rafmacalaba/datause-encoder-data huggingface dataset:
encourage cooperative behavior as countries attempt to deal with such environmental changes through coordination.

# <u>4.1.3.2.</u> _<u>Geography</u>_

The physical attributes of shared rivers have long been recognized as important to understanding conflict and cooperation over transboundary water (e.g. Le Marquand 1977; Toset et al. 2000; Brochmann and Hensel 2009; Brochmann and Gleditsch 2012). In particular, empirical studies considering the river basin as the unit of analysis find that the greater the significance of the particular basin to the respective countries the higher the cooperation evinced. In addition, the more control a country has over a given river basin the less cooperation (or more conflict) is evinced because that country perceives more benefits from unilateral action. (Espey and Towfique 2004; see also Gleditsch et al. 2006: 369).

Since we consider the river basin as the main unit of analysis, we adopt a rationale and methodology similar to Espey and Toufique (2004). We expect less cooperative behavior in cases where the percentage of the basin within the boundaries of one country is higher compared to the other country. On the other hand, we expect more cooperative behavior in cases where the river basin comprises a larger percentage of the countries’ territory.

We derive two geographic measures to proxy for the level of control a given country in the dyad and the entire country dyad has over the basin as well as two similar measures proxying for the importance of the basin to the individual country as well as the country dyad. The proxy for contol is derived by calculating the percentage of the total river basin within the boundaries of each country while the proxy for importance is derived by calculating the size of the river basin within the country as a percentage of the total area of the country. Two numbers are then derived for that dyad, a ratio derived value and a sum derived value, so as to gauge asymmetry and country dyad-wide nuances. The variables are derived and calculated based on OSU’s International River Basin Register.

# <u>4.1.3.3.</u> _<u>Governance and democracy</u>_

Based on the Democratic Peace Theory, scholars have claimed that regime type should also matter for explaining conflict and cooperation over the environment. Neumayer (2002a), for example, 13
# **Table 9. Development stage and environmental effects**

||**Lgro**<br>|**wth**|**LPgr**<br>|**owth**|
|---|---|---|---|---|
||**Coef**|**se**|**coef**|**se**|
|Outage|**-0.0004**|0.007|**-0.015**|0.014|
|Outage * Poor (i.e., low-income countries)|**0.001**|0.015|**0.106***|0.042|
|Overdraft facility|**0.017***|0.008|**0.048****|0.016|
|Overdraft facility * Upper Middle (i.e., upper-middle and above)|**-0.015**|0.012|**-0.059***|0.023|
|Trade credit|**-0.011**|0.013|**0.071****|0.023|
|Trade credit * Upper Middle|**0.0004**|0.015|**-0.062***|0.027|
|Trade credit * Poor|**0.018**|0.019|**-0.090***|0.039|
|Capacity agglomeration|**0.044***|0.018|**-0.010**|0.028|
|Capacity agglomeration * Poor|**-0.007**|0.033|**0.144***|0.062|
|Controls in Table 4 panel B|||||
|N|54,|960|44,|279|
|Country, industry, year FEs|Y|es|Y|es|
|adjusted R-squared|0.1|31|0.2|86|

*, ** represent statistical significance at the 5 and 1 percent levels. Heteroskedasticity-corrected standard errors clustered at the country level in columns indicated by “se”.

Those variables without significant interaction terms with the two income dummies have similar coefficients as in Table 5 and are not reported.

43
draw resources from and compete with the formalized business sector (this has been categorized as the ‘parasitic view’)<sup>4</sup> ; by contrast, a competing view is that widespread informality represents a ‘segmentation’ between a formal labor market, the business environment they experience, and their informal analogs (Perry et al 2007; Maloney 2007; Kanbur, 2017; Ulyssea, 2020). Understanding the estimates and nature of informal sector businesses informs policy, particularly considering the absence of consistently effective policies to address informality, e.g., the limited success of formalization efforts (see Bruhn and McKenzie, 2014; Floridi et al 2020).

