rafmacalaba/datause-encoder-data huggingface dataset:
Despite a relatively intense and durable civil conflict, Nepal has experienced steady growth in real gross GDP (5% per year between 1995/96 and 2003/04), an additional increase in disposable income due to huge flows of remittances from abroad (representing 12.4% of GDP), seen a steady decrease in poverty over the period (from 42% in 1995/96 to 31% in 2003/04), as well as an improvement in human development indicators such as primary school enrollment (up from 57% to 73%) and child mortality, which decreased by 5% per year (World Bank, 2005; Macours, 2011).

Nepal comprises seventy five districts divided between five development regions: Far Western, Mid-Western (where the conflict started), Western, Middle, and Eastern. The Middle region is richest, and, just before the start of the conflict, the prevalence of poverty was increasing as one moved further West (World Bank, 2005). The intensity of conflict varied widely across districts, and I use this variation in order to identify the effect of conflict.

# 3.2. Conflict

Nepal was an absolute monarchy until 1990. Despite multiparty democratic elections being held in 1991, a Maoist insurgency broke out in 1996, only to end in 2006. The insurgency started in February 1996 in the Rolpa district. At first, it was concentrated in a few Communist strongholds in Western Nepal, but by the end of the war, conflict-related casualties were recorded in 73 out of the 75 Nepalese districts. The Maoist presence varied from sporadic attacks to the organization of their own local governments and law courts. Over the course of the conflict, Maoists attacked government targets such as army barracks, police posts, local government buildings (Do and Iyer, 2010). They were also reported to terrorize, loot, abduct, and physically assault civilians (Bohara et al., 2006). On the other hand, government security forces also killed civilians and were accused of using children for spying, torturing, displacing and summarily convicting civilians (Bohara et al., 2006).

The principal objective of the insurgents was to obtain the creation of a constituent assembly to draft a new constitution. Other important aims were stated to be: land redistribution and equality for all castes, language groups, and women.

# Figure 2 goes about here

A crucial moment in the conflict was the end put by the Maoists to a short-lived cease-fire agreed in 2001. From then on, the government's response intensified dramatically, involving the Royal Nepal Army, leading to an escalation of violence (see Figure 2). Another cease-fire was 8
Footnotes from Table 2-1: Spectrum of entry policies

- Tynan, Nicola and Prof. Cowen, Tyler (1998) _The Private Provision of Water in eh and 19ih Century London,_ First Draft, May 1998, Department of Economics, George Mason University

- ii Solo, Tova Maria & Snell, Suzanne (May 1998) _Water and Sanitation Services for the Urban Poor - Small Scale Providers: Profiles and Typology_ UNDP-World Bank Water and Sanitation Program, page 9, citing Randall Crane article in Water and Development announced in Voices from the City, the Peri-urban network newsletter, April 1994

- **iii** Solo & Snell, (1998)

- iv Economist 12 August 1995 _Investing in Africa: A new scramble_

- v Whittington, Lauria and Mu (1989) _Paying for urban services: A study of water vending and willingness to pay for water in Nitsha, Nigeria_ Infrastructure and Urban Development Department, Case Study, Report INU 4o; cited in Lee and Annas (1992) p.1088

- **vi** Drangaert,Pili, Malgarejo, Silvio, Kemper, Karin, and Bakalian Alex, _Water Supply in Paraguay: The Role of Small Entrepreneurs_ World Bank (1997)

- **vii** Solo & Snell (1998)

- **viii** Solo & Snell (1998)

- **ix** Solo & Snell (1998) p 9

- x Solo & Snell (1998)

- xi Solo & Snell (1998)

- **xii** Solo & Snell (1988)

- **xiii** London Economics research

- **xiv** Lee, Kyu Sik and Anas, Alex (1992) "Costs of Deficient Infrastructure: The Case of Nigerian Manufacturing" _Urban Studies, VoL 29_

- xv London Economics research

- **xvi** Lee & Anas (1992)

- **xvii** Lee and Anas (1992) p.<sup>10 8 8</sup>

- **xviii** Economist 12 August (1995)

