rafmacalaba/datause-encoder-data huggingface dataset:
**Table 12: Determination of employment and earnings at the regional and national levels using level of schooling as indicator of human capital**

||Central|Region|Eastern R|egion|Zagreb|Region|Adriatic No|rth Region|Adriatic So|uth Region|
|---|---|---|---|---|---|---|---|---|---|---|
||Coefficient|t-statistics|Coefficient<br>t|-statistics|Coefficient|t-statistics|Coefficient|t-statistics|Coefficient|t-statistics|
|_Estimation of income equations (depen_|_dant variable:_|_monthly earning_|_s (in thousand kun_|_a))_|||||||
|age|0.144|11.9|0.119|8.87|0.070|4.24|0.130|5.47|0.051|2.64|
|age*age|-0.002|-11.23|-0.001|-7.81|-0.001|-2.68|-0.001|-4.58|0.000|-1.69|
|Basic education|0.486|6.98|0.405|4.59|0.612|3.61|0.374|1.75|0.711|4.31|
|Lower secondary vocational|||||||||||
|education|1.456|21.34|1.231|14.26|1.325|7.94|1.278|6.12|1.420|9.07|
|Higher secondary education|1.845|14.15|1.434|11.06|1.768|9.79|1.293|5.07|1.531|7.83|
|Two-year post secondary education|2.667|28.48|2.560|22.81|2.339|13.22|2.241|9.82|2.568|15|
|University education and above|3.995|44.48|3.104|29.39|3.473|20.26|3.073|13.85|3.202|18.99|
|sex (male=0, female=1)|-0.651|-20.12|-0.589|-14.81|-0.563|-16.45|-0.909|-16.38|-0.825|-16.53|
|constant|-1.235|-4.8|-0.597|-2.03|0.536|1.37|-0.305|-0.56|0.953|2.19|
|_Estimation of selection equations_|||||||||||
|age|0.193|37.89|0.194|33.38|0.265|43.55|0.252|31.37|0.219|33.68|
|age*age|-0.002|-40.2|-0.002|-35.5|-0.003|-47.38|-0.003|-33.9|-0.003|-36.01|
|Basic education|-0.082|-2.18|-0.056|-1.27|0.309|4.31|0.199|2.36|-0.019|-0.31|
|Lower secondary vocational|||||||||||
|education|0.089|2.39|0.367|8.54|0.805|11.72|0.523|6.45|0.351|5.99|
|Higher secondary education|-0.489|-7.71|0.069|1.05|0.253|3.26|-0.035|-0.34|0.074|0.98|
|Two-year post secondary education|0.356|6.08|0.874|12.81|0.969|12.33|0.713|7.36|0.736|10.51|
|University education and above|0.374|6.57|1.101|16.75|1.013|13.94|0.710|7.72|0.831|12.2|
|sex (male=0, female=1)|-0.198|-8.93|-0.400|-15.53|-0.106|-4.7|-0.178|-5.64|-0.181|-6.63|
|married|0.228|9.57|0.247|9.15|0.202|7.75|0.123|3.4|0.185|6.2|
|household head|0.105|4.01|0.205|7.01|0.176|6.94|0.186|5.22|0.322|10.13|
|constant|-3.342|-34.99|-3.751|-34.1|-5.289|-41.49|-4.728|-28.66|-4.353|-34.03|

Note: "No school or uncompleted basic education" as reference.

22
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1.00<br>0.20 EE<br>0.10<br>1990 1995 2000 2005 2010 2015<br>-~O-WSF evolution -Q—-GAUD -Q-GAIA -Q-—GISA -@Q—GHSL<br><!-- End of picture text -->

<!-- Start of picture text --> _<br>1.00<br>wada re ee<br>1990 1995 2000 2005 2010 2015<br>—©@-WSF evolution —Q@—-GAUD -@-GAIA -@—GISA -—@Q—GHSL<br><!-- End of picture text -->
# Accelerating Poverty Reduction in a Less Poor World: The Roles of Growth and Ine ualit <u>q y</u>

**Pedro Olinto** _World Bank_

## **Gabriel Lara Ibarra**

_World Bank_

## **Jaime Saavedra-Chanduvi**

_World Bank_ JEL Codes: I32, O15 Keywords: Income growth, inequality, elasticity of poverty, Arellano Bond estimator
- 41 -

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**_Energy demand models for policy_** **_<u>formulation</u>_**

# **A1.3.1.4 Hybrid models**

# **NEMS [National Energy Modelling System]**

NEMS was designed and primarily used by the US Department of Energy for preparing the Annual Energy Outlook. It is a model of energy-economy interaction that is used to analyse the functioning of the energy market under alternative growth and policy scenarios. The model uses a time horizon of about 25 years (up to 2030 for the present version).