Existing methodologies, such as household-based sampling, are not designed to measure informal sector businesses directly. Such methods often do not capture informal sector businesses at their point of business or operation; when they do, businesses are reached via the places of employment of household members and, consequently, they are unable to estimate business density in a geographical area. Likewise, informal sector businesses are, in all but a few cases, excluded from economic censuses, which tend to be infrequent and expensive. Informal sector businesses, virtually by definition, are also not reached by official surveys proceeding from administrative sampling frames. By contrast, the proposed ACS method reaches units at their point of operation and is designed to measure informal sector businesses _per se_ ; this paper builds upon existing ACS literature and shows that this method not only provides a precise, unbiased population estimate but can also provide practitioners with large fieldwork efficiency gains when compared to other methods.

However, the ACS method does bring trade-offs and challenges. As noted above, to operationalize such a survey, a clearly delimited population of inference is needed. In this case, this consists of un-registered business activity, within a delineated geographic area (e.g., a set urban area). This implies that informality in non-covered locations, rural or excluded cities, will not fall within the purview of these surveys. The approach also implies that portions of labor and fiscal informality do not fall within the coverage of the survey, an argument to include potentially sensitive topics on the informal activities of formally registered firms in traditional firm-level surveys.<sup>5</sup> Practitioners should note that such a methodology, unlike household- or labor-surveylinked methods, is not designed to measure household activity; as such, additional care must be

> 4 See La Porta and Shleifer (2014) for a discussion.

> 5 The authors, for example, are also experimenting on different ways to ask for these sensitive topics in the Enterprise Surveys of the World Bank.

4
15

# **Table 5. Full Results**

|**Estimates forgraduate(1 =graduate, 0 = non-gr**|**aduate)**||
|---|---|---|
||**Logit**|**Probit**|
|GNIper capita/operational cutoff|0.091***|0.088***|
||(4.94)|(5.14)|
|Logofyears over cutoff - capped at 9|0.122***|0.108***|
||(5.10)|(5.58)|
|Logofyears above cutoff in excess of 9|−0.033**|−0.035|
||(−2.45)|(−2.44)|
|Creditworthiness|0.006***|0.006***|
||(4.65)|(5.14)|
|Povertyheadcount|−0.332|−0.383**|
||(−1.54)|(−1.97)|
|Logofpopulation|0.057***|0.051***|
||(4.91)|(5.13)|
|Logof life expectancy|0.694***|0.682***|
||(3.09)|(2.96)|
|Urbanpopulation share|0.422**|0.413**|
||(2.49)|(2.54)|
|Institutional development|0.465*|0.516**|
||(1.80)|(2.31)|
|Resource rents/GDP|−0.017|−0.024|
||(−0.08)|(−0.11)|
|Logof OECD GNIper capita/cutoff|1.383***|1.338***|
||(7.32)|(7.36)|
|Povertyheadcount ×pre-1999|0.44|0.543|
||(1.57)|(2.08)|
|Logof life expectancy×pre-1999|−0.022|−0.026|
||(−0.41)|(−0.52)|
|Urbanpopulation share ×pre-1999|0.281|0.28|
||(0.81)|(0.88)|
|Institutional development ×pre-1999|−0.038|−0.049|
||(−0.15)|(−0.21)|
|Resource rents/GDP ×pre-1999|0.924**|0.835**|
||(2.06)|2.49|
|Angola dummy|0.126|0.149|
||(1.19)|(1.56)|
|_Number of observations_|_705_|_705_|
|_Number of countries_|_65_|_65_|
|_pseudo-R_<sup>_2_</sup>|_0.78_|_0.78_|
|_Mean dependent variable_|_0.27_|_0.27_|
|_Correctly predicted(%)_|_94.9_|_94.5_|

_Note:_ t-statistics (in parentheses, beneath marginal effects) are adjusted for clustering by country; * indicates significance at the 10 percent level, ** at the 5 percent level, and *** at the 1 percent level for two-tailed tests.
_Table 2. Sample and project area by district compared to South Punjab_