- **xix** ESMAP (1991)

- xx ESMAP (1998)

- xx. London Economics research London Economics research

- **xxiii** London Economics research

- **xxivi** London Economics research

- **xxv** London Economics research

- xxvi London Economics research
impact. The expected sign of population is ambiguous. Although there are more jobs available in large states, there are also possible congestion costs. In column 1, all coefficients have the expected sign and are significant, except for the population variable.<sup>23</sup> Table 1: Sectoral Market Access and Bilateral Migration

|Dep.|ln(migrants_ijs_|_t_/stayers_iist_)|||||
|---|---|---|---|---|---|---|
|variable:|||||||
||(1)|(2)|(3)|(4)|(5)|(6)|
|||_benchmark_<br>_I_||PPML|||
|_ijst_<br>_MA_<br>∆<br>|0.617<sup>_a_</sup>|0.571<sup>_a_</sup>|0.745<sup>_a_</sup>|0.983<sup>_a_</sup>|0.846<sup>_a_</sup>|0.544<sup>_a_</sup>|
||(0.086)|(0.097)|(0.116)|(0.259)|(0.280)|(0.114)|
|_ijst_<br>_w_<br>∆<br>|0.251<sup>_a_</sup>|0.311<sup>_a_</sup>||0.170<sup>_a_</sup>|0.041|0.294<sup>_a_</sup>|
||(0.044)|(0.048)||(0.056)|(0.033)|(0.050)|
|_ijt_<br>_u_<br>∆<br>|-0.262<sup>_a_</sup>||||-0.268<sup>_a_</sup>||
||(0.077)||||(0.066)||
|_ijt_<br>_pop_<br>∆<br>|-0.031||||-0.368||
||(0.745)||||(0.621)||
|_ijt_<br>_death_<br>∆<br>|-0.129<sup>_c_</sup>||||-0.078||
||(0.074)||||(0.061)||
|_ijst_<br>_supply_<br>∆<br>||||||0.034<sup>_a_</sup><br>(0.009)|
|_ij_<br>_FE_|yes|yes|yes|yes|yes|yes|
|_st_<br>_FE_|yes|yes|yes|yes|yes|yes|
|_it_<br>_FE_&<br>_jt_<br>_FE_||yes|yes|yes||yes|

23 We do not adjust standard errors for the fact that the market access and wage variables are themselves estimated. Bootstrapping standard errors is prohibitive given the already considerable computational requirements for the construction of each of these variables.

16
# **Appendix A: Potential Selection Bias for Missing Children**

The 3908 rural households included in the Priority Survey provide data on 1442 children between 6 and 60 months of age. As shown in Appendix Table 1, the three provinces in which all children were exposed to the war (Bubanza, Bujumbura Rural, and Cibitoke) and the two provinces in which no children were exposed to the war (Cankuzo and Rutana) are excluded from the baseline analysis. There are 85 and 129 children, respectively, in these two regions, which leaves a total rural sample of 1228 children. An additional 166 children cannot be included in the child health analysis because of missing height data.<sup>26</sup> There are 35 children excluded from the analysis due to measurement errors in either height or age (for instance a height of 775 cm for a 16-month old child).<sup>27</sup> This large number of children (201) with missing information potentially poses a selection bias problem. We use two alternative approaches to evaluate this problem and find consistent results indicating selection bias is unlikely to be a significant concern.

The first approach is to compare the 1027 children who were included in the analysis with the 201 excluded children along as many observable dimensions as possible. Results for this comparison are presented in Appendix Table 2, which shows the mean differences in covariates for the included and excluded children. The children appear to come from similar types of households, with the percentage of literate mothers, household head’s education level, age, and marital status not showing any significant differences across the two groups. Average age and gender of the child also appear to be similar for both groups of children. Finally, the excluded children are no more likely to have been exposed to the civil war shock. Since none of the covariates are significantly different across the two groups of children, this suggests that, at least along observable dimensions, selection effects are likely to be minimal or absent.

