rafmacalaba/datause-encoder-data huggingface dataset:
- (iii) If the quality of labor needed increases with the size and value of the capital equipment, larger enterprises would be offering a higher wage to attract a pool of applicants in which workers of potential quality are better represented.

In sum, while the wage-efficiency relationship operates generally as an independent factor influencing wages, it is partly not separable from the enterprise size effect, in so far as it is stronger in larger firms.

# **4.6 Increasing Returns to Scale and Profit Sharing**

Another route through which enterprise size could affect the level of wages is through the production side. Generally firms with higher capital intensity would have higher value added (and rent) per worker. If there is increasing returns to scale then we would expect to see labor productivity increase with enterprise size even after controlling for the increase in capital intensity. Such a relationship might exist because of technological factors, or imperfections in the markets for products or other inputs. In any event increasing returns to scale provide an opportunity for value added per worker to increase in such a way that the surplus after payments to factors of production increase with size. This surplus is potentially available for sharing between profits and wageseither through bargaining or a cooperative solution.

In the work of Teal discussed earlier the variable 'profits per worker' does show up as a significant explanatory variable in the earnings function, along with the 'log of enterprise size' and 'unionization'. Profit sharing thus exists as an independent effect in the Ghana manufacturing sector. At the same time production function analysis for a sample of firms in the sector showed that value added per worker increased strongly with enterprise size, even after we have controlled for the increase in mechanization as measured by the capital-labor ratio (Mazumdar 1994). The increasing returns to scale which this relationship implies derives both from technological and product market characteristics. The quantitative importance of such factors in Ghanaian manufacturing can be compared with the results from similar analysis undertaken for selected samples of manufacturing enterprises in other African countries. They are as follows:

**Table 4.3: Elasticity of Value Added per Worker with respect to Capital and Labor**

|__________<br>**|**|**Kenya**|**Zambia**|**Zimbabwe**|**Cameroon**|**Ghana**|
|---|---|---|---|---|---|
|Capital-labor<br>ratio|0.24|0.18|0.31|0.28|0.23|
|Employment<br>size|0.09|0.19|0.10|0.17|0.05|

_Source: generatedfrom Mazumdar (1994)_ 26
measure cases in which conflict exists, but is held in check by the presence of a UNPKO, so that battle deaths fall below the threshold for conflict. The second is a dummy variable equal to one if at least 5 percent of the population of a country is involuntarily displaced as refugees across international borders, and zero otherwise. This measure is intended to capture episodes of conflict that may not cross the threshold of battle deaths but nevertheless are sufficiently severe to cause significant involuntary displacement of people.<sup>11</sup> The threshold of 5 percent is somewhat arbitrarily selected, and corresponds to the 99th percentile of all non-zero refugee observations in the data.

Figure 3 helps to visualize the three criteria we use to identify conflict, using the Central African Republic as an example. The dotted lines show the number of battle deaths and the ratio of displaced population as a fraction of the population of the country of origin. The horizontal lines represent the thresholds for battle deaths and total displacement ratio. Whenever the dotted line is above the corresponding horizontal line, or there is a UN peacekeeping mission, the corresponding year is coded as a conflict year.

Figure 4 displays the contribution of individual criteria to identifying conflict country-year observations in our full dataset. Over half of the conflict country-year observations are captured only by the battle deaths criteria, while just 6 percent are captured only by the refugees share of total population (by country of origin). The UN Peacekeeping Mission presence criteria account for a further 19 percent of the conflict-year observations while the remaining 24 percent are identified jointly by at least two of the three criteria.

We next translate these annual country-year observations on conflict into “conflict episodes” that constitute our estimation sample.<sup>12</sup> We are primarily interested in predicting the onset of conflict, rather than its duration. We therefore identify conflict episodes as the first year in which one of the three indicators signals conflict. In many countries, conflict years are immediately preceded by other conflict years. To make sure that only distinct episodes of conflict are identified, we drop conflict episodes that are preceded by conflict in any of the previous three years.<sup>13</sup> Non-conflict episodes are defined as non-overlapping periods of five consecutive years in which none of the three events related

> 11 Data comes from UNHCR Population Statistics. Data on IDPs are published by the Internal Displacement Monitoring Centre (IDMC) and have various challenges in collection, hence we do not include these data in the analysis. For an in depth discussion please see World Bank (2016)

> 12 This methodology is similar to Kraay & Nehru (2006), who define episodes of debt servicing difficulties in a analogous way.

