rafmacalaba/datause-encoder-data huggingface dataset:
nevertheless notable how much difference there is in the elasticity based on the NSS consumption growth rates versus the NAS growth rates for the post-1991 period; the surveybased elasticities are about twice as high (in absolute terms) as the national accounts based elasticities. The much lower NAS elasticities reflect the NAS-NSS drift (much faster NASbased growth relative to that based on the NSS) post-1991.

Our results are qualitatively robust to the choice of poverty measure. However, it is notable that the growth elasticities tend to be highest (in absolute value) for SPG and higher for PG than H. The higher growth elasticity of PG than H implies that the depth of poverty (as measured by the mean poverty gap relative to the poverty line) is also reduced by growth. Similarly, the even higher elasticity of SPG implies that inequality amongst the poor (as measured by the coefficient of variation) is reduced by growth. Thus the impacts of growth within and between sectors are not confined to households in a neighborhood of the poverty line.

To summarize: The responsiveness of poverty to growth when measured from the surveys is generally greater in the post-1991 period. This is also true of the responsiveness of the headcount index to growth measured through the national accounts, but not for the two poverty gap indices (PG and SPG), which are similarly responsive to growth in the two periods.

# **5. Poverty and the urban-rural pattern of growth**

Our proposed test for whether the sectoral composition of growth matters entails estimating the following regression equation on the discrete time-series data on poverty and growth: Here  is the discrete-time difference operator (such that  _xt_  _xt_  _xt_  1 ), _sit_  _= nit_  _it /_  _t_ is sector _i_ ’s share of mean consumption at survey round _t_ and  _it_ is the mean consumption for sector _i_ . The  's are parameters to be estimated. To motivate this test regression, notice that, under the null hypothesis that  _u_   _r_   _n =_  , equation (2) collapses to:  ln _Pt =_   ln  _t +_  _t_ (3) 17
survival and find that selection on efficiency exists among larger firms but less so for small firms. Supporting these results, Shiferaw (2009) finds that the probability of firm exit is lower in expanding industries and that productive firms are less likely to exit among Ethiopian manufacturing firms.

Finally, the literature examines the impact of macroeconomic and other shocks on private sector behavior. For instance, large macroeconomic disturbances are found to affect the firm turnover process. A study of new firms in the U.S. finds that startup rates are substantially shaped by both macroeconomic fluctuations as well as industry-specific characteristics; macroeconomic expansion serves as a catalyst for startup activity, but new-firm startups are promoted by a low cost of capital as well as high unemployment rates (Audretsch and Acs, 1994). Levinsohn (1999) investigates employment patterns in Chilean manufacturing firms following a major trade liberalization between 1975-1979, which made the economy more outward-oriented, removed quantitative trade restrictions and slashed tariff rates, privatized firms, relaxed government controlled prices, and reformed financial markets. He finds that in the years following trade liberalization, firm size matters for job creation and destruction; job churning is very high in both expanding and contracting industries; and job creation and destruction varies with whether the firm is operating in the tradable or non-tradable sectors. The importance of firm size in determining survival is supported by another study that examines firms in Ghana, Kenya, and Tanzania during the 1990s, when market reforms were implemented to eliminate protection of the domestic firms (Harding et al., 2004). To summarize, the literature in developing countries generally finds that the main determinant of firm exit is size, with small firms having much higher exit rates than large firms.

# **3. Macroeconomic background and government reforms**

In 2010, Cote d'Ivoire ranked 149 out of 177 in the UNDP "Human Development Index" (UNDP, 2010).<sup>7</sup> This followed a period of political instability and civil war (2002 to 2003). Yet, Cote d'Ivoire remains one of the largest economies in the region and accounts for close to 40% of the

> 7 This section draws on Trebesch (2008), Reuda-Sabater and Stone (1992), and Library of Congress Country Studies (1988).

6
machines/vehicles to natural forests and government ceiling on the amount of forest products (e.g., wood for producing woodchips) that can be extracted annually. While the extraction of forest products is simpler as compared to natural forests, the annual ceiling on the amount of forest products is also applicable for the community forests. These limitations imply that the supply of woodchips would be much smaller compared to the demand for the large-scale switching of coal to fuelwood in the brick industries. The limited supply and regulatory hurdles cause the fuel wood supply unreliable thereby discouraging brick kilns from switching to fuelwood. Large-scale switching of coal to wood chips looks unlikely in the brick industries as long as existing forest regulation is not changed. It needs strong political will and enhanced monitoring capacity.