The second approach modifies the method proposed by Fitzgerald, Gottschalk, and Moffit (1998) to analyze attrition in a panel data setting. In Appendix Table 3, we present results of a probit regression analysis where the dependent variable is the probability of being included in the sample. For the 1027 children included in the health analysis, the variable is coded one and for

> 26 The survey does not provide any reason for this non-measurement of certain children.

> 27 We adopt the approach of Alderman, Hoddinott, and Kinsey (2006) in excluding all children with a z-score of less than -6 or greater than 6, as these extreme scores are probably due to measurement errors.

16
The sample used in this study is a pure cross-section of SMEs in the manufacturing sector. Based on the size stratification used by ES for sampling purposes, we define SMEs as all firms with fewer than 100 permanent full-time employees at the beginning of the growth accounting period.<sup>3</sup> We focus on the manufacturing sector to keep the sample relatively homogenous Each country and firm is included only once. The most recent round of ES in the country is used. The firms were surveyed between 2006 and 2018. For our baseline results, the sample consists of 20,295 manufacturing SME firms spread out across 109 countries.

# 2.1 _Estimation Methodology_

The baseline regression exercise involves estimating the following equation: where subscript _i_ denotes the firm, _j_ the country to which the firm belongs. _Y_ is the annual growth rate of employment over the last three years; _Informal Competition_ is a measure of competition that the firm faces from the informal sector firms. Initial Employment is the (log of) level of employment at the beginning of the growth period. _YFE_ denotes dummy variables for the survey year (Year fixed effects) and _IFE_ denotes dummy variables for the industry to which the firm belongs (Industry fixed effects). _Firm Controls_ and _Country Controls_ include various firm- and country-level controls, respectively. _u_ is the error term.

> 3 The sample of large firms is used in one of the robustness checks (section 5.4) to compare the results for SMEs with those for large firms.

8
E-3 interregional variation in industrial pollution. This project should help identify the policies which have reduced industrial pollution most cost-effectively under different conditions. By quantifying the effect of country- and region-specific policy and economic variables, it should also provide the basis for adjusting IPPS to conditions in a wide variety of national and regional economies.

We have learned a number of valuable things from first-phase development and application of IPPS:

- @ Industrial pollution problems vary substantially across countries, and across regions within countries. We have therefore estimated intensities for a large number of air, water and toxic pollutants. To illustrate, at the broadest level of pollutant aggregation, IPPS intensity estimates are available for the sum of all toxic pollutants released to all media (air, water, land). At the narrowest level, separate intensities have been estimated for air, water and land release of over 100 toxic pollutants.

- @® Complementary economic data for developing countries can be somewhat randomly available by variable and level of aggregation. We have therefore found it useful to estimate IPPS parameters at the 2-, 3-, and 4-digit levels of aggregation in the International Standard Industrial Classification (ISIC). At each ISIC level, we have estimated pollution intensities, or emissions per unit of activity, using all three economic variables which are commonly available: Value of output, value added and employment. For cases where extremely detailed data are available, we have also estimated sectoral parameters at the U.S. 4- and 5-digit SIC levels. In the latter case, the estimates include some information for over 1,000 industry sectors.
the sector to GDP from 1.4 percent in 1996 to 2.8 percent in 2005<sup>10</sup> . However the agriculture sector remains the main source of employment and represents one third of GDP.

More specifically, Tanzanian exports have been characterized by a slight decrease in exports concentration at the product level over the 2003-2009 period. Table 2 indicates a decrease in the share represented by Gold exports from 18 to 11 percent in 2003 and 2009 respectively. Nevertheless Gold remains the most important export and overall the top exports in the mining sector represent over 20 percent of total exports in both years. The trend is clearly marked by an increase in the importance of the mining sector (with gold and precious ores as main products) at the expense of agriculture goods, in terms of value of exports.

