rafmacalaba/data-use-mentions-extended huggingface dataset:
around 4.7 percent. Finally, the bottom line of panel A shows the statistics for the banks’ ratio of exposure to the energy sector in the month prior to the energy price shock. On average, the exposure is around 8 percent, with banks in the bottom decile having zero exposure to the energy sector, whereas banks in the top decile have 19.6 percent exposure.

Table 1, panel B, reports the loan characteristics of firms operating in energy-related sectors. Although the average bank loan of an energy firm is around Mex$89 million, the median loan size is Mex$1.5 million. Interest rates average 11.4 percent, with loans in the bottom decile having rates as low as 4.4 percent and in the top decile 18 percent. The maturity of loans is on average around two years, with loans in the bottom decile having maturities of around two months, whereas the top decile has maturities of four years.<sup>10</sup> These short maturities are consistent with most loans being destined for working capital. Collateral rates average 14.6 percent of the value of the loan and, again, there is great variation in the amount of collateral required across loans. Although the median loan is uncollateralized, loans in the top decile require collateral of more than half their value. The loan characteristics of firms operating in non-energy sectors display similar patterns as those of firms in the energy sector (Table 1, panel C).

The last two panels in Table 1 present summary statistics for real outcomes at the firm-year level and aggregate production at the state-quarter level. As panel D shows, the median bank debt of firms according to the credit registry is around Mex$730,000. From the Orbis data set, we find that the median liabilities of firms are around Mex$350 million, with median assets and revenues of around Mex$1.1 billion and Mex$800 million, respectively. We construct _AvgExposureEnergym,Aug14_ , a
# **8. Conclusion and policy implications**

Understanding trends in firm dynamics, productivity growth and job creation is essential to design policy that facilitates the growth of the private sector. This paper explores firm-level dynamics in South Africa by providing a detailed set of descriptive data along with regression analysis of employment, wage, productivity, and other firm dynamics. A key contribution is the international firm-level data comparisons used to benchmark South African data. These comparisons provide valuable insight into the structure, performance and peculiarity of the South African economy in comparison to selected advanced and developing countries.

In the descriptive analysis, we find that the role of firm entry and exit in net job creation is negligible in South Africa. This is in contrast to other fast-growing developing countries such as Vietnam where firm entry constitutes the predominant form of job creation in other countries. Job creation is taking place predominantly by large and old firms and net job creation is mainly driven by the services sector, followed by the construction and commerce sectors.

The life cycle of surviving micro firms for which we observe entry shows that the average size of these firms increased from 4 employees to almost 17 over the 5-year period from 2010 to 2014. This growth trajectory compares favorably to other countries such as Vietnam, Kosovo and Côte d’Ivoire where the average size of surviving micro firms increased at a slower rate over a longer period of 9 to 12 years. This suggests that micro firms that are able to survive over the short to medium term experience relatively faster growth, or are able to expand more rapidly, than the growth experienced by surviving micro firms in countries such as Vietnam.

In our static decompositions, we find that the construction, business and finance and other services sectors did not allocate labor efficiently over the period. There is also a sharp decline in aggregate productivity in manufacturing, construction and commerce. When we decompose aggregate productivity growth, we find that firms that expand productivity are decreasing their employment shares (i.e. they are becoming more productive
million USD and total agricultural exports by 278 million USD. That is, foreign exchange “savings” through lower imports are _more than completely offset_ by a corresponding reduction in agricultural exports, in contrast to a mere 48% “offset factor” in our main estimates (see Section VII.B).<sup>88</sup> In the absence of fertilizer subsidies, there is a closer correspondence between the import price of fertilizer and its marginal revenue product. As a result, the reduction in fertilizer use induced by the ban decreases agricultural export revenues and fertilizer imports by roughly similar amounts.

# **VII.E Influence of Modeling Trade Policy: Iceberg Trade Costs versus Quantitative Restriction**

In our model, trade policy is represented by QRs. This differs from much of the NTM literature, which models NTMs as an increase in multiplicative “iceberg” trade costs.<sup>89</sup> These modeling choices are often not innocuous: while QRs raise prices and create rents that can be captured by certain domestic agents, the price increases resulting from iceberg trade costs are typically modeled as losses, with no one benefiting from them. Therefore, using iceberg trade costs to model what is

> homothetic model matches the ILO data much more closely than the homothetic model.

> 87This also implies a smaller gap in welfare losses between workers and farmers relative to the case with subsidies (Column 1 of Table VI), leading us to underestimate how unequal the incidence of the ban is across occupations.

> 88The 48% offset factor is computed by comparing the decrease in agricultural exports (596 million USD) to the decrease in fertilizer imports (1.253 billion USD) in our main estimates displayed in Table IV.

