rafmacalaba/datause-encoder-data huggingface dataset:
senior management time to the consulting program.<sup>7</sup> We compared these program firms with the 49 non-program firms and found no significant differences in observables.<sup>8</sup> The experimental firms have typically been in operation for 20 years and all are familyowned. They all produce fabric for the domestic market, and some also export. Table 1 reports some summary statistics for the textile manufacturing parts of these firms (many of the firms have other businesses in textile processing, retail and real estate). On average these firms had about 270 employees, current assets of $13 million and sales of $7.5m a year. Compared to US manufacturing firms these firms would be in the top 2% by employment and the top 5% by sales,<sup>9</sup> and compared to India manufacturing in the top 1% by both employment and sales (Hsieh and Klenow, 2010). Hence, these are large manufacturing firms.<sup>10</sup> These firms are complex organizations, with a median of 2 plants per firm (plus a head office in Mumbai) and 4 reporting levels from the shop-floor to the managing director. In all the firms, the managing director is the largest shareholder, and all directors are family members. One firm is publicly quoted on the Mumbai Stock Exchange, although more than 50% of the equity is held by the managing director and his father.

In Exhibits (1) to (7) in the Appendix we include a set of photographs of the plants. These are included to provide some background information to readers on their size, production process and initial state of management. Each plant site involves several multi-story buildings (Exhibit 1). The plants operate a continuous production process that runs constantly (Exhibit 2). The factories’ floors were rather disorganized (Exhibits 3 and 4), and their yarn and spare-parts inventory stores lacked any formalized storage systems (Exhibits 5 and 6).

# **III. THE MANAGEMENT INTERVENTION**

## **_III.A. Why use management consulting as an intervention_**

The field experiment aimed to improve management practices in the treatment plants. To achieve this we hired a management consultancy firm to work with the plants as the easiest way to

> 7 The main reasons we were given for refusing free consulting were that the firms did not believe they needed management assistance or that it required too much time from their senior management (1 day a week). But it is also possible these firms were suspicious of the offer, given many firms in India have tax and regulatory irregularities. 8 For example, the program firms had slightly less assets ($12.8m) compared to the non-program firms ($13.9m), but this difference was not statistically significant (p-value 0.841). We also compared the groups on management practices using the BVR scores, and found they were almost identical (difference of 0.031, p-value 0.859). 9 Dunn & Bradstreet (August 2009) lists 778,000 manufacturing firms in the US with only 17,300 of these (2.2%) with 270 or more employees and only 28,900 (3.7%) with $7.5m or more sales.

> 10 Note that most international agencies define large firms as those with more than 250+ employees.

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**Figure 11: Labor market incentives for second earners for families with 1 child**

<!-- Start of picture text --> Second Earner Moving from Inactivity into MW Employment<br>(Principal Earner Fixed at MW)<br>44300<br>39440<br>34580<br>29720<br>24860<br>20000<br>Net+500+CCC CCS 25% CCS 50% CCS 75% CCS 100%<br>(PLZ)<br>Annual Net Household Earnings<br>47 95 95 96 97 97 98 99 99 100 101 102 102 103 104 104 105 106 106 107 108 109 109 110 111 111 112 113 113 114 115 116<br><!-- End of picture text --> Examining the income distribution of top earners in families in Poland reveals that the bottom 20 percent make less than MW.<sup>24</sup> This means that the aforementioned incentive issues occur mostly in families at the bottom 20 percent of the income distribution, whom programs such as the 500+ benefit were partly designed to support.

When income of the principal earner is fixed at median wage for this family type, in this case 81 percent of AW,<sup>25</sup> even without a subsidy for childcare there is a gain in income by PLN 3,851 when the secondary earner moves into MW employment (Appendix F). It is important to note that while there is no disincentive in this case, there is still no large economic incentive for the second earner to enter the labor force because of the additional 100 percent of MW income that the secondary earner gets, only 18 percent is not taxed. Only at a subsidy of 50 percent of the childcare costs the family receives a considerable net income gain (around half of the MW). Therefore, while the disincentive effects are strongest for the poorer deciles of the population, they persist.

# **_Married Couple with 2 Children_**

While for married couples with 2 children the situation looks similar, it illustrates the underlying system of benefits and their disincentive effects well. Because the calculation of the means-tested aspect of the 500+ benefit for the first child is based on the number of individuals in the family, the withdrawal of the benefit occurs at a much higher point of the income schedule. This allows to separately observe the two aspects of the tax-benefit system under scrutiny. First, we will examine what happens when the principal earner is fixed at MW while the secondary earner is moving from inactivity to an MW job.

