rafmacalaba/datause-encoder-data huggingface dataset:
I1 groups clearly bear a heavier burden of income losses during periods of economic decline in Africa.<sup>6</sup>

**Table 7: Relative importance of mean and distribution in the evolution of poverty incidence**

||_Percentagechangein_<br>_meanpercapita_|_Percentage_<br>_changein_|_Poverty_<br>_Elasticity_|_Explainedb _|_ychanigesin:*_|
|---|---|---|---|---|---|
||_expenditure_|_poverty_<br>_headcount_|_wrtmean_<br>_expenditure_|_Mean_|_Distribution_|
|**Ghana**||||||
|1992-1998|23.7|-23.5|-0.99|-0.93|-0.06|
|**Madagascar**||||||
|1993-1997|-17.5|4.7|-0.27|-0.77|0.50|
|1997-1999|0.6|-2.7|-4.51|-0.79|-3.72|
|**Mauritania**||||||
|1987-1995|49.5|-39.7|-0.82|-0.74|-0.07|
|**Uganda**||||||
|1992-1997|17.1|-21.4|-1.21|-1.C07|-0.15|
|**Zimbabwe**||||||
|1991-1996|-28.8|34.6|-1.23|-2.22|0.99|

* Decompositions based on Kakwani and Pemia (2000); _Source:_ World Bank data and country studies under _Dynamics of Poverty_ study.

To assess further the extent to which these episodes of growth and recession are 'pro-poor' we follow Kakwani and Pemia (2000) in defining, <!-- Start of picture text --> 0 = 7<br>77g<br><!-- End of picture text --> where _i7_ is the _observed_ elasticity of headcount poverty with respect to changes in mean expenditure, and **_17g_** is the elasticity of headcount poverty assuming the distribution of income did not change during the period. 0 can be defined as an index of 'pro-poor ,growth.' Growth can be considered pro-poor if 4 > 1.7 Table 8 compares estimates of 4) for these five African countries with recent experience in Asia. On the basis of this sample of countries, growth and recession episodes in Africa have tended to be pro-poor, and indeed more so than the Asian experience.

> ' This is defined as the proportionate change in headcount poverty divided by the proportionate change in mean per capita household expenditure. For details of the method used see Kakwani and Pernia (2000). 6 The tendency for income inequality to narrow as higher income groups bear the brunt of economic recession was also noted by Grootaert (1996) in analyzing poverty changes in Cote d'Ivoire in the 1980s. 7 When mean household expenditures are declining, P= **Tlg/T1,** so that a recession would also be considered pro-poor if 4 > 1.
28 electoral checks are few, perhaps because of laws limiting political competition, executive control of the legislative apparatus is usually strong. Legislative seats depend on party nomination, which in turn is usually vested in the executive in such countries. The Index also does not take into account electoral rules that influence party control over members, an issue even in OECD countries. Where party control is weak and the same party controls both the legislative and executive branches of a presidential government, this index would understate the level of checks and balances by coding the country as not having a divided government. To adjust for these circumstances, the DPI includes two new variables, CHECKS1 and CHECKS2. These variables count the number of veto players in a political system, adjusting for whether these veto players are independent of each other, as determined by the level of electoral competitiveness in a system, their respective party affiliations, and the electoral rules. Both variables take into account the Legislative Index of Electoral Competitiveness. If the index is less than four, the two measures are set equal to one since, regardless of the formal constitutional arrangements in a system, where legislative elections are uncompetitive, constitutional checks on officials are unlikely to be binding.

