rafmacalaba/data-use-mentions-extended huggingface dataset:
To see whether the results reported in Table 3 are sensitive to different measures of corruption and regulation, we also estimated the model using the averages of bribery depth ( _Corruption_ 1), bribery incidence ( _Corruption_ 2) and number of visits or required meetings with tax officials ( _Regulation 1_ ) over all other firms in the industry-size-city cell. As shown in Table A.6 in the appendix, the coefficient of the interaction term between corruption and regulation is still negative and significant, indicating that the results are robust to different measures of corruption and regulation in the ES data.

# **6. Conclusion**

Using nationally representative survey data from 39,732 private firms in 111 economies in six different regions of the world, we estimated the relationship between firm productivity and corruption, and how this relationship varies with the level of business regulation. We find robust evidence that on average there is a negative relationship between corruption and productivity, and this negative relationship becomes stronger at higher levels of regulation. In fact, at low levels of regulation (for example, at the 25<sup>th</sup> percentile of regulation), there is no significant relationship between corruption and productivity. These findings are consistent with the findings of many prominent studies, such as Shleifer and Vishny (1993), Djankov et al. (2002), Banerjee (1997) and Aidt (2016), pointing out that corruption and excessive regulation reinforce each other and together they are more detrimental to economic performance.

As discussed in the paper, even though we incorporate a wide range of control variables and fixed effects and use cell averages of corruption and regulation, we still cannot entirely rule out the endogeneity problem. As panel data and instrumental variables become available, future studies can address this issue more accurately.

26
Sen, A., 1997. Editorial: Human Capital and Human Capability, _World Development,_ 25 (12), 1959-61 Sen, A, 1999 _Development as Freedom_ , Oxford, Oxford University Press Sen, A., 2013. The ends and means of sustainability. _Journal of Human Development and Capabilities_ , 14(1), pp.6-20.

Simon, J., Anand, P., Gray, A., Rugkåsa, J., Yeeles, K. and Burns, T., 2013. Operationalising the capability approach for outcome measurement in mental health research. _Social Science & Medicine_ , 98, pp.187-196.

Singh, P., 2011. We-ness and welfare: A longitudinal analysis of social development in Kerala, India. _World Development_ , 39(2), pp.282-293.

Stewart F., 2005 Groups and capabilities, _Journal of Human Development,_ 6 (2), pp185-204 Sugden, R., 1993. Thinking as a team: Towards an explanation of non-selfish behavior. _Social Philosophy and Policy_ , _10_ (1), pp.69-89.

Suppa, N., 2015. Capability deprivation and life satisfaction. Evidence from German panel data. _Journal of Human Development and Capabilities_ , 16(2), pp.173-199.

32
# **1. Introduction and background**

South Africa’s economy continues to struggle with slow growth, high levels of inequality and unemployment. The South African economy grew by an annual average of 1.9 percent between 2010 and 2016, well below the levels required to address structurally high levels of unemployment (National Treasury, 2017). Exploring dynamics at the firm level is critical to strengthening our understanding of the performance of the private sector and its contribution to growth. In this study, administrative tax data, which are increasingly being used for empirical research, are used to explore firm, employment and productivity dynamics trends for South African formal businesses.

The level of detail available in administrative tax data allows for examination of sectors in unprecedented detail as analysis can proceed at the level of each individual firm. An analysis of total factor productivity (TFP) in South Africa using these data yielded the first firm-level productivity estimates for South Africa’s manufacturing sector (see Kreuser and Newman, 2018).<sup>2</sup> Other studies, such as Edwards et al. (2018), found that firms that engage in international trade employ more people, pay higher wages, have more capital, and exhibit higher levels of productivity.<sup>3</sup> 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.

