a country ( _own_ ) shows a positive coefficient sign, but is insignificant (columns 1 and 5). The interaction terms with a sector’s average percentage of FDI sales to the domestic market in a country ( _market_ ) has a significantly positive effect on domestic firms’ productivity in the OLS regressions (column 2), confirming the hypothesis that market-seeking FDI is more likely not only to provide a higher spillover potential, but also to translate into more positive actual spillovers.

Since the _market_ variable might be influenced by the productivity of domestic competitors, we additionally instrument for the interaction term of foreign output share with the _market_ variable in column 7. We choose the sectoral average number of days to clear direct exports through customs in a country (in natural logarithms) from the Enterprise Surveys Indicator Database as an inverse instrument. One could argue that longer waiting times at customs motivate foreign firms to rely more strongly on the host market for sales. The coefficient signs in the IV 2SLS regressions are also positive, but insignificant (columns 6 and 7). However, the Hansen J-test of over-identifying restrictions implies that the instruments are not accepted in these specifications.

Analogously, a sector’s average percentage of domestic input purchases of FDI firms in a country ( _inp_ ) might be endogenous, as the sectoral share of inputs purchased locally might be

> 18 Görg and Strobl (2001) and Görg and Greenaway (2004), however, argue that studies using cross-sectional data tend to find positive FDI spillovers, which they relate to potential time-invariant effects across units (firms or sectors) that are correlated with the FDI presence variable without being caused by it. Interestingly, we find negative FDI spillover effects regardless of whether we control for this potential endogeneity (IV 2SLS) or not (OLS).

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**Table 6: Firm Investment and the Intensity and Purpose of Mobile Money Use**

|**Dependent variable: Firm purchased fixed assets**<br>**Y:1 N:0**||**Pro**|**bit**||
|---|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|% of total labor cost paid using mobile money (0 if<br>not adopted or no payment)|0.016***<br>(0.005)||||
|% of raw material cost paid using mobile money (0 if<br>not adopted or no payment)||0.022***<br>(0.007)|||
|% of utility bill paid using mobile money (0 if not<br>adopted or no payment)|||0.009**||
||||(0.004)||
|% of annual sales from customer payments using<br>mobile money (0 if not adopted or||||0.019***<br>(0.006)|
|Log of size|0.246***|0.262***|0.209**|0.260***|
||(0.085)|(0.090)|(0.086)|(0.088)|
|Top manager experience in sector (years)|-0.002|-0.003|-0.003|-0.004|
||(0.007)|(0.007)|(0.007)|(0.008)|
|Log of sales per worker (outliers removed)|-0.027|0.002|-0.031|0.001|
||(0.040)|(0.044)|(0.042)|(0.045)|
|Log of age of firm|-0.129|-0.125|-0.129|-0.092|
||(0.111)|(0.117)|(0.116)|(0.120)|
|Firm is part of a larger firm Y:1 N:0|-0.073|-0.107|-0.131|-0.088|
||(0.239)|(0.250)|(0.227)|(0.232)|
|Exports 10% or more of sales Y:1 N:0|-0.104|-0.056|0.005|-0.071|
||(0.224)|(0.237)|(0.228)|(0.225)|
|Foreign ownership Y:1 N:0|0.202|0.283|0.370|0.333|
||(0.245)|(0.269)|(0.264)|(0.264)|
|Establishment has a line of credit or loan at this time<br>Y:1 N:0|0.239|0.256|0.255|0.273*|
||(0.157)|(0.163)|(0.161)|(0.163)|
|Firms use email to interact with clients/suppliers Y:1<br>N:0|0.119|0.173|0.168|0.197|
||(0.177)|(0.184)|(0.188)|(0.194)|
|Establishment has checking or savings account at this<br>time Y:1 N:0|0.089|0.046|0.112|0.012|
||(0.272)|(0.223)|(0.277)|(0.219)|
|Log of mean distance - bank (km)|-0.768***|-0.848***|-0.854***|-0.897***|
||(0.214)|(0.224)|(0.218)|(0.217)|

26
those to be procured as comparators. In sum, transparency reforms will almost certainly have to be complemented by a variety of other actions and policies.

