rafmacalaba/datause-encoder-data huggingface dataset:
# **III. Context: Sectoral Reallocation of Resources and Enterprise Dynamics**

Over the past thirty years since 1980, there have been significant changes in the underlying structure of the Sri Lanka economy from agriculture to manufacturing and services. In terms of value added, manufacturing has remained roughly constant at 29 percent of GDP, while the value addition of agriculture has declined by 17 percentage points to 12 percent as the services sector has increased its share to 59 percent of GDP (Figure 1, panel A). The proportion of the largely non-traded services sector in Sri Lanka is large even by South Asian standards, where the size of the services sector stands out relative to other developing regions of the world. Services-led growth has become more pronounced in recent years putting it closer to the composition of economic growth in OECD countries than other developing countries. Over 60 percent of real GDP growth recorded from 2000 is accounted for by growth in the services sector (Figure 1, panel B).

Sectoral reallocation from agriculture to manufacturing has played an important but less significant role in Sri Lanka’s labor productivity growth than for other South Asia economies. Over the past several decades, Sri Lanka has not benefitted from sectoral resource reallocation to the same degree as its South Asian neighbors. Table 1 provides estimates of economy-wide productivity enhancements from sectoral reallocation based on national accounts data. The contribution of sectoral reallocation to overall growth is given by: where GDP1980−2008 is the annual growth rate of GDP from 1980 to 2008, ki,1980 is the share of employment in sector _i_ (indexed by agriculture, manufacturing and services) in 1980, and LaborProdi,1980−2008 is the average annual growth in labor productivity from 1980 to 2008. Over this time period, broad sectoral reallocation contributed approximately 15 percent to average annual growth in output per worker in Sri Lanka (0.5/3.1), as compared to 25 percent in India (1.1/4.4), just under 40 percent in Bangladesh, and nearly all of the productivity growth in Nepal.

Figure 2 shows the path of structural change from the early 1980s to 2008 in terms of employment share changes in agriculture, manufacturing and services relative to GDP per capita, for Sri Lanka and for other South Asian and international comparator countries. As illustrated in Panel A, Sri Lanka’s transition out of agriculture has largely kept pace with its level of development. Panels B and C, however, show that while the share of employment in both manufacturing and services has increased, resource changes over this period are larger in the service sector relative to the manufacturing sector. To the extent that Sri Lanka’s employment share patterns may over time become more aligned with average patterns across countries as it continues to develop, this suggests that there is a potential for further strong growth in 7
Guharoy, D. (2008) ‘Without Quality Education, The Country Will Change Fundamentally,’ _Jakarta Post_ , 4 March.

Hadiz, V. (2003) ‘Reorganizing Political Power in Indonesia: A Reconsideration of So-called ‘Democratic Transitions’, _Pacific Review_ , 16 (4), pp.591-611.

Hadiz, V. (2010) _Localizing Power in Post-Authoritarian Indonesia: A Southeast Asia Perspective_ , Stanford: Stanford University Press.

Hameiri, S. (2007) ‘Failed States or Failed Paradigm? State Capacity and the Limits of Institutionalism’, _Journal of International Relations and Development_ , 10, pp.122-149.

Hudson, D. and A. Leftwich (2014) _From Political Economy to Political Analysis_ , Canberra: Developmental Leadership Program.

Ilfiyah, A., F. Hendri, R. Rasiki, and R. Yudhistira (2015) _Kegagalan Pemerataan Guru: Evaluasi SKB 5 Menteri Tahun 2011 Tentang Penataan dan Pemerataan Guru PNS di Indonesia_ , Jakarta: ICW.

Irawan A., Eriyanto, L. Djani, and A. Sunaryanto (2004) _Mendagangkan Sekolah_ , Jakarta: Indonesia Corruption Watch.

_Jakarta Post_ (2011) ‘Govt to Cap Local Budgets for Personnel’, 14 September, p.13.

Jalal F. and B. Musthafa (eds.) (2001) _Education Reform in the Context of Regional Autonomy: The Case of Indonesia_ , Jakarta: MoNE, Bappenas, and World Bank.

Jalal F, M. Samani, M. Chang, R. Stevenson, A. Ragatz, and S. Negara (2009) _Teacher Certification in Indonesia: A Strategy for Teacher Quality Improvement_ , Jakarta: MoNE, World Bank, and Dutch Embassy.

_Jawa Pos_ (2014) ‘Empat Daerah Sukses Program Pemerataan Guru’, 14 October, available at: <u>http://www2.jawapos.com/baca/artikel/8100/empat-daerah-sukses-program-pemerataan-guru.</u> Kelsall, T. (2008) _Going With the Grain in African Development?_ Africa Power and Politics Discussion Paper No 1, London: Overseas Development Institute.