> 89One exception is Atkin et al. (2024), who explicitly model a discretionary import licensing system.
# **B Survey data construction**

## **B.1 Chinese Family Panel Studies (CFPS)**

Baseline (2010):

- Employment status: defined by the answer to question G3 from the Adult questionnaire (“Do you currently have a job”).

- Occupation type: for respondents with non-missing employment status, defined by the answer to question G303 of the Adult questionnaire (“What is the overall type of your current job?”). Self-employment and wage workers exclude workers who are currently primarily engaging in agricultural work (question G4).

- Income: from question K601 (”Your personal income (from all sources) last year was yuan”) in the Adult questionnaire.

Third follow-up <u>(2016):</u>

- Occupation type: from variable jobclass (generated from the answers to job module [EHC-Job] in the Adult questionnaire).

- Migration: constructed using changes in province or county since baseline. Validated using answers to the migration module [EHC-RESI]. The current version of CFPS only reports province for this module. We could thus manually check that the current province in 2016 corresponds to the province reported in AR101 (“What was your address when interviewed in“ [CAPI] Load CFPS2014 ~~t~~ ime””) for respondents that report an error in the 2014 filed residence, or the province reported in AR201 (“What is your current primary address”) when the respondent reports to have moved with respect to 2014 (question AR2, “Is “ [CAPI] Load CFPS2014 ~~r~~ esidence”your current address?”).

- Notes: CFPS does not track international migrants.

## **B.2 Egypt Labor Market Panel Survey (ELMPS)**

Baseline (1998)

- Employment status: from chapter 1, question 1501 (“Did you participate in any employment during the past three months ending 31 October 1998?”).

48
###

Figure 3: Change in firm trade outcomes for GVC firms and non-GVC firms

## (a) Customs data: Change in value of exports and imports

<!-- Start of picture text --> 10 10<br>4<br>0 0<br>-10 -4 -1 - 11 -3 -2 -10 -14 -6 -12 -9<br>-20 -16 -20<br>-24<br>-30 -30 -28<br>-33<br>-40 -37 -40<br>- 42<br>-50 -50<br>Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4<br>Quarter of 2020 Quarter of 2020<br>Non-GVC Firms GVC Firms Non-GVC Firms GVC Firms<br>(b) BPS data: Change in share of exports and imports<br>1<br>0 0<br>-10 -8 -10<br>-11<br>-20 -20<br>-19<br>-30 -30<br>Non GVC firm GVC firm Non GVC firm GVC firm<br>P-value of test that GVC firms are different than non-GVC firms: 0.035 P-value of test that GVC firms are different than non-GVC firms: 0.088<br>Change in firm exports Change in firm imports<br>Change in share of exports over sales (p.p)<br>Change in share of imports over inputs costs (p.p)<br><!-- End of picture text --> _Notes:_ The sample includes in panel a 153,038 firm-quarter observations in the left plot and 579,123 firm-quarter observations in the right plot in 20 countries and in panel b 617 exporting firms in the left plot and 1,024 importing firms in the right plot in 13 countries.

The
variations. These determinants of productivity are associated with the efficiency with which factors, technology and material inputs are utilized. They reflect the level of competence of the entire production unit taken as a whole and depend mostly on the quality of management. The rapidly growing literature on technical efficiency is largely concerned with estimating the extent of this source of productivity variance (Coelli et al 1999). In practice however, effects deriving from scale and location externalities, tend to get bundled in with purely internal effects in the estimation of technical inefficiency.

Technical inefficiency introduces a plant-specific _diminution_ of operational productivity in relation to the domestic best practice productivity that can be realized in a particular industry. Using plant level data for four major Indian industries at the three digit level of industrial aggregation, we examine the variation of this technical inefficiency component in relation to various locational and other economic variables. In particular we are interested in testing whether mean technical efficiency is higher - and its variance lower - in relatively modern or technology intensive sectors. Higher efficiency is expected in such sectors for several reasons, which include the adoption of efficient production technologies, higher skill levels of the workforce, a greater likelihood of knowledge diffusion and uniformity of efficiency in the plants being examined.

India has one of the oldest and most widely developed manufacturing sectors in the developing world. Since it has functioned to a large extent like a closed economy for most of the post-1948 period, it is a prime candidate for the study of industrial heterogeneity as described above. Additionally, since significant liberalization has been introduced into some sectors in the early 1990s, the forces of enhanced market competition are likely to have produced further differentiation. In this study, the variation of technical inefficiency, estimated by the stochastic frontier method, is examined in relation to scale, location, extent of infrastructure investment and other determinants.

