rafmacalaba/datause-encoder-data huggingface dataset:
location choices by Indian manufacturing establishments and their associated productivity consequences (e.g., Lall et al. 2004, Lall and Mengistae 2005, Deichmann et al. 2008, Fernandes and Sharma 2011).

Section 2 of this paper describes our establishment-level data. Section 3 provides greater detail on the growth of unorganized tradable industries and 1-person establishments. Section 4 analyzes explanatory factors for these trends. The fifth section quantifies local productivity consequences, and Section 6 considers contemporaneous growth across the establishment size distribution to the development of 1-person establishments. The last section concludes.

# **2. Indian Manufacturing Establishment Data**

Our analysis builds upon repeated cross-sectional surveys of manufacturing establishments carried out by the government of India for the fiscal years of 1989, 1994, 2000, 2005, and 2010. In all cases, the survey was undertaken over two fiscal years (e.g., the 2000 survey was conducted during 2000-2001), but we will only refer to the initial year for simplicity. The organized and unorganized sectors of Indian manufacturing are surveyed separately, as described next. In every period except the last one, our surveys for the organized and unorganized sectors were undertaken contemporaneously. In the last period, we combine the 2009-2010 survey for the organized sector with the 2010-2011 survey for the unorganized sector. We will again refer to this period as 2010 for simplicity.

The organized sector comprises establishments with more than 10 workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act. The organized sector accounts for over 80% of India’s manufacturing output, while the unorganized sector accounts for over 80% and 99% of Indian manufacturing employment and establishments, respectively (Table 2b Panel A of this paper; Ghani, Kerr and O’Connell, 2013a).

The organized manufacturing sector is surveyed by the Central Statistical Organization through the Annual Survey of Industries (ASI). Our data for the unorganized sector come from the National Sample Statistics (NSS). These surveys are used for many published reports on the 5
A large fraction of the households are also producers of maize. In 2016/17, for example, about 72 percent of Malawians produced maize, and more than 80 percent of Malawians residing in rural areas grew maize. These shares are as a proportion of all households, not just farm households. Of course, almost all farm households grow maize, and nearly all households consume maize. In rural areas, 82 percent of households grow maize, compared to 30 percent of urban residents. Across regions, the North has a lower share of maize-producing households (74 percent), compared to the South (81 percent) and Central (85 percent) regions. A similar pattern of production is observed in 2010/11.

**Table 2: Maize production, sale, and consumption patterns in rural/urban areas and by region**

||Sample N|% HH<br>producing<br>maize|% HH<br>selling<br>maize|% HH<br>consuming<br>maize|Share of maize<br>consumption in total<br>food expenditure(%)|
|---|---|---|---|---|---|
|**2016/17**||||||
|_Urban-Rural_||||||
|Urban|2,272|29.2|5.0|97.2|24.5|
|Rural|10,175|81.8|12.2|96.4|41.2|
|_Region:_||||||
|North|2,491|63.3|12.0|90.1|32.0|
|Central|4,220|75.2|16.9|98.2|39.5|
|South|5,736|70.1|4.7|96.3|37.8|
|Malawi|12,447|71.7|10.8|96.6|38.0|
|**2010/11**||||||
|_Urban-Rural_||||||
|Urban|2,233|33.0|3.4|97.6|16.5|
|Rural|10,038|87.2|14.2|97.0|31.1|
|_Region:_||||||
|North|2,302|79.2|14.8|94.0|27.5|
|Central|4,217|83.9|17.3|97.6|27.7|
|South|5,752|74.1|7.6|97.4|30.1|
|Malawi|12,271|78.8|12.5|97.1|28.8|

_Source_ : Authors’ calculations based on IHS3 and IHS4 data. _Note:_ HH = households.

While most farm households produce maize, the share of households engaged in maize sales is minimal, indicating that many of the maize producers are subsistence farmers, producing mainly for their consumption. Even if 72 percent of households grew maize in 2016/17, only 11 percent of households sold maize. Across regions, a larger share of households in the rural Central region (19 percent) engages in maize sales, compared to the South region (5 percent). To make matters worse, farm households typically sell their maize immediately after harvest, when prices are generally low. Maize harvest usually occurs during March–June, mostly in April, when half of the farmers reported starting harvest (Figure 5, panel a). Soon after, newly harvested maize enters the market in May, and farmers complete most maize sales by August/September. In general, the households selling later are also those that undertake maize harvest later in the season (Figure 5, panel b). Limited abilities of farmers to hold maize stocks due to the constraints in storage capacity, high post-harvest losses, lack of access to finance, and other factors contribute to early disposal of maize harvest. Only a few households wait until December–February when prices are generally 16
Mozambique, and Comoros have a similar discrepancy. Despite these differences, of the 27 countries which have data from both sources, all but three show the same sign for the difference between the enrollment of girls and of boys (the exceptions are Bangladesh, Indonesia, and Zimbabwe where the difference is close to zero in any case).