> 13 An important point of the World Development Report 2011, was that modern violence comes in various forms and repeated cycles.

18
# **4.3 Identifying Supra-Regional Trends**

Nearly all the metrics discussed thus far are founded on static concepts. As such, there is an unintended and potentially misleading implication that all industries are equally viable candidates for development, from both regional and supra-regional perspectives. This conclusion could lead a regional policymaker to work toward the recruitment or promotion of a targeted industry that simply might not be competitive in a given region. One broad brush approach that has allows the decomposition of changes in industrial structure into components that reflect national (supraregional) trends, industry mix, and region-specific characteristics is called Shift-Share Analysis. This method has been in use for decades and owes its longevity to simplicity of implementation, interpretation, and extension.<sup>8</sup> Shift-Share Analysis has been used in a variety of policy contexts, including the identification of regional comparative advantages and other region-specific, relative strengths and weaknesses.

# **_Shift-Share Analysis_**<sup>**_9_**</sup>

Once again, we use regional employment by industry as our indicator of industrial structure, although personal income or output data by industry would be equal informative when these data if and when these data are available. This method, however, requires industrial structure data for two different time periods. While there are numerous extensions, including dynamic shift-share (Barff and Knight 1988) and the incorporation of spatial structure (Nazara and Hewings 2004), the presentation below reflects the method in its most fundamental form.

With data for two different time periods, we use time _t_ − 1 as a reference to the beginning period and time to the ending period. Our industrial structure changes will thus refer to the period spanning time to time . Our variables are defined as follows:

> <sup>_e_</sup> _tr_<sup>- total employment in region</sup> at time

8 More sophisticated methods called structural decomposition analyses have been developed using input-output accounts as empirical foundations. See Lahr and Dietzenbacher (2017) for details.

9 This section draws heavily on Jackson and Haynes (2009), where various issues involved in selecting an appropriate time period for analysis, data development, and methodological extensions are addressed.

31
- [3] ANDERON.S, S.ERTAC, U.GNEEZY, J.A.LIST AND S.MAXIMIANO (2013). “Gender, Competitiveness and Socialization at a Young Age: Evidence from a Matrilineal and a Patriarchal Society.” _The Review of Economics and Statistics_ 95: 1438–1443.

- [4] BECKER.G.S AND H.G.LEWIS (1973) "On the Interaction between the Quantity and Quality of Children." _Journal of Political Economy_ 81(2): S279‐S288.

- [5] BENABOU.R AND J.TIROLE (2011) "Identity, Morals, and Taboos: Beliefs as Assets." _The Quarterly Journal of Economics_ 126(2): 805‐855.

- [6] BANDIERA.O, N.BUEHREN, R.BURGESS, M.GOLDSTEIN, I.RASUL, S.GUESCI AND M.SULAIMAN (2015) “Women's Empowerment in Action: Evidence from a Randomized Control Trial in Africa.” Working Paper.

- [7] CARVALHO.L.S, S.H.BARCELLOS AND A.LLERAS‐MUNEY (2014). “Child Gender and Parental Investments in India: Are Boys and Girls Treated Differently?” _American Economic Journal: Applied Economics_ , 6(1): 157‐189 _._

- [8] BEYEZA‐KASHESYA.J, S.NEEMA, A.M.EKSTROM, F.KAHARUZA, F.MIREMBE AND A.KULANE (2010) “"Not a Boy, Not a Child": A Qualitative Study on Young People's Views on Childbearing in Uganda.” _African Journal of Reproductive Health_ 14(1): 71‐81.

- [9] BERGE.L, I.O.K.BJORVATN, A.J.G.PIRES AND B.TUNGODDEN (2015). “Competitive in the Lab, Successful in the Field?” _Journal of Economic Behavior & Organization_ , 118: 303–317.

- [10] BERTRAND.M (2010) “New Perspectives on Gender.” in _Handbook of Labor Economics_ , O. Ashenfelter, and D. Card, eds. Amsterdam: North‐Holland, 1545 – 1592.

- [11] BOOTH.A.L AND P.J.NOLEN (2012). “Choosing to Compete: How Different are Girls and Boys?” _Journal of Economic Behavior & Organization_ , 82: 542–555.

- [12] BREER.P.E AND E.A.LOCKE (1965). _Task Experience as a Source of Attitudes_ . Homewood, IL: The Dorsey Press.

- [13] BRIM.O.G (1958) "Family Structure and Sex Role Learning By Children: A Further Analysis of Helen Koch's Data." _Sociometry_ 21: 1‐16.