Page (1984) argues that lower efficiency variation is to be expected in relatively modern industries due to a higher degree of homogeneity in the vintage of capital stock and labor force characteristics.
by Development Partner Groups and Country Groups</u>**

|**Group**|**No**|**FCS (Rank)**|**Non-FCS (Rank)**|**FCS + Non-FCS**<br>**(Rank)**|
|---|---|---|---|---|
|Bilateral|22|-15.9 (3)|-40.8(3)|-56.7 (3)|
|MDBs|4|54.0 (1)|150.9 (1)|204.9 (1)|
|Other MOs|4|51.7 (2)|128.2(2)|180.0 (2)|
|Vertical funds|2|-36.2(4)|-113.2(4)|-149.3 (4)|

_Source: Author_ There are two findings. First, there are no differences in ranking among the groups between FCS and non-FCS (e.g. bilaterals are on average the lowest performers in both FCS and non-FCS countries). Second, among the four groups, MDBs perform best followed by other MOs, vertical funds and bilaterals. Table 5 provides us with a general sense that relative development partner performance is quite similar between FCS and non-FCS. However, development partner performance may still differ between FCS and non-FCS on particular indicators.

Table 7-A and B includes the average standardized scores by indicator.

13
paise per quintal. This was increased to Re 1 per quintal in 1971, to Rs 6 in 1975, and to Rs 12 per quintal in 1988, at which level it still remained in 1995. This compares with free market prices which were reported to be of the order of Rs 100 to Rs 150 per quintal in the late 1980s and early 1990s, and international prices estimated to be over Rs 1 00/quintal during the same period. The controlled domestic prices and estimated fob export prices all expressed in constant 1981 Rupees are compared in Fig. 15. In real terms, there was a big increase in the controlled domestic price in 1975, but it was still less than one fifth of the fob price. After 1975 it declined in real terms and in 1994 was lower than it had been in 1975.

Figurel5: **Controlled domestic prices and estimated fob prices of Indian molasses in 1981 Rupees, 1965-94** <!-- Start of picture text --> 80 +FOB PRICE AT INDIAN PORT<br>c 70 -u-CONTROLLED PRICE<br>'2<br><!-- End of picture text -->

**Source: Annex 10.**

Since molasses exports have consistently been suppressed both by direct controls and indirectly by the allocation of supplies at low prices to distilleries and industrial alcohol producers, we have treated it as an exportable in estimating its nominal protection. Both domestic and international transport costs are high relative to international prices, and the assumptions made about the exporting areas in India and the international markets that would be supplied have a considerable influence on the estimates. As regards world prices, we found thatexcept in years with very low volumes- Indian unit export values have fairly closely followed changes in US tank car prices at New Orleans, which are the industry standard indicators of world prices (see Annex 9). Indian fob export unit values are also close to export unit values in Pakistan, which consistently exports much larger quantities than India.
62|39.39|18.32|<0.001|
|Total hired labor|48.23|85.98|61.12|29.39|35.61|<0.001|
|Total labor(HL + HH)|97.87|195.36|109.74|68.78|53.93|<0.001|
|Observations|1039|109|415|477|38||

Notes: Mean values are shown with standard deviations in parentheses. P-values of continuous variables are obtained by one way anova test for equality of mean values and P-values of binary variables are obtained by Pearson’s _χ_<sup>2</sup> -tests for equal proportions. All labor data are expressed in person-days/ha.

4.3 Econometric results

# **_4.3.1 Factors explaining the adoption of modern farm technologies_**

Table 5 presents the estimated results from the first stage of the multinomial endogenous treatment effects model. The following insights regarding the correlates of the adoption of different technology packages emerge. First, those with access to credit and wealthier households are more likely to adopt improved farm technologies in rice production, highlighting the continuing importance of access to financial markets and the ability to take risks. Second, contact with extension services and the number of plot visits by extension agents is also positively correlated
useless at times.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|At times think I am no good at all<br>(Rosenberg)|At times I think I am no good at all.<br>To what extent do respondents: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement about them|
|**Secondary**<br>**Outcome**|**Psychological well-being index**|Summary index of 9 questions on well-<br>being in last 2 weeks and stress in last 4<br>weeks.<br>The questions used here belong to two<br>different sets of questions. The first set is<br>the 5 WHO questions on well-being, and<br>the second set is the Cohen stress scale<br>4 questions.|
larger changes in the adoption of recommended practices among control farmers near treated farmers. We find no evidence of any such spillovers.

> 35We test whether the lower adoption of _T_ 1 (relative to _T_ 2) is due to proximity to other study farmers as disagreement across recommendations results in lower willingness to follow their own recommendations. We examine this hypothesis using again plot-level GPS locations. In particular, we examine whether farmers in _T_ 1 with plots close to those of other treated farmers (controlling for the total number of plots from nearby study farmers) were

19
# **Executive Summar** **<u>y</u>**

The objective of the report is to review the literature on the links between energy access, welfare, and gender in order to provide evidence on where gender considerations in the energy sector matter and how they might be addressed. Prepared as a background document for the 2012 World Development Report on Gender Equality and Development, as well as a part of the Social Development Department‘s ongoing work on gender and infrastructure, the report describes and evaluates the evidence on the links between gender and energy focusing on the following areas: increased access to woodfuel through planting of trees and forest management; improved cooking technologies; and access to electricity and motive energy.