With regard to trade partners, Tanzanian exports have experienced an increased geographical diversification over the 2003-2009 period, as shown in Table 3. The share of exports to each destination was more evenly distributed in 2009 compared to 2003, when almost one third of exports were concentrated in the EU market. Nevertheless, the top ten exports still represent almost half of total exports; there was just a slight decrease from 44 to 43 percent in 2003 and in 2009 respectively. It is interesting to note that the EU has shifted from being the top destination (in 2003) to second top destination in (2009). Most importantly, China, which was not among the top exporters in 2003, has risen from the fifth place in 2006 to the third place in 2009, moving down Japan from the third to the seventh place in 2003 and 2009, respectively. In general, the rank of Tanzanian trade partners (for both imports and exports) varies considerably from year to year. However, a number of trends are visible. The rise of China as an export market is due to the signature of a special trade agreement in 2007, which guarantees access for Tanzanian goods into China<sup>11</sup> . China has also become the main source of inputs (displacing South Africa) followed by Kenya and India.

Prices play an important role in the Tanzanian economy. The 2007/2008 year was characterized by a considerable increase in food prices for most food crops, due to worldwide decline in food supplies. The Bank of Tanzania reports that production of many traditional crops (except for coffee) increased due to improved weather conditions (compared to the drought in 2006). Moreover, the development in the production of cashew nut was led by the introduction of the Warehouse Receipt Payment System (WRPS): this was aimed at allowing farmers to realize better prices for their products, with a particular focus on cash crops. In 2007/08, world demand and improved quality of traditional export crops pushed their prices upward. More specifically cotton unit price increased in 2007/2008 due to a decline in global

> 10 Economist Intelligence Unit (2007).

> 11 Economist Intelligence Unit (2007)

8
the samples of all, industrial, and developing<sup>countries.'9Table</sup><sup>**5**andFigure2report</sup> the globalization measure.

There are two very clear results. The<sup>firstisthattheshareof variance explained</sup> **by** global factors or globalization<sup>measure hasincreasednotably inthelast</sup><sup>**15**years,</sup> from less than **10** percent to over **60** percent.<sup>Furthermore,theincreaseisstatistically</sup> significant, as implied **by** the<sup>**95**percentconfidencebandsshowninFigure2a, derived</sup> from a bootstrapping procedure.

The second result is that the globalization<sup>measurehas been largerfor industrial than</sup> for developing countries (Figure **2b).** This<sup>is naturalgiventhattheformerarearguably</sup> more integrated economies. However,<sup>thisdifferencehasshrankovertheyears:fifteen</sup> years ago the share of global factors<sup>wastwiceaslargeinindustrialthandeveloping</sup> countries, but **by** the end oft he 1990's the gap<sup>wasreducedto</sup><sup>**30**percent.Indeed,the</sup> same bootstrapping procedure used<sup>above shows thatin the period</sup><sup>**1989-93**the estimates</sup> of the contribution of the globalf actor<sup>for industrialcountrieslieoutside the</sup><sup>**95**percent</sup> confidence bands (not shown to avoid cluttering<sup>thegraph)constructedfor developing</sup> countries, suggesting that the difference<sup>betweenthe twosetsof countriesregarding the</sup> role of,global forces was significant.<sup>After1994,this ceasestobethecase.</sup> Where does the increased importance<sup>oftheglobalfactorscomefrom?Isitdueto</sup> the direct impact or its indirect impact<sup>via thecorrelationwiththe localfactors?This</sup> is an important question not only because<sup>one would liketo checkthatour identification</sup> procedure is not eluding the contribution<sup>ofglobalfactors,butalsobecauseforthe</sup> reasons argued above, according to<sup>whichwe expectforeign directinvestment to be more</sup> exposed to global factors, we should<sup>alsoexpectlocal variablestoalso bemoreexposed</sup> to them.