The model employs a technologically rich representation of the energy sector and covers the spatial differences in energy use in the US. The demand-side is disaggregated into four sectors, namely industry, transport, residential and commercial but both industry and transport are further disaggregated to capture the specific features of energy intensive users and alternative modes of transport. This is a hybrid model because it uses the details found in engineering-economic models but retains the behavioural analysis found in topdown models.

The supply-side of the model contains four modules – one each for oil and gas supply, gas transportation and distribution, coal supply and renewable fuels. The oil and gas supply module captures the onshore, offshore and non-conventional oil and gas supply and generates oil and gas production functions. The gas transportation module tracks the supply of natural gas across regions and allows an analysis of the pipeline and storage capacity constraints and capacity needs as well as pricing of natural gas. The coal supply module considers the mining of coal, its transportation and pricing in various regions of the country and generates the coal production functions.

There are two conversion modules, namely for electricity and petroleum product markets. These modules consider the technological characteristics of electricity supply and refining. The basic structure of the NEMS model is shown in Fig. A1.4.

137
9

# **Natural gas supply and deliveries**

The natural gas market in the United States is the largest in the world, with a supply of 24.3 trillion cubic feet in 1995. Almost 77 percent of this supply was produced domestically in 1995. The rest came from storage withdrawals and imports, each of which accounted for 12 percent. Gas production is concentrated in a large producing region along the Gulf Coast in Louisiana and Texas; smaller producing regions are in Alaska, the Southwest, and the central United States. Imports from Canada provide an important share of gas supply in consumer areas in the Northeast, the Midwest, and the Pacific Northwest.

Natural gas goes primarily to consumption (89 percent in 1995) and additions to storage (11 percent). Natural gas exports are minimal (table 2).

**Table 2 Supply and Disposition of Natural Gas in the United States, 1995**

||Volume (millions of cubic feet)|Percentage of total|
|---|---|---|
|Supply|||
|Domestic dry production|18,708,969|76.85|
|Withdrawals from storage|3,024,548|12.42|
|Imports|2,841,048|11.67|
|Balancing item|-230,002|-0.94|
|Total|24,344,563|100.00|
|**Disposition**|||
|Additions to storage|2,609,779|10.72|
|Exports|154,119|0.63|
|Consumption|21,580,665|88.65|
|Total|24,344,563|100.00|

_Source:_ U.S. Department of Energy, Energy Information Administration.

Total consumption by end users was 19.7 trillion cubic feet in 1995 (table 3). There are four main categories of consumers: industrial (which accounted for 44 percent of total consumption in 1995), residential (25 percent), electric utilities (16 percent), and commercial (15 percent). Consumption increased steadily between 1930 and the early 1970s, from 1.2 trillion cubic feet to almost 20 trillion cubic feet in 1973. It plummeted during the oil crisis in the late 1970s and early 1980s, but then began increasing again during the past seven years as a result of growth in consumption by industrial consumers and electric utilities (figure 4).
Table 5: The Impact of Initial Conditions on Employment of Refugees by Years Since Migration