Moreover, to be effective transparency norms need to be enforceable. Of particular importance here are domestic challenge procedures and the costs and benefits of multilateral dispute settlement. To be effective, domestic challenge mechanisms need to provide firms with an opportunity to protest before the procurement process is completed, as well as thereafter (Alam, 1995). In the procurement context, the set of losers is usually small. In discretionary, non-transparent procurement systems losing firms have little incentive to protest against irregularities because of the power of procuring entities to black list them. In general, given the sunk costs of participation in the bidding process there is only an incentive to protest if expected returns outweigh expected costs of protesting. In the absence of effective compensation, and thus limited returns to, and incentives for, individuals bringing cases, there may be a need for public action to enforce norms and penalties that are strong enough to be of deterrent value. An ombudsman, an activist and free press, and surveys of the type discussed in section IV above, are all potential complementary mechanisms to ensure accountability.

The foregoing discussion suggests that procurement reform ought not to be limited to improving transparency—there is a plethora of complementary actions that will generally be needed on the part of governments and stakeholders. In short, from a development perspective, it is probably necessary to go beyond improving transparency alone. But this poses something of a problem in the context of trade negotiations where the natural extension of any negotiations is precisely what many WTO members are opposed to. If progress, therefore, is unlikely at the WTO, what alternatives exist for international cooperation on procurement reform?

# **_Voluntary cooperation: ‘soft law’ as a first step (or an end point)_**

Given the uncertainty regarding the returns to multilateral rule making, one alternative way forward might be to explore the scope for _more extensive_ voluntary cooperation between nations.<sup>24</sup> One option could involve the creation of a mechanism through which

> 24 It would incorrect to suggest that voluntary cooperation and norm formation in the area of government procurement is new. One prominent initiative is the United Nations Commission on International Trade

29
# **3. Public and Private Wages in India (knowledge to date)**

The paucity of evidence on the magnitude of the wage differentials for the Indian labor market as a whole is usually blamed on the scarcity of suitable data. Even though many agencies collect some information on employment and wages, the comparability of public and private sector wages is complicated because wage data are rarely collected in conjunction with information on the qualifications of workers. Thus, the notion of alternative wages is rarely used. Yet, some data from formal sources exist and several authors have analyzed wages and wage differentials using individual-level data. Overall, the information on the differences in wages is limited to the _factory sector_ , to the segment of the labor market, which employs highly skilled workers, and to some geographic areas.

Data on wages from the 1997-98 Annual Survey of Industries (Central Statistical Organization, Government of India), which covers the _factory sector_ , suggest that the public sector offers higher remuneration for workers compared to the private sector. There are substantial variations in pay within the public sector. Workers employed in units owned by the Central government receive wages that are nearly twice as high as the sector average; followed by workers in units owned jointly in the public sector. Workers in “wholly” private enterprises appear to receive the lowest wages among all considered categories (Table 4).<sup>2</sup> Several authors have attempted to account for differences in the human capital of workers in the public and private sectors while investigating wage differentials. Most of these studies used the survey of Degree Holders and Technical Personnel (DHTP) conducted, along with the 1981 Census of India, on behalf of the Division of Scientific and Technical Personnel of the Council for Scientific and Industrial Research. Data were collected on the basis of a 20% Census sample in 12 states and using the complete enumeration samples in other states and union territories.

> 2 The 1999-2000 Annual Survey of Industries data on wages has not been released as yet. Various media sources state that the high differential between public and private workers in the factory sector remained. In particular “According to the Annual Survey of Industries for 1999-2000, in the top ten industrialized states, the average annual wage difference between public and private sector industrial workers varies between Rs 12,000 and Rs 90,000. Industrial workers in the public sector are earning 15 to 80% more than the wages earned by workers in the private sector... In Haryana, the wage differential is around 36%, while in Uttar Pradesh it is 45%.” by Mamata Singh at: http://www.rediff.com/money/2002/may/22psu.htm).

8
(Dunn et al. 1986), the Spanish-speaking version of the much-used Peabody Picture Vocabulary Test (PPVT). The TVIP has been used widely to measure development among Latin American children—see Paxson and Schady (2007) for a comparison of vocabulary scores between children in Ecuador and the U.S., and Schady et al. (2015) for evidence on levels and socioeconomic gradients in the TVIP in five Latin American countries, including Ecuador. The other three tests were taken from the WoodcockJohnson battery of achievement tests (Woodcock and Muñoz-Sandoval 1996) and have been applied in other evaluations of interventions in settings similar to ours, including in Ecuador (Paxson and Schady 2011; Araujo et al. 2016).