We have decomposed the explained variance<sup>**by**the globalfactorintothe component</sup> coming directly from the global factor<sup>(i.e.,</sup><sup>_Var_</sup><sup>**_(i_**</sup> **_)_**<sup>**_'ZG))_**and thatcomingfrom the impact</sup>

> 1<sup>9</sup> That is, for a given estimation window,<sup>weallowthedata butnot theestimated parametersto</sup> vary across the three samples, i.e., all,<sup>industrial,anddevelopingcuntries.Forall samples,weusethe</sup> estimated parameters corresponding<sup>tothesampleofallcountries.Thisallowsusto focuson the effects</sup> of different data on explanatory<sup>variablesacrosssamples.</sup>

**19**
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36
sector growth is 5.4 percent per year.<sup>12</sup> The contributions to growth are as follows: agriculture 35 percent, manufacturing 18 percent, services 39 percent, and public services 8 percent. While the economy shows the expected movement towards higher value-added manufacturing and services sectors, the fact remains that agriculture will remain to play an important role in medium-term growth, given its significance in the initial period.

**Figure 2 Sources of growth in the three model scenarios**

<!-- Start of picture text --> Annual growth rate, 2004-2015 (%)<br>8<br>7<br>6<br>5 Productivity<br>4 Capital<br>Labor<br>3<br>2<br>1<br>0<br>Baseline Accelerated MDG<br>growth achievement<br><!-- End of picture text --> If realized, the strong growth performance of the baseline scenario puts Ghana in a good position to reach the key poverty MDG well ahead of schedule. In fact, the latest GLSS-5 survey with poverty data for 2005/6 shows that to a large extent, this is already happening. As shown in Table 5, the poverty headcount for the year 2015 in the baseline scenario is estimated to be at 11 percent, thus exceeding with a large margin the target of 26 percent. This remarkable pace of poverty reduction is underpinned by strong growth in per capita household consumption and a moderate decrease in inequality, with the Gini index _declining_ from 0.39 in 2004 to 0.38 in 2015 and the Theil index falling from 0.26 to 0.25 over the same period. The growth incidence curve of Figure 3 shows the consumption gains across the percentiles of the distribution, clearly illustrating the propoor bias of the growth process. The largest gains—approximately 25 percent more than the average—are observed among the poorest households in Ghana, and every member of the poorest one-half of the population enjoys above-average consumption gains. This propoor distributional shift is largely explained by robust growth in unskilled wages, which is underpinned by a well-performing educational system that dampens the growth in unskilled labor supply by keeping children in school longer (see the discussion below for more about labor market results).

> 12 Given that factors are mobile across sectors and there is no sector-biased productivity gain, the growth differential across sectors is explained by the fact that consumption elasticity with respect to income is higher for manufacturing and services. Therefore, with the large income growth recorded in these scenarios demand tends to shift away from agricultural goods and towards manufacturing and services.

11
# **Table A-1:  OLS log earnings regressions**

# **<u>(i) Bolivia</u>**

|Variable label|White Men|Indigenous Men|White Women|Indigenous<br>Women|
|---|---|---|---|---|
|Primary|0.40*|0.39**|0.57*|0.56**|
||(0.20)|(0.12)|(0.22)|(0.16)|
|Secondary|0.66**|0.65**|0.97**|0.78**|
||(0.20)|(0.13)|(0.24)|(0.18)|
|Tertiary|1.00**|0.92**|1.21**|1.17**|
||(0.21)|(0.14)|(0.25)|(0.18)|
|Age|0.10**|0.05**|0.08**|0.07**|
||(0.01)|(0.01)|(0.02)|(0.02)|
|Age^2|-0.001**|-0.001**|-0.001**|-0.001**|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Formal sector wage employee|0.61**|0.73**|0.66**|0.62**|
||(0.12)|(0.10)|(0.16)|(0.19)|
|Informal sector wage employee|0.50**|0.75**|0.23|0.25*|
||(0.09)|(0.07)|(0.14)|(0.12)|
|Public sector employee|0.29*|0.71**|0.42**|0.97**|
||(0.12)|(0.09)|(0.13)|(0.12)|
|Urban|1.04**|1.22**|1.19**|1.08**|
||(0.12)|(0.07)|(0.19)|(0.12)|
|Constant|2.83**|3.32**|2.58**|2.42**|
||(0.29)|(0.25)|(0.43)|(0.40)|
|Number of Observations|923|1510|586|753|
|Adjusted R-squared|0.40|0.51|0.38|0.39|

Standard errors in parentheses; ** significant at 99% level; * significant at 95% level 32