|||Main sample||R|estricted sam|ple|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Variable × Years since migration_|||||||
|Attitudes X 0-2 years|0.00802<br>(0.00565)|0.0143<sup>_∗∗∗_</sup><br>(0.00544)|0.0117<sup>_∗∗_</sup><br>(0.00533)|0.00689<br>(0.00943)|0.0162<sup>_∗_</sup><br>(0.00944)|0.0122<br>(0.00948)|
|Attitudes X 3-5 years|0.0422<sup>_∗∗∗_</sup><br>(0.00537)|0.0374<sup>_∗∗∗_</sup><br>(0.00554)|0.0347<sup>_∗∗∗_</sup><br>(0.00531)|0.0375<sup>_∗∗∗_</sup><br>(0.00911)|0.0389<sup>_∗∗∗_</sup><br>(0.00936)|0.0360<sup>_∗∗∗_</sup><br>(0.00924)|
|Attitudes X 6-10 years|0.0378<sup>_∗∗∗_</sup>|0.0317<sup>_∗∗∗_</sup>|0.0306<sup>_∗∗∗_</sup>|0.0227<sup>_∗∗∗_</sup>|0.0176<sup>_∗∗_</sup>|0.0159<sup>_∗_</sup>|
||(0.00513)|(0.00527)|(0.00516)|(0.00830)|(0.00852)|(0.00840)|
|Attitudes X 11-15 years|0.0287<sup>_∗∗∗_</sup><br>(0.00542)|0.0232<sup>_∗∗∗_</sup><br>(0.00572)|0.0218<sup>_∗∗∗_</sup><br>(0.00548)|0.0205<sup>_∗∗_</sup><br>(0.00849)|0.0162<sup>_∗_</sup><br>(0.00874)|0.0145<sup>_∗_</sup><br>(0.00850)|
|Attitudes X 15+ years|0.0316<sup>_∗∗∗_</sup>|0.0194<sup>_∗∗∗_</sup>|0.0175<sup>_∗∗∗_</sup>|0.0129|0.00664|0.00438|
||(0.00599)|(0.00629)|(0.00603)|(0.0108)|(0.0105)|(0.0104)|
|Unemployment X 0-2 years||-0.0121<sup>_∗_</sup>|-0.0134<sup>_∗∗_</sup>||-0.00926|-0.0113|
|||(0.00656)|(0.00642)||(0.0107)|(0.0105)|
|Unemployment X 3-5 years||-0.0262<sup>_∗∗∗_</sup>|-0.0268<sup>_∗∗∗_</sup>||-0.0195<sup>_∗_</sup>|-0.0205<sup>_∗_</sup>|
|||(0.00629)|(0.00609)||(0.0109)|(0.0108)|
|Unemployment X 6-10 years||-0.0276<sup>_∗∗∗_</sup><br>(0.00606)|-0.0287<sup>_∗∗∗_</sup><br>(0.00588)||-0.0276<sup>_∗∗∗_</sup><br>(0.0104)|-0.0288<sup>_∗∗∗_</sup><br>(0.0102)|
|Unemployment X 11-15 years||-0.0268<sup>_∗∗∗_</sup><br>(0.00609)|-0.0279<sup>_∗∗∗_</sup><br>(0.00594)||-0.0270<sup>_∗∗_</sup><br>(0.0115)|-0.0280<sup>_∗∗_</sup><br>(0.0113)|
|Unemployment X 15+ years||-0.0349<sup>_∗∗∗_</sup>|-0.0362<sup>_∗∗∗_</sup>||-0.0293<sup>_∗∗_</sup>|-0.0309<sup>_∗∗_</sup>|
|||(0.00670)|(0.00664)||(0.0125)|(0.0127)|
|Network X 0-2 years|||-0.00211<br>(0.00265)|||-0.00727<br>(0.00572)|
|Network X 3-5 years|||-0.00781<sup>_∗∗_</sup><br>(0.00312)|||-0.00704<br>(0.00571)|
|Network X 6-10 years|||0.00546<sup>_∗∗_</sup><br>(0.00271)|||0.00350<br>(0.00515)|
|Network X 11-15 years|||0.00457|||0.00709|
||||(0.00279)|||(0.00532)|
|Network X 15+ years|||0.00368<br>(0.00348)|||0.00365<br>(0.00617)|
|Country of birth FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Years since migration FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Arrival year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Canton of arrival X Current year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Education FE|No|No|No|Yes|Yes|Yes|
|Observations|696135|696135|696135|127737|127737|127737|

Dependent variable is Employment dummy. Network is defined as log (nb of co-nationals+1). All regressions also include age, age squared, gender, marital status. Specifications (1) and (4) also include the variables Unemployment and Network, (2) and (5) also include Network. Standard errors are clustered at the level Canton of arrival _×_ Year of arrival. * p < 0.10, ** p < 0.05, *** p < 0.01 31
- Acemoglu, Daron, and Pascual Restrepo. 2018. “The race between man and machine: implications of technology for growth, factor shares, and employment.” _American Economic Review_ 108 (6): 1488–1542.

- ~~.~~ 2019a. “Automation and new tasks: how technology displaces and reinstates labor?”

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- Aral, Sinan, Yannis Bakos, and Erik Brynjolfsson. 2018. “Information technology, repeated contracts, and the number of suppliers.” _Management Science_ 64 (2): 592–612.

- Asker, John, Allan Collard-Wexler, and Jan De Loecker. 2014. “Dynamic inputs and resource (mis) allocation.” _Journal of Political Economy_ 122 (5): 1013–1063.