## _Evolving policy context_

The South African economy developed around the mining industry, exploiting the country’s rich resource endowment. This led to the underdevelopment of downstream industries – as cheap energy provided by the state electricity utility allowed for the exploitation of South Africa’s mineral wealth and the development of capital-intensive upstream industries (Roberts
subsequent regressions, we aggregated the 75 districts into 15 sub-regions (the five regions interacted with the three types of terrain). More specifically, we took this step in order to reduce the number of regional parameters in the estimation of the labor supply equation, which controls for region-specific effects using regional fixed effects. If we were to use district level aggregation, we would have (over and above the other coefficients in the model) 75 such districtlevel parameters versus the 15 sub-regional parameters that we currently have. Since the districts are aggregated up to the sub-region level, all the information contained at the district level is still reflected in the sub-regional coefficients.

Conflict measures cannot be used directly in the estimations since they are likely to be co-determined with other variables that might affect women‟s employment. For example, subregions with higher rates of poverty also had more intense conflict (Do and Iyer 2010). We hypothesize that geographical measures from a pre-conflict time period provide the exogenous variation required to identify the effect of conflict on women‟s work. To test this hypothesis, we used the conflict and geographical indicators from Do and Iyer (2010), supplemented with additional geographical and weather data on Nepal from Sharma and Subedy (1994). In the first stage, the number of state-caused and Maoist-caused deaths from 1996 to 2006 was regressed on four indicators of geographical status and two indicators of weather status from 1994, a preconflict year. Indicators of geographical status include the proportion of a sub-region that is forested, altitude of the sub-region as a proxy for mountainous/hilly territory, the number of major rivers in a sub-region normalized by area, and the total length of the road network normalized by the area of the sub-region in 1994. Indicators of weather-related status include average annual rainfall normalized by area of the sub-region and average temperature of the subregion. As noted above, all indicators cover time periods that preceded the conflict (1994). The 9
, digital collateral (Gertler et al. 2021), or microequity and mutuality (Cordaro et al. 2022) may also help expand women’s access to credit. These products show promise, but so far, there is no causal evaluation estimating their effects on the performance of women-led businesses.

# 4.3 In-kind or targeted larger cash grants or loans

A series of experimental studies have found that cash grants to micro or small firms improve business outcomes primarily for business led by men (see Jayachandran 2021 for a review). One key reason is that women entrepreneurs often do not manage to invest in their own business because they use the funds to satisfy other household demands or family requests, or they invest in their husbands’ business (Jakiela

> 14 A complementary strategy to increase access to finance for women-led businesses is to increase women’s control over assets; for example, by encouraging joint titling for spouses. There is causal evidence that joint titling can increase women’s access to capital in agricultural settings (Ali et al. 2014).

> 15 Digital footprints, which include mobile phone usage, social media data, and digital transaction records from mobile money, banking, or e-commerce platforms, can be valuable for credit assessment. For instance, IFC (2022) describes how a digital distribution platform active in Indonesia and the Philippines plans to use the digital footprint of women-owned firms to refine its credit-scoring algorithm.

16 Digital collateral refers to a digital technology that enables the lender to temporarily disable the use of the product serving as collateral.

> 17 In Africa, uncollateralized high-interest rate loans of small size offered via mobile money have shown large uptake rates (Robinson et al. 2022). However, concerns arise from opaque loan terms and hidden costs, coupled with the low financial literacy of many clients (Brailovskaya et al. 2021).

18 Alibhai et al. (2022) collaborated with microfinance institution in Ethiopia to randomize the offer of uncollateralized loans among 131 women who had passed a psychometric test
Panel A of table 1 shows that our frame includes 75,571 farmers, more than double the 36,184 in the Form 29 survey as used by SSSU. Of these, 21% are legal entities, 11% FOPs, and 68% individuals overall with the latter concentrated in the farm size class below 50 ha of which 82% are individuals and 9% each FOPs and legal entities. Legal entities dominate for larger farm size classes, making up 58%, 76%, and 97% of those in the 50-120, 120-500, and > 500 ha group, respectively. Coverage in terms of farm numbers by the SAR is better than that of Form 29 for the below 120 ha group (with 3.8 and 1.1 times the number of farms in the < 50 and 50-120 ha) but worse above 120 ha with SAR containing only 0.62 and 0.44 times the number of operations in the 120-500 and the >500 ha group, respectively.

