rafmacalaba/datause-encoder-data huggingface dataset:
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- 32 -
have contributed significantly toward convergence in aggregate labor productivity. Interestingly, productivity growth in agriculture was the main driver of aggregate convergence. Nevertheless, agricultural productivity in low-income countries still markedly lags and is weakly converging relative to advanced countries. As for manufacturing, they find evidence in support of unconditional convergence consistent with Rodrik (2013) albeit with a slower convergence rate. In line with Bakker et al. (2020), their findings also imply addressing barriers to sectoral reallocation through human capital development, good governance, and a favorable business environment would boost productivity growth and accelerate convergence.

# **_Regional convergence_**

Studies have also examined the patterns of regional inequalities and convergence within countries. Gennaioli et al. (2014), based on regional data from 83 countries, show that rich countries experienced faster regional convergence than poor countries, mainly attributed to better capital markets in the former and limited factor mobility in the latter. Their results further show that the pace of convergence across regions within countries is relatively faster than the one observed across countries.Yet, the regional convergence rate is generally slow, as indicated by lower resource mobility. This is despite regions being more likely to exhibit homogeneity in technology, productivity, and institutions compared to countries.

Examining the determinants of regional growth, Gennaioli et al. (2014) show that geography (e.g., resource endowment, temperature, proximity to the ocean) and human capital (e.g., educational attainment) are important factors but not culture (e.g., ethnic heterogeneity, trust) and institutions (e.g., business environment) factors. More importantly, they highlight the crucial role of human capital through the mechanisms of worker education, entrepreneurial/managerial education, and externalities while noting the ease with which firms, workers, and entrepreneurs move across regions than countries.

Coming to country-specific studies, Kinfemichael and Morshed (2019b) argue that the slowdown of labor productivity convergence in recent years in the United States is possibly due to factors such as the decline in cross-state migration, increase in housing costs in major cities, and strengthening of agglomeration economies. During the period 2000-2012 for Peru, Iacovone et al. (2015) report evidence of labor productivity cross-region convergence in manufacturing and mining sectors, but 7
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# REFERENCES

- ACEMOGLU, DARON, SIMON JOHNSON, AND JAMES A. ROBINSON, “Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution,” The Quarterly Journal of Economics 117, no. 4 (2002), 1231–1294.

- AKCIGIT, UFUK., HARUN ALP, AND MICHAEL PETERS, “Lack of Selection and Limits to Delegation: Firm Dynamics in Developing Countries,” NBER Working Paper no. 21905, 2017.

- AGHION, PHILLIPE, AND PETER HOWITT, “A Model of Growth through Creative Destruction,” _Econometrica_ , 60, no. 2 (1992), 323–351.

- AMINADAV, GUR, AND ELIAS PAPAIOANNOU. “Corporate Control Around the World,” Revised draft of NBER WP 23010, (2016).

- AMIT, RAPHAEL, YUAN DING, BELÉN VILLALONGA, AND HUA ZHANg, “The role of institutional development in the prevalence and performance of entrepreneur and family-controlled firms”, _Journal of Corporate Finance_ (2015), 284-305.

- ATKIN, DAVID, FRANCISCO BUERA, AMIT KHANDELWAL, AND YONGSEOK SHIN, “Constraints to the Growth of Firms: All in the Family?,” Meeting Paper 1191 (2013), Society for Economic Dynamics.

- Anderson, Ronald, and David Reeb. 2003. "Founding-Family Ownership and Firm Performance: Evidence from the SandP 500." The Journal of Finance 58 (3): 1301-1328.

- BENNEDSEN, MORTEN, KASPER NIELSEN, FRANCISCO PEREZ-GONZALES, AND DANIEL WOLFENZON, “Inside the Family Firm: The Role of Families in Succession Decisions and Performance”, _The Quarterly Journal of Economics_ , 122, no. 2 (2007), 647–691.

- BERLE, ADOLF, AND GARDINER MEANS, The Modern Corporation and Private Property (New York: Harcourt, Brace and World, [1932] 1968).

- BERTRAND, MARIANNE, “CEOs,” _Annual Review of Economics_ , 1, no. 1 (2009), 121-150.