King, D. (1995/6) ‘Bureaucracy and Implementation of Complex Tasks in Rapidly Developing States: Evidence From Indonesia’, _Studies in Comparative International Development_ , 30 (4), pp.78-92.

Kingdon, G. and M. Muzammil (2009) ‘Political Economy of Education in India: The Case of Uttar Pradesh’, _Oxford Developmemt Studies_ , 37 (2), pp.123-144.

_Kompas_ (1970) ‘PGRI Bernaung Dibawah Sekber Golkar’, 11 July.

_Kompas_ (2002) ‘Penerimaan CPNS di Daerah Bernuansa KKN’, 14 November, p.19.

_Kompas_ (2010b) ‘PGRI Desak Surat Edaran Menpan Dicabut’, 26 November.

_Kompas_ (2010b) ‘SKB Redistribusi Guru Disiapkan’, 9 June, available at: <u>http://edukasi.kompas.com/read/2010/06/09/20065384/SKB.Redistribusi.Guru.Disiapkan,</u> accessed 14 September 2015. _Kompas_ (2015a) ‘Kualitas Guru Masih Rendah’, 8 July, p11.

53
Table F.4: Productivity and Technology Sophistication Controlling for ln(K/L), GBF and SSBF MOST

|VARIABLES|(1)<br>ln(VAPW)|(2)<br>ln(VAPW)|
|---|---|---|
|ln(K/L)|0.26***<br>(0.04)|0.26***<br>(0.04)|
|GBF EXT|1.67***<br>(0.32)||
|GBF EXT<sup>2</sup>|-0.21***<br>(0.05)||
|GBF EXT*AGRI||1.84***<br>(0.50)|
|GBF EXT*MANF||-0.29***<br>(0.10)|
|GBF EXT*SVC||0.30***<br>(0.10)|
|SSBF MOST*Agriculture|0.08|0.15|
||(0.25)|(0.25)|
|SSBF MOST*Food Processing|0.28<br>(0.18)|0.29<br>(0.18)|
|SSBF MOST*Apparel|0.51***|0.46***|
||(0.15)|(0.14)|
|SSBF MOST*Retail and Wholesale|-0.03|-0.13|
||(0.14)|(0.14)|
|SSBF MOST*Other Manufacturing|0.15*|0.12*|
||(0.08)|(0.07)|
|SSBF MOST*Other Services|0.18<br>(0.25)|0.19<br>(0.26)|
|Var(ABF EXT)|0.22**|0.30***|
||(0.10)|(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|
|Observations|2,519|2,519|
|R-squared|0.55|0.55|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

97
The results of the estimation are displayed in Table 1. The first column presents the estimates for the entire world. Columns 2 and 3 show the estimates for the two income categories of countries. The parameter of the speed of adoption ( δ 2 ) is higher for low and lower-middle income countries than for upper-middle and high income countries. This difference suggests that low income countries have a higher adoption speed, which is consistent with the hypothesis that low income countries are ‘catching up’, and also consistent with the hypothesis that followers tend to adopt technologies faster than leaders.

**<u>Table 1:</u> Estimates of the S-shape Function for Internet Users**

||Entire World|Low and Lower-<br>Middle Income<br>Countries|Upper-Middle and<br>High Income<br>Countries|
|---|---|---|---|
||[1]|[2]|[3]|
|0<br>δ|16.31***|6.99|40.94***|
||(0.5)|(0.28)|(0.87)|
|1<sup>δ</sup>|-5.83***|-9.57***|-5.76***|
||(0.14)|(0.26)|(0.16)|
|2<br>δ|0.49***|0.7***|0.53***|
||(0.02)|(0.02)|(0.02)|
|Observations<br>|15|15|15|
|R<sup>2</sup>|0.99|0.99|0.99|

Notes: Standard errors in brackets. *, **, ***: significant at the 10, 5, and 1 percent level, respectively.

Figures 3, 4, and 5 display the actual and predicted percentages of Internet users in the world during the years 1990-2004 and for each of the country income groups for the same period of time. The first thing to notice is that the fit of the data is very accurate as the R<sup>2</sup> ’s of the regressions in Table 1 indicate. Moreover, in particular for upper-middle and high income countries one can appreciate a very clear S-shape pattern—while the diffusion of Internet is very slow in the initial years, it speeds up in the middle years, and than finally slows down in the last periods. As mentioned above, the fact that our dataset contains information from the very first years of Internet diffusion helps explain why we obtain a complete S-shape curve while most of the related literature does not.