Table 1 panel B illustrates that the farms covered by Form 29 and SAR cultivate 27.85 and 11.6 million hectares, respectively with 25% of Form 29 area in ACUs and 25% in AACs. While they comprise less than a quarter of the number of registered farms, legal entities operate more than 90% of the area registered in SAR overall (33%, 64%, 83%, and 97% of area in the <50, 50-120, 120-500, and > 500 ha groups, respectively). The total area registered in SAR amounts to 42% of the area in Form 29 at the national level and 150%, 127%, 67%, and 37% of which in the <50, 50-120, 120-500, and > 500 ha groups, respectively.<sup>12</sup> To quantify the extent of informality in Ukraine’s agriculture sector, we use crop maps for 2020 elaborated using the methodology described in Kussul _et al._ (2017
an explicit rationale for including Cost Efficiency analysis and just under half explicitly reported the limitations of the Costing exercise. Our analysis suggests the results of available Cost Efficiency analyses from WB impact evaluations should be used carefully. Such analyses often quantify a portion of the program’s benefits, and frequently suffer from missing information and missing cost data.

In sum, our analysis reveals the need to elevate reporting transparency and the need to develop standards of reporting so that the available evidence can accumulate and be used to improve the use of resources in development cooperation. In addition, our analysis suggests the need to examine the root causes underscoring the wide variation in cost-efficiency reporting across evaluators. It is difficult to know why VFM analysis is so infrequent in the absence of further inputs from IE producers and consumers.

# **QUESTION 2: WHAT ARE THE EXISTING INCENTIVES AND BARRIERS FACED BY PRODUCERS AND USERS, BOTH AS INDIVIDUALS AND INSTITUTIONS, FOR VFM INCORPORATION?**

The descriptive analytic findings on the state of VFM in IE are symptoms of underlying problems faced in their production, dissemination, and use. We found three primary reasons for the underproduction of VFM in IE—organized around three principal actors: impact evaluators, policymakers, and institutions.

**Finding 1:** The expected payoffs to producing efficiency analyses is dulled by the (inaccurate) perception that policymaker demand for VFM is low when it is more accurately described as uncertain.

As shown in Table 3.1, our sample of IE producers most frequently cite a lack of policymaker demand as the reason for little VFM observed in IEs. IE-producing interviewees supported this argument with three main points: first, interviewees questioned the cost data’s relevance to a policymaker’s individual contextual considerations. Second, many interviewees felt that the political calculus, rather than a project’s investment case, dominates a policymaker’s decision-making. This view of political economy dynamics undercuts incentives to generate efficiency analysis and often detrimentally influences the analysis itself. Third, IE and VFM producers
the new stations and local demand/supply shocks. Moreover, the construction of the line also experienced multiple delays given changes in the regulatory framework and the 1994 financial crisis.<sup>11</sup> The line is approximately 20 kms long and has 21 stations. It connects the metropolitan area of the city with some adjacent municipalities in Mexico State, such as Ecatepec de Morelos and Ciudad Nezahualcoyot. These areas are characterized by high poverty rates, low education, and high informality rates.<sup>12</sup> As a result, line B has the distinct feature of connecting informal workers with formal jobs. To date, it is the line with the fourth-highest number of passengers in the network. The total cost of this line, including the net present value of service operations, maintenance, and other overheads, was approximately $2,900 million in 2014 USD dollars, which represents 0.7% of the total GDP of Mexico City.

Figure 1 depicts a map of the Mexico City subway system in 2000, highlighting the lines that I use in my empirical strategy. Line B (purple) connects the northeastern area, including locations in the State of Mexico, with the center of the city. I also use line C and line 12 for robustness checks. Line C (green) was planned as a feeder line in the early 2000s, similar to line B; however,

> 11The initial plan of the Government was to finish the line in 1997. However, they finished the construction of the entire line in 2002.

> 12In the Appendix, I relate census tract characteristics before the shock to line B to show this result.

