rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Difference between MNE and National Average (%) Share of firms with positive spending on R&D (%)<br>-30 -20 -10 0 10 20 30 40 SO 60 60 10 20 30 40 50 60 70 80 90 100<br>dia —_——___<br>a | Guatemala eo<br>| I Chile t—eo<br>ss) ChinaPeru 1——_o——_-eoo<br>| L UgandaKenya -—_oceHo<br>Argentina toc<br>DRC Leo<br>Mexico -—®<br>Venezuela to—e<br>Colombia e——___+_____o0<br>Poland -—_oo<br>Zambia te<br>Ethiopia || H——-o———_<br>Ecuador al<br>Morocco t—_e—o<br>Mali +e<br>Nigeria Ho<br>Ghana »<br>Malaysia oo<br>Vietnam eo<br>Malawi »<br>Philippines oi<br>Romania a<br>| IndonesiaRussia ||||-—®@—4<br>Céte d'Ivoire ||eO<br>| UzbekistanEgypt _90<br>a . e ° '<br><!-- End of picture text -->

<!-- Start of picture text --> Difference between MNE and National Average (%) Share of firms acquiring fixed assets (%)<br>-40 -20 0 20 40 60 60 10 20 30 40 50 60 70 80 90 100<br>l Chile Heo<br>India oe<br>Russia -——_____———_8<br>Mali tH——_o——_<br>Peru H—_o-0<br>) I | Vietnam H——_o<br>Kenya -——_o—-<br>Guatemala H—_e—_o<br>Romania +e<br>Ghana He<br>Mexico H—_®<br>Poland +o<br>Ethiopia o+——_—_—_e<br>China Ho<br>Morocco eo<br>Nigeria H_o—<br>Argentina toe<br>DRC oe<br>Madagascar He<br>Malawi eo<br>Philippines te<br>Angola eto<br>Cote d'Ivoire eo<br>Venezuela oe<br>Uganda te<br>Colombia e———_©<br>Egypt te<br>Indonesia Feo<br>Uzbekistan eo<br>| Malaysia leo<br>MNE 10 MNE 50 MNE 50 MNE 10 All firms<br><!-- End of picture text -->
||**Table**|**A11: Interacti**|**on term result**|**s forgender sp**|**ecific labor law**|**s**|||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Exports (%, cell average)*Women<br>can work in the same industries as<br>men Y:1 N:0|0.166<br>(0.102)|0.198**<br>(0.100)|0.195*<br>(0.103)|0.206**<br>(0.102)|0.254**<br>(0.107)|0.272***<br>(0.100)|0.273***<br>(0.101)|0.280***<br>(0.098)|
|Exports (%, cell average)|0.091*|0.089*|0.076|0.074|0.072|0.030|-0.698|-0.723|
||(0.055)|(0.052)|(0.053)|(0.054)|(0.057)|(0.055)|(0.487)|(0.507)|
|Women can work in the same|-5.258**|-4.799**|-4.890**|-3.761*|-4.087*|-4.652*|-3.052|-3.746|
|industries as men Y:1 N:0|(2.278)|(2.213)|(2.220)|(2.151)|(2.405)|(2.459)|(3.170)|(3.127)|
|Exports (%, cell average)*GDP|||||||0.080|0.096|
|per capita (logs)|||||||(0.054)|(0.072)|
|Exports (%, cell average)*WBL||||||||-0.165|
|Index||||||||(0.370)|
|No. of Workers (logs)*Women|||||||-0.394|-0.202|
|can work in the same industries as<br>men Y:1 N:0|||||||(0.539)|(0.529)|
|No. of Workers (logs)*WBL||||||||-2.942*|
|<br>Index||||||||(1.712)|
|No. of Workers (logs)||0.543**|0.707***|0.745***|0.910***|0.916***|1.114***|3.066**|
|||(0.242)|(0.255)|(0.278)|(0.289)|(0.290)|(0.360)|(1.225)|
|GDP per capita (logs)||15.421***|15.569***|17.722***|18.160***|7.974|6.209|6.291|
|||(4.869)|(5.019)|(5.067)|(5.441)|(5.494)|(5.559)|(5.524)|
|Herfindahl-Hirschman Index|||-7.033**|-5.831*|-9.966***|-7.021**|-7.080**|-6.775*|
||||(3.314)|(3.329)|(3.855)|(3.446)|(3.491)|(3.482)|
|Age of Firm (logs)|||-0.525|-0.912***|-0.688*|-0.834**|-0.846**|-0.834**|
||||(0.356)|(0.336)|(0.358)|(0.355)|(0.356)|(0.358)|
|Foreign Ownership (proportion)|||-0.854|-0.085|0.117|0.183|0.169|0.218|
||||(0.842)|(0.878)|(0.968)|(0.961)|(0.960)|(0.961)|