Another comparison one can make on the basis of these data is that to the stock of education as reported by Barro and Lee (1993) which has been used in numerous papers to investigate the determinants of growth. Table 4 reports the average years of schooling of the female population over 15 from the DHS data as well as the average years of schooling of the population over 15 based on the Barro-Lee (BL) data. Here the DHS imply a stock of schooling that is slightly higher than that in the alternative data source: the mean of the average years of schooling among women 15 and older across all the Countries is 3.7 in the DHS data and 2.6 in the BL data. A possible explanation for this is that the DHS are from a period spanning 1990 to 1998 whereas the BL data are an estimate for 1990. T he discrepancy for some countries is substantial ranging from a high of 3.2 years in (Zimbabwe where the DHS imply an average of _5.9_ years and the BL where the average is 2.7 years) **to** -1. 1 (Pakistan where the DHS imply 1.8 years and BL estimate 2.8).

Focusing on the male/female ratio in the stock of education, the DHS tend to imply a lower degree of male advantage. The cross-country average male/female ratio from the DHS is 1.58 whereas that in BL is 1.73. Again, this would be true if male advantage were declining over time and the DHS were capturing a later period. In some countries the discrepancy is especially large, for example in Ghana BL imply that men have 2.6 times the schooling of women but the DHS implies they have only 1.46 times as much. Other countries where the difference is large are Bangladesh, Haiti, Mali, Niger, Nepal, Rwanda and Togo.

In summary, the aggregate statistics based on the DHS are similar to those that are frequently used to describe education outcomes across countries, although there is a larger 14
# _Appendix Table A.7. Summary Statistics_

|||Total|||Unskilled|||Skilled||
|---|---|---|---|---|---|---|---|---|---|
||||Change|||Change|||Change|
||2002|2004|<br>2002-2004|2002|2004|<br>2002-2004|2002|2004|<br>2002-2004|
|**Personal Characteristics**||||||||||
|Observations|1746|1746||1065|1065||656|656||
|Female (%)|.36<br>|.36<br>||.31<br>|.31<br>||.46<br>|.46<br>||
||(.48)|(.48)||(.46)|(.46)||(.50)|(.50)||
|Minority (%)|.073|.073||.066|.066||.087|.087||
||(.26)|(.26)||(.25)|(.25)||(.28)|(.28)||
|Education (years)|9.25|9.38||7.21|7.30||12.41|12.61||
||(4.23)|(4.26)||(3.55)|(3.59)||(3.08)|(3.03)||
|Potential Experience|18.63|20.45||18.01|19.86||19.58|21.33||
|(years)|(10.26)|(10.32)||(10.66)|(10.75)||(9.64)|(9.66)||
|Married (%)|.68|.71|.025|.60|.63|.030|.80|.82|.015|
||(.47)|(.45)|(.20)|(.49)|(.48)|(.21)|(.40)|(.38)|(.18)|
|Urban (%)|.40<br>|.40<br>||.30<br>|.30<br>||.55<br>|.55<br>||
||(.49)|(.49)||(.46)|(.46)||(.50)|(.50)||
|Monthly wage|735.16|922.31||576.26|695.17||976.34|1260.19||
|(1,000 dong)|(596.44)|(715.31)||(415.99)|(504.16)||(745.21)|(848.34)||
|Log monthly wage|13.27<br>|13.49<br>|.22<br>|13.06<br>|13.24<br>|.18<br>|13.59<br>|13.86<br>|.27<br>|
||(.70)|(.72)|(.51)|(.66)|(.67)|(.55)|(.64)|(.63)|(.44)|
|**Province-specific Characteristics**||||||||||
|Observations|61|61|61|||||||
|Export Index|2.35|2.93|.59|||||||
||(4.76)|(6.10)|(1.44)|||||||
|Export US Index|<br>.42<br>|<br>.76<br>|<br>.34<br>|||||||
||(.82)|(1.65)|(.84)|||||||
|US tariff cut measure|||.45<br>(.87)|||||||
|Share of agriculture 2002 (%)|.620<br>(.178)|||||||||
|Share of construction 2002 (%)|<br>.041<br>(.022)|||||||||
|Share of government 2002 (%)|<br>.053<br>(.018)|||||||||
|Share of industry 2002 (%)|<br>.113<br>(.075)|||||||||
|Share of service 2002 (%)|<br>.174<br>(.096)|||||||||

_Sources_ : Author’s calculation based on the VHLSS 2002, VHLSS 2004 and the _Enterprise Survey_ data. _Note_ : Between parentheses are the standard deviations.