Unsurprisingly, performance on the four tests is positively correlated in our data. The lowest correlation, 0.34, is between vocabulary and number identification, and the highest, 0.64, is between number identification and basic arithmetic. As with the CLASS, the magnitude of the correlations likely reflects that different tests measure different dimensions of knowledge or achievement, but also measurement error in the tests.

# **3. Cross-sectional associations between CLASS and achievement**

To motivate our analysis, we first calculate the associations between the CLASS and child test scores. Panel A of Table 2 reports the results from regressions of overall achievement on the CLASS, with the sample limited to children in the control group. Column (1) shows that a 1 SD increase in the CLASS is associated with a 0.097 SD increase in test scores (p-value: <0.001). In the following columns we report the association between individual domains of the CLASS and achievement. These columns, and in particular the specification in column (5), which includes all three CLASS domains at the same time, show that _Classroom Organization_ is most strongly associated with test scores. In contrast, the association between _Emotional Support_ and achievement is not significant once other CLASS domains are included as controls.

To further explore the associations between the CLASS and achievement, we next make use of data from another experiment, carried out in a different sample of 200 schools in Ecuador (see Araujo et al. 2016; Campos et al. 2020). In that experiment, children were randomly assigned to classrooms within schools in kindergarten and were then reassigned to different classrooms in every grade between 1<sup>st</sup> and 6<sup>th</sup> grades. Much as in the coaching pilot, teachers were filmed, and the video was used to calculate teacher CLASS scores. At the end of each grade, children were given a large battery of age-appropriate tests in math and language. In 1<sup>st</sup> grade, these tests included the same math and language tests as were applied in the coaching pilot (as well as other tests). For the calculations we report below, we limit the sample to teachers and children in 1<sup>st</sup> grade and use only those tests that were also applied in the coaching pilot, scored and aggregated in the same way.

9
hours of Science than teachers in the Control and Sequence Groups. This is important, as increasing class teaching hours is associated with improvements in learning outcomes (OECD 2016). All of these differences are statistically significant.

# Follow-Up

Finally, this study explores the possibility of the longer-run impact of the different training interventions evaluated. As shown above, a structured sequence using an inquiry-based approach has a positive effect on how students learn a specific pedagogical content. Although learning of a particular topic is likely to have a limited aggregate impact over time for a typical student, what is clearly required for a potential long-lasting effect of teacher training is that the targeted teachers adopt new teaching practices consistently over time. Thus, the obvious follow-up question is whether teachers under our treatment groups continued using the sequence when teaching the same topic the following year. To answer this question, participant teachers in the Sequence and Couches Groups were contacted after the intervention to inquire about whether they continued using the sequence provided the prior year, even when this time their students were not going to be externally assessed.

In fact, almost every “treated” seventh-grade Science teacher that remained in the same school continued using the sequence (100 percent in the Sequence Group and 89 percent in the Coaches Group) (see <u>table 11). This is an encouraging finding, as it provides evidence suggesting a persistent change in teaching</u> practices.

However, between 67 percent and 73 percent of the participant teachers were reassigned to either other grades or other schools. That means that teaching turnover may dissipate part of the effect of the training. This is an issue that goes beyond the scope of our paper but increases its relevance when assessing the longrun impact of training initiatives.

28
Table 7. Cotton in district GJ8 of Gujarat: average and weighted average loss costs at 70% and 90% indemnity levels

|**Insurance**|**Area Sown**|**Probable Yield**|**Average histo**<br>|**rical loss cost**<br>|
|---|---|---|---|---|
|**Unit**|**2007 (ha)**|**(kg/ha)**|**70% indemnity level**<br>**(** **)**|**90% indemnity level**<br>**(** **)**|
|[1]|[2]|[3]|[4]|[5]|
|GJ8_1|29,000|2,787|17%|21%|
|GJ8_2|20,300|2,631|14%|19%|
|GJ8_3|21,894|1,820|26%|37%|
|GJ8_4|26,440|2,204|19%|24%|
|GJ8_5|38,000|2,739|10%|17%|
|GJ8_6|16,500|2,714|8%|14%|
|GJ8_7|10,000|2,243|23%|31%|
|GJ8_8|18,000|2,251|19%|26%|
|GJ8_9|4,810|2,441|18%|23%|
||Weighte|d average loss cost|16%|23%|

The indemnity level for each district may then be chosen as a function of the weighted average loss costs at 70% and 90% indemnity levels (see Table 8). Cutoffs and in Table 8 should be chosen to target commercial premium rates. An increase in or would increase the average indemnity level across the mNAIS portfolio, and commensurately increase the average commercial premium rate and government subsidy rate.