Without subsidies for childcare costs, there is a net income loss (PLN 1,122) for moving into MW employment for the second earner. While providing a 25 percent subsidy for childcare costs leads to a marginal increase in net income, when moving from inactivity to MW employment (PLN

> 24 This includes all types of incomes that are used to determine eligibility criteria for benefits and taxes in Poland, including farm income.

> 25 81 percent of AW = PLN 36,904 annual gross income.

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computed using questions that capture the degree of stringency of banking regulation in engaging in securities, insurance, real estate, and nonfinancial business activities. The index ranges between 3 and 12, with higher values indicating stricter regulation.

# **Global Data Regulation Dataset**

Produced as part of World Bank (2021), the Global Data Regulation Diagnostic summarizes the laws and regulations concerning key aspects of a data economy for 80 countries. Following Chen (2021), the empirical analysis below relies on an index of enablers that facilitate use/reuse of data and another index of safeguards that protect the rights of market players in the data economy. The enablers index covers three dimensions: e-commerce, data produced for public purposes (e.g., public policy), and data collected by the private sector for business purposes. The safeguards index covers four dimensions: personal data, nonpersonal data, cybersecurity, and cross-border data. The indexes range from 1 to 17 with higher values indicating that a country has more of the regulations that enable data use/reuse,<sup>11</sup> or safeguard against its misuse. Index scores are aggregated across survey responses. For example, countries received one point if they had a law or regulation that explicitly governs e-commerce/e-transactions. The full list of the survey questions used to score countries is found in Annex 2 of Chen (2021).

# **Human Development Index**

Collected as part of UNDP (2022), the Human Development Index (HDI) is a summary measure of average achievement on three key dimensions of human development: a long and healthy life, being knowledgeable, and having a decent standard of living.<sup>12</sup> The health dimension is measured using life expectancy at birth; knowledge using expected years of schooling and mean

> 11 However, the highest possible value on the enablers index is 16.

> 12  UNDP (2022). Data available at: https://hdr.undp.org/data-center/documentation-and-downloads.

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10 of various outcomes for members of the population. The equal-opportunity view, however, focuses upon the distinction between circumstances and effort. Thus, to assess the desirability of a system, it requires not only the data just mentioned, but also knowledge of the _type_ of each individual. It is a non-consequentialist measure, for it will assess differently the same outcome for two individuals, if they have different circumstances. Utilitarianism condemns inequalities if their elimination would increase average or total welfare (however it is measured); opportunity egalitarianism condemns them to the extent they are due to circumstances beyond the control of the individuals concerned. The views are quite different.

Opportunity egalitarianism is not only a superior ethic to utilitarianism, it is the one implicitly endorsed by members of many societies. Suppose one asks the proverbial man on the street, “Do you think that the inequality between the rich Mr. _A_ and poor Ms. _B_ is unjustified?” it is unlikely that he will answer, “Only if a redistribution from _A_ to _B_ would increase their total welfare.” But he might well answer, “It depends upon how hard they each worked.” In other words, the popular views of justice are not consequentialist, they are based upon notions of _desert,_ and desert is based upon measurements of effort. Our man on the street must know more than the aggregate distribution of the objective to assess whether that distribution is fair – he must know the (disaggregated) distributions of the objective by type. The source of the inequality matters, ethically speaking, but these sources are ignored by looking only at outcomes<sup>7</sup> .

7 There is substantial survey and experimental work which examines the views that people in various societies have concerning distributive justice. An extensive discussion of this literature is found in Gaertner and Schokkaert (2012).
To recap the main points of the paper before we move on to the implications for policy, SSA has an extremely difficult contracting environment. SMEs respond to this poor environment by creating architectures of relation-based governance that substitute for missing market-supporting institutions. In the shock-prone business climate of the region, however, it is hard in some cases to create and sustain cooperative, self-enforcing relationships, because economic instability can undermine repeated game incentives. Tight, ethnically-based networks emerge and thrive in this setting, as there is an added need for the information and collectivist system of enforcement that a network can provide to help make and sustain relational contracts. The RPED surveys show that there is a coordination failure in the indigenous-African manufacturing business community that keeps firms from developing an effective multilateral reputation mechanism analogous to that found in ethnic minority networks. We show that the social capital in minority ethnic networks confer major benefits on member firms, facilitating larger size at entry, higher productivity, and faster growth. But there are features of these networks that can produce a kind of “lock-in:” stable networks and static patterns of business exchange. Lock-in is reinforced by the economic conditions and sparse business environments in SSA. Stable and cohesive, ethnically-based networks can have adverse consequences for income distribution, resource allocation, and competition. “Outsiders” can be excluded from supplier credit and many other business transactions. Adverse network effects fall hardest on indigenous-African firms.