51
ultimately contributed to policy reversals in some of these systems since the financial crisis.<sup>19</sup> The paper also discusses how regulations could be adapted to incentivize long-term investment horizons by introducing outcomebased benchmarks.<sup>20</sup> Another form of regulation, which can distort pension fund investment and skews portfolios to short-term instruments, is switching. DC style, open pension systems, which allow members to switch between providers – often at will – have to hold more liquid portfolios to accommodate unexpected outflows. This again forces pension funds into shorter-term, more liquid instruments and does not allow for full diversification into longer-term asset classes, and prevents pension funds from having their potential impact on capital market development. Musalem and Pasquini (2012), examining the pension system in 27 countries from 1990-2007, find that: “ _occupational schemes tend to generate higher returns than do personal pension schemes and closed schemes tend to generate higher returns than do open schemes_ .” Evidence of such impact can be found in the United States in the difference between portfolio construction and investment performance of (closed) DB and (open) DC pension funds. There is also a difference in asset allocation between fund type in Australia – with open retail funds showing higher levels of cash and lower exposure to other/ illiquid assets than closed industry and corporate funds. As with US DB vs DC schemes, this translates into higher returns for corporate and industry vs. retail funds.

**Table 1: USA DB vs. DC Asset Mix + Performance**

|**Asset Class**|**Asset Mi**|**x % AUM**|**Returns( **|**see note)**|
|---|---|---|---|---|
|**(ranked by returns)**|**DB**|**DC**|**DB**|**DC**|
|Private Equity|4%|n/a|12.6%|n/a|
|Real Assets|5%|n/a|9.3%|n/a|
|Small CapStock|6%|7%|10.2%|8.4%|
|Employer Stock|0%|21%|n/a|8.6%|
|Fixed Income|31%|10%|6.8%|6.7%|
|Hedge Funds|2%|n/a|7.7%|n/a|
|Stock U.S. Large Cap|26%|30%|6.8%|6.1%|
|Stock Non U.S. or Global|23%|7%|6.7%|6.5%|
|Stable Value/GICs|n/a|17%|n/a|4.9%|
|Cash|2%|8%|2.9%|3.2%|
|**TOTAL**|**100%**|**100%**|**7.9%**|**6.9%**|
|No. of observations|3,083|1,995|||

Source: CEM<sup>21</sup> Notes: Asset Mix 17 years ending 2013 = arithmetic average of annual asset mix weight / Returns 17 years from 1997 to 2013. Returns are the geometric average of the annual averages for each asset class. Hedge funds were not treated as a separate asset class until 2000, so 60% stock 40% bond returns were used as a proxy for 1997-1999.

> 19 See Schwarz and Arias (2014), Price and Rudolph (2013) which raise these issues through also stress that the main driver behind the second pillar reversals was fiscal pressures.

> 20 Continuing the airplane analogy, without them Blake et at (2008) describe managing pension funds as the equivalent of worrying about air turbulence on a flight without considering whether the plane is actually going to reach its destination. 21 Data taken from CEM Database, as presented by Mike Heale, presented at the IOPS/AIOS International Seminar on Pension Systems, San Jose, Costa Rica, February 25, 2015.

9
17 accounted for by the relatively high share at' chemicals and energy products in total exports - these products face quotas and special product-specific charges. Eco-labelling requirements are largely responsible for the relatively high share (about 6 percent) of sub-Saharan Africa's non-oil exports that face post-Uruguay Round NTMs. The cost of compliance with these requirements may be relatively low. However, Varangis et. a]. (1993, p.20) suggest that their adoption could have potentially negative mimplications for African tropical trinter exports.