Table 8. Indemnity level loss cost cutoffs

|**Rule**|**Indemnity limit**|
|---|---|
|District Loss Cost at 90% indemnitylimit less than x|90%|
|District Loss Cost at 70% indemnitylimitgreater thany|70%|
|Otherwise|80%|

Note that selecting and to be between and is broadly equivalent to the CV rule under the current NAIS, based on the Normal Theory Method. Using this indemnity level cutoff rule it is possible to calculate the indemnity levels for cotton for all districts in Gujarat (column [4] of Table 9). Under the NAIS NTM method, if the coefficient of variation (CV) of yields for the last ten years was less than 16, corresponding to an NTM pure premium rate of 2.9%, an indemnity level of 90% was chosen. If the CV was greater than 31, corresponding to an NTM pure premium rate of 2.4%, an indemnity level of 60% was chosen. Otherwise an indemnity level of 80% was chosen.

16
that SBP incurs in offering them is quite substantial.

In light of our results, some policy implications emerge. First, to make the schemes more cost effective, it is important that the SBP reassesses the refinancing rates it offers to commercial banks. Lending at negative real interest rates is costly and distorts the allocation of credit at the aggregate level as Zia (2008) has shown. Second, the schemes could be more impactful if opened to all firms in all sectors in the economy. In particular, it is likely that new firms, or firms that are diversifying into new markets or products, may benefit more from access to export finance. Prioritizing new export ventures when allocating the funds is likely more efficient than continuing to lend to established exporters.

# **References**

- Akg ̈und ̈uz, Y. E., S. H. Kal, and H. Torun (2018): “Do subsidized export loans increase exports?” _The World Economy_ , 41, 2200–2215.

- Amiti, M. and D. E. Weinstein (2011): “Exports and Financial Shocks,” _The Quarterly Journal of Economics_ , 126, 1841–1877.

- Antr`as, P. and C. F. Foley (2015): “Poultry in Motion: A Study of International Trade Finance Practices,” _Journal of Political Economy_ , 123, 853–901.

- Berman, N. and J. H ́ericourt (2010): “Financial factors and the margins of trade: Evidence from cross-country firm-level data,” _Journal of Development Economics_ , 93, 206–217.

- Bernard, A. B., J. B. Jensen, S. J. Redding, and P. K. Schott (2007): “Firms in International Trade,” _Journal of Economic Perspectives_ , 21, 105–130.

- Brun, M., J.-P. Gambetta, and G. Varela (2020): “Slow Rockets and Fast Feathers or the Link between Exchange Rates and Exports: A Case Study for Pakistan,” World Bank Policy Research Working Paper wps9353, World Bank.

- Cadot, O., A. M. Fernandes, J. Gourdon, and A. Mattoo (2015): “Are the benefits of export support durable? Evidence from Tunisia,” _Journal of International Economics_ , 97, 310–324.

- Caliendo, M. and S. Kopeinig (2008): “Some Practical Guidance for the Implementation of Propensity Score Matching,” _Journal of Economic Surveys_ , 22, 31–72.

- Ch ́avez, J. F., A. C. Novelli, and M. Perez Leon (2020): “Export Subsidies in Emerging Markets During the Great Trade Collapse,” _Economics Bulletin_ , 40, 1879–1892.

