rafmacalaba/datause-encoder-data huggingface dataset:
21 Table 5: Trade Regime and Price Distortion Ranking Measures

|Trade Openness<br>(most open t|Measure, 1988<br>o least open)|Price Distortion Measure, 1990<br>(least distorted to most distorted)|
|---|---|---|
|Singapore|1|12|
|HongKong|2|2|
|Malaysia|3|13|
|Thailand|9|6|
|Sri Lanka|10|1|
|Indonesia|11|23|
|Pakistan|13|7|
|Philippines|15|15|
|Bangladesh|19|3|
|Nepal|na|5|
|India|na|20|
|Fiji|na|19|

Source: Leamer, Edward (1988), "Measures of Openness" in Robert Baldwin, ed. Trade Policy Issues and Empirical Analysis, Chicago: University of Chicago Press, and Dollar, David, "Outward-Oriented Developing Economies Really do Grow more Rapidly: Evidence from 95 LDCs 1976-85." mimeo.

Table 6: Domestic Saving, Investment and GDP: 1980-90 Average Annual Percentages

||Low Inflation<br>Countries<br>4|<br>Moderate Inflation<br>Countriesb|High Inflation<br>Countries'|
|---|---|---|---|
|Domestic Saving|Rate<br>33.0|21.8|5.7|
|Investment Rate|<br>30.8|25.5|18.4|
|GDP Growth Rate|<br>7.2|5.1|2.7|

Notes: a - Average annual inflation rate of 6 percent or less; b - Average annual inflation rate between 6 and 10 percent; c - Average annual inflation rate greater than 10 percent.

Source: ADB, Asian Development Outlook 1991.
grown in several countries—e.g., Iraq, Afghanistan, Nigeria, Pakistan, and the Syrian Arab Republic. These groups have been characterized by their oppression of women and minorities. Previous evidence on violent conflict has focused on analyzing the impact on the targeted areas. A handful of cross-country panel studies have analyzed the economic impact of conflict on areas located far from conflict incidents. Yet, the spatial effects of Islamic extremist groups have not been analyzed before. Given the misogynistic nature of their operations and the strong displacement of the population, a better understanding of the spatial dispersion of their effects on women’s empowerment is needed. This paper has aimed at filling this gap.

We estimated the spatial spillover effects of conflict in Nigeria. In the past decade, the country experienced an increase in violent attacks due to religious extremism and territorial disputes. We estimated spatial spillover impacts of violent events conducted by the Islamist extremist group Boko Haram and, for comparison, militant Fulani herders on and outcomes for women and men.

We found that distant Boko Haram attacks exert positive spillover effects on economic well-being at the household level. This result is driven by an increase in employment prospects among women and men. The effects on the former are concentrated among Christian women and do not generalize well to women of traditionalist and Islamic orientation. Importantly, the improved economic well-being of the household translates into the economic empowerment of women. Specifically, we found a strong increase in their likelihood of owning assets and using modern contraceptive methods. However, even though distant attacks of the extremist group also reduce the propensity of women to regard domestic violence perpetrated by their partner as acceptable, we find a strong surge in the likelihood that a woman reports being pushed, slapped, or punched by her husband. A possible explanation is that for many Nigerian women and men the use violence to sanction women for neglecting children and burning food is justified. When women expand time spent on economic activities it may become increasingly challenging for them to cater to the daily needs of all family members, especially those of the children (Deschênes _et al._ , 2020). For men, we find no significant spillover effects on attitudes 37
- 2 -

targeting using household survey data. Section IV applies this method using data from Cote d'Ivoire. The fifth section discusses some ways in which the analysis could be extended, and the last section concludes the paper.

# II. Principles of Targeting Transfers to the Poor

The objective of targeting transfers to the poor is to reduce measured poverty given a fixed amount of money available for such transfers.21 This requires an aggregate index, or measure, of poverty for use - in comparing the outcom-s of different transfer schemes. Formally, let y (y1,y2 ,...yn) be the distribiution of incomes over the population in question. If an individual has an income which puts him or her below some pre-specified poverty line z, that person is categorized as poor.31 The measure of poverty is an index which indicates the aggregate amount of poverty, usually giving heavier weight to those who are deeper in poverty. It is a function of the distribution of incomes y and the poverty line z:

## p = P(Y;z)

(1)

- 21 At this point assume that all transfers must be non-negative, so that the fixed amount of money must be positive. The analysis here can be extended to allow for negative transfers (i.e. taxes), as discussed briefly in Section V.