Table 4 provides a less aggregated view of the Uruguay Round's achievement by showing how the pre- and post-Uruguay Round NTM trade coverage ratios should change for selected developing countries. These projections are made under the constant trade value assumiption and, as a result, overstate the imnportance of nontariff measures that will remain after the Round."1 Even so, the imnpact on some countries is dramatic. The NTM coverage ratio for all Sri Lankan exports, for example, - declines from over 50 percent to under 1 percent an even greater reduction occurs within "other manuifactures' (see Table 1 for a list of products included in this group). A dec-line of 40 perentage points or more is. also projected for Bangladesh and Pakidstan. The declines for all of these countries are accounted for by the preponderance of textiles and clothing products in their total exports. V. Policy Substitution T'he impending elimination of the bulk of NTMs faced by developing countries in industria country markets is a remarkable achievement, notwithstanding the fact that the phase-out will take up to ten years and that agricultural tariffication might in some cases lead to short-tern increases in protection 15Analysis of our underlying trade and trade barrer information indicate that several two-digit SITC chemical products like mineral tars and crude compounds (SfTC _52),_ manufactured fertilizers (SITC _56),_ or plastic materias (SitC _58)_ will have trade coverage ratios ranging from _5_ to 10 percent due to the imposition of quotas and restrivctv licensing regulations. Tariff quotas will apply to over 10 percent of wood manufactures, wlbile nonautomatic licensing regulations will be applied to between _5_ and 12 percent of medicinal product imporbs (SLTC 54) and manufactred fertilizers (SITC _56)._ Tariff quotas and specia product specific charges (lie retaliatoy duties) wHil remain on a few products, but their coverage ratios will not exceed 2 percet Special entry regulations, like eco-labeung requirements, wll continue to be appled to a fay high of some crude material products classified in SITC 2 as well as true, wood and leather manufacur.
# _16_ **_How_** _Fwat_ **_Mar Chine Irdmrbv Grow?_**

doubled between 1978 and 1987, imply still larger increases in the _mumsinal_ prices paid even by high-priority energy consumers in China's industrial economy, Even where energy prices have not risen steeply, reports suggesting widespread energy rationing point to the binding nature of energy constraints across much of Chinese energy. Under these conditions, managers seeling higher financial returns will impute a high opportunity cost to inessential energy consumption even if direct costs are low. It thus seems reasonable to conclude that many Chinese managers feel intense pressure to economize on energy consumption.

Changes in industrial energy productivity can be decomposed into three components: changes in the branch structure of industry that decrease the reladve weight of energy-intensive industries, reductions in energy requirements for producing specific commodities, and changes in the commodity structure of output within individual branches of industry. We have already seen that Chinese industry experienced no significant change in branch structure during the past decade (Table 3).

Review of Chinese publications, which include numerous descriptions of physical input-output coefficients related to energy consumption, sugest only modest gains from reduced unit energy requirements for specific products. Electric power consumed in producing one ton of crude oil or raw coal increased every year during 1980/85 (Energy 1986, pp. 86, 504]. Coal consumption per idlowatt of power turned out by large power plants or per ton of cement produced by major plants declined, but by less than five percent [ibid., 508, _527]._ Since the share of output coming from small plants, which are criticized for their excessive energy requirements, has risen in many branches, the potential for major growth of energy productivity from reduced unit energy requirements for specific products seems very limited.

This leaves structural change within individual branches of industry as the main locus of improved energy productivity for Chinese industry during 1980/85. Tlhis conclusion is problematic because substantial intra-branch restructunng appears limited to a few branches - machinery, chemicals, and perhaps textiles. This outcome draws attention to the possibility that a portion of the productivity gains repot.ed in Table 8 may be attrbutable to measurement error. To explore the possibility of measurement error, and also to investigate the branch pattern of cnange _ip._ sarmgy productivity, we turn to an examination of energy data for 15 branches of Chinese industry during 1980/85.

Table 9 pnsents energy consumption data for 15 branches during 1980/85. Panel B uses these figures to derive annual percentage changes in energy productivity (GVIO/E, where E represents energy consumption in terms of standard coal) for 15 branches. These data conta a number of improbable items: can we believe, for example, that energy productivity in food processing rose by 27.3 percent during 1984/85, or that energy productivity in machine-building rose by 21.1 percent in the same year?
had been serving farmers for 9 months while the oldest had been irrigating for 42 months at the time of the survey (Figure 1). 41 of the 50 systems were less than two years old and only one system was older than three years.