76
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**Chart 4**

<!-- Start of picture text --> Turkey: Real Interest rates<br>Ex-post annual return on 3-month T-BilIs<br><!-- End of picture text --> <!-- Start of picture text --> 150<br>1994 MS : 294<br>1994 M6 :202<br>-50<br>-100<br>3-month T-Bill Return in US$ --- 3-month T-Bill Return in TL---LBO<br><!-- End of picture text --> Given the oDpenness of financial markets in Turkey, the starting point is the uncovered interest parity condition. It states that with no barriers to capital mobility, risk neutrality, and no country risk, returns for simtilar types and maturity of assets would be equalized through arbitrage, and deviations from parity will be unpredictable and white noise (Frankel & Okongwu 1995, Edwards, 1998). The fact that there maybe restrictions or taxes on capital flows, and there ar-e foreign exchange and country risks can put a wedge between domestic and foreign rates, which will leave investors indifferent between holding foreign and domestic instruments. This portfolio equilibrium differential can be expressed in real terms as:

# R(TR) = R(US) + R + TX ± U(t)

where R(TR) and R(US) are real interest rates in US$ terms in Turkey and abroad (US or euro dollar markeit) respectively, R is the risk premium, which can be further decomposed into the country pr-emium, i.e. the compensation for holding claims on a country that is perceived to be riskier than claLims on the US; and the exchange rate risk premium, i.e., the compensation for holding currencies that are perceived to be riskier than dollars. TX is tax equivalent of any restrictions of capital inflows, and U(t) is an iid random variable.
|P103922|2010|2011|1/7/2011|2 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P103922|2010|2012|10/1/2011|3 Closed|Yemen,Republic of|
|P103922|2010|2012|6/29/2012|4 Closed|Yemen,Republic of|
|P103922|2010|2013|2/10/2013|5 Closed|Yemen,Republic of|
|P103922|2010|2014|7/30/2013|6 Closed|Yemen,Republic of|
|P103922|2010|2014|2/25/2014|7 Closed|Yemen,Republic of|
|P103922|2010|2014|6/25/2014|8 Closed|Yemen,Republic of|
|P103922|2010|2015|12/8/2014|9 Closed|Yemen,Republic of|
|P103922|2010|2020|6/28/2020|10 Closed|Yemen,Republic of|
|P103927|2007|2009|8/18/2008|1 Closed|Ghana|
|P103927|2007|2010|9/27/2009|2 Closed|Ghana|
|P103941|2008|2008||0 Closed|Moldova|
|P103941|2008|2009|4/28/2009|1 Closed|Moldova|
|P103946|2007|2009|8/14/2008|1 Closed|Yemen,Republic of|
|P103946|2007|2010|9/2/2009|2 Closed|Yemen,Republic of|
|P103946|2007|2010|6/7/2010|3 Closed|Yemen,Republic of|
|P103950|2008|2008||0 Closed|Madagascar|
|P103950|2008|2009|12/27/2008|1 Closed|Madagascar|
|P103950|2008|2009|6/25/2009|2 Closed|Madagascar|
|P103950|2008|2010|12/31/2009|3 Closed|Madagascar|
|P103950|2008|2010|6/30/2010|4 Closed|Madagascar|
|P103950|2008|2011|5/4/2011|5 Closed|Madagascar|
|P103950|2008|2012|8/9/2011|6 Closed|Madagascar|
|P103950|2008|2012|1/27/2012|7 Closed|Madagascar|
|P103950|2008|2013|8/3/2012|8 Closed|Madagascar|
|P103950|2008|2013|3/13/2013|9 Closed|Madagascar|
|P103950|2008|2014|12/9/2013|10 Closed|Madagascar|
|P103950|2008|2014|6/21/2014|11 Closed|Madagascar|
|P103950|2008|2015|10/30/2014|12 Closed|Madagascar|
|P103960|2007|2008||0 Closed|Indonesia|
|P103974|2009|2009||0 Closed|North Macedonia|
|P103974|2009|2010|7/28/2009|1 Closed|North Macedonia|
|P103974|2009|2010|4/30/2010|2 Closed|North Macedonia|