- [14] BUSER.T, L.GEIJTENBEEK AND E.PLUG (2015) “Do Gays Shy Away from Competition? Do Lesbians Compete Too Much?” Working Paper No. 9382. Institute for the Study of Labor (IZA).

- [15] BUSER.T, M.NIEDERLE AND H.OOSTERBEEK (2014) “Gender, Competitiveness and Career Choices.” _Quarterly Journal of Economics_ , 129 (3): 1409‐1447.

- [16] CAMERON.L, N.ERKAL, L.GANGADHARAN AND X.MENG (2013). “Little Emperors: Behavioral Impacts of China's One‐Child Policy”. _Science_ 22 339(6122): 953‐957.

- [17] CARDENAS.J.C, A.DREBER, E.VON.ESSEN AND E.RANEHILL (2012). “Gender Differences in Competitiveness and Risk Taking: Comparing Children in Colombia and Sweden.” _Journal of Economic Behavior & Organization,_ 83: 11–23.

11
teachers, and parents are more informed than are national decision-makers in the areas that should be a priority for improving learning quality. Thus, by empowering local authorities to make spending decisions, resources will be allocated more strategically for better quality. Eskeland and Filmer (2002) similarly find that the autonomy of teachers, principals, and parents to make organizational and pedagogical decisions and the participation of parents in schools significantly increased primary school test scores in Argentina. Available reports on TEEP indicate that several aspects of SBM may have been conducive to improved student performance: improved school management through intensive principal and head-teacher training, identification of school-level needs and allocation of resources to those specific needs, greater community attention to schools’ and students’concerns.

Fourth, the study does not examine the distribution of effects across different types of schools, both in terms of specified outcomes – student achievement – as well as unintended effects, such as toll on the principal’s and teachers’ time in community engagement. For example, Berlinski and Galiani (2007) find that Argentina’s decentralization of secondary schools significantly increased test scores overall but decreased scores for schools in poor areas and in provinces with pre-decentralization fiscal deficits. On the other hand, King and Ozler’s (1998) study of School Autonomy, a Nicaraguan SBM program, found that autonomous schools, most of which were catering to deprived areas, yielded results comparable to those of other schools. Unintended effects, or processes that eventually undermine desired effects, may exist as well. The Implementation Completion Report for the TEEP project indicates that, indeed, community engagement demanded that principals and teachers spend considerable time on community relations in addition to their administrative and pedagogical responsibilities, a commitment that several were beginning to find burdensome.

Thus, answers to questions regarding the conditions under which the different SBM models work and which implementation processes are effective are critically important from a policy and program design and implementation perspective. A new evaluation would present an opportunity to study such issues through focusing on such issues as well.

Despite these limitations, this study intends to contribute to the growing body of studies that examine school based management as a tool for improving student outcomes. This study provides an initial case study on the Philippines, an area where SBM has not yet been evaluated. Thus, this analysis suggests there are possible benefits from SBM that may be applicable in the country as a whole.

16
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

|Utility|**Co**<br>**reco**<br>[1]|**st**<br>**very**<br>[2]|**E**<br>[3]|**fficienc**<br>[4]|**y**<br>[5]|[6]|**Eq**<br>[7]|**uity**<br>[8]|[9]|Equity score|**Tot**<br>Efficiency<br>score|**al score**<br>Cost-<br>recovery<br>score|Total<br>score|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|DUWASA|0|0|0|0|0|0|—|1|1|2|0|0|2|
|MWSA|0|0|0|0|0|0|—|1||1|0|0|1|
|NWSC|1|0|0|1|0|0|1|1|1|3|1|1|5|
|eThekwini|1|1|0|0|1|1|—|1|0|2|1|2|5|
|Johannesburg|0|0|1|1|1|1|—|1|0|2|3|0|5|
|Tygerberg|0|0|0|1|1|1|—|1|0|2|2|0|4|
|Drakenstein|0|0|0|0|1|1|1|1|—|3|1|0|4|
|SWSC|0|0|1|0|0|1|—|1|—|2|1|0|3|
|NWSC|0|0|1|0|0|1|1|—|1|3|1|0|4|
|LWSC|0|0|0|0|0|0|—|1|1|2|0|0|2|

_Source:_ AICD WSS Survey Database, 2007.