Switching from coal to biomass for brick production can reduce PM2.5 emissions, but the extent of the reduction depends on the type of biomass used and the efficiency of the combustion process (kiln technology type). If biomass is efficiently burned, it produces fewer PM2.5 emissions compared to coal (US EPA, 1995). Cleaner and more homogenous biomass fuels, such as pellets, also tend to burn more completely than fuelwood. There are some technical constraints to switching biomass from coal in brick kilns. According to (Timilsina and Malla, 2023), some respondents to their rapid survey said that biomass fuels do not provide enough heat (measured in temperature) needed for quality brick production (i.e. technically advanced kilns including Zig Zag kilns). Fuels with gross calorific values (GCV) above 7,000-kilo calories (kcal) per kg are needed to attain a temperature of more than 900 C for good quality bricks, whereas biomass fuels can provide less than 5,000 kcal/kg.<sup>21</sup> On the other hand, any type of biomass fuel can be used in fixed kilns. In western Nepal, many brick producers use biomass fuels in their fixed kilns.

6.3 Electric kilns could be the ideal technology, but it is not available in Nepal Electric kilns would be an ideal option for brick production in Nepal due to the clean hydropower supply in Nepal. Electric kilns not only eliminate environmental pollution from the brick industry but also help improve trade balance and foreign currency reserves by substituting the imported coal used in the brick industry with domestically produced hydroelectricity. However, such a

> 21 <u>htps://www.ecostan.com/calorifc-value</u>

31
<!-- Start of picture text --> a. Efficiency of ordinary growth, 1992-2014<br>0.0 ‘“ ia a8 <3 ‘ *<br>5 ini hes<br>=gp 25 ~ i ks<br>3 art,<br>3 5.0<br>a=]<br>B<br>3g<br>a<br>Lo)2<br>-10.0<br>0.3, 04 0.5 0.6<br>Gini coefficient, 1992<br><!-- End of picture text --> <!-- Start of picture text --> a. Efficiency of inequality-adjusted growth, 1992-2014<br>0.0 & She Gl %, /<br>5 ? eee.<br>= : ee *<br>p25 ao nde<br>& s ae oN<br>3 5.0 “aS ~<br>Lo} b<br>6 a .<br>B > 2<br>3g 5<br>a<br>Lo)2<br>-10.0<br>0.3, 04 0.5 0.6<br>Gini coefficient, 1992<br><!-- End of picture text -->
LAO PEOPLE'S DEM. REP.

# 1. Recent Trends

Nest recent **census (00s** _Nar._ 85): 35U **0MP** per capita (U.S. dollars, 1966): 219 Region: **Asia** Income Group: Low

|Population<br>***n**<br>**_**structure|**Unit**of measure|195|1965|1990|
|---|---|---|---|---|
|Totat<br>Population<br>**(mid-year)**|Thouwsnds|3205|3594|4186|
|Age structure|||||
|0-4|Thousands|532<br>|622<br>|799<br>|
|5-14<br>|Thousands<br>|816<br>|912<br>|1055<br>|
|15-64|Thousands|1769|1957|2206|
|65+|Thousands|as|103|125|
|Dons ity|Pop. per sq.<br>km.|14|16|18|
|Urbanization|Percent<br>urban|13|16|19|
|School-age<br>population|||||
|<br>6-11|Thousands|501|560|657|
|12-17|Thousands|429|471|531|
|Women of childcbering<br>**ae**<br>15-49|Thousands|751|834|942|
|**Vital**<br>**rtoo and**policy|Unit of**_mesure_**|1975-80|1960-5|**1986-90**|
|Amualgrowth rate|Percent|1.2|2.3|3.1|
|Fortility|||||
|Total fertility rate(TFR)|Births<br>perwoman|6.7|6.7|6.7|
|Crude birth rate (CBR)|Births per 1000pop.|45|45|48|
|Mortality|||||
|Life expectoncy at birth (c0)|||||
|Males|Years|42.1|44.5|47.0|
|**Femles**|Years|45.0|47.5|50.0|
|infant mortality rate (IMR)|Per 1000 live births|135|122|110<br>|
|Under 5 mortality risk (q5)|1000 q5|201|192|183|
|<br>Crude deathrate<br>(CDR)|Deaths per 1000 pop.|21|19|17|
|Net migration|Thousands||-20|-5|
|Govt.**ass*ssment**<br>of|||||
|Population growth rate||Too low|Too Low|Too low|
|Fertility||Too low|Too low|Satisfactory|
|Contraeption|Unit of**measur**||Previous|**Lat_t**|
|Contraceptive<br>**prevalence**|percent of worried||||
|<br> <br>Yoer|women 15-49 using||||
|Contraceptive mix|Percent using||||
|<br>PiLL|||||
|lud|||||
|F**em e**sterillation|||||
|Nate**steritization**|||||
|Condom|||||
|Other modern|||||
|<br>Traditional|||||