- 31 For clarity of exposition, assume for the moment that we can treat individuals as living in separate households. The framework easily extends to the case where individuals live in households and per capita income is used as a measure of poverty status. This is done in the empirical section of this paper.
<!-- Start of picture text --> Calinski.Harabasz Davies.Bouldin Hartigan<br>1.65 -<br>60-<br>1.60 - 25 -<br>55-<br>1.55 -<br>50- 20-<br>1.50 -<br>45 -<br>1.45 - 15-<br>40-<br>1.40 -<br>Krzanowski.Lai MSE Silhouette<br>2.5-<br>3000 -<br>0.25 -<br>2.0-<br>2000 -<br>1.5- 0.20 -<br>10- 1000 -<br>234562345623456<br>K<br><!-- End of picture text -->
# **Figure 24. Workforce development and economic upgrading**

<!-- Start of picture text --> TYPE OF WORK<br>AGRICULTURE APPAREL AUTOMOTIVE IT HARDWARE BUSINESS SERVICES<br>KNOWLEDGE-INTENSIVE<br>HIGH-SKILLED, TECHNOLOGY-<br>INTENSIVE WORK<br>MEDIUM SKILLED, MIXED<br>PRODUCTION TECHNOLOGIES<br>LOW-SKILLED, LABOUR-<br>INTENSIVE WORK<br>SMALL SCALE, HOUSEHOLD-<br>BASED WORK<br>ECONOMIC UPGRADING<br><!-- End of picture text --> Source: Barrientos, Gereffi and Rossi, 2011

# **PART 4 – INCREASING THE VALUE FOR TRADE IN GVCs**

As explained in Part 1, participation in GVCs is both a source of opportunities and risks for developing countries. A number of policies could help make the balance tip in favor of the benefits and minimizing the risks.

While the mixed impact of GVCs is recognized (Sturgeon and Memedovic, 2010), the economic and business literature tends to put greater emphasis on the benefits. For example, studies suggest that firms that trade tend to be larger, earn higher profits, spend more on R&D, and pay higher wages than firms that do not (Bernard, Jensen, Redding and Schott, 2007). Access to a range of competitively priced foreign intermediate goods has been crucial to achieving higher productivity in both OECD and emerging countries such as India and China (Miroudot, Lanz and Ragoussis, 2009; Goldberg, Khandelwal, Pavcnik and Topalova, 2010). Trade, investment and knowledge flows that underpin GVCs can provide mechanisms for rapid learning, innovation and industrial upgrading in developing countries (Lall, 2000; Humphrey and Schmitz, 2002). Because GVC-linked transactions and investments typically come with quality control systems and prevailing global business standards that exceed those in developing countries, suppliers and individuals in developing countries can be “pushed” to acquire new competencies and skills though their participation in GVCs (Sturgeon and Memedovic, 2010). Finally, GVCs tend to compress the development experience, making non-linear catch up possible, as has been the case for China (Whittaker, Tianbiao, Sturgeon; Mon Han O. Toshi, 2010; Breznitz and Murphree, 2011).

33
# **_Household Welfare_**

For the final part of our analysis, we look at whether there is any impact on household welfare (see Table 8). We find no impact on food expenditure when either one or both spouses are targeted. This might be due to the fact that farmers in Tigray and Amhara regions are mainly engaged in crop production and do not spend much on food. When both spouses are targeted, we find that by endline households have increased their nonfood expenditure in the last twelve months—including school fees, agricultural service costs, clothing and household fabric— though this result is only significant at the 10 percent level. This result suggests an important added value for targeting both spouses, in addition to the effects of targeting either spouse individually. When we look at the joint effect, we find that it increased the nonfood spending by Br 1,358. To put that into context, the mean nonfood expenditure for control households at baseline was Br 4,618. Importantly, we can reject the null hypothesis that this joint effect is equal to zero. We find no similar effect when only one spouse is targeted.