The report is intended to complement ongoing efforts to formulate a gender business plan for the Sustainable Development Network of the World Bank. It focuses on reviewing the academic evidence and does not aim to offer specific operational recommendations which are better left for the gender business plan and other documents that follow up on the 2012 World Development Report.

Focusing on the academic peer-reviewed literature, fairly inclusive screening criteria are applied when selecting the evidence to consider. Thus, much of this review is based on quantitative studies with samples that are relatively small, yet sufficient to support multivariate regressions to control for confounding effects. Thus, we include studies even if they were not experimental or quasiexperimental; used cross-section measures instead of a before and after; and relied on proxy indicators of welfare outcomes.

The main finding of this review is that energy interventions can have significant gender benefits which can be realized via careful design and targeting of interventions based on a context-specific understanding of energy scarcity and household decision-making. The Bank may also want to consider a program of rigorous impact evaluations and participatory monitoring of household energy projects.

While Sub-Saharan Africa (SSA) has particularly low electrification rates, rural electrification is a policy goal in many countries and 68 developing countries have set explicit targets for electricity access. In most countries, electricity is
The results presented also highlight the need to upgrade the knowledge production function used in endogenous growth models by allowing not just knowledge complementarities, but also substitution or competition effects. The evidence supports these considerations and highlights the need for new theoretical research that could investigate the growth and welfare implications of international knowledge spillovers. By the same token our results highlight the pertinence of studying different types of innovation as the nature of international knowledge spillovers could be different, depending on the type of innovation, be they product-quality upgrading, the introduction of new product varieties by firms, or patentable commercial inventions.

# **References**

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- **, C. Harris, and J. Vickers** , “Competition and Growth with Step-by-Step Innovation: An Example,” _European Economic Review_ , 1997, _Papers and Proceedings_ , 771–782.

- **, M. Dewatripont, and P. Rey** , “Competition, Financial Discipline, and Growth,” _Review of Economic Studies_ , 1999, _66_ (4), 825–852.

- **Allison, Paul and Richard Waterman** , “Fixed-Effects Negative Binomial Regression Models,” _Sociological Methodology_ , 2002, _32_ , 247–265.

- **Almeida, R. and A.M. Fernandes** , “Openness and Technological Innovations in Developing Countries: Evidence from Firm-Level Surveys,” _Journal of Development Studies_ , 2008, _44(5)_ , 701–727.

- **Audrestsch, D. and M. Feldman** , “R&D Spillovers and the Geography of Innovation and Production,” _American Economic Review_ , 1996, _86(3)_ , 630–640.

- **Backus, D., P. Kehoe, and T. Kehoe** , “In Search of Scale Effects in
regression. So if we ...nd a negative e¤ect of land restrictions in OLS regressions that omit land productivity controls but include human capital controls, this can be taken as particularly strong evidence.

The OLS regressions reported later provide strong evidence of a signi...cant negative e¤ect of land restrictions on equilibrium wage and suggest that the magnitude of this negative e¤ect decreases with distance. While the OLS estimates may be acceptable as credible evidence of a negative e¤ect of land restrictions on equilibrium wage, they cannot give us an estimate of the magnitude of this e¤ect because of the possible downward bias from omitted productivity heterogeneity. We provide estimates of the causal e¤ect of land restrictions on the wage using an instrumental variables approach that corrects for the possible downward bias.

While the unusual positive land productivity correlation in Sri Lanka is useful for our empirical analysis, there is a second and equally important advantage of Sri Lanka as a case study to understand the e¤ects of land restrictions. The history of malaria infestation from 16th to early 20th centuries, and the unique role it played in the land policy provides a credible source of exogenous variations in the LDO restrictions. The variations in the historical malaria prevalence across DSDs are useful especially because a nationwide malaria eradication program was implemented as early as 1947 (Brown, 1986, Lucas, 2010), long before the data used for this study were collected (we use HIES 2002). We develop an instrumental variables approach that exploits this exogenous source of variations to identify and estimate the e¤ect of LDO restrictions on wages. To ensure the validity of the exclusion restrictions, we control for a set of covariates including current malaria parasite infection rate, individual and DSD level education, age (to capture cohort e¤ects), and district ...xed e¤ects. In a recent paper, Lucas (2010) shows that the cohorts that were exposed to severe malaria in the late 1930s had lower