# **III. Implications for Institutions and Policies in SSA**

We are now in a better position to address the questions posed at the beginning of this paper. Do interventions to assist SMEs in developing private institutional support arrangements make sense for countries in Sub-Saharan Africa? And, if interventions are required, what types of policies or programs might be needed?

We have shown that African SMEs lacking access to formal institutions and facing market failures and high transaction costs have been able to engage in a range of productive activities and gain access to needed working capital by creating workable private institutional arrangements to govern these transactions. We have also shown that 32
**_Energy demand models for policy_** **_<u>formulation</u>_**

The inaccurate characterization of energy systems in energy demand models can lead to incorrect policy prescriptions having implications for long-term energy system development and for sustainability. Clearly the dynamics of economic growth and consequent energy implications are poorly understood in developing countries, which in turn results to inadequate infrastructure development or poorly adapted development. An example can be provided from the Indian power sector (see Bhattacharyya, 2008). Recently, concerned with the growing capacity shortage in the country studies and plans were undertaken to determine the long-term capacity needs. A comparison of such estimates from the government agencies with those from the World Energy Outlook 2007 (WEO 2007) (where India and China’s needs were specifically considered) showed a great divergence in the estimates, essentially originating from the diverging assumptions and modeling approaches. While Indian studies used simple, aggregated forecasting techniques, IEA relied on a bottom-up demand forecasting approach. The demand forecasts by the government agencies are significantly higher than that of the WEO 2007 (IEA, 2007). Bhattacharyya (2008) concludes that “If the lower end of the capacity requirement as suggested by the IEA is really what is required to meet the demand, there would be an excess capacity of above 200 GW by 2030, for which the country would be paying a high cost, as the investment could have been better utilized in other areas.” The shortage and excess capacity situations found in the developing countries can often be related to the inaccurate demand estimations that fail to consider the specific features of these economies.

Similar problems arise while considering the issue of energy access in developing countries. Clearly, the top-down approach of demand analysis is inappropriate in dealing with such cases due to the prevalence of informal economic activities, reliance on nonmarketed fuels to a large extent, and use of inefficient technologies that do not represent the most efficient production frontier. There can be significant differences in the consumption behavior between urban and rural areas and within rural areas across various geographic zones as well by income class. Inadequate representation of such characteristics hinders any search for policy interventions for addressing the issue of access to clean energies. As widespread reliance on dirty energies has local as well as 95
|**label**<br>**Description**<br>log expenditure<br>Per capita Household Expenditure expressed in Loga-<br>ritm. This variable is the expenditure aggregate con-<br>structed by the World Bank for the purpose of poverty<br>measurement<br>Household Size<br>Number of Household Members living in the household<br>at the moment of the interview<br>antsShare of Dependent Members<br>Dependent household members, defned as individuals<br>aged less than 15 and over 64 living in the household,<br>as a share of total household members<br>State<br>Categorical variable where each level is one of the<br>36 Nigerian States plus the Federal Capital Territory<br>of Abuja: Abia; Adamawa; Akwa Ibom; Anambra;<br>Bauchi; Bayelsa; Benue; Borno; Cross-rivers; Delta;<br>Ebonyi; Edo; Ekiti; Enugu; Gombe; Imo; Jigawa;<br>Kaduna; Kano; Katsina; Kebbi; Kogi; Kwara; Lagos;<br>Nassarawa; Niger; Ogun; Ondo; Osun; Oyo; Plateau;<br>Rivers; Sokoto; Taraba; Yobe; Zamfara; FCT Abuja<br>LGA<br>Categorical variable where each level is one Local Gov-<br>ernment Areas. Of the total 774 LGAs Nigeria is di-<br>vided into, the dataset contains observations from 467<br>LGAs.<br>Type of Settlement<br>Type of settlement (1=rural; 0=urban)<br>HH Gender<br>Gender of the Household Head (1=male; 0=female)<br>HH Age<br>Age of the household head<br>HH Year of Education<br>Number of years of education attained by the house-<br>hold head<br>HH Employed<br>Status in employment of the household head (1=em-<br>ployed; 0=unemployed or inactive)<br>HH Married<br>Marital Status of the household head (1=married;<br>0=Divorced, separated, widowed, or never married)<br>HH Principal Language<br>Principal language spoken in the household (1=Hausa;<br>2=Igbo; 3=Yoruba; 4=Other)|
|---|
|**Variable**<br>ln<br>exp<br>hhsize<br>demo<br>sh<br>depent<br>state<br>lga<br>rururb<br>hh<br>sex<br>hh<br>agey<br>hh<br>eduyrs<br>hh<br>empl<br>hh<br>marstat<br>hh<br>language|

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intense competition raises prices above marginal cost, reducing output demanded by consumers and, therefore, reduces labor demanded by producers.<sup>24</sup> An increase in prices also reduces real wages, which could theoretically reduce the supply of labor if the substitution effect outweighs the income effect or boost labor supply if the income effect outweighs the substitution effect. The impact of competition on wages has been relatively less examined than the impact on employment and productivity. Existing studies have focused on the effect on wage inequality rather than on wage levels or growth – and these have been mixed in the findings.<sup>25</sup> We are not aware of any literature in the South African context that has linked competition, as proxied by a behavioral indicator such as markup, to employment and wages.