One interesting thing to notice in Figures 4 and 5 is that in low and middle income countries diffusion of Internet accelerates in the last years, whereas in upper middle and high income countries the process significantly flattens out. This graphical evidence also suggests that low income countries are catching up with upper-high income countries in the diffusion of Internet.

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arrangements. This need not imply that ejido<sup>agriculture isless</sup> efficient than private agriculture (in plots of similar size, with<sup>similar access to</sup> water, etc.). In fact, the empirical evidence<sup>on this score is ambiguous: "It</sup> is almost impossible to draw hard and fast conclusions<sup>about the relative</sup> productivity of private farms and ejidos in<sup>the recent period" (Heath, 1990,</sup> p. 3). Yet, when the restrictions are imposed<sup>they imply that the returns to</sup> land are below the maximum attainable. Moreover,<sup>the rural poor own land</sup> under both tenure systems, and poverty<sup>is associated with both51.</sup> In addition, they are also affected by regulations on large<sup>private farms through</sup> its effects on the demand for labor.

Two, regulations in agriculture have an effect<sup>on the market for credit,</sup> particularly in the ejidos. Since ejido land cannot<sup>be mortgaged, most credit</sup> for the ejidos comes from public sources<sup>52</sup> ; in<sup>addition, individual</sup> ejidatarios are not allowed to contract credit<sup>on their own, but must do so as</sup> a joint operation of the community (although this changed<sup>very recently).</sup> Joint provision of credit creates three problems.<sup>First, there is no</sup> 53 mechanism to insure that credit is allocated<sup>to efficient producers</sup> Second, the joint nature of credit creates a free<sup>rider problem resulting in</sup> large default rates. In effect, credit becomes<sup>a subsidy to production and</sup> consumption (Yates, op. cit., p. 209). The associated<sup>subsidies to public</sup> agricultural credit institutions are probably<sup>regressive, since there is no</sup> mechanism to insure that the subsidized credit<sup>goes to the poorest</sup>

> **5 1As** mentioned in section IV, the IES does not allow one to connect earnings with asset ownership. Nevertheless,<sup>household studies report low</sup> incomes for small scale private agricultural<sup>producers and ejidatarios, so it</sup> seems safe to assume that some of both<sup>are among the poor; see Finkler (1978)</sup> and Roberts (1982).

52Commercial banks can lend to ejidatarios, but do so accompanied by guarantee schemes provided by second<sup>tier institutions.</sup> 53A related problem is that credit is given in kind which appears to create problems with timely delivery, an issue<sup>of particular importance in</sup> agriculture. In addition, "..partly in order<sup>to achieve economies of scale in</sup> procurement, (Banrural) operates with a standarized<sup>input package that is</sup> insensitive to regional variations in input<sup>requirements and prices" (Heath,</sup> op. cit., p. 21).
**Table 1A: Descriptive Statistics on Parents-Children Sample**

|**Sample**|**Ful**|**l sample**||**M**<br>**Primary scho**|**eans of Varia**<br>**ol as the cut-of **|**bles for Differen**<br>**f**|**tGroups **<br>**Junior mid**|**dle as the cut-off**|
|---|---|---|---|---|---|---|---|---|
||**Mean**|**Standard**<br>**deviation**|**Farm**<br>**low edu**|**Off-farm**<br>**low edu**|**Farm**<br>**high edu**|**Off-farm**<br>**high edu**|**Farm**<br>**high edu**|**Off-farm**<br>**high edu**|
|**_Daughters_**|||||||||
|Daughters' age|24.3|7.1|28.5|24.0|22.4|21.2|22.1|20.7|
|Daughters' years of schooling|8.6|2.6|7.4|8.5|9.0|9.6|9.2|9.9|
|Dummy = 1 if daughter more than primary schooling|0.8|0.4|0.7|0.8|0.9|0.9|0.9|1.0|
|**_Sons_**|||||||||
|Sons' age|25.6|7.2|29.0|26.1|24.0|22.5|23.8|21.7|
|Sons' years of schooling|8.9|2.4|7.9|8.9|9.1|9.8|9.4|10.2|
|Dummy = 1 if son more than primary schooling|0.9|0.3|0.8|0.9|0.9|1.0|0.9|1.0|
|**_Father_**|||||||||
|Father's age|53.2|8.4|58.4|53.9|50.8|49.0|50.1|48.0|
|Father's years of schooling|6.4|3.0|3.6|4.4|8.1|8.6|9.5|9.8|
|Dummy = 1 if father more than primary schooling|0.5|0.5|0|0|0.9|1.0|1.0|1.0|
|Dummy = 1 if father more than middle schooling|0.2|0.4|0|0|0.2|0.4|0.8|0.8|
|Dummy = 1 if father in off-farming|0.3|0.5|0|0.9|0|0.9|0.0|0.9|
|**_Mother_**|||||||||
|Mother's age|51.0|8.4|55.8|51.2|48.8|47.2|48.1|46.2|
|Mother's years of schooling|4.5|3.1|2.4|3.2|5.5|6.3|6.4|7.1|
|Dummy = 1 if mother more than primary schooling|0.3|0.4|0|0|0.4|0.5|0.6|0.6|
|Dummy = 1 if mother more than middle schooling|0.1|0.3|0|0|0.1|0.2|0.3|0.4|
|Dummy = 1 if mother in off-farming|0.1|0.3|0|0.3|0|0.3|0.0|0.3|
|**_Household per capita_**_income (yuan_)|||||||||
|income in 2002|2725|2239|2189|3159|2449|3445|2551|3692|
|3-year average income (2000-2002)|2561|1857|2111|2947|2318|3170|2411|3369|
|5-year average income(1998-2002)|2376|1680|1972|2730|2155|2918|2236|3102|