This study therefore presents an early assessment of the functioning and the impact of JCLISs. There has not been a drought year since most of these systems started working. Nor have they been used during the Kharif season critical for the household food security of farmers, since it was a good monsoon year. Norms around water allocation, water fees, and fee collection, and resource mobilization for repair and maintenance of system water sources, pumps, panels, and distribution networks are still evolving. Only a few instances of physical or technical breakdown in the sampled irrigation systems have taken place, and the water user groups managing them therefore have not experienced stresses that would test their solidarity, discipline, initiative, and resourcefulness.

**Figure 1. The frequency distribution of months since the sampled JOHAR systems started irrigating farmers' fields (as of 1 September 2021)**

<!-- Start of picture text --> 20<br>12<br>9<br>5<br>4<br><1 year 12-18 months 18-24 months 24-30 months 30-42 months<br><!-- End of picture text --> _Source: Created by authors using primary data collected from system operators_ 47 of the 50 systems in the sample are powered by solar energy. The remaining three use diesel pumps. Each solar pump was connected to 15-16 solar panels with a total capacity of 4.7 to 5.6 kilowatt (kW). The capacity of the three diesel pumps varied between 5 and 8.5 horsepower (hp). 44 of the 50 system operators knew the design command area of their system. The average design command area of JCLIS in our sample is 13.7 acres (95 percent confidence interval: 11.4 to 16.0 acres) and a typical system benefits between 15 and 18 farmers. According to the operators' report, 9
vestment debt inflows and outflows also appear to be highly correlated, presumably due to financial arms of large corporates in such countries, while public sector inflows are broadly negatively correlated with other inflows.

EMs do not display correlations as strong as those of AEs at this level of disaggregation, but it is still easy to see that the strongest positive correlation is that of other investment debt outflows of banks with bank inflows in either portfolio debt or other investment debt. An interesting feature of the emerging markets panel is that outflows of public other investment debt have a strong negative correlation with inflows of other investment to banks. This suggests that there is more to understand about the relationship between the banking sector and the public sector, particularly when it comes to EM capital flows.

# **4.2 Panel Regressions: Capital Inflows by Sector**

We next examine the of sectoral to risk measured response capital inflows global appetite, by the VIX (push factor), and to the domestic business cycle, measured by countries’ own GDP growth (pull factor), in a panel regression setup with our quarterly data. We focus on a very simple specification to illustrate our results: Our dependent variable is capital flows as a percent of GDP. _INFLOWit_ is a measure of capital inflows for country _i_ in quarter _t_ . We examine inflows by capital flow type as well as by sector. _GDPit_ is quarterly GDP from Datastream and national sources. The dependent variables are capital flows expressed as a percent of GDP. _αi_ is a country fixed effect.

28
**_Figure 3: Private Returns to Education in Mozambique_**

<!-- Start of picture text --> 250%<br>200%<br>150%<br>100%<br>50%<br>0%<br>EP1 EP2 ES1 ES2 Technical Technical Teacher Higher<br>(Grades 1-5) (Grades 6-7) (Grades 8-10) (Grades 11- Training 1 Training 2 Education Education<br>12)<br><!-- End of picture text --> Source: World Bank (2008b) based on data from Mozambique’s household survey ( _IAF_ ) in 2003.

Working in Mozambique is not, however, the only option for the skilled. The low availability of skills locally is also a consequence of skilled emigration from Mozambique. Table 1 shows that as of 2000, university-educated Mozambicans abroad represent 45% of all university educated citizens living in Mozambique. This rate is huge compared to other SADC countries, and is only smaller than that for the island-state of Mauritius.<sup>6</sup> In contrast, migration rates for secondary-educated and primary-educated Mozambicans are more in line with those for other SADC countries. Thus, emigration from Mozambique is highly skewed towards university-educated individuals, which are likely to include professionals. It is likely that much of the emigration took place in the period of civil unrest. But if these rates of emigration are continuing, then that is because the high returns to tertiary education in Mozambique are still lower than those abroad.

Note that Table 1 refers to Mozambican emigrants working in OECD countries. Also note that the emigration rate is given by the stock of Mozambican emigrants with tertiary education degrees (whose total number is 10,696) in 2000 as a proportion of the total labor force in Mozambique with tertiary education. South Africa is also a significant destination for Mozambicans. The South African population censuses of 1996 and 2001 report the presence of, respectively, 1,513 and 2,788 Mozambicans with tertiary education working in South Africa.

> 6 The SADC countries are Angola, Botswana, the Democratic Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, the Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe.

14