|**District**|**N**|**umber of s**|**chools**|**Prim**|**ary enrol**|**lment**|
|---|---|---|---|---|---|---|
||**South**<br>**Punjab**|**PHCIP**|**Analytical**<br>**Sample**|**South**<br>**Punjab**|**PHCIP**|**Analytical**<br>**Sample**|
||2,145|473|126|269,425|71,282|17,888|
|**Bahawalnagar**|(13%)|(13.9%)|(14.1%)|(12.5%)|(13.5%)|(13.1%)|
||1,652|400|107|196,742|52,606|14,436|
|**Bahawalpur**|(10%)|(11.8%)|(12%)|(9.2%)|(10%)|(10.6%)|
||1,242|270|72|152,944|39,264|10,023|
|**Bhakkar**|(7.6%)|(7.9%)|(8.1%)|(7.1%)|(7.4%)|(7.4%)|
||1,567|194|45|203,814|31,322|6,967|
|**D.G. Khan**|(9.5%)|(5.7%)|(5%)|(9.5%)|(5.9%)|(5.1%)|
||929|235|63|101,302|33,400|8,677|
|**Khushab**|(5.7%)|(6.9%)|(7%)|(4.7%)|(6.3%)|(6.4%)|
||1,506|454|117|187,575|65,181|15,778|
|**Layyah **|(9.2%)|(13.4%)|(13.1%)|(8.7%)|(12.4%)|(11.6%)|
||750|241|63|110,317|38,601|10,146|
|**Lodhran **|(4.6%)|(7.1%)|(7%)|(5.1%)|(7.3%)|(7.5%)|
||1,201|282|80|144,850|44,758|12,465|
|**Mianwali**|(7.3%)|(8.3%)|(8.9%)|(6.7%)|(8.5%)|(9.2%)|
||1,731|372|96|266,126|67,497|17,512|
|**Muzaffargarh**|(10.5%)|(10.9%)|(10.7%)|(12.4%)|(12.8%)|(12.9%)|
||2,773|165|45|386,011|22,974|5,848|
|**Rahim Yar Khan**|(16.9%)|(4.9%)|(5%)|(18%)|(4.4%)|(4.3%)|
||942|314|80|129,564|60,853|16,441|
|**Rajanpur**|(5.7%)|(9.2%)|(8.9%)|(6%)|(11.5%)|(12.1%)|
|**Total**|**16,438**|**3,400**|**894**|**2,148,670**|**527,738**|**136,181**|

Source: Author’s calculations using the Punjab Annual School Census, 2021.

# **Tools**

The AIM-ECD DA (or AIM-ECD for short) was used to measure early childhood developmental outcomes for children aged 4-6 years old (Pushparatnam et al., 2021; World Bank, 2021). The Teach Early Childhood Education (Teach ECE) tool was used to conduct classroom observations to capture the quality of teaching practices and interactions between teachers and children (Ding et al., 2024; Pushparatnam & Ding, 2021). Each tool is described below.

The AIM-ECD DA is a tool that measures early childhood development through a set of activities that a trained enumerator engages in with a child. The AIM-ECD DA contains items in four critical domains of early development: Early Numeracy, Early Literacy, Executive Function, and Social-Emotional development and shares items from the Measure of Learning and Quality Outcomes Module on Early Learning and Development (MELQO MODEL) as well as the International Development and Early Learning Assessment (IDELA) (Pisani et al., 2018; Raikes et al., 2019).

6
**<u>Figure G1: Unconditional quantile regression of height-for-age z-scores on food security measures, limited controls (Pakistan)</u>**

<!-- Start of picture text --> Per Capita Caloric Availability 1-SSR<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>1-SSEXR DDS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>RIF-OLS 95% Confidence Interval OLS<br>RIF-OLS<br>.4<br>.4<br>.2<br>.2<br>0<br>0<br>-.2<br>-.2<br>-.4 -.4<br>.6<br>.8<br>.4<br>.6<br>.2<br>.4<br>0<br>.2<br>-.2<br>0<br>-.4<br>-.2<br><!-- End of picture text --> 82
# **Table 7. Is the SADC region different?** **<u>The dependent variable is ln(FDI per capita + constant)</u>**