70
associated 134-by-134 matrices of wage bills and remittance flows for the benchmark year. Also, the GMig2 database provides total population headcounts by region of origin and residence.

In this analysis, the model is implemented with the databases aggregated to consider 24 regions and countries and seven sectors.<sup>8</sup> The selection of the regional aggregation aims to balance tractability with the desire to separately capture major migration corridors, to separate high-income regions from other regions, and to separate as far as possible low-income regions from middleincome regions.

# **2.3 Scenarios**

The first of three scenarios considered in this analysis is a baseline of the global economy out to 2030. The baseline population and labor force paths are drawn from the medium variant projections of the United Nations’ World Population Prospects 2015 Revision (United Nations 2015). The labor productivity parameters are calibrated residually so that the baseline scenario replicates real GDP growth projections from World Bank (2015b) till 2017 and from the OECD projections documented in IIASA (2013) till 2030. The exogenous baseline migrant worker stocks by host region and origin are assumed to evolve in proportion to host regions’ given total labor force path, i.e. the 2007 benchmark shares of workers by origin and skill class in each host region’s total labor force are preserved. In the subsequent scenarios below, all changes are always relative to the baseline. For example, an increase reported in one variable means an increase over the corresponding value of the same variable in the baseline.

The second scenario considers a gradual increase in labor migration from low- and middle-income (LMI) regions to high-income countries (HIC), in the spirit of the exercise of Rodrik (2001), Walmsley and Winters (2005), World Bank (2006), and Walmsley, Winters, and Ahmed (2011). It is assumed that existing binding restrictions on LMI-HIC migration to each of the six HIC regions are relaxed such that the total labor force in each of the host regions rises eventually by three percent relative to the baseline. The migration increase is gradually phased in over the period 2010 to 2020, and beyond 2020 the LMI migrant stocks remain three percent above baseline levels. The net inflows required to reach the migrant stock deviations from the 2030 baseline levels have by assumption largely taken place gradually over the period up to 2020. The distribution of the additional LMI migrants by region of origin in each host region is assumed to be _pro rata_ of the corresponding baseline distribution. Globally, the migrant share of high-income countries’ labor forces rises from 8.1 percent in 2007 to 11.4 percent in 2030. Within the migrant labor forces of high-income countries, the share of migrants from low- and middle-income countries rises from 64.6 percent in 2007 to 74.3 percent in 2030.

> 8 The 24 countries and regions are listed in Table A1. The seven sectors are Agriculture, Oil and Gas Extraction, Other Natural Resource Extraction, Food Processing, Other Manufacturing, Electricity and Gas Distribution, and Other Services.

7
Imbens, Guido and Karthik Kalyanaraman 2012. “Optimal Bandwidth Choice for the Regression Discontinuity Estimator,” Review of Economic Studies (2012), 1–27.

Imbens, G.W., & Lemieux, T. 2008. “Regression discontinuity designs,” Journal of Econometrics, 142, 615–635 MacDonald, Glenn and Chris Robinson 1985, “Cautionary Tails About Arbitrary Deletion of Observations,” Journal of Labor Economics 3: 124-152 Morduch, Jonathan. 1998. Does Microfinance Really Help the Poor? New Evidence from Flagship Programs in Bangladesh. New York University. Department of Economics. Available at http://nyu.edu/projects/morduch/documents/microfinance/Does_Microfinance_Really_Help.pdf Phillips, P.C.B.. 1983. Exact small sample theory in the simultaneous equations model. In M.D. Intriligator and Z. Griliches (eds.), Handbook of Econometrics, Volume I, chapter 8, 449-516, NorthHolland, Amsterdam.