**_Notes_** . 1) Observations: 7264 for full sample: 2916 for daughters and 4348 for sons; 2) Observations for the four groups in the primary school cutoff:  farm and low edu (2494), off-farm and low edu (621), farm and high edu (2403), off-farm and high edu (1746); 3) Observations for the junior middle school cut-off: farm and high edu (636 ), off-farm and high edu (735 ); 4) For primary school cutoff, "High edu" stands for more than primary schooling and "low edu" stands for primary schooling or less; 5) For junior middle school cut-off, "High edu" stands for more than junior middle schooling
**Table 22. Significance of legal and regulatory obstacles: Pricing, technology, or product restrictions**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0173|-0.0153|||-0.0293|-0.0410|||
||(0.0648)|(0.0622)|||(0.115)|(0.114)|||
|Export % of sales|-0.000473|0.000114|-0.000257|0.000350|-0.000852|9.13e-06|-0.000343|0.000574|
||(0.000845)|(0.000834)|(0.000848)|(0.000826)|(0.00149)|(0.00153)|(0.00152)|(0.00154)|
|Share of sourcing via imports|0.00286***|0.00190**|0.00258***|0.00142|0.00488***|0.00320*|0.00459***|0.00253|
||(0.000919)|(0.000931)|(0.000929)|(0.000932)|(0.00161)|(0.00171)|(0.00166)|(0.00174)|
|High-income source country|-0.139|-0.0279|-0.125|-0.0155|-0.230|-0.0472|-0.204|-0.0162|
||(0.0889)|(0.0870)|(0.0890)|(0.0877)|(0.151)|(0.154)|(0.155)|(0.158)|
|>250 employees|-0.0207|0.0230|-0.0221|0.0172|-0.0343|0.0498|-0.0320|0.0502|
||(0.0812)|(0.0781)|(0.0810)|(0.0792)|(0.142)|(0.143)|(0.143)|(0.146)|
|Cumulative inv. >US$10M|0.0356|0.0337|0.0249|0.0245|0.0547|0.0526|0.0324|0.0300|
||(0.0770)|(0.0749)|(0.0771)|(0.0750)|(0.133)|(0.136)|(0.136)|(0.139)|
|Years in country|0.00306|0.00119|0.00312|0.000998|0.00518|0.00199|0.00511|0.00135|
||(0.00234)|(0.00225)|(0.00238)|(0.00230)|(0.00401)|(0.00399)|(0.00416)|(0.00417)|
|Level of foreign ownership (%)|-0.00149|0.000305|-0.00115|0.000774|-0.00249|0.000377|-0.00203|0.00115|
||(0.00135)|(0.00136)|(0.00134)|(0.00133)|(0.00240)|(0.00251)|(0.00245)|(0.00249)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.014|0.066|0.031|0.085|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Restrictions on setting prices, production technology, or format of products]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