- BERTRAND, MARIANNE, SIMON JOHNSON, KRISLERT SAMPHANTHARAK, AND ANTOINETTE SCHOAR, “Mixing Family with Business: A Study of Thai Business Groups and the Families Behind Them,” _Journal of Financial Economics_ , 88, no. 3 (2008), 466-498.

- BERTRAND, MARIANNE, PARAS MEHTA, AND SENDHIL MULLAINATHAN, “Ferreting out Tunneling: An Application to Indian Business Groups.” _The Quarterly Journal of Economics_ 121–148, (2002).

- BERTRAND, MARIANNE, AND ANTOINETTE SCHOAR, “The Role of Family in Family Firms,” _Journal of Economic Perspectives_ , 20, no. 2 (2006), 73-96.

- BLOOM, NICHOLAS, AND JOHN VAN REENEN, “Measuring and Explaining Management Practices Across Firms and Countries,” _Quarterly Journal of Economics_ , 122, no. 4 (2007), 1351-1408.

- BLOOM, NICHOLAS, RAFFAELLA SADUN, AND JOHN VAN REENEN, “The Organization of Firms Across Countries,” _The Quarterly Journal of Economics_ , 127, no. 4 (2012), 1663–1705.

- BLOOM, NICHOLAS, CHRISTOS GENAKOS, RAFAELLA SADUN, AND JOHN VAN REENEN, “Manage-

23
suggest 6 lags of the two variables. As before, the asset ratio is significant for the entire sample, and Europe and for Latin America at the 7% level. However, the variance is now significant for the whole sample, Europe, and Latin America although it enters with incorrect sign in the latter and in Asia+. The variables taken together are statistically significant for all except the Asia+ regression, although again, the overall explanatory power is under 5% of the variance.

The results improve if we work with R/M2 as suggested by Calvo and Mendoza. The sample size increases for Europe because Sweden publishes M2 and the U.K. publishes a proxy for M2 (the retail component of M4) but neither publish MI. RIM2 is of the predicted sign for all but Asia+ although it is now not significant within Latin America. The variance is very significant and of the correct sign for all except Asia+. Again, all the regressions, with the exception of Asia+ are very significant.

The poor performance of the model for Asia+ may results from two factors. First, since in none of the countries was there a true speculative attack across the sample period, the movements in the standardized index may represent noise unrelated to speculative pressure. Moderate depreciations designed to preserve competitiveness in Korea, or Israel will get very large weight, yet occur in relatively healthy macro-environments. The fact that the model predicts so poorly in this case may be considered support for it overall. It also suggests that, for the other regions, the index is not just picking up noise. It may also be, however, that despite the loosening of capital controls over time, some countries, like Korea, still managed short term flows and therefore do not correspond well to the model.

The fact that the variance now enters with the correct sign in the Latin subsample is supportive of the variance of M2IR being the more appropriate of the two monetary aggregates.

14
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Table 4: Formal labor market participation

||(1)<br>In FLM<br>Basic Model|(2)<br>In FLM<br>IV Approach|
|---|---|---|
|Employment Growth_t−_1|0.0020**<br>(0.0008)|0.0259**<br>(0.0105)|
|Time-variant controls|✓|✓|
|Family-fxed efects|✓|✓|
|Time-fxed efects|✓|✓|
|F-Statistic||25.32|
|Mean FLMP|0.2158|0.2158|
|Average transition probability|0.0265|0.0265|
|N|52,020,809|51,354,456|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 **Notes:** The table presents the main results from model (1) in column (1) and from the IV approach as specified in model (2) in column (2). The dependent variable is _a family has at least one member in formal dependent employment_ . The regression controls for family- and timefixed effects. Robust clustered standard errors at the municipality level are shown in parentheses.

and significant. Hence, an increase of 10% employment growth at the local level increases the probability that a family leaves BF by 0.342 percentage points (column (4) Table 5). The average employment growth in our study period was 2.4%, which then, in turn, caused an increase in the average transition probability by 0.08 percentage points: from 8.61% to 8.69%. It should be remembered that Brazil went through different economic cycles in our study period. While there were economic upturns, there were also economic downturns, which in part explain the small results.