# _Employment growth_

To test the relationship between competition and employment growth in South Africa we use the following specification: ∆lnl�� �α� �α� ln μ���� �Z��β�α�∆lnw�� �α� ln TFP���� �α� ∆ln l���� �δ� �γ� �ε�� [3] Where l�� is employment in firm i at time t. Z�� is a vector of control variables that includes export status (using the same categorizations explained in the section above), firm age, whether the firm is a new entrant, sales share, and log of lagged capital stock.<sup>26</sup> We also control for real wage growth. Since we do not observe true firm level wage rates, we impute the level of firm wages (w��) using data on the total wage bill divided by firm-level employment.

The findings reported in Table 4 suggest that firms that face lower competition (i.e. those that have higher pricing power) have lower employment growth – and this holds whether we control for productivity growth or not. A 10 percent reduction in average observed markups leads to a 0.12 percent increase in employment growth. While the impact on employment growth may seem small,

> 24 See for example Spector (2004) and Blanchard and Giavazzi (2003) for examples of theoretical work; and Griffith, Harrison, and Macartney (2007); Fiori et al. (2012); and Nicoletti and Scarpetta (2005) for examples of empirical work. World Bank – OECD (2017) also contains a review of the links between competition and employment outcomes.

> 25 See World Bank – OECD (2017) for a review on the links between competition and wages.

> 26 Alternative specifications replacing lagged productivity with contemporaneous productivity growth were also tested as robustness checks (not shown). Results were consistent with those shown here.

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at baseline. However, entrepreneurs in the treatment group who did not participate in the training, as well as entrepreneurs in the control group also did better at answering two of these questions at follow‐up than at baseline.

In Table 7, we thus turn to estimating the causal impact of the training on business and financial knowledge, using the specification described in Section V.III. Here, our measure of business and financial knowledge is the sum of correct answers to the three questions that were included in the follow‐up survey, as explained in the previous paragraph. The result in Column 1 indicates that the average treatment effect of the training on business and financial knowledge is positive, but not statistically significant. We then examine whether this treatment effect differed by whether the entrepreneur had a baseline financial literacy level above or below the median. Column 2 shows that the effect of the training on business and financial knowledge is positive and statistically significant for individuals with below median financial literacy at baseline. For these individuals, the training increased the business and financial knowledge score by 0.239 compared to the control group mean of 0.897. On the other hand, for individuals with above the median financial literacy at baseline, the training appears to have had no effect on our measure of business and financial knowledge. This does not necessarily imply that individuals with above median financial literacy at baseline did not learn anything in the training since the course content was much richer than what is captured by the three business and financial knowledge questions included in our follow‐up survey. In particular, the course 20
terms of wages and agricultural income. In summary, even if trade liberalization has produced beneficial effects across all income groups, at the same time, income inequality has risen as richer households have gained more both in absolute and percentage terms.

Since trade liberalization favors particular endowments relative to others, and since those endowments are unlikely to be evenly across regions, figure 5.3 illustrates the welfare effects for each of the 32 Mexican states.<sup>33</sup> All the states have benefited from the trade liberalization process of the 1990s, but to different degrees. The states that have benefited the most from trade liberalization are the ones near the United States border, especially the states close to the California border. While regions closer to the Texas border have also greatly benefited, but to a slightly lesser extent. Large beneficiaries are also some of the states in the northern region, such as Aguascalientes, Durango, San Luis Potosi and Nayarit. Virtually all of the states in the central region, with the exception of those immediately surrounding Mexico City, have experienced a welfare gain between 2 and 4 percent, while the urban area of Mexico City gained around 4.5 percent. Finally, trade liberalization has produced the most modest gains for southern states, where welfare effects are in most cases lower than 2 percent.

The results of equation (5.1) produces welfare changes for each household, hence a further way to explore the results is to examine if some particular class of households may have benefited more or less from trade liberalization. To do so, the percentage gains or losses of each household are regressed on a series of variables. The estimation takes the form:

> 33 ENIGH data is not collected to be representative at the state level. To produce welfare aggregate at the regional and state level in this paper the average level of expenditure and income shares by state are assumed to reflect those of the underlying population.

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