For values of the electoral competitiveness index of four or more, the variable CHEGKS1 is increased by the number of veto players in the system. In presidential systems, it is increased by one for the president and one for each legislative chamber. However, if elections are conducted under closed list rules (closed list equals one) and the president’s party is the largest government party in a particular chamber, then it is assumed that the president exercises substantial control over the chamber and it is not counted as a check. For

> 3 The values of CHECKS1 and CHECKS? are nearly invariant to the use of the Executive or Legislative Index of Electoral Competitiveness.
# **_Firm Characteristics_**

# **i. Research and Development**

In 1993 R&D increased both wages and productivity. Wages increased by 2 percent, and productivity increased by 20 percent (see Table 18). However, this trend did not continue in 1999 since R&D and productivity were found to be strongly negatively correlated. However, foreign R&D was found to have a positive effect on both wages and productivity, by 19 and 90 percent respectively. 13

||||**Table18:Res**|**earch& De**|**velopment**||||
|---|---|---|---|---|---|---|---|---|
|**Variable**||**1**|**993**|||**1**|**999**||
|**Rd**|**Wage**|**P>Izi**<br>|**Productivity**|**P>Izl**|**Wage**|**P>Izl**|**Productivity**|**P>Izl**|
|ooled|0.02|0.08|0.20|0.00|0.02|0.52|<br>-0.20|0.03|
|Micro|-0.14|0.09|0.23|0.13|0.03|0.81|0.59|0.05|
|Small|0.00|0.94|0.10|0.04|0.30|0.00|<br>_-1.15_|0.00|
|Medium|0.02|0.37|0.10|0.00|-0.15|0.01|0.17|0.23|
|Large|0.03|0.21|0.29|0.00|0.02|0.73|-0.12|0.44|

_Source_ Author's calculations based on ENTRAM-ENESTYC.

## ii. **Ownership and Export Orientation**

The following two tables illustrate two important firm characteristics effects on wages and productivity. The two variables that are used are ownership structure and export orientation.

It is presumed that a firm with partial foreign ownership is more likely to be affiliated with international markets than a strictly Mexican-owned firm. In both 1993 and 1999, foreign ownership had a positive effect on productivity and wages, controlling for both firm and individual characteristics (see Table 19). Firms mostly owned by foreigners are statistically more productive than firms owned by locals, and they pay higher wages. In 1993 mostly foreign-owned firms were 54 percent more productive, and they paid 7 percent more than firms that had little or no foreign ownership. In 1999

> 1<sup>3</sup> Results that include foreign R&D are available upon request.

20
business grow”. The mean owner is very confident in their ability to do 5 out of 12 tasks, and winning the competition has no impact on this measure. This measure captures a combination of actual skill and confidence, and suggests little change in E.<sup>19</sup> Mentoring measures whether the business owner has a mentor they talk to about business matters. This is significant for existing firms in the unconditional regressions, but with one exception, not in the results conditional on operating a business. Network measures the number of other firm owners the business owner discusses business matters with, which is again not significant in the conditional regressions. This lack of change in self-efficacy, mentorship, or networking, suggests the impacts of winning are unlikely to reflect better business knowledge.

Formality measures whether the firm has a registered business name, municipal license, and income tax registration. We see a large increase in this measure, which is consistent with the winners needing to register in some form to receive grant payments. The existing global evidence suggests that formality per se does not have measureable impacts on firm productivity or performance for most firms (Bruhn and McKenzie, 2014).

Finally, in terms of access to other sources of capital, there is no significant change in the likelihood of receiving investments from outside equity partners, and a small, but statistically significant reduction in the likelihood of getting a formal loan in the year prior to the second round survey for existing firms. Thus the grant does not appear to have crowded in other sources of finance.

Table 9 examines the impact of winning on the input use of existing firms. Column 1 shows that owners are devoting more hours to their businesses. Recall we have already seen firms are using more labor in the form of employees. Column 2 examines a final source of labor, which is hiring outside consultants. There is no significant impact on this margin for existing firms after conditioning on remaining in business, although perhaps a shift in timing of this use for new firms. The remaining columns show that winning firms are using more capital stock. They have higher

> 19 Note this also helps rule out the potential concern that the control group has become less self-confident as a result of not winning.

28
public transfers in Moldova and Georgia, for example, represent less than 10 percent of GDP. Since wages and transfers can be measured quite well by household surveys, whereas other sources of income, such as from informal self-employment, are notoriously hard to measure with any precision, such compositional effects have serious implications for the accuracy with which inequality is measured. For this reason, Table 1 is a poor guide to describing inequality in the case of low income CIS countries such as Armenia, Georgia, the Kyrgyz Republic and Moldova.