Pitt, Mark M. 1999. Reply to Jonathan Morduch’s “Does Microfinance Really Help the Poor? New Evidence from Flagship Programs in Bangladesh.” Department of Economics. Brown University. Available at http://www.brown.edu/research/projects/pitt/.

Pitt, Mark M. 2011a. Response to Roodman and Morduch’s “The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence.” Available at http://www.brown.edu/research/projects/pitt/.

Pitt, Mark M. 2011b. Overidentification Tests and Causality: A Second Response to Roodman and Morduch. Available at http://www.brown.edu/research/projects/pitt/.

Pitt, Mark M. 2012. “Gunfight at the NOT OK Corral: Reply to ‘High Noon for Microfinance’ ,” Journal of Development Studies, Dec. 2012, 48:12, 1886-1891. Expanded version available at http://www.brown.edu/research/projects/pitt/.

Pitt, Mark M., and Shahidur R. Khandker. 1998. The Impact of Group-Based Credit on Poor Households in Bangladesh: Does the Gender of Participants Matter? Journal of Political Economy 106(5): 958–96.

Pitt, M.M. and Khandker, S.R. (2012) Replicating Replication: Due Diligence in Roodman and Morduch’s Replication of Pitt and Khandker (1998). Working Paper 6273. World Bank.

Pitt, Mark M., 2014. Response to ‘The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence’, Journal of Development Studies, 2014 (in press).

Roodman, David. 2011. Due Diligence: An Impertinent Inquiry into Microfinance. CGD Books.

Roodman, David and J. Morduch. 2009. The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence, Working Paper 174, Center for Global Development.

Roodman, David and J. Morduch. 2014. The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence, Journal of Development Studies.

19
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|Gross State Domestic Product<br>(GSDP)|Gross State Domestic Product (base<br>2011-12)|2005 to 2019|RBI|
|---|---|---|---|
|Per capita GSDP (PGSDP)|The ratio of Gross State Domestic<br>Product to the total population (base<br>2011-12)|2005 to 2019|RBI|
|Urbanization Rate (UR)|The ratio of the urban population to<br>the total population|2005 to 2019|RBI|
|Investment (INV)|Gross fixed capital formation as the<br>ratio of GSDP|2005 to 2019|RBI|
|Capital expenditure<sup>9</sup>(CEXP)|Capital expenditure as a ratio of<br>GSDP|2005 to 2019|RBI|
|Population density (PDEN)|Population density (per sq.km.)|2005 to 2019|Census, 2011|
|Internal debt (DEBT)|The internal debt of state<br>government as the ratio of GSDP|2005 to 2019|RBI|
|Political Dummy (Dummy)|Dummy variable to capture whether<br>both State and central govt. are from<br>the Same party or in the same<br>coalition|2005 to 2019|Authors’<br>compilation|
|Share of Services sector to<br>GSDP (SSER)|Share of Services sector to GSDP|2005 to 2019|RBI|
|Share of the manufacturing<br>sector to GDP (SMNF)|Share of the manufacturing sector to<br>GDP|2005 to 2019|RBI|
|Schedule Cast and Schedule<br>Tribe people (SC/ST)|Percentage of Schedule Cast and<br>Schedule Tribe people to the total<br>population|20005 to 2019|Census, 2011|

Source: Author’s compilation.

*2005 to 2019 refers to Financial Year (FY) 2004-2005 to FY 2018-19. Therefore the estimation period includes 15 years of annual data.