32
|**Table 3: Linear Effects of Insu**<br>_Sample of 56 countries, 1976-2004_<br>_Dependent Variable: Growth in real_<br>_Estimation Method: GMM-IV System_|**rance Variables**<br>_(5-year period obse_<br>_GDP per capita_<br>_Estimator_<br>**[1]**|**on Economic G**<br>_rvations)_<br>**[2]**|**rowth**<br>**[3]**|**[4]**|**[5]**|
|---|---|---|---|---|---|
|Constant|0.115 ***|0.127 ***|0.162 ***|0.151 ***|0.151 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Log. Initial GDP per capita|-0.009 ***|-0.012 ***|-0.015 ***|-0.015 ***|-0.014 ***|
||_0.001_|_0.000_|_0.000_|_0.000_|_0.000_|
|**Private Credit to GDP**|**0.010 ****|**0.007 ****|**-0.003**|**-0.005**|**0.002**|
||**_0.014_**|**_0.033_**|**_0.650_**|**_0.476_**|**_0.755_**|
|**Stock Market Turnover**||**0.026 *****|**0.025 *****|**0.026 *****|**0.025 *****|
|||**_0.000_**|**_0.000_**|**_0.000_**|**_0.000_**|
|**(Life + Non-Life insurance) to GDP**|||**0.138 *****<br>**_0.000_**|||
|**Life premiums to GDP**||||**0.151 ****<br>**_0.050_**||
|**Non-Life premiums to GDP**|||||**0.251 *****<br>**_0.000_**|
|Degree of openess|0.399 ***|0.598 ***|0.501 *|0.564 **|0.313|
||_0.001_|_0.004_|_0.060_|_0.019_|_0.146_|
|Governement consumption|-1.858 ***|-1.679 ***|-2.206 ***|-1.889 ***|-1.988 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Inflation|-0.003 ***|-0.003 ***|-0.003 ***|-0.003 ***|-0.003 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|Terms of trade|0.024 ***|0.046 ***|0.043 ***|0.041 ***|0.048 ***|
||_0.003_|_0.000_|_0.000_|_0.000_|_0.000_|
|Human Capital Investment|0.042 ***|0.037 ***|0.055 ***|0.054 ***|0.057 ***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|**_Period shifts_**||||||
|Period 81-85|-0.016 ***|-0.016 ***|-0.015 ***|-0.015 ***|-0.016 ***|
|Period 86-90|-0.007 ***|-0.011 ***|-0.012 ***|-0.012 ***|-0.012 ***|
|Period 91-95|-0.017 ***|-0.020 ***|-0.023 ***|-0.023 ***|-0.024 ***|
|Period 96-00|-0.017 ***|-0.023 ***|-0.027 ***|-0.027 ***|-0.027 ***|
|Period 01-04|-0.027 ***|-0.033 ***|-0.040 ***|-0.038 ***|-0.039 ***|
|Countries|56|56|56|56|56|
|Observations|263|263|263|263|263|
|Specification tests (p-values)||||||
|-Sargan test|0.732|0.766|0.448|0.447|0.525|
|-2nd. Order Correlation<br>P-values in Italics. ***, **, * denote statistical|0.252<br>significance at the 1, 5,|0.586<br>and 10 percent level.|0.425|0.429|0.484|

In addition, Table 4 shows the coefficients of life and non-life insurance premiums to GDP by income group. In the case of life insurance, only high-income countries would drive the results shown in Table 3 because the coefficient on life insurance for developing countries is not significant. An increase in one standard deviation in life insurance premiums to GDP in high-income countries only, ceteris paribus, would imply an 10
**<u>Table 24: Improvement in GOT and returns to cotton</u>**

|**Ginning Outturn**<br>**(% lint)**|**Ginner’s Profit**<br>**(USD per MT seed cotton)**<br> <br>|
|---|---|
||**FAM-low**<br>**FAM-high**|
|38.0%|(3.78)<br>(6.23)|
|38.5%|3.02<br>0.57|
|39.0%|9.83<br>7.38|
|39.5%|16.63<br>14.18|
|40.0%|23.44<br>20.99|

# **IX. REVIEW OF OTHER VALUE CHAINS AND RELATED STUDIES**

96. For the sake of completeness, we reviewed a number of value chain studies undertaken in Malawi during the last five years. We reviewed studies on completed value chains for dairy, poultry, tea, sugar, fish farming, cassava, coffee and horticultural products, especially paprika (see Koester et. al 2004; RATES 2003; Agar et al. 2007; 2008; GoM 2008; World Bank 2008). There are important variations in most of these studies in terms of scope and methodologies. For example, while our methodology considers the entire length of the chain from input supply to distribution of the final processed (or semi-processed) commodity, most of the studies reviewed only concentrate on some parts of the value chain. Most studies focus on the mapping of supply chains and describe the costs along the chain, but do not benchmark the domestic costs with parity prices. As a result most of the studies do not assess competitiveness. The results from these studies are summarized in Table 25<sup>15</sup> .