When the female spouse is targeted, we find an initial significant negative impact on the values of productive assets. The size of this impact holds at the endline, though is no longer significant. This decrease in productive assets could potentially explain the increased savings reported when the female spouse is targeted via WIF.

27
yearly data are scarce and of dubious quality. Although some low income countries have annual data dating back to 1971, time series with less than 40 observations are insucient to estimate the process (2.2) in a reliable way. For example, even with 40 observations per equation, a vector autoregression with six endogenous variables, a constant, one lag on the endogenous variables, and no exogenous variables has 63 parameters to estimate with 240 observations, less than 4 observations per parameter.

To cope with this problem, I pool data from several low income countries allowing for partial heterogeneity in the form of country xed-eects and country-specic exogenous variables, but assuming common slope coecients and covariance matrix across countries. Thus, I replace the specication (2.2) with a vector autoregression applied to a panel of cross-country and time series data (Panel VARX) represented by Here, countries are indexed by _i_ = 1 _,_ 2 _, ..., N_ ; the time index is _t_ = 1 _,_ 2 _, ..., Ti_ , where _Ti_ denotes the number of usable observations per country; and _p_ and _q_ denote the number of lags on the endogenous and exogenous variables respectively. Thus, the total number of observations is _T_ =<sup>�</sup><sup>_N_</sup> _i_ =1<sup>_Ti_.</sup> In addition, _αi_ is a vector of country xed-eects and the residuals _εi,t_ are i.i.d. shocks satisfying _E_ ( _εi,t_ ) = **0** , _E_ � _εi,tε_<sup>_′_</sup> _i,t_ � = Ω for all _i_ and _t_ , _E_ � _εi,tε_<sup>_′_</sup> _i,s_ � = **0** for all _i_ and _t_ = _s_ , and _E_ � _εi,tε_<sup>_′_</sup> _j,s_ � = **0** for any _s_ , _t_ and _i_ = _j_ . The slope coecients Θ _j_ and Φ _k_ , and the covariance matrix Ω are common across countries. Thus, the methodology computes the dynamic response on the endogenous variables of a generic low income country. Note, however, that there are two sources of heterogeneity across countries. The rst takes the form of a xed-eect aecting the intercept of the regression. The second is that the realization and stochastic process followed by the exogenous variables _Xi,t_ could dier across countries.

9
Global GIS (Geographic Information System) database that was released in 2002.<sup>10</sup> We categorize Uganda regions into two groups: regions with below-median road density and regions with above-median road density. The road network in Uganda is shown in Figure S2-4 in the appendix. As can be seen, while the road network covers most of Uganda, there does appear to be a higher concentration of roads in the southeast of Uganda, near the capital city of Kampala and in the northwestern part of Lake Victoria.

# **3.3.2 Initial regional economic development**

Regional economic development at the beginning of the migration period is proxied by the average intensity of nightlights in each region in 2001. Satellite imagery of nightlights are provided by the United States Airforce Defense Meterological Satellite Program (DMSP) since 1992 and measure the intensity of lights at night around the globe at the approximately 1 squared kilometer grid cell level. More specifically, each satellite of the DMSP observes every location on the globe at some point in time at night, between 8:30 and 10:30 pm. These images are then processed to remove intensity due to moonlight, late lighting during summer months, auroral activity and forest fires. The remaining light intensity, arguably due to human activity, is then averaged on an annual basis and normalized to scale of integers ranging from 0 (no light) to 63. One should note that these nightlights data have been shown to constitute good proxies for GDP and its growth (Henderson, Storeyguard and Weil (2012)), especially in African countries where national income figures are widely thought to be unreliable (Behrman and Rosenzweig (1994), Heston (1994)). Moreover, they have been argued to serve as an alternative measure of local income where disaggregated

> 10This database is available at http://www.agiweb.org/pubs/globalgis/description.html.