**Model Specifications:** To identify the various factors that explain differential development of infrastructure (physical, social and financial) across states, the study uses the following three models: P P P i 0<sup>+ ββ</sup> 1<sup>P+ ββ</sup> 2<sup>(G</sup> i 3<sup>(P</sup> i 4<sup>UUPP</sup> i 5 i<sup>= ββ</sup> 6 i<sup>P</sup> 7<sup>P</sup> ii−1i 8<sup>GGPP/PPG</sup> i<sup>GGPP)</sup> 9<sup>+ ββ</sup> i 10<sup>VV/C</sup> P<sup>CCPP)</sup> i<sup>+ ββ</sup> 11<sup>+</sup> i _...................... (1)_ ββ PPGGCCPP + ββ CCPPCC + ββ GGCCGGSS + ββ GGSSP + ββ GGCCEESS + ββ GGP PP + ββ GG+ uu The expected sign of (β1, β2, β3, β4, β5, β6, β8 and β10) > 0, whereas the coefficient of β7 and β9 < 0. P i 0 1 P 2(G i 3(P i 4 i + 5 i 6 i 7 ii−1i 8 G i 9 i 10 P P P i 11<sup>GG+ εε</sup> i GGP PP = μμ + μμ GGP PP + μμ GGPP/PPG GGPP) + μμ VV/C CCPP) + μμ UUPP μμ PPGGCCPP + μμ CCPPCC + μμ GGCCGGSS + μμ CCPP + μμ GGCCEESS + μμ ............<sup>+ μμ</sup> _....... (2)_

The expected sign of (μ1, μ2, μ3, μ4, μ5, μ6, μ10) > 0, whereas the coefficient of μ7 and μ9 are < 0.

> 9 Capital expenditure consists of all possible items of investment.

14
very low level of exports and rural population dependent on subsistence agriculture, which are all common symptoms of an economy unable to make a meaningful structural transformation out of low productivity agriculture. In 1985, Nepal’s GNI per capita was the lowest among the 140 countries for which data were available; life expectancy at birth was 50.1 years and ranked 165 out of 193 countries. Sanitation facility coverage was almost entirely absent for the rural poor and very limited even in urban areas.<sup>4</sup> The share of agriculture in GDP, an indicator often used as a proxy for economic development, was as high as 65 percent in the 1960s and 1970s (World Bank 1991) and remained at 52 percent in 1985, the fifth highest in the world, trailing only the poorest countries in Sub-Saharan Africa at that time (Table 1).<sup>5</sup> The 1991 report concluded that poverty would most likely persist in Nepal for years to come; it projected poverty incidence to remain flat at 40 percent by 2010 under a relatively optimistic scenario of constant inequality (World Bank 1991).

**<u>Table 1: Where Nepal Ranked in Socio Economic Indicators circa 1985 and 2010</u>**

||'|circa1985<br>||'|circa2010<br>||
|---|---|---|---|---|---|---|
||||||||
||epas<br>Rank|oa umer<br>of Countries|Year|epas<br>Rank|oa umer<br>of Countries|Year|
|GNI per capita, Atlas method<br>(current US$)|140|140|1985|172|188|2010|
|Agriculture, value added (% of<br>GDP)|5|113|1985|10|176|2010|
|Life expectancy at birth, total<br>(years)|165|193|1985|135|200|2010|
|Mortality rate, infant (per 1,000<br>live births)|17|186|1985|63|192|2010|
|Improved sanitation facilities (% of<br>populationwithaccess)|159|162|1990|157|194|2010|
|Primary completion rate, both<br>sexes (%)|63|84|1988|24|124|2012|
|Youth Literacy (15- 24 Years Old)|18|18|1981|44|51|2011|
|AdultLiteracy|18|18|1981|46|50|2011|

Source: World Development Indicators (World Bank 2016b) Contrary to the prediction, Nepal made significant progress in reducing poverty in a span of a decade and a half. While the poverty headcount rate had remained virtually unchanged at approximately 40 percent until 1995 (World Bank 1999), it declined to 31 percent by 2003 (World

> 4 According to the 1984/85 Multi-Purpose Household Budget Survey, only 1 percent and 6 percent of the poor had access to a latrine in the rural terai and hills, respectively. In urban hills and terai, approximately 50 percent and 90 percent of the poor had no access to a latrine (World Bank 1991). The earliest available national estimate of the access to improved sanitation facilities was 4 percent as of 1990 (World Bank 2016b).

> 5 The four countries with a higher share of agriculture in GDP were Somalia, Burundi, Ethiopia and Uganda.

3