39
|Study and Location|Definition of<br>Empowerment|Measurement<br>Concept|Data Sources|Focus|Conclusions|Implication|Scope|
|---|---|---|---|---|---|---|---|
||must occur at<br>each of 3 levels:<br>micro (attitude,<br>feelings and<br>skills), interface<br>(participation<br>and action<br>immediately<br>around the<br>individual) and<br>macro (beliefs,<br>action and<br>effects)|statements (33 on<br>the micro, 15 on the<br>interface and 13 on<br>the macro level)<br>after pretesting,<br>implementation, and<br>validity testing|an experimental<br>design in eight<br>Free to Grow<br>Groups<br>(company<br>presenting self-<br>development<br>programs in<br>organizations),<br>combined with<br>qualitative data<br>collection|instrument to<br>measure the<br>outcomes of<br>empowerment<br>and increase<br>accountability<br>in adult<br>education<br>programs|needs analysis<br>(baseline<br>assessment), long<br>term trends and<br>effects, proof of<br>impact,<br>comparison of<br>methods, and<br>action research|interpretations of<br>the definitions<br>and validating<br>with an objective<br>measurement tool|_the questionnaire,_<br>_which is proving hard_<br>_to access at the present_<br>_time.]_|
|Kroeker (1995)<br>_Nicaragua_|Reversing the<br>process of<br>alienation and<br>disbelief in<br>change and<br>increasing<br>access to<br>resources and|Seven months of<br>participant<br>observation and four<br>follow-up visits|An agricultural<br>cooperative in<br>Nicaragua in<br>1989, literature<br>review,<br>interviews of key<br>informants, and<br>visits to 15|Factors<br>enhancing and<br>impeding<br>individual,<br>organizational<br>and societal<br>empowerment|Factors enhancing<br>empowerment at<br>each level<br>(personal,<br>collective and<br>societal) are sense<br>making and<br>informal|Interrelated levels<br>of empowerment<br>are necessary:<br>personal,<br>organizational,<br>and societal|No specific instrument|
||control over the<br>conditions and<br>decisions that<br>affect one’s<br>personal life and<br>environment||cooperatives<br>around the<br>country||consciousness<br>raising processes;<br>impeding factors<br>are fears of<br>speaking in<br>meetings and<br>reluctance to face<br>crises,<br>environment and<br>outsiders|||
|UNDP (1995)<br>_Human_<br>_Development Report_|Unspecified|Gender<br>Empowerment<br>Measure (GEM):<br>index based on<br>women|Data about<br>percentage of<br>women in<br>parliament,<br>among|Comparing<br>abilities of<br>women and<br>men to<br>participate|Measure of<br>whether women<br>can take active<br>part in economic<br>and political|Participation and<br>measure of<br>gender inequality<br>in political and<br>economic life|National level<br>(typically); opportunity<br>structure; status of<br>empowerment/outcome.<br>Political and economic|

40
studies to rely on recall data, sometimes dating back several years, along with several aggregations, which are prone to recall bias (e.g., Gibson and Kim, 2007; Beegle et al., 2012) and aggregation bias (Sharma and Gibson, 2019; Rockmore, 2017; Rockmore et al., 2020) that may significantly affect statistical estimates of the impacts of violent conflicts. Besides limiting our understandings of the nature and consequences of violent conflicts, such data-related limitations are, among others, likely to hinder the speed and capacity of humanitarian organizations to target and deliver lifesaving humanitarian assistance to affected populations (Baker et al., 2020).

We make several key contributions to this literature, including to addressing some of the above data-related limitations. First, to our knowledge, we provide the first quantitative _ex durante_ study of the microeconomic consequences of an active large-scale conflict, giving insights into the immediate effects of war. Second, in addition to examining food security – a natural focus of war’s impacts on immediate welfare outcomes – we also document disruptions to household participation in livelihood pursuits and food markets, giving insights into sectoral and geographical patterns of resilience. We then discuss how such _ex durante_ monitoring and analysis may inform post-conflict recovery efforts. Finally, we discuss how similar high-frequency phone surveys and related remote data collection efforts could best be mobilized for monitoring of similar conflict contexts in other settings in the future.

Our analysis is enabled by combining High-Frequency Phone Surveys (HFPS) with conflict events data to identify the impact of the conflict on welfare outcomes.<sup>7</sup> The HFPS are monthly phone surveys that cover all regions of Ethiopia and span April 2020-May 2021, with multiple waves before and after the outbreak of the civil war. The HFPS sample is drawn from a nationally representative face-to-face survey fielded in 2019 (the 4<sup>th</sup> round of the Ethiopian LSMSISA). These combined data offer several advantages and a unique opportunity to link households’ welfare outcomes to exposure to conflict events. First, the spatiotemporal coverage of the HFPS data permits the construction of aggregate (affected versus unaffected regions) and disaggregated (household-level) measures of exposure to conflict. Importantly, because the HFPS surveys are georeferenced, we were able to merge the household data with granular conflict events data from the Armed Conflict Location and Event Data (ACLED) project. Second, the HFPS data also allow us to go beyond standard welfare measures that are typically assessed in conflict studies. In

> 7 The HFPS data were collected by the World Bank in partnership with the Central Statistical Agency of Ethiopia and were designed to monitor the local impacts of the COVID-19 pandemic.

4