16
**Table 3.  US Agricultural Imports:  Preference Coverage,**<sup>**1**</sup> **Utilization,**<sup>**2**</sup> **and Average Nominal Tariff Preference,**<sup>**3**</sup> **2003**

|||**AGOA**|||**GSP**||
|---|---|---|---|---|---|---|
||**Coverage (%)**|**Utilization (%)**|**Av. Tariff Preference (%)**|**Coverage** **(%)**|**Utilization** **(%)**|**Av. Tariff Preference (%)**|
|**_Chad_**|||||||
|**_Congo (DROC)_**<sup>**_4_**</sup>|0|||100|100|1.0|
|**_Ethiopia_**|39|6|1.3|100|92|3.1|
|**_Gambia_**|||||||
|**_Guinea_**|0|||100|100|6.1|
|**_Lesotho_**|||||||
|**_Madagascar_**|0|||100|71|3.7|
|**_Malawi_**|92|30|9.2|100|49|6.3|
|**_Mali_**<sup>**_5_**</sup>|||||||
|**_Mauritania_**|||||||
|**_Mozambique_**|0|||100|100|3.4|
|**_Niger_**<sup>**_5_**</sup>|(.)|0||100|0||
|**_Rwanda_**|||||||
|**_Sao Tome/Prin._**|||||||
|**_Sierra Leone_**|60|0||100|33|11.3|
|**_Tanzania_**|59|94|6.8|41|67|2.8|
|**_Uganda_**|42|68|2.3|100|72|0.7|
|**_Zambia_**|41|0||100|59|5.8|

1 Ratio of eligible imports to total dutiable imports.

2 Ratio of imports entering under preference to total eligible imports.

> 3  Difference between nominal _ad valorem_ tariff equivalent and  nominal preferential tariff.  Covers all HS 8-digit lines with eligible US imports in 2003. 4 AGOA benefits delayed until 10/31/03.

5 AGOA eligibility as of 12/03.

(.) Indicates less than one percent.

33
(Lee, 2002). Second, to account for the small number of clusters in each of our treatment groups, we estimate alternative p-values using randomization inference (Athey and Imbens, 2017).

We estimate separate effects for our full sample and for the subsample of households with a child aged 0 to 2 as well as estimating separate child-level effects for children within this age-range to examine whether childcare centers have differential impacts for women with young children.<sup>11</sup>

# **4 Results**

## **4.1 Baseline Characteristics**

Table 1 presents balance on baseline women’s and household characteristics for the full sample. Women in our sample are aged 31 on average and two thirds are in a monogamous marriage. Approximately 46% of respondents have no formal education. In comparison, 64.5% of women in Burkina Faso and 48.7% of women living in urban areas have no formal education according to the latest Harmonized Survey on Household Living Conditions, conducted in 2018/2019. Thus, education levels in our urban sample are similar to those for women in the urban population in general. Almost 98% of women report having worked for at least one hour in the last 30 days, which is consistent with respondents’ participation in the public work activities. On average, women in our sample worked 33 hours per week in the last month. Time use data reveal that in the last 24 hours, women in our sample spent on average of 6.7 hours caring for their children, 2.5 hours on domestic chores and 3.4 hours doing paid work. Women in the control group spent more time working per week (37 hours for women in the control group, compared to 28 hours on average for women in the treatment group, _p<_ 0.05), and more time on domestic chores (2.6 hours in the control group, compared to 2.4 hours in the treatment group, _p<_ 0.05).

Women earn an average monthly total income of approximately 19,000 FCFA at baseline. This is lower than the 37,000 FCFA monthly payment from the public works, which reflects program implementation challenges. Public works programs aim to provide temporary employment opportunities for the most vulnerable, but by the time of the baseline survey (four months into the program) public works participants were yet to receive their allowances because of persistent payment delays during the study period.

> 11We do not have enough statistical power to detect heterogeneous treatment effects by age of the youngest child. We therefore present these results as suggestive evidence.

14
rural areas (Zheng 2007), the result here further confirms that e-commerce appears to yield benefits broadly shared among participants in an equitable way within the Taobao Villages.