97. However, notwithstanding these differences, most studies arrive at similar conclusions in terms of the key factors that affect Malawi’s agricultural trade performance. Cost of inputs, transport and trader margins feature highly. Apart from tobacco, the other value chains that have promising prospects include sugar, dairy and poultry (for import substitution) and horticulture.

**<u>Table 25: Summary findings of main value chain studies in Malawi</u>**

|Study|Commodity<br>assessed|Year<br>completed|Main conclusions|
|---|---|---|---|
|Market Survey –<br>Dairy Processors<br>Association Final<br>Report|Dairy|2008|Domestic production still low (at only 60%<br>of demand) and uncompetitive. As such<br>Malawi makes up through imports of UHT<br>and powdered milk. To promote local<br>production, there is need improve animal<br>husbandry techniques, improve access to<br>feed and artificial insemination. There is<br>also need to promote hygiene and cooling<br>facilities atmilkbulking grouplevel.|
|Cassava Value Chains<br>in Nkhotakota,<br>Salima and Lilongwe|Cassava|2008|Cassava production is less costly and<br>competitive than maize. Due to shorter<br>shelf life, net farm profit increases if<br>farmers produce for processing into starch<br>and other adhesives. There is need to<br>formalize standards withtheMalawi|

15 See Koester et al. (2004); RATES, (2003); Kadale Consultants, (2007); (2008), MoAFS (2008/09).

47
sufficient effort by people who do not live in the village. For example, everyone in any village could be given access to a ventilated improved pit latrine in a matter of hours if a sufficient number of outsiders descended upon a village to install such latrines. An “output” is an indicator whose value does not depend entirely on the intensity of effort by outsiders and depends on a range of complex decisions by villagers, such as school attendance or child stunting.

The results are presented in two formats. A series of figures grouped by indicator show the trends by country for the Millennium Village, for the country overall, for rural areas of the same country, and for rural areas of the province or region where the Millennium Village is located (Figures 1–9).<sup>7</sup> For each country, we also present the complete results in a single table, showing standard errors for the DHS-based statistics. The tables also indicate the trend for each indicator in the Millennium Village, and the difference between this trend and the trend in the surrounding area (Tables 1–3).<sup>8</sup> In the figures we assume a linear trend in all indicators. This assumption is not highly problematic, given that under the most straightforward hypothetical deviation from linearity—exponential growth—the assumption of linearity provides a conservatively optimistic view of the intervention’s impact.<sup>9</sup> _Inputs: Relative Trends Inside and Outside Millennium Villages_ _Access to improved sanitation_ . Figure 1 shows that access to improved sanitation improved markedly across all three countries, as well as in the rural areas of the regions in which the MV in each country is located. In Nigeria, access at the MV intervention site rose at approximately the same rate as access in the surrounding region. In Ghana, access at the MV intervention site rose more rapidly than in the surrounding region. (MVP 2010c does not report this statistic for the MV in Kenya.) For this and all subsequent statistics, the exact numbers and standard errors are presented in Tables 1–3.

> 7 The indicators shown in the figures as “MV region, rural” are for rural households in the region in which the Millennium Village is located. This is rural Ashanti in Ghana, rural Nyanza in Kenya, and the rural Northwest Region in Nigeria. The Nigeria DHS does not provide state-level data in 2003. 8 Standard errors are not reported in the interim MV report, so we are not able to report standard errors for the indicators for the MV sites, the trends at the MV sites, or the differences between the trends at MV sites and the surrounding areas.

> 9 For example, Figure 6 shows a higher linear slope at the intervention sites than in the surrounding region, but all points in the graph could hypothetically lie on roughly the same exponential growth curve.

12
ences in the average size distribution, but it also underestimates the TFP gains associated with their removal by between 4 to 8 percent.

A limitation of our study is its inability to pinpoint any concrete distortion underlying both the idiosyncratic distortions and the nonregulatory component of entry barriers. Our goal here was to respark interest in a type of distortion that was previously deemed weak in explaining cross-country differences in income. We leave a more thorough investigation of the drivers of entry taxes for future research.

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