Third, there are serious issues of under-reporting and non-response. Richer households, for example, tend to be increasingly missed by sample surveys. In practice, countries undertake different degrees of adjustment to correct for non-response but, in doing so, make a number of assumptions which can undermine comparability. In Russia, unlike in any other country, the increasing gap between reported incomes and estimates from macroeconomic sources is arbitrarily assigned to the top decile of households as “undeclared” incomes (World Bank 2005d), limiting comparability with other data on income distribution.

Fourth, correction for regional price differences is not normal practice in many statistical offices.<sup>1</sup> Fifth, the use of equivalence scales has not been converging towards a single standard.<sup>2</sup> All of this implies that, while official data can suggest that inequality has increased in all countries in transition, the magnitude of such increases is less certain. Despite these limitations, data such as those reported in Table 1 are used to generate “stylized facts” and draw far-reaching conclusions on the evolution of inequality in transition. [Ivashenko (2002)].

# **III. TOWARDS COMPARABLE DATA ON INEQUALITY IN TRANSITION**

The lack of consistency of “official” data on inequality prompted the creation of comparable and consistent inequality statistics based on primary records from household surveys across the transition countries of Eastern Europe and the former Soviet Union.<sup>3</sup> Most of these surveys are conducted by statistical offices and are, in that sense, “official”. But the way in which primary data were used led to indices that are different from the numbers reported in Table 1.

First, the preferred measure of welfare is consumption rather than income. The choice of consumption was dictated by practical considerations. While data on incomes remain 1 When such corrections are practiced, they tend to reduce inequality as measured by Gini by between 1-3 percentage points.

2 The use of Eurostat equivalence scale rather than per capita with ECA household structure typically reduces the value of Gini index by about 2 percentage points.

> 3 Copies of much of the survey data conducted in the region are stored in the World Bank ECA regional data archive.  At the time  of writing the archive contained primary unit record data from recent household surveys for twentyfour countries spanning the period 1998-2004

8
**Appendix Table A10: List of Business Practices Measured in Surveys**

|**Label**|**Name of the Practice**|**Survey Question**|
|---|---|---|
|**Joint**|**Decision-Making:**||
|||With whom do you discuss your business most often?<br>|
|||a) Family member|
|||b) Personal friend|
|_J1_|Di bi ih fil b|c) Business friend in neighborhood<br>|
||scusses usness wt amy memers|d) Business friend outside of neighborhood<br>|
|||e) Supplier<br>f) Local official (“Kepala RT/RW/Kelurahan”)<br>) Preman|
|||g<br>h) No one|
|_J2_|Discusses business with business friends|(see above:_J1_)|
|_J3_|Discusses business matters with anyone|(see above:_J1_)|
|_J4_|Discusses business sales|What kind of business topics do you discuss with other<br>|
|||people?<br>a) Sales|
|||<br>b) Selling price<br>c) Best-selling products<br>d) Discounts|
|||e) Promotion, marketing, advertizing<br>f Gt fdi|
|||) overnmen unng<br>g) Other financing opportunities<br>h) Arisan (“ROSCAs”)<br>i) Supplier(s)|
|||<br>j) Purchasing prices<br>k) New brands or products<br>l) Business practices<br>m) Business lan|
|||p<br>n) Market trends|
|||o) Business rumors<br>p) Security-related issues<br>q) Most profitable products<br>r) Assets owned|
|||s) New assets|
|||t) Others|
|_J5_|Discusses selling prices|<br>(see above:_J4_)|
|_J6_|Discusses best-selling products|(see above:_J4_)|
|_J7_|Discusses financing opportunities|(see above:_J4_)|
|_J8_|<br>Discusses buying prices|<br>(see above:_J4_)|
|_J9_<br>_J10_|Discusses business practices<br>Discusses business plan|(see above:_J4_)<br>(see above:_J4_)|
|_J11_|<br>Joint decisions on business sales|<br>What kind of business decisions do you make with other<br>|
|||people?<br>a) Sales|
|||<br>b) Sellin rice|
|||g p<br>c) Best-selling products<br>d) Discounts|
|||e) Promotion marketing advertizing|
|||, ,<br>f) Government funding<br>g) Other financing opportunities<br>h) Arisan (“ROSCAs”)<br>i) Sulier(s)|
|||pp<br>j) Purchasing prices<br>|
|||k) New brands or products|
|||l) Business practices<br>m) Business plan<br>|
|||n) Market trends<br>|
|||o) Business rumors<br>p) Security-related issues|
|||<br>q) Most profitable products<br>|
|||r) Assets owned|
|||s) New assets<br>t) Others|
|_J12_|Joint decisions on selling prices|<br>(see above:_J11_)|
|_J13_|Joint decisions on best-sellers|(see above:_J11_)|
|_J14_|Joint decisions on financing opportunities|(see above:_J11_)|
|_J15_|Joint decisions on buying prices|(see above:_J11_)|
|_J16_<br>|Joint decisions on new products<br>|(see above:_J11_)<br>|
|_J17_<br>_J18_|Joint decisions on business practices<br>Joint decisions on businessplan|(see above:_J11_)<br>(see above:_J11_ )|

