rafmacalaba/datause-encoder-data huggingface dataset:
Agriculture and services are the main sources of employment in Sudan, with the agricultural sector largely dominated by women, according to some measures. In 2014, the agricultural sector accounted for almost half of Sudan’s total employment, which included 60 percent of Sudan’s employed women and 80 percent of Sudan’s employed rural women. Sudanese households whose primary source of income is derived from agriculture, rather than from other segments of the economy, are generally poorer, more vulnerable, and less resilient.

Having _wasta_ —a connection to someone who can “get things done”—plays a key role in enabling, or inhibiting, Sudanese women’s and youth’s access to the labor market. _Wasta_ helps members of all demographic groups access employment opportunities so long as they stay within their geographic, socioeconomic, and so on context. If, however, as implied earlier, a male youth from Nyala tries to “use” his “Nyala _wasta_ ” in Khartoum, he is probably not going to be successful, unless he is solely interested in seeking employment in a “Nyala enclave.”

# **Potential areas for policy and programmatic action**

This study has brought to light several areas in which targeted policies and programs have the potential to help Sudan’s women and youth improve their economic circumstances. The following five areas are briefly explored with the idea that discussions with a broad range of stakeholders will serve to further refine them:

- Expand the opportunities for women to access finance.

- Improve the access to and quality of agricultural jobs.

- Combine efforts to build human capital with activities aimed at strengthening agency.

- Identify and develop marketable skills that increase opportunities for service sector employment and for entrepreneurial activities.

- Create an enabling environment that lifts gender-specific constraints.

The five areas are intentionally broad though by no means arbitrary. They draw on international best practices from the Middle East and Sub-Saharan Africa and from a variety of sectoral initiatives (World Bank, 2008, 2017). The goal is to appeal to as wide an array of potential stakeholders as possible. The policy actions include short-, medium-, and long-term options. The analysis in this report suggests that the greater share of the policies and programs that will serve to improve Sudanese women’s and youth’s economic circumstances for the better will necessitate outlooks that focus on the longer term. However, there are some low-hanging fruits that can be achieved in the short term and have the potential to make significant impact. The reason the actions are not separated into these three groups is to avoid possible misinterpretation that medium- and long-term goals can be set aside for now. Rather, work on achieving some medium- and long-term goals also needs to start now while working to achieve short-term objectives. The policy action areas are summarized in a matrix (Table 2).

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- 21 -

**Table** **<u>5. Spatial autoregressive estimation without instruments</u>**

||(1)||(2)||(3)||
|---|---|---|---|---|---|---|
|ln_RDDN_|0.465<br>(0.161)|***|||||
|ln_TCITY_|||-0.209<br>(0.094)|**|||
|ln_TPORT_|||||-2.113<br>(1.209)|*|
|ln_NUM_|0.106||0.120||0.118||
||(0.082)||(0.079)||(0.077)||
|ln_POP_|0.490|**|0.417|*|0.496|**|
||(0.224)||(0.219)||(0.239)||
|ln_WAGE_|0.193||0.057||0.138||
||(0.157)||(0.160)||(0.162)||
|ln_EDU1_|2.404|**|2.615|**|2.656|**|
||(1.199)||(1.121)||(1.163)||
|ln_EDU2_|-3.549||-1.675||-0.544||
||(3.284)||(3.256)||(3.479)||
|ln_RENT_|0.166||0.212|*|0.198|*|
||(0.130)||(0.120)||(0.119)||
|ln_AGV_|-0.064|**|-0.102|***|-0.120|***|
||(0.032)||(0.028)||(0.035)||
|ln_TAX_|0.063||0.178||-0.230||
||(0.277)||(0.288)||(0.377)||
|ln_POWER_|-0.125||-0.061||-0.516||
||(0.590)||(0.759)||(0.759)||
|constant|-19.656|***|-19.064|***|-7.131||
||(3.063)||(3.303)||(7.469)||
|Obs|118||118||118||
|λ|0.347|***|0.289|***|0.318|***|
||(0.093)||(0.096)||(0.090)||
|ρ|0.407||0.615||-0.147||
||(0.947)||(1.018)||(0.904)||

Finally, from the policy point of view, one may be interested in asking which transport infrastructure is most important to attract more firms. The above analysis includes only one transport variable in each regression, and thus, technically speaking, cannot answer this question. There is an obvious constraint on available instrumental variables.

With two more instruments constructed: (i) distance to a line between Dar es Salaam and Mwanza, and (ii) distance to the border line between Kenya and Tanzania, the spatial
job attributes, since both are normal goods. Consequently, estimates of the hedonic wage equation will be biased in a direction opposite to the (true) compensating differential. Various approaches have been employed to deal with the problem, including the use of panel data (Duncan and Holmlund, 1983) and instrumental variables (Garen, 1988). While we lack panel data, participation in the safe sex training program provides us with a valuable instrumental variable that is able to correct the bias inherent in OLS estimates of effect of condom use on wages of sex work.