||**The**|**Pooled Sap**|**mle**|**The similar-**|**income sample**|
|---|---|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|ln(schoolingt-1)|0.198|0.200|0.138|0.218|0.065|
||(0.170)|(0.166)|(0.177)|(0.169)|(0.175)|
|ln(phone densityt-1)|0.243**|0.231**|0.261**|0.159|0.121|
||(0.116)|(0.111)|(0.114)|(0.110)|(0.106)|
|the adult share of populationt-1|4.391**|4.456**|2.505|4.914**|3.621*|
||(2.103)|(2.086)|(2.097)|(2.080)|(2.000)|
|urban sharet-1|0.334|0.348|0.145|0.378|0.456|
||(0.614)|(0.610)|(0.616)|(0.653)|(0.642)|
|Ln(population)|-0.123*|-0.124**|-0.009|-0.177***|-0.047|
||(0.063)|(0.063)|(0.068)|(0.061)|(0.064)|
|Relative exchange ratet-1|-0.002*|-0.002|-0.002**|0.003|0.001|
||(0.001)|(0.001)|(0.001)|(0.008)|(0.009)|
|Ln(GDPpct-1)|0.275*|0.268*|0.326**|0.300*|0.376**|
||(0.148)|(0.149)|(0.151)|(0.159)|(0.156)|
|Mean GDPpc growth|0.075***|0.074***|0.072***|0.069***|0.068***|
|in the previous 5 yrs|(0.015)|(0.015)|(0.013)|(0.015)|(0.014)|
|S.D. GDPpc growth|-0.890|-0.860|-1.304*|-0.693|-1.071|
|in the previous 5 yrs|(0.714)|(0.706)|(0.738)|(0.680)|(0.715)|
|Rulelawt-1|0.025||0.036|0.018|0.014|
||(0.056)||(0.055)|(0.057)|(0.058)|
|Control of Corruptiont-1||0.064||||
|||(0.070)||||
|Trade/GDPt-1|||0.702***||0.960***|
||||(0.160)||(0.195)|
|SADC dummy|0.018|-0.011|-0.117|0.049|-0.085|
||(0.305)|(0.306)|(0.286)|(0.287)|(0.255)|
|Number of observations|3,265|3,265|3,187|2,311|2,269|
|Adjusted R2|0.442|0.443|0.455|0.376|0.409|

Note. ***, **, and * represent statistical significance at the 1, 5 and 10 percent levels. Standard errors clustered at the country level. The constant in the dependent variable is 1.4.

29
represent examples of “interlocking” conflict, as did Zimbabwean support for Frelimo and Renamo attacks on Zimbabwean civilians on the eastern border. Violence was not contained inside Mozambique; it also occurred within Zimbabwe and South Africa. Finally, the disruption of regionalized links—South African and Zimbabwean support for their respective allies— facilitated the peace.

Another example of interdependent violent conflict and its consequences for peace making comes from the Sudan–Uganda interaction. Uganda supported southern opponents of the Khartoum government after Khartoum began to support opponents of the Uganda government in northern Uganda in 1986 and subsequently within Sudan (Prunier 2004). With the Comprehensive Peace Agreement of January 2005 that ended the war between southern Sudan and the Khartoum government, security problems in northern Uganda subsided, although the Lord’s Resistance Army opponents of the Kampala government remain active in parts of CongoKinshasa, the Central African Republic, and southern Sudan. From an econometric perspective, to the extent that conflicts end because regional conflicts wind down, there is correlation in the outcomes and, presumably, the error terms among the countries in samples used to study the duration and ending of civil wars.

Congo-Kinshasa (former Zaire and henceforth Congo, when the context is clear) is a major participant in another regional war complex that we call the Great Lakes complex. Table 2 illustrates how four datasets used by econometricians all classify Congo in the 1990s as having experienced civil war. The MGB studies code a civil war based on the concept of “internationalized armed conflict” from 1996 to 2000 as intensity 3 and that from 1997 to 2001 as intensity 1–3. Their coding rule does not indicate whether the years coded as civil war constitute one or more such wars. The other two datasets code two civil wars each. Fearon and 27
Ver ́ıssimo, D., Schmid, C., Kimario, F., & Eves, H. (2018). Measuring the impact of an entertainmenteducation intervention to reduce demand for bushmeat. _Animal Conservation_ , _21_ (4), 324–331.

- Ver ́ıssimo, D., & Wan, A. K. Y. (2019). Characterizing efforts to reduce consumer demand for wildlife products. _Conservation Biology_ , _33_ (3), 623–633.

- Wakefield, M. A., Loken, B., & Hornik, R. C. (2010). Use of mass media campaigns to change health behaviour. _Lancet_ , _376_ (9748), 1261–1271.

- Walelign, S. Z., Nielsen, M. R., & Jacobsen, J. B. (2019). Roads and livelihood activity choices in the Greater Serengeti Ecosystem, Tanzania. _PLoS ONE_ , _14_ (3), e0213089.

- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. _The American Statistician_ , _70_ (2), 129–133.

- Wells, G. J., Ryan, C. M., Das, A., Attiwilli, S., Poudyal, M., Lele, S., Schreckenberg, K., Robinson, B. E., Keane, A., Homewood, K. M., Jones, J. P. G., Torres-Vitolas, C. A., Fisher, J. A., Ahmad, S., Mulligan, M., Dawson, T. P., Adams, H., Siddappa Setty, R., & Daw, T. M. (2024). Hundreds of millions of people in the tropics need both wild harvests and other forms of economic development for their well-being. _One Earth_ , _7_ , 311–324.

- West, S., Haider, L. J., Masterson, V., Enqvist, J. P., Svedin, U., & Teng ̈o, M. (2018). Stewardship, care and relational values. _Current Opinion in Environmental Sustainability_ , _35_ , 30–38.

- WildAid. (2020). 2019 Annual Report. https://wildaid.org/wp- content/uploads/2020/06/ WildAid ~~A~~ nnual-Report-2019.pdf

- WildAid. (2021). Understanding Urban Consumption of Bushmeat in Nigeria. https://wildaid. org/wp-content/uploads/2021/01/Nigeria-Bushmeat-Consumption-Survey-Report.pdf

39
79 EUROPE AND U.S.S.R. Population projection (thousands) SunMry for 25 Year Periods

|I . .......<br>|.......................<br>||..<br>...........|......|......................||||
|---|---|---|---|---|---|---|---|---|
|AGE GROJP|1985|2000|2025|2050|2075|2100|2125|2150|
|. .............................||.....|........<br>...........||....................||||
|TOTAL M+F|770751|820747|863674|865251|870352|878531|880799|881371|
|MALES|||||||||
|0-4|28951|27846|27972|28134|28234|28256|28256|28256|
|5-9|28985|28412|27905|28070|28198|28238|28236|28235|
|10-14|29297|28853|27926|28029|28164|28223|28220|28219|
|15-19|30121|28625|27758|<br>27991|2S109|<br>28193|28192|28193|
|20-24|32320|28678|27512|27880|28024|28144|28149|28151|
|25-29|31566|28896|27385|27659|27928|28081|28103|28103|
|30-34|29175|29635|27910|27557|27837|28017|28057|28055|
|35-39|25766|31695|28217|27490|27736|27937|27996|27993|
|40-44|21320|30778|27823|27202|27610|27812|27895|27894|
|45-49|24681|28129|27610|26752|27337|27580|27698|27703|
|50-54|21388|24369|27329|26209|26769|27160|27308|27330|
|55-59|20722|19451|27082|25887|25965|26404|26575|26613|
|60-64<br>|15260|21097|27160|24735|24654|25116|25299|25352|
|65-69|9735|16666|23712|22186|22398|23050|23219|2328|
|70-74|10033|13844|18292|18827|19038|19832|20009|20095|
|75,+|12162|14072|22202|29659|30467|31949|32559|32779|
|TOTAL|371482|401046|423795|424268|428469|433991|435m|436260|
|FEMALES|||||||||
|0-4|28002|26861|26847|26891|26889|26892|26892|26892|
|5-9|27769|27543|26879|26888|26892|26892|26890|26889|
|10-14<br>|27873|28094|26975|26897|26896|26890|268s8|26888|
|15-19|28900|27936|26886|26922|26892|26885|26885|26885|
|20-24|31027|27711|26757|26901|26879|26877|26881|26883|
|25-29<br>|30552|27795|26770|26793|26864|26874|26878|26878|
|30-34|28534|28791|27434|26813|26855|26873|26872|26871|
|35-39|25483|30842|27935|26878|26847|26865|26860|26857|
|40-44|21518|30254|27698|26736|26839|26837|26830|26830|
|45-49|25529|28073|27335|26506|26747|26765|26763|26768|
|50-54|2277|24813|27175|26325|26497|26627|26638|26643|
|55-59|24159|20580|27711|26625|26250|26383|26402|26404|
|60-64|20948|23652|28852|26482|25833|25946|25966|25963|
|65-69<br>|14443<br>|20041<br>|26846<br>|25115|24771|25083|25085|25083|
|70-74|16679|19217|22582|22745|22783|23301|23323|23327|
|75.|25076|27496|35199|45465|47149|48549|48973|<br>49052|
|TOTAL|399269|419700|439879|440983|441883|444539|445027|445112|
|**BIRTH**<br>RATE||14.4|13.2|12.8|12.8|12.7|12.6|12.6|
|DEATH RATE||10.2|11.2|12.8|12.6|12.3|12.5|12.6|
|NET MIGRATI|ON<br>RATE|.1|.0|.0|.0|.0|.0|.0|
|GROWTH<br>RATE||.42|.20|.01|.02|.04|.01|.00|
|TOTAL FERTIL<br> <br>|ITY<br>|1.963<br>|2.008|2.069|2.066|2.063|2.063|2.063|
|**e(O)**<br>**-** BOTH|SEXES|73.79|75.63|77.28|78.75|79.59|79.60|79.60|
|**I-**<br>BOTH S|EXES|20.0|14.1|9.7|6.8|5.3|5.3|5.3|
we developed our pseudo-random shocks to crop yields by first estimating the variancecovariance matrix of crop yields (C) across regions and crops. Then, we used the Cholesky decomposition to obtain a lower triangular matrix, _U_ , where C= UU<sup>T</sup> . Pre-multiplying a matrix of independent, standard normally distributed shocks obtained from a random number generator by the lower-triangular matrix _U_ created a new matrix with variance-covariance matrix C.