27
Table 6: Effects of LEAP on pro-social laboratory behavior

||(1)<br>Risk taking|(2)<br>Impatience|(3)<br>Altruism|(4)<br>Public good|(5)<br>Trust|(6)<br>Trustworthiness|(7)<br> <br>Average Effect|
|---|---|---|---|---|---|---|---|
|_Full sample_||||||||
|LEAP|0.10|0.01|49.62|0.05|38.73|0.01|0.07|
||(0.11)|(0.34)|(57.44)|(0.05)|(84.68)|(0.02)|(0.06)|
|Constant|2.19**|3.41**|742.37**|0.64**|620.00**|<br>0.15**||
||(0.07)|(0.24)|(35.13)|(0.04)|(39.47)|(0.01)||
|N|524|510|524|523|251|271|524|
|_IDPoor 1&2_||||||||
|LEAP|0.08|0.07|72.86|0.05|79.56|0.00|0.08|
||(0.13)|(0.36)|(65.57)|(0.06)|(107.55)|(0.02)|(0.07)|
|Constant|2.18**|3.41**|706.21**|0.64**|524.10**|<br>0.15**||
||(0.09)|(0.26)|(43.41)|(0.04)|(62.37)|(0.02)||
|N|349|341|349|349|165|182|349|
|_Non-poor_|0.13|-0.11|-0.98|0.06|-48.16|0.02|0.05|
|LEAP|(0.17)|(0.49)|(96.27)|(0.07)|(141.78)|(0.03)|(0.09)|
||2.21**|3.41**|817.65**|0.65**|809.52**|<br>0.14**||
|Constant|(0.12)|(0.34)|(64.11)|(0.06)|(91.54)|(0.02)||
||175|169|175|174|86|89|175|

Note: Columns 1-6 report OLS estimates with village-clustered standard errors. Column 7 reports the standardized average effect, as described in Kling et. al. (2004) and Clingingsmith et. al. (2009), across the measures in columns 3-6

* = significant at the five percent level, **=significant at the one-percent level

36
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influenced by domestic firm productivity. We therefore additionally instrument for the interaction term of foreign output share with the _inp_ variable in column 9, using the sectoral average number of days to clear imports through customs in a country (in natural logarithms) from the Enterprise Surveys Indicator Database as an inverse instrument. The argument for this choice of instrument would be that longer waiting times at customs may motivate foreign firms to source more inputs from the domestic market. The IV 2SLS regressions show that the interaction term with _inp_ has a positive coefficient sign (columns 8 and 9), while it is negative in the OLS regression (column 3). Both interaction terms are insignificant. The interaction term with sectoral FDI technology intensity in a country ( _tech_ ) has a negative but insignificant impact in both regressions (columns 4 and 10), rejecting the findings of a positive impact (e.g. Buckley et al. 2007; Lin et al. 2009).

Unfortunately, many results fall short of the thresholds of statistical significance, which could be related to heterogeneous productivity levels of domestic firms (Girma and Görg 2007). We therefore examine the dynamics of productivity at different points of the distribution in a next step.

# **_The Role of Domestic Firm Productivity_**

We split our sample into three productivity groups: LP <= 33.33 percentile (low-productivity firms); 33.33 percentile < LP <= 66.67 percentile (medium-productivity firms); and LP > 66.67 percentile (high-productivity firms). The IV 2SLS regression results by productivity level are shown in Appendices 5 to 7. We detect clear differences across the three types of domestic firms. The foreign output share variable mostly shows negative coefficient signs for all types of firms, but is rarely significant. However, the coefficients decline with firm productivity, suggesting that higher productivity reduces negative FDI spillovers. Capital intensity shows a constantly positive effect on labor productivity, but the impact is strongest for high-productivity firms, followed by lowproductivity firms and medium-productivity firms.

The interaction term with foreign ownership share ( _for_ ) is insignificant for all types of firms (column 1). The interaction term with FDI sales to the domestic market is positive and significant for both low- and medium-productivity firms, but more so for low-productivity firms (columns 2 and 3). While market-seeking FDI provides a higher spillover potential, the underlying transmission channels, however, seem to be different for both types of firms. It is more likely that mediumproductivity firms benefit from competition and demonstration effects, while low-productivity firms may rather benefit from potential integration in supply chains and labor turnover.

21
_Figure 1: Trend of TVET Enrollment, Graduates, Assessed, and Certified (1995–2016)_ <!-- Start of picture text --> 2,500,000<br>2,000,000<br>1,500,000<br>1,000,000<br>500,000<br>0<br>Enrolment Graduates Assessed Certified<br><!-- End of picture text --> Source: _Administrative data, many years, TESDA_ 17