_Note:_ The utility scores 1 against a specific criterion according to:

- [1] Cost Recovery: O&M cost recovery

- [2] Cost Recovery: Capital cost recovery

- [3] Efficiency: No fixed charge or minimum consumption charge

- [4] Efficiency: Metering ratio is higher than sample average (77%)

- [5] Efficiency: The price of the last block meets the capital cost

- [6] Small piped consumers (at 4 m3) pay less than average piped consumers (at 10 m3) [7] Standpost consumers pay less than small piped consumers (at 4 m3)

- [8] Connection cost as share of GNI per capita is lower than sample average (27%)

- [9] Residential consumers pay less than nonresidential consumers at 100 m3 of consumption

- = data not available.

45
# **Dep. variable: Log consumption per person**

# **Dep. variable: Log income per person**

||**Mode**<br>**OL**<br>**Param.**|**l (1)**<br>**S**<br>**t-stat**|**Mod**<br>**IV**<br>Param.|**el (1)**<br> <br>**t-stat**|**Mod**<br>**OL**<br>**Param.**|**el (2)**<br>**S**<br>**t-stat**|**Mod**<br>**restr**<br>**Param.**|**el (2)**<br>**icted**<br>**t-stat**|**Mod**<br>**OL**<br>**Param.**|**el (1)**<br>**S**<br>**t-stat**|**Mod**<br>**IV**<br>**Param.**|**el**_(1)_<br> <br>**t-stat**|**Mode**<br>**restri**<br>**Param.**|**l (2)**<br>**cted**<br>**t-stat**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Household has electricity|0.2578|27.01|0.2571|26.98|0.2581|27.11|0.25B1|27.10|0.2161|19.63|0.2154|19.57|<br>0.2145|19.51|
|Social capital index|-0.0043|_-0.12_|-0.0083|-0.23|0.0210|<br>0.41|0.0190|0.48|0.0041|0.14|0.0018|0.06|0.0573|1.63|
|lnfTastructure capital index|0.1384|4.70|0.1299|441|0.1201|2.76|0.1360|4.61|0.1413|4.91|0.1334|4.66|0.1371|4.77|
|Commercial capital index|0.1041|3.33|0.1072|3.43|0.1210|2.86|0.1231|3.76|0.1159|4.14|0.1185|4.21|0.0997|3.17|
|Diversity of employment|0.0597|3.69|0.0509|3.75|0.0437|2.46|0.0406|2.31|-0.0618|-3.17|-0.0602|-3.08|-0.0853|-4.20|
|_Shock_**_variables:_**|||||||||||||||
|Labormarketshock:S(L)|-0.1190|-9.29|-0.0141|-0.25|0.1070|1.91|0.1238|2.49|-0.1657|-12.19|-0.0454|-0.84|0.1072|2.24|
|El Nirtoshock: S(E)|-0.0502|-5.11|-0.0639|-2.71|-0.0481|-1.35|-0.0351|-1.32|-0.0756|-6.90|-0.0918|-3.72|-0.0930|-3.18|
|Joint lab. Market-EI Nito shock: S(LE)|-0.0924|-7.54|-0.0204|-0.84|0.0337|0.70|||-0.1468|-11.96|-0.1056|-4.49|||
|S(L)Avg. years of educationi of adult houschold<br>members|||||-0.0202|-4.31|-0.0215|-4.93|||||-0.0217|-4.70|
|S(L)*No of male adults (at least IS years)|||||-0.0167|-1.60|-0.0113|-1.19|||||||
|S(L)*No of female adults (at least 15 years)|||||-0.0143|-1.33|-0.0147|-1.45|||||-0.0226|-2.01|
|S(L)'Diversity of employment|||||0.0253|1.56|0.0263|1.63|||||||
|S(L)*Member of a cooperative or NGO|||||-0.0411|-0.98|||||||||
|S(L)'Owns land|||||-0.0022|-0.05|||||||-0.0957|-1.80|
|S(L)*Social capital index|||||0.0358|0.55|||||||||
|<br>S(L)*lnfrastructure capital index|||||0.0359|0.57|||||||||
|S(.)'Commercial capital index|||||-0.1382|-2.16|-0.1092|-2.54|||||-0.0767|-1.82|
|S(E)*Avg. years of education of adult household<br>members|||||0.0027|0.95|||||||||
|S(E)*No of male adults (at least 15 years)|||||-0.0083|-1.17|||||||||
|S(E)*No of female adults (at least 15 years)|||||0.0015|0.19|||||||||
|S(E)'Diversity of employment|||||0.0257|2.34|0.0287|2.96|||||0.0408|3.78|
|S(E)*Member of a cooperative or NGO|||||-0.1098|-3.91|-0.0877|-3.75|||||||
|S(E)'Owns land|||||-0.0356|-1.49|-0.0395|-1.77|||||-0.0782|-2.93|
|S(E)*Social capital index|||||-0.0855|-1.46|-0.0691|-1.58|||||-0.1034|-2.18|
|S(E)infrastructure capital index|||||0.0077|0 15|||||||||
|S(E)*Commercial capital index|||||0.0347|0.64|||||||0.0620|1.62|
|S(LE)'Avg years of education of adult household<br>|||||-0.0108|-2.90|-0.0095|-3.53|||||-0.0184|-6.67|
|members|||||||||||||||
|S(LE)*No of male adults (at least 15 years)|||||-0.0087|-1.07|||||||||