Domestic output shocks are only indirectly related to whether the Q-SSM will be triggered because the change in imports resulting from the shock depends on a number of other factors. To translate the changes in domestic productivity into changes in imports and, from there, duties and domestic prices, we use the GTAP model into which we feed the set of simulated productivity changes. The imperfect substitution between imported and domestic goods in this model reduces the magnitude of the import shocks relative to those expected in a model with perfect substitutes, making our results conservative estimates of the measured impacts of the measure.

# **Macro model**

The initial part of the analysis was undertaken using version 8 of the GTAP global CGE model originally described in Hertel (1997). This model allows us to simulate the implications of changes in agricultural yields (productivity) for numerous variables of importance such as domestic prices and wage rates. To facilitate the analysis, we run the Monte Carlo simulations, with the 57 available commodities aggregated into six—maize, wheat, rice, soybeans, other food, and the rest of the economy. We focus on these commodities because they account for a large fraction of global calorie consumption, because their output is frequently variable, and because it seems likely that they would loom large in the use of the Q-SSM given the sensitivity of these products to policy makers. We aggregate the available 129 countries and regions into 32 blocs used to represent our sample countries and the rest of the world.

In each of our simulations, we assume a short-run closure where the allocation of land, labor and natural resources to each commodity is fixed. The reason for this assumption is that the size of the SSM response will generally become known only when most agricultural input decisions have already been taken and little can be done to adjust output. Once a crop has experienced a poor harvest, the level of the resulting import surge is determined simultaneously with the corresponding duty change.

9
# **1. Introduction**

The Indian economy has undergone remarkable changes since the early 1980s. India's GDP per capita grew at an annual rate of 3.8% during the 1980-2000 period as compared to 1.7% during the 1950-80 period. Much of this expansion is attributed to the services sector which grew at 5.1% per annum (in per capita terms) increasing its share in GDP from 38% in 1980 to 49% in 2000. Some of the key development policy questions today are whether India's improved performance is sustainable and whether other developing countries can emulate India. While growth is a complex and elusive phenomenon, it may be helpful to identify specific factors that help explain India's growth pattern.

The present paper focuses on the role of the highly skilled. Using panel data for the fourteen major states of India over the 1980-2000 period, we examine the performance of per capita GDP and value added in agriculture, manufacturing (registered and unregistered) and services. Controlling for other factors, and using the system-GMM and the traditional instrumental variable methods to address endogeneity concerns, we find that greater availability of skilled workers had a statistically significant and positive impact on per capita output of the aggregate services sector, but no significant impact on agriculture, and more surprisingly, no impact on manufacturing. One reason for the much stronger impact on services could be that, relative to manufacturing sectors, the service sectors are more skill intensive and therefore more likely to benefit from an expansion in human capital. We test for this idea using data at the disaggregated level (within services and registered manufacturing sectors) and find strong evidence in its favor.

That skilled labor has played an important role in India's overall performance and especially so in the services sector has provoked much discussion but only limited 1