Table 5: Exit probability

||(1)<br>Exit BF<br>Basic Model|(2)<br>Exit BF<br>Basic Model|(3)<br>Exit BF<br>IV Approach|(4)<br>Exit BF<br>IV Approach|
|---|---|---|---|---|
|Employment Growth_t−_1|0.0036***|0.0036***|0.0334**|0.0342**|
||(0.0013)|(0.0013)|(0.0148)|(0.0151)|
|Time-variant controls||✓||✓|
|Family-fxed efects|✓|✓|✓|✓|
|Time-fxed efects|✓|✓|✓|✓|
|F-Statistic|||25.34|25.32|
|Mean exit probability|0.0861|0.0861|0.0861|0.0861|
|N|52,319,618|52,020,809|51,655,314|51,354,456|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 **Notes:** The table presents the main results from model (1) in columns (1) and (2) and from the IV approach as specified in model (2) in columns (3) and (4). The dependent variable is _a family left BF_ . The regression controls for family- and time-fixed effects. Robust clustered standard errors at the municipality level are shown in parentheses.

18
# Table 3: Input-tariff liberalization and firms’ decision to import capital goods

|Depend|ent variable: dumm|y equal to one if t|he frm_i_imports ca|pital goods in_t_.|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Input tariff(s)(t-1)|-0.166***|-0.149**|-0.152**|-0.152**|-0.151**|-0.012|
||(0.056)|(0.060)|(0.060)|(0.060)|(0.069)|(0.078)|
|Input tariff(s)(t-1)_×_imported inputs_>_0||||||-0.170**|
|||||||(0.066)|
|Capital goods tariff(s)(t-1)||-0.165**|-0.168**|-0.170**|-0.168*|-0.173**|
|||(0.073)|(0.073)|(0.073)|(0.086)|(0.083)|
|Output tariff(s)(t-1)||-0.057|-0.057|-0.053|-0.026|-0.030|
|||(0.047)|(0.047)|(0.047)|(0.041)|(0.040)|
|Herfndhal index(s)(t-1)|||0.044|0.045|0.022|0.024|
||||(0.049)|(0.049)|(0.059)|(0.059)|
|Age(t-1)||||-0.004|-0.016|-0.011|
|||||(0.028)|(0.033)|(0.034)|
|Capital intensity(t-1)||||0.013**|0.014**|0.015*|
|||||(0.006)|(0.007)|(0.007)|
|Imported inputs_>_0|||||0.333***|0.405***|
||||||(0.028)|(0.041)|
|Firm fxed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fxed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|14,680|14,680|14,680|14,680|14,680|14,680|
|R-squared|0.036|0.036|0.036|0.036|0.093|0.094|

_Notes_ : The dependent variable is a dummy for firm _i_ having positive imports of capital goods in year _t_ . Output tariff(s)(t-1) are MFN applied tariffs from WITS-WB dataset at the 3 digit industry level and input and capital goods tariffs are constructed separately using these output tariffs and India 1993 inputoutput matrix. Importer inputs is a dummy equal to one if the firm imports intermediate goods. Herfindahl index measures the concentration of sales of the industry. Capital intensity is measured by capital stock over sales of the firm. The Prowess dataset reports the year of creation of the firm that allows to construct the age of the firm. Heteroskedasticity-robust standards errors are reported in parentheses. Errors are corrected for clustering at the 3-digit industry level.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5 and 10 percent levels respectively.

_The heterogeneous effects of input tariff cuts_ 33
Interestingly, for the top income earners in Malawi we find saving rates that are very similar to (or slightly above) the saving rates of the top income earners in the United States. The saving rates for the top 10% of the income distribution are 42% for rural Malawi, 46% for urban Malawi, and 36% for the United States.<sup>47</sup> In rural and urban areas, the top 1% and top 5% of the income distribution share a very similar saving rate. For the top 1%, the saving rate is 0.72 in rural areas and 0.74 in urban areas; for the top 5%, the saving rate is 0.37 in rural areas and 0.36 in urban areas. Across the income-poor households, we find a larger negative saving rate in Malawi than in the United States. The saving rates for the bottom 10% of the income distribution are, on average, -5.0 in rural Malawi and -12.0 in urban Malawi, and this figure is -1.30 for the United States. That is, unlike at the top of the income distribution, the rural-urban differences in saving rates are large at the bottom of the income distribution.