|P103974|2009|2011|9/23/2010|3 Closed|North Macedonia|
|P103974|2009|2011|1/10/2011|4 Closed|North Macedonia|
|P103974|2009|2012|11/27/2011|5 Closed|North Macedonia|
|P103974|2009|2012|3/27/2012|6 Closed|North Macedonia|
|P103974|2009|2013|11/12/2012|7 Closed|North Macedonia|
|P103974|2009|2014|7/1/2013|8 Closed|North Macedonia|
|P103974|2009|2014|12/18/2013|9 Closed|North Macedonia|
|P103974|2009|2014|6/20/2014|10 Closed|North Macedonia|
|P103974|2009|2015|1/22/2015|11 Closed|North Macedonia|
|P103974|2009|2016|7/11/2015|12 Closed|North Macedonia|
|P103974|2009|2016|1/7/2016|13 Closed|North Macedonia|
|P103974|2009|2016|6/18/2016|14 Closed|North Macedonia|
|P103974|2009|2017|12/15/2016|15 Closed|North Macedonia|
|P103974|2009|2017|6/14/2017|16 Closed|North Macedonia|
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Despite lengthy transit times for imports, it is less expensive in most cases to move freight by rail than by road, giving rail the competitive edge. On average, it costs between $100 and $320 per tonne to move imports by rail and between $120 and $280 per tonne to move them by road. The only case where rail is more expensive than roads is in transporting freight to Lubumbashi. This is in part because of the large economic cost of long waits for locomotives at the border between Zambia and the Democratic Republic of Congo. Of the various factors (including delays at borders, long dwell times for railwaybound freight, and lengthy administrative processes), dwell times at ports and high transport costs compose the largest share of the total cost of moving freight (figure 2.4).

**Figure 2.4 Cost of importing goods: Road versus rail routes**

# a. Total cost (US$ per tonne)

<!-- Start of picture text --> 400<br>300<br>200<br>100<br>0<br>rail road rail road rail road rail road rail road<br>Port Locomotive Interchange Transport Border Administrative<br>b. Composition of cost (% of total cost)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>rail road rail road rail road rail road rail road<br>Port Locomotive Interchange Transport Border Administrative<br>Lusaka Lusaka Durban- Durban-<br>via alternative modes Durban-Gaborone Durban-Gaborone Durban-Harare Durban-Harare Durban-Lusaka Durban-Lusaka Dar es Salaam- Dar es Salaam- Lubumbashi Lubumbashi<br>Shares of costs of importing freight<br>Durban-Gaborone Durban-Gaborone Durban-Harare Durban-Harare Durban-Lusaka Durban-Lusaka Dar es Salaam-Lusaka Dar es Salaam-Lusaka Durban-Lubumbashi Durban-Lubumbashi<br>alternative modes<br>Costs (US$ per tonne) of importing freight via<br><!-- End of picture text -->

- b. Composition of cost (% of total cost)

_Source:_ Based on data collected from World Bank 2009; AICD ports and railway databases; Nathan Associates 2010; Teravaninthorn and Raballand 2009; and World Bank 2010.

16
- 10 -

countries, sociocultural and religious barriers limit their mobility as well. This limits their ability to attend training courses outside their villages. Mobile extension training courses can overcome these barriers. In Zambia, mobile training increased the numbers of farm vomen in extension training to about one-third of participants (Safilios-Rothchild 1985). Extension courses which use decentralized centers can also provide transportation, day care, and residential facilities for women or break courses into several shorter modules.