This table lists the business practices measured in the baseline and followup surveys for this study, along with the corresponding survey question for each practice.
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# 3. **DATA SOURCES, VARIABLES AND SECTORS SELECTED**

# **(a) Data sources**

The plant level data for this study are drawn from the Annual Survey of Industries (ASI), which is published by the Central Statistical Office of the Government of India. The individual plant or "factory" is the unit of analysis used in the survey and the data are based on returns provided by factories<sup>2</sup> . The ASI covers factories registered under sections 2m(i) and 2m(ii) of the Factories Act 1948, employing 10 or more workers and using power, and those employing 20 or more workers but not using power on any day of the preceding 12 months.

Data on various firm level production properties such as output, sales, value added, labor cost, employees, capital, materials and energy use have been used in our study. We closely examined data quality issues by examining the data as well as reviewing the comments of other researchers on this issue. We dropped observations for which information was either incomplete or where the sum of various components were found to be inconsistent with reported aggregates, on the basis of usual accounting principles. The geographic attributes allow us to identify each firm at the district level<sup>3</sup> .

Goldar's (1997) insightful review indicates problems with depreciation figures for capital as well as the head count index for measuring labor input. In this paper, we use the gross (undepreciated) value of capital stock to circumvent the depreciation issue, which appears to be justified in a cross sectional analysis. In addition, to capture variations in hours of work, we use employee man-days instead of the number of employees as the variable to best represent labor input.

# **(b) Variables selected to explain heterogeneity**

We have used several variables to explain inter-firm variations in efficiency, even when they are part of the same industry sector. For example, let us consider the effect of firm age on productivity. The Schumpeterian approach of "creative destruction" suggests that new inventions make old technologies or products obsolete. Hence, one would expect newer entrants to be more productive than older plants. Newer plants are likely to have better technology embodied in new capital as well as easier access to "off-the-shelf' technological and managerial advances, obtained from consulting services and suchlike.

6
# **II.** **<u>Metrics of Job Creation and Firm Size</u>**

The last two decades have witnessed a substantial expansion of firm-level data that has in turn led to deepened analysis of the patterns of employment and job dynamics. These data helped to address important gaps, including the expansion of coverage to include both services and smaller firms, while building longitudinal databases has resulted in more precise measurements at shorter intervals, capturing entry and exit effects.<sup>2</sup> Importantly, these datasets incorporate firm-level characteristics, allowing for the comparison of the relative share of various groups of firms to the levels of employment and job growth (or contraction). As a result, the inclusion of these characteristics has effected a debate about where people are employed as well as where jobs are created or eliminated. Yet, despite this explosion of analysis in developed economies, similar sources are often unavailable in developing economies.