29
# **4.2 Structural VAR specification**

Since my time series is short relative to the number of parameters, I consider the following panel vector auto-regression(PVAR) model for _Xi,t_ = [ _Ti,t, Gi,t, Yi,t_ ]<sup>_′_</sup> , after taking out country specific deterministic time trend and demeaning _Xi,t_ : This specification allows me to pool estimates of the lag coefficient matrices in the VAR and hence to reduce the total number of parameters estimated, while still having country-specific reduced form shocks.<sup>2</sup> Let _ui,t_ = [ _u_<sup>_T_</sup> _i,t_<sup>_, uG_</sup> _i,t_<sup>_, uY_</sup> _i,t_<sup>] be reduced-form shocks</sup> and _εi,t_ = [ _ε_<sup>_T_</sup> _i,t_<sup>_, εG_</sup> _i,t_<sup>_, εY_</sup> _i,t_<sup>] be structural shocks, the following system is estimated for each</sup> country i: Here the structural errors satisfies _E_ [ _εi,t_ ] = 0 and _E_ [ _εi,tε_<sup>_′_</sup> _i,t_<sup>] =</sup><sup>_I_.</sup> I discuss the PVAR identification strategy proposed by Mertens and Ravn(2014) for this system, which is based in part on assumptions from Blanchard and Perotti(2002). In the later, identification involves imposing certain values on some parameters in the system above. First and foremost, because of decision and recognition lags, it is presumed that discretionary responses of spending to cyclical movements of output is unlikely, at least at quarterly frequency. Hence, _γY_ = 0. Second, one remains agnostic about whether spending or tax decisions come first. This means one of _θG_ and _γT_ is set to zero and the other can be estimated. It turns out that the answer doesn’t depend much on which one is set to zero and I have _γT_ = 0. Finally, _θY_ , the elasticity of tax revenue with respect to output, is calibrated to OECD estimates(See Giorno et al.(1995) for details of the calibration procedure). For instance, _θY_ is set to 2.08 for the case of the United States. The following decomposition is used in the calibration of _θY_ :

> 2Panel VAR estimation is carried out with Abrigo and Love(2015)’s package in STATA.

17
More generally, the observed increase in internal migration in recent years may also be attributable in part to the emergence of a young and growing population in Vietnam that enjoys greater freedom and a larger array of economic opportunities than earlier generations. As of 2004, over one-fifth of the population was aged less than 15 years, and the four to 19 age-group grew by over 10% between 1989 and 1999. This yielded annually about 1.5 million new entrants for the labor force over this period. During the 1990s, agriculture was central to job creation for the growing labor force. The set of policy initiatives associated with land reform, trade liberalization, and the promotion of the household sector were crucial in providing conditions for robust growth in the agricultural sector, and the resultant improvement in the living standards of rural households (World Bank, 1998; Benjamin and Brandt, 2004). However, the gains from correcting these distortions were not sustainable given the agricultural sector’s inability to absorb the growing labor force and sustain the type of poverty reduction witnessed in the early 1990s (Van de Walle and Cratty, 2004; World Bank, 2006). The share of agriculture in total employment declined from more than two-thirds in 1990 to around 58% in 2004. The underemployment rate has also shown a tendency to increase in rural areas with GSO (2006) recording an average rate of 25% in recent years.

In contrast, the emergence of a vibrant private sector, which was given impetus by the introduction of the Enterprise Law of 2000, created new wage employment opportunities in urban areas.<sup>1</sup> During the period 1993-2002, the average real wage rate grew rapidly by an average rate of 12% per annum with the strongest growth observed in urban areas, especially in the Southeast region (Pham and Reilly, 2007).

3