## (e) Lack of Education

1.29 Throughout most of Suù-Saharan Africa, the adult male literacy rate is almost twice that of females. hales are almost twice as likely to be enrolled in secondary school. Moreover, gender-based educational discrepancies tend to be greater in countries where incomes are lower or where Islam is widely practiced.

1.30 In its study of extension for women in five African countries, FAO (Gill 1987) reported that the vast majority of rural women were illiterate. Percentages of rural women unable to read or write were available in four of the countries:

Country % of women unable to read and write Kenya 30 Malawi 68 Sierra Leone 94 Zimbabwe 30

1.31 Women's access to agricultural extension and their ability to comprehend and use technical information is compromised when they lack basic education. Studies supported by the World Bank have demonstrated the critical link between farmer efficiency and farmer education (Jamison and Lau 1982). The impact of education on efficiency is likely to be particularly strong when modern as opposed to traditional agricultural techniques are being introduced.

# r

### Box 3. **EDJCATION AND** FARUER PRODUCTIVITY

Lockheed, Jamison, **and** Lau (1980) examined 18 studies consisting of 37 sets of farm data. Controtling for other variables, they estimated the effect of education on farmer efficiency. They conctuded that farm productivity increases an average 7.4X if a farmer comptetes four years of etementary school. One of the few studies to compare the efficiency of female and mate farmers concluded that both mate and female farmers in the Vihiga Division of Western Kenya achieved higher maize yietds when they had four or more years of education. (Moock 1973)

1.32 This has implications for how extension messages are delivered. Because of their lower level of education, women art less able than men to respond to written extension materials. Other forms of communication are
- Unicef and WHO. 2012. _Progress on Drinking Water and Sanitation: 2012 Update_ . Joint Monitoring Programme for Water Supply and Sanitation.

- Vogl, Tom. 2014. “Height, Skills, and Labor Market Outcomes in Mexico.” _Journal of Development Economics_ 107:84–96.

- Watson, Tara. 2006. “Public Health Investments and the Infant Mortality Gap: Evidence from Federal Sanitation Interventions on U.S. Indian Reservations.” _Journal of Public Economics_ 90 (8-9):1537–1560.

30
in ICT has not occurred in ICT producing sectors but rather in ICT using sectors. In contrast, both investment in ICT and productivity improvements have been much more moderate in the EU during the same period (von Ark et al, 2008). For comprehensive surveys see Draca, Sadun and Van Reenen (2007) and Syverson (2011).

Firm level studies are scarcer, partly due to the endogeneity in input choice that arises in the estimation of production function, and also due to the difficulty in measuring ICT capital at the firm level. In particular, one potential problem is that the direct cost of purchase of ICT does not fully represent the cost of adjusting the organization of the firm to deal with the new technology. To address this issue several firm level studies have highlighted the complementarities between ICT, organizational capacities, and human capital. Bresnahan, Brynjolfsson and Hitt (2002) run their own survey and find that ICT is complementary to decentralized decision making, self-managing teams, worker autonomy, broader responsibilities of workers, and skills. They attribute part of the large estimated returns to ICT to be derived from the change in organizational capacities. Further support for the organizational capacities hypothesis is provided by Brynjolfsson and Hitt (2003). They study investment in computers and find that the explanatory power of computers on firm output growth is substantially larger in the medium run than in the shorter run, suggesting that ICT capital becomes more productive as firms have time to adjust their labor force, managerial practices, and overall firm capabilities.

Bloom, Draca and Van Reenen (2016) apply the organizational capacities hypothesis to explain the differences in the contribution of ICT to productivity growth between the US and Europe. Using a panel of firms they show that US multinationals operating in Europe obtain larger productivity gains from ICT compared to European firms. They further find evidence that the productivity advantage of ICT in US firms is due primarily to different management practices.

# **3. Model**

In this section we describe a partial equilibrium model of firm heterogeneity and investment in ICT. We start describing a situation in which firms produce using skilled and unskilled labor. We then allow for investment in ICT. The model is based on the task production function of Acemoglu and Autor (2011), extended to allow for firm heterogeneity as in Melitz (2003) and rent-sharing wage schedules as in Amiti and Davis (2011).