In contrast, when we rank households by wealth, we find larger discrepancies in the saving rates between rural and urban areas across the top wealth groups. The top 1% (and 5%) of the wealth distribution saving rate is 0.08 (and 0.09) in rural areas and 0.42 (and 0.08) in urban areas. This difference reinforces the idea of a larger persistence of wealth in urban areas compared with rural areas. The bottom of the wealth distribution in rural and urban areas behave similarly with a negative saving rate of -0.60 and -0.87, respectively. In terms of land, only the top 1% land-richest households show positive saving rates in rural areas.

To summarize, only the top income earners in Malawi are able to save, and they do so with saving rates that are high and similar to those of the top income earners in the United States. Now, with such similar saving rates, can the top income earners in Malawi accumulate as much wealth as the top income earners in the United States? This is the question we turn to next.

> 47Here, we compare in a bit more of detail the saving rates by income decile between the United States from the 1992 CEX as documented in Sabelhaus and Groen (2000) and the saving rates that we find for Malawi (ISA 2010):

|||||Saving R|ates by|Income|Deciles||||
|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|
|U.S. CEX 1992|-1.3|-0.63|-0.66|-0.12|0.00|0.05|0.10|0.19|0.26|0.36|
|Malawi — Rural|-5.0|-2.48|-1.57|-1.27|-0.97|-0.76|-0.46|-0.30|-0.06|0.44|
|Malawi — Urban|-12.0|-3.81|-2.47|-1.65|-1.41|-1.12|-0.90|-0.32|-0.22|0.47|

The income decile 1 represents the bottom 10% of the income distribution and the income decile 10 represents the top 10% of the income distribution. This table excludes 73 observations with either zero or negative income.

22
|9|Agriculture; forestry and fishing|1032|720|69.8|
|---|---|---|---|---|
|10|Accommodation and food service activities|1320|924|70.0|
|11|Administrative and support service activities|1692|1212|71.6|
|12|Water collection, treatment and supply|2460|1908|77.6|
|13|Human health and social work activities|2808|2184|77.8|
|14|Other service activities|1104|876|79.3|
|15|Construction|1368|1224|89.5|
|16|Public administration and defense|2964|2736|92.3|
|17|Information and communication|2988|2760|92.4|
|18|Financial and insurance activities|4440|4140|93.2|
|19|Wholesale and retail trade; repair of motor vehicles and<br>motorcycles|1212|1224|101.0|
|20|Arts, entertainment and recreation|1500|1824|121.6|
|21|Transportation and storage|1668|2904|174.1|

Source: ILOSTAT database, International Labor Organization Table 6 shows the gender wage gap in Pakistan across different industry sectors in 2016. A very skewed gender pay gap exists in the manufacturing sector with women earning only 37.2 percent of wages as compared to men for the same work. Other major sectors where majority of women are employed show similar trends, for instance, in agriculture, forestry and fishery group females gender wage gap is 69.8 percent, i.e., women earn 30.2 percent less as compared to men. The only sector which provides higher wages to female workers compared to male counterpart is transport and storage industry (with gender wage gap of 17.4 percent), however, this sector has very low concentration of women workers. Thus, many different factors determine the wage gap in Pakistan, women are generally concentrated in a smaller number of specific industry groups than men, and these occupations usually pay lower wages. In occupations where there are fewer women, they tend to earn relatively higher wages.<sup>20</sup>

# 2.3.4 Sri Lanka

Sri Lanka is the second best among South Asian countries in terms of gender equity with regard to attainment of education, health and other social indicators for women, however, female labor force participation has remained pegged at a low level of 30-35 percent of the workforce for the past two decades. An ILO (2016) study on women’s labor force participation in Sri Lanka highlighted that combination of demand-side constraints such as discrimination in employment, differences in the quality of jobs available to women versus the quantity of such jobs, wage discrimination faced for doing exactly the same job, etc. result in lower participation of women into the workforce than men.<sup>21</sup> In another way, Sri Lanka is distinct compared to other countries in the region is that it has less dependence of women in agriculture and other informal activities. The 2014 labor force survey in Sri Lanka shows that the highest percentage of women workers are in the

> 20 Saeed, Muhammad B. (2017).

21 International Labor Organization 2016. Factors affecting women’s Labor force participation in Sri Lanka, ILO Country Office for Sri Lanka and the Maldives. Colombo.

19