In September, 1992 the All India Institute of Public Health and Hygiene began a program in Sonagachi that attempted to provide basic health care facilities to sex workers and their families in Sonagachi while also educating them about HIV/AIDS and the methods to aid in its prevention. The HIV/AIDS information was communicated in a manner that proved to be both innovative and effective. Initially, a group of twelve sex workers were recruited to become peer educators. They were given intensive training on AIDS and other aspects of health care, provided with green coats to identify them as medical workers, and sent into the community to promote safe sex practices. The primary tool they employed for this purpose was a flip chart that used a series of pictures to explain the nature and progression of the HIV virus, its effect on the human immune system, and how the use of condoms was the most effective method of preventing the disease. They also carried condoms with them to distribute to the sex workers free of cost while demonstrating their proper use. Sex workers who wished to use condoms, subsequent to meeting the peer educator could pick them up for free from nearby locations, thus condoms were available in unlimited supply at zero cost.

The peer educators would contact about 40 to 50 sex workers every day and by the time the survey was conducted in 1993 about 53 per cent of them had been contacted in this manner. It is important to note that those who were contacted were not selected on the basis of their income, age, schooling, or any other characteristic: the peer educators would simply target a set of brothels in an area of Sonagachi and move on to another area the next day<sup>4</sup> . Thus, while the selection was not random in a technical sense, it was also not systematically related to any observed or unobserved attributes relevant to this analysis.

Prior to this contact with the peer educators, even if the sex workers had heard of AIDS most did not have any detailed knowledge of the disease or a sense of how safe sex practices could prevent its transmission. Thus flip charts represented the first time that most the sex workers were receiving accurate and complete information on AIDS. Since the flip charts were not administered in a systematic manner 4 The brothels are not located in a systematic pattern. Each street in Sonagachi has a mix of "high-class" and "low - class" establishments that sometimes co-exist within the same building.

7
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# Table E.8 Distance Within Which 50% of GNP-Adjusted Trade Takes Place

|||1970||1985|||
|---|---|---|---|---|---|---|
|'SIC||distance<br>(miles)|country<br>per cent|distance<br>(miles)|country<br>per cent|1985:<br>1970|
|332|FURNITURE|645|4.9|645|4.5|<br>1.0|
|353|PETR. REF.|1132|10.5|727|5.1|<br>0.6|
|356|PLASTIC NEC|705|5.4|743|5.2|<br>1.1|
|314|TOBACCO|776|7.0|743|5.2|<br>1.0|
|380|METAL SCRAP|822|7.4|745|5.3|<br>0.9|
|354|MISC. PETR. & COAL|1070|9.6|748|5.7|<br>0.7|
|342|PRINTING|785|7.0|776|6.4|<br>1.0|
|369|OTHER NON-METAL|910|8.2|794|6.6|<br>0.9|
|381|METAL PRODUCTS|1012|9.2|794|6.5|<br>0.8|
|362|GLASS|1070|9.6|1030|8.5|<br>1.0|
|351|CHEMICALS|1354|13.8|1098|9.2|<br>0.8|
|361|POTTERY|794|7.1|1168|10.2|<br>1.5|
|385|PROF., SCI., MEAS. EQ|1191|11.5|1191|10.5|<br>1.0|
|352|OTHER CHEMICALS|1452|14.5|1210|10.7|<br>0.8|
|355|RUBBER|1168|11.1|1221|11.1|<br>1.0|
|371|IRON & STEEL|1214|11.9|1265|11.7|<br>1.0|
|313|BEVERAGES|1762|18.7|1266|11.8|<br>0.7|
|321|TEXTILES|1421|14.2|1341|12.6|<br>0.9|
|324|FOOTWEAR|840|7.6|1354|12.6|<br>1.6|
|382|MACHINERY|1363|13.9|1363|12.8|<br>1.0|
|384|TRANSPORT EQUIP.|1926|20.1|1363|12.8|<br>0.7|
|331|WOOD|1485|15.3|1421|13.0|<br>1.0|
|341|PAPER|1554|16.0|1472|13.8|<br>0.9|
|383|ELECT. MACH.|1452|14.5|1551|14.5|<br>1.1|
|322|APPAREL|705|5.4|1571|14.8|<br>2.2|
|372|NON-FERROUS METAL|<br>1579|16.5|2229|20.2|<br>1.4|
|323|LEATHER|1098|10.0|2596|22.3|<br>2.4|
|312|OTHER FOOD|3826|27.5|3539|24.5|<br>0.9|
|390|OTHER MANUF.|1846|19.3|3918|27.0|<br>2.1|
|311|FOOD|5647|43.5|3933|27.3|<br>0.7|

Note: These distances correspond to the distance between the pair of countries at which the cumulative ratio of GNP-adjusted exports is equal to 50% of the total. The country ratio corresponds to the percentage of pairs of countries among which these exports take place.
**Table 7 – Triple Differences for Log of Markup (ACF-DLW)**

|VARIABLES|(1)<br>Base|(2)<br>Firm-level|
|---|---|---|
|MobileAccess*Post*Local|-0.414**<br>(0.183)|-0.383**<br>(0.183)|
|Observations|2,597|2,545|
|R-squared|0.075|0.095|
|Year FE|NO|YES|

Note: Column (1) is the baseline specification. Column (2) adds firm-level controls, including firm size, firm age, and capital intensity. Robust standard errors are clustered on Woreda. Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) One consequence of stiffer price competition would be increased investments and efforts for productivity enhancement. As profit margins become razor thin, cost cutting is the main way to remain competitive. One key way to achieve lower manufacturing costs is through investments in technology upgrades as well as better organization of production.

Thus, one implication of our conjectured effect of the 3G network is that productivity will go up. We check this hypothesis by assessing if there is a differential positive impact on TFP for nonexporters, other things equal, located in woredas most exposed to 3G network penetration.