# _Products and preferences_

In a given industry there is a continuum of product varieties, each variety indexed by � , with the total mass of varieties given by � . Varieties are horizontally differentiated and enter 8
**TABLE 8. Wage by worker type. ICT intensity**

||Mana<br>|gers<br>|Skilled<br>|Workers<br>|Unskilled<br>|Workers<br>|
|---|---|---|---|---|---|---|
||FE|FE‐2SLS|FE|FE‐2SLS|FE|FE‐2SLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ICT|0.013|0.28***|‐0.0031|0.12***|0.016**|0.11***|
||(0.012)|(0.054)|(0.0085)|(0.037)|(0.0072)|(0.027)|
|ICT|‐0.00032|0.22***|‐0.016|0.080**|‐0.0069|0.046|
||(0.016)|(0.060)|(0.011)|(0.039)|(0.0095)|(0.030)|
|ICT * Prod|0.022|0.061**|0.021*|0.047**|0.041***|0.080***|
||(0.016)|(0.031)|(0.012)|(0.020)|(0.011)|(0.018)|
|ICT|0.0030|0.24***|‐0.011|0.11***|0.0074|0.086***|
||(0.012)|(0.053)|(0.0090)|(0.037)|(0.0078)|(0.026)|
|ICT * Skills|0.035**|0.074**|0.031**|0.060***|0.035***|0.074***|
||(0.017)|(0.029)|(0.012)|(0.020)|(0.011)|(0.017)|
|Observations|2,246|2,246|2,333|2,333|3,212|3,212|

Dependent variable: change in log average wage of managers (columns 1 and 2), skilled workers (columns 3 and 4) and unskilled workers (columns 5 and 6). Regressors: ICT investment dummy, and ICT investment dummy interacted with firm‐level indicator variables defined as in Table 5. Instruments defined as in Table 3. Robust standard errors in parenthesis.

32
to a political economy environment conducive to elite cooperation around resource management and accountable relations between political elites and citizens. In addition, even within states that are marked as fragile or resource cursed, there are conditions that can produce pockets of state capacity. These capacities may inhere in particular institutions or sectors. In Nigeria for example – a country rich in natural resources and with a complex federal system - the public sector is constituted, especially at the federal level, by a vast array of institutions of quite different calibers and exhibiting markedly different standards of efficiency. Some Nigerian institutions in particular sectors, states, or organizations work much better than others. Over the last two decades, at various times the Supreme Court and the Economic and Financial Crimes Commission have exhibited (if not consistently) some important examples of independence and autonomy.