The resulting literature is far from unanimous. And conflicting findings are likely to be the result of underlying data sources – including whether data are panel or cross-sectional – and their coverage. Further, the metrics used also affect findings, stemming from issues such as what point is used as the starting base and how firms are classified. In fact, varying results can be obtained even using seemingly straightforward categories such as a firm’s size or its age. Adding further complications, whether data is collected at the “establishment” or “firm” level has non-trivial implications for the debate. An establishment is commonly understood to mean a singular physical business entity, while a firm is defined as the legal entirety of a business. In other words, a firm can encompass several establishments, a so-called multi-establishment firm. Measures of employment may be affected by the presence of multi-establishment firms as these firms may increase employment by opening new establishments rather than expanding employment in existing establishments.<sup>3</sup> Ayyagari et al (2014) note that the occurrence of multi-establishment firms in the developing world is infrequent and is likely to have little effect in those economies; however, the average number of multi-establishment firms in the World Bank’s Enterprise Surveys (which have been conducted in 215 rounds largely in the developing world) is 15 percent, indicating that this distinction must be taken into account.

> 2 Notably, the US Bureau of Labor Statistics (BLS) has developed and leveraged the microdata in the Business employment dynamics (BED) database for well over a decade (see Pivetz et al (2001) for example).

> 3 In estimating firm-level figures for employment, moreover, changing ownership structure, either through mergers, acquisitions, or legal maneuvering, may have a substantial effect Haltiwanger et al (2013) and Pinkston and Speltzer (mimeo).

3
Table 8: Global Policy Selectivity and Chinese Investments (2000–2014)

|Dependent Variable:|CPIA, log||||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged GPS|0.060<sup>_∗∗∗_</sup>|0.054<sup>_∗∗_</sup>|0.091<sup>_∗_</sup>|0.029<sup>_∗∗_</sup>|0.017|0.064|
||(0.011)|(0.024)|(0.046)|(0.012)|(0.019)|(0.039)|
|Large Chinese Investments|0.391<sup>_∗∗∗_</sup><br>(0.072)|0.461<br>(0.278)|0.433<br>(0.268)||||
|Lagged GPS* Chinese Investments|-0.117<sup>_∗∗∗_</sup><br>(0.019)|-0.134<sup>_∗_</sup><br>(0.072)|-0.127<sup>_∗_</sup><br>(0.069)||||
|Large IDA Aid Amounts||||-0.034|-0.004|-0.005|
|||||(0.086)|(0.075)|(0.074)|
|Lagged GPS*Large IDA Aid Amounts||||0.011<br>(0.022)|0.016<br>(0.019)|0.017<br>(0.019)|
|Region-Year FE|No|Yes|Yes|No|Yes|Yes|
|GPS without outliers|No|No|Yes|No|No|Yes|
|N|1954|1954|1954|1954|1954|1954|
|R-squared|0.023|0.158|0.158|0.002|0.159|0.159|

Dependent variable is the log of the overall CPIA in recipient countries. GPS is the log of the share of policy-selective aid disbursed globally. A recipient received large Chinese or IDA investments if total investments over the entire time period are above the 75th percentile. All regressions use recipient-year observations and include a constant term that is not reported. Year level clusterrobust standard errors reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent thy difference is that compared to other donors, particularly bilateral donors, the number of recipient countries – with 52 on average in a given year – is very low.

For our estimation, we generate a Chinese investment dummy variable that equals one for a recipient if total investments over the entire time period exceed the 75th percentile, which corresponds to investments of about 2.4 billion USD. We include all investments recorded by AidData, which include ODA- and OOF-like flows. Because that dataset covers the period from 2000 to 2014, we use that time span in the regressions. Columns (1) to (3) in Table 8 present the results. As expected, we find a large negative and statistically significant coefficient on the interaction term in all three regression, implying that the incentive effect of policy-selective aid is not present in recipients of large Chinese investments. This result is robust to the inclusion of region-year fixed effect and holds for both our GPS measures, outlier adjusted or not. In Columns (4) to (6) in this table we report results where we apply the same methodology used for China for aid allocated by IDA.<sup>6</sup> We do not find an interaction effect for 6Note, however, the difference that for IDA the financial flows are all ODA, whereas for China they include 22