7
subsidies in the Turkish context. The most closely related study to ours is the study by Betcherman et al. (2010) who estimate the effect of two employment subsidies targeted to firms with more than 10 employees in Turkey. Similarly, as the subsidy studied in this paper, the subsidy was geographically targeted which allowed the authors to carry out a difference-in-difference estimation to estimate the effects of the subsidy. However, contrary to our study, they use aggregate data on labor market outcomes at the regional level to study those impacts. In addition, the subsidy was at the time only targeted to firms of more than 10 employees. In addition, it was applicable only to additional hires while the subsidy we study in this paper is applied to all employees. They find that the subsidies had a sizeable and positive impact on registered employment.

Uysal (2013), Ayhan (2013), and Balkan et al. (2016) study the effect of another employment subsidy in Turkey targeted to women and to youth introduced in 2018. All three papers carry out a difference-in-difference estimation to estimate the employment effects of the subsidy, with Ayhan (2013) conducting a triple difference-in-difference strategy. They use nationally representative survey data on workers from the Household Labor Force Survey (HLFS). The three studies report different results on the impact of the subsidy, although they all find that treatment effects tend to be larger for women. Uysal (2013) uses aggregate labor market data by demographic groups and emphasizes that the program has been effective for older rather than younger women, while Ayhan (2013) uses micro-level data in order to control for 6
2model, and estimated using pooled cross-section time-series data for subSahara Africa during 1965-82.

Any study which attempts to relate government fiscal policies with output growth rates must confront the theoretical problem that while taxes and an inefficient government sector may reduce the level of GDP, it i. not clear that the rate of growth of GDP should be affected. Lucas (1985), and Manas-Anton (1985) have emphasized that taxation and (most) government policy will have no effect on long-term growth rates. The first question to be addressed, then, is why should tax rates affect output growth rates?

The answer is that static tax distortions do affect output growth along a transition pa.. -- or a sequenced change in the level of output -- by encouraging the flow of investment and labor se into sectors which largely escape taxation. The expansion of these liv&.-Iy taxed (or even subsidized) sectors will lead to lower overall capital and labor productivity. Hence for a given rate of investment and ]4bor supply grow..h, output growth is likely to decline. Alternatively, if the lightlytaxed sectors provide positive benefits (e.g., export-ori.ented industries), then taxes which direct more resources into these socially productive activities can augment output growtn rates. If the economy is on a steadystate growth path, taxation will have no effect. Ultimately, the effect of taxation on output growth is an empirical question.

While Landau (1983, 1986) has found an often negative impact of the level of government spending on growth rates, Ram (1986) has emphasized that the change in government spending is the theoretically correct factor in explaining a change in output. Regressions which follow Ram's formulation indicate that during the period 1965-73, the high
ours is one of the first that provides extensive evidence on globally engaged firms based on a large sample of nearly 65,000 firm-observations in 45 developing countries. _Second_ , our work shows not only the initial impact of the pandemic but also documents the differences in recovery patterns of globally engaged firms, comparing them with domestically-oriented firms, using three rounds of BPS data with nearly 27,000 panel firms. _Third_ , while samples in the BPS data are not stratified by global integration status, our results are validated using customs data from the Exporter Dynamics Database (EDD), which covers the universe of exporters and importers in a large sample of countries. _Fourth_ the initial impact and recovery are measured for a range of performance metrics of firms, including not only total sales but also financial fragility, employment, trade outcomes (exports and imports, trading partners), as well as digitalization, product innovation, and access to public support. _Finally_ , our work highlights the critical heterogeneity in the response of globally engaged firms by their internal and external traits, the attributes of the sector that they operate in, and the characteristics of the country of their location.

The rest of the paper is organized as follows. Section 2 discusses the related literature and Section 3 describes the data and methodology. Using the BPS data, Section 4 presents evidence that globally engaged firms are indeed more capable, which sets them apart during the COVID-19 crisis period.

4
factors influence agricultural productivity and market access, thereby affecting food security (Jawid & Khadjavi, 2019).