**Figure 10. Stratification results by wealth**

<!-- Start of picture text --> 120% 40000<br>95% 98% 97%<br>100%<br>30000<br>80%<br>57%<br>60% 20000<br>33,944 32,906<br>40% 25,360 26,996<br>10000<br>20%<br>0% 0<br>Wealth Wealth Wealth Wealth<br>quartile 1 quartile 2 quartile 3 quartile 4<br>control mean<br>Growth rate of income relative to Absolute income increase in RMB<br><!-- End of picture text -->

# _4.3.3 Stratification by education level of household head_

Rural e-commerce has made markets easier to access for rural residents, but it is still technology-intensive, and it may be hard for rural residents with limited education to fully take advantage of its benefits. To explore this hypothesis, we stratify the full sample into four subgroups according to the educational level of household head. The results in Figure 11 show that all education levels attain positive gains from e-commerce. Ranging from having completed primary school or less, middle school, high school, and technical school, the e-commerce effects are 28,237 yuan, 31,736 yuan, 28,947 yuan, and 45,777 yuan, respectively. Having completed technical school has the highest absolute gains from e-commerce among all educational levels. Excluding technical school education, having more general education does not seem to increase the gains from e-commerce.

This finding suggests that holding other observable factors equal, having vocational training is better than general education in facilitating e-commerce success. In other words, skills attained from vocational training could be more relevant to e-commerce than general education. For example, regular high school curriculums usually do not include management, business operation, or any technical skills, instead, they include liberal arts and science classes that may not be directly related to e-commerce. However, technical training in vocational schools can be targeted on specific skills. For example, curriculums in vocational schools can include web page design and retailing that are helpful to e-commerce operations. Additionally, vocational training, such as gardening, cooking, and tailoring, can give e-shop owners the practical skills and knowledge needed to understand the markets for the products they may produce and sell through e-commerce. In the Taobao Village Survey, data also show that e-commerce training is one of the top three supports e-tailers need from the government. These pieces of evidence, while far from conclusive, suggest that collective action from the villagers or government support in designing complementary training programs can go a long way to promote further e-commerce development.

21
# **2.3 Creative Content Sector**

Creative contents industry represents the latest strategic industry focus in the government’s attempt to re-make Singapore into an innovative city.

As yet there is no standard way of defining the creative content sector, hence estimates of its size and growth may vary according to the scope of industries included. The ERC report, using a somewhat broad definition, estimated the creative cluster to have a value of almost S$5 billion in 2000, contributing to about 3% of GDP. Another estimate of the contribution of the creative content sector is available from Toh et al (2003). Using a slightly narrower definition than used by the ERC report (covering mainly IT services, advertising, interior graphics and fashion design, architectural services, arts/antiques trade, crafts, photography, and industrial design), they found the creative industries to have a value added of $2.98 billion in 2000, contributing to about 1.9% of GDP. They also estimated the employment in the creative industries in 2000 to be 46,850, with labor productivity to be $63,500 per worker.

Both the ERC Report and Toh et al (2003) included under creative contents industry a number of sub-sectors that have already been covered under the KIBS sector (IT services, architectural services, etc.). To avoid too much overlap with the KIBS sectors, for the purposes of our study, we took a narrower definition of the creative content sector to encompass only the software development, publishing, music, film, broadcasting and dramatic arts industries. Based on this narrower scope, we estimated the sector to have generated a total value added of $1.6 billion and employment of about 26,500 in 2001, making it the smallest of the three innovative sectors within our study (see Table 2.8).

Despite its relatively small size, the creative content sector has grown moderately fast over the last 15 years. Value added grew at an average of 7.4% per annum between 1986 and 2001, while operating receipts grew by about 10%. However, employment has grown more slowly, at 3.9% p.a. from 1986 to 2001. Compared to the high-tech manufacturing sector and the KIBS sector, labor productivity within the creative contents sector has grown the slowest, at 3.4% per annum, from $37,300 in 1986 to $61,200 in 2001.

Like the knowledge-intensive business services, the creative content sector is populated with mainly small companies. Indeed, the growth of the industry over the years has been accompanied by the entry of many small-sized firms, resulting in the average firm size falling from about 20 people per firm in 1986 to about ten people in 2001. Similarly, value added per firm fell from $0.73 million in 1986 to $0.62 million in 2001.

The composition of the creative contents sector changed significantly over the period 19862001. Reflecting Singapore government’s high policy emphasis on promoting ICT development, the software development industry experienced the fastest growth over the years to become the sector’s largest industry, accounting for about 70% of the creative content sector’s total value added, and over 16