**Figure 8. Budget, PFM and revenue mobilization reforms: the role of fragility and natural resources**

<!-- Start of picture text --> Quality of budgetary and financial Quality of budgetary and financial<br>management management<br>3.25<br>FCV non FCV 3.20 Resource-rich Non resource-rich<br>3.15<br>3.50<br>3.10<br>3.00 3.05<br>2.50 3.00<br>2.00 2.95<br>2.90<br>1.50<br>2.85<br>1.00<br>2.80<br>0.50 2.75<br>0.00 2.70<br>Efficiency of revenue mobilization Efficiency of revenue mobilization<br>4.00 FCV non FCV 3.60 Resource-rich Non resource-rich<br>3.50 3.50<br>3.00 3.40<br>2.50 3.30<br>2.00 3.20<br>1.50 3.10<br>1.00 3.00<br>0.50 2.90<br>0.00 2.80<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> In this overall context of poor governance, corruption remains high in SSA, with little improvement in the last twenty years. Since 2000, the SSA region has consistently ranked at the bottom of world regions on the Worldwide Governance Indicator on Control of Corruption, a composite indicator that captures perceptions of the extent to which public power is exercised for private gain, including both petty and 13
|COMPONENTS OF<br>THE WELFARE<br>DISASTER RISK|PARAMETER(S)|INDICATORS AND/OR MODELS|POLICIES TO REDUCE THE WELFARE<br>DISASTER RISK|
|---|---|---|---|
|Hazard|The<br>probability<br>of<br>occurrence of disasters<br>( for various events);|Hazard maps for countries can provide this information<br>(with uncertainty) based on historical data; hazard maps<br>can be adjusted based on climate models to investigate<br>future conditions(even larger uncertainty)<br>Alternatively, each country could build a set of “typical<br>events” covering various hazards (e.g., earthquake,<br>storm, etc.) and various intensities (e.g., return periods<br>of one,ten,and 100years).<br>Alternatively, a simple analysis can classify countries in<br>terms of none, low, medium, and high hazards for the<br>main hazards(storms,flood,earthquake,etc.)|Climate change mitigation and local<br>environmental protection|
|Exposure|The exposure to possible<br>disasters, represented by<br>the total affected capital<br>( ) and the number of<br>affected households( )|If hazard maps are available (at least for the set of<br>“typical disasters”), then asset and population maps can<br>be used to identify the exposure to these hazards.|Land-use planning and risk-sensitive<br>urban plans<br>Information and education<br>campaigns on risk maps|
|Vulnerability|The vulnerability of the<br>exposed capitalV(or|Inventory of building and infrastructure vulnerability and<br>robustness assessment|Building<br>norms,<br>reinforced<br>infrastructure|
||equivalently, total asset<br>losses )|Fraction of the population covered by an early warning<br>system and with ability to prepare and evacuate|Early warning system and evacuation<br>schemes.<br>Information and education<br>campaigns on risk maps|
|Macro-economic<br>resilience ( )|The interest rate and<br>marginal<br>capital<br>productivity ( );|Macroeconomic data provide this information|Policies<br>to<br>improve<br>the<br>macroeconomic context|

34
diversification, tobacco-farming households often grow additional crops. Almost all of the tobacco producing households also reported growing non-tobacco crops in the current planting season. Regardless, smallholders who planted and tended to labor-intensive tobacco made a significant production decision. Malawian farm households typically experience inadequate food production, with a well-defined hungry season before early green maize is edible for consumption. By directing a portion of their land away from food production, tobacco growers reduced the amount of on-farm food available for their families. In fact, the IHS2 data indicate that with respect to non-tobacco counterparts, tobacco-producing farm households, on average, record lower values for (i) the share of all agricultural plots that is cultivated with maize, and (ii) the share of total farm area that is devoted to maize, and that the differences are statistically significant at least at the 5 percent level.

The main outcome of interest in our analysis exploring the nutritional impact of household tobacco production is stunting, a robust long-term measurement of childhood undernutrition that compares a child’s height and age to a globally representative World Health Organization (WHO) reference population (Habicht et al., 1974).<sup>13</sup> Abnormally short children are defined as having a z-score of two standard deviations or more below the mean of the WHO population (Waterlow et al., 1977; Dibley et al., 1987). A wide range of literature has demonstrated that malnutrition in general, and height-for-age in particular, affects long-term wage, health and educational attainment opportunities (Strauss and Thomas, 1998; Chang et al., 2010; Gandhi et al., 2011).<sup>14</sup> Stunting has been shown to accurately measure long-term childhood nutrition levels (Beaton et al., 1990). In comparison to standard reference populations, stunted children experience greater probabilities of early mortality, decreased physical capabilities and diminished mental capacity (Grantham-McGregor et al., 2007; Fawzi et al., 1997). Over 30 percent of the children under the age of 5 around the world were estimated to be stunted in 2005 (Black et al., 2008). Researchers attribute a large number of childhood deaths in Africa to undernutrition and have called for future Malawian health interventions to focus on stunting (Espo et al., 2007). Recent updates to the WHO’s Child Growth Standards have further emphasized the importance of nutritional deficiencies in newborn rural Malawian children (Prost et al., 2008).

> 13Stunting is identifiable within the child’s first year. We use anthropometric measurements for children from six to sixty months of age, which only eliminates one observation from the sample.

> 14See Hoddinott et al. (2008) and Maluccio et al. (2009) for discussion of a positive nutritional shock in Guatemala, which is shown to increase long term economic productivity and educational attainment.

9