Seasonality affects agricultural productivity and food security (Aweke et al., 2022; Tay et al., 2023). Harsh winter weather often decreases agricultural productivity, leading to food shortages. While food availability generally increases during the summer, extreme heatwaves and droughts can reduce crop yields. Spring is important for planting, with favorable weather boosting productivity, whereas fall marks the harvest season, but unpredictable weather can impact crop quality and yield. To account for seasonal variations, we included three seasonal dummy variables as additional controls in our analysis.

## 3.3 Data Preparation

The rainfall and NDVI data were initially divided into 401 administrative codes and aggregated at the administrative division level. Each month included three data points corresponding to the 1st, 11th, and 21st days. For rainfall, we aggregated these data points using sum and mean operations to calculate average and total rainfall per month. We then standardized the monthly rainfall values using Z-scores within each market to create a rainfall anomaly variable, representing long-term rainfall variability. This approach aligns with methodologies used by Ngoma et al. (2023) and Penson et al. (2024) to capture deviations from normal rainfall patterns. A similar procedure was applied to the NDVI data. We aggregated NDVI data to obtain average NDVI per month and calculated NDVI anomalies by standardizing the average NDVI values as Z-scores within each administrative division.

To account for extreme weather events such as droughts and floods, we modeled anomalies asymmetrically. Excessive rainfall increases flood risk, while rainfall shortages increase drought risk; both conditions can exacerbate food insecurity (Coumou & Rahmstorf, 2012). We created two dummy variables for anomaly types—one indicating positive anomalies (flood anomalies) and the other indicating negative anomalies (drought anomalies). We then generated interaction terms by multiplying these dummies with the anomaly variable, allowing us to separately assess the impacts of floods and droughts.

Data on agricultural wages, exchange rates
." _Review of African Political Economy_ , **_41_** (141), 406-423. Boone, C. 2017. "Legal Empowerment of the Poor through Property Rights Reform: Tensions and Trade-Offs of Land Registration and Titling in Sub-Saharan Africa." World Institute for Development Economic Research (UNU-WIDER), WIDER Working Paper Series: 037. Brits, A. M., C. Grant and T. Burns. 2002. "Comparative Study of Land Administration Systems with Special Reference to Thailand, Indonesia and Karnataka (India)." _World Bank Regional Land Workshop in Phnom Penh, Cambodia_ . Bruce, J. W. and S. E. Migot-Adholla. 1994. _Searching for Land Tenure Security in Africa._ Kendall/Hunt Publishers, Dubuque, IA. Carter, M. R. and P. Olinto. 2003. "Getting Institutions "Right" for Whom? Credit Constraints and the Impact of Property Rights on the Quantity and Composition of Investment." _American Journal of Agricultural Economics_ , **_85_** (1), 173-186. Chapoto, A., T. S. Jayne and N. M. Mason. 2011. "Widows' Land Security in the Era of Hiv/Aids: Panel Survey Evidence from Zambia." _Economic Development and Cultural Change_ , **_59_** (3), 511-547. Chen, C. 2017. "Untitled Land, Occupational Choice, and Agricultural Productivity." _American Economic Journal: Macroeconomics_ , **_9_** (4), 91-121. Chernina, E., P. Castaneda Dower and A. Markevich. 2014. "Property Rights, Land Liquidity, and Internal Migration." _Journal of Development Economics_ , **_110_** , 191-215. Chitonge, H. 2016. "Zambia at 50: The Persisting Challenges of Economic Structural Transformation." _Development Southern Africa_ , **_33_** (6), 790-805. de Janvry, A., K. Emerick, M. Gonzalez-Navarro
|40|40|40|40|45|46|41|41|41|41|41|41|41|41|41|
|22|Other goods-producing<br>sectors|6|6|6|6|6|6|6|6|6|6|4|6|6|6|6|6|6|6|6|6|
||**Total**|1,609|1,624|1,774|1,783|1,792|1,920|1,856|1,924|1,891|1,948|2,020|1,440|1,454|1,450|1,432|1,425|1,414|1,394|1,396|1,396|

Note: Table reports number of lines at 10-digit level with a specific tariff greater than zero by industries of the Russian input-output table. Mapping from 10 digit codes to industries of the Russian input-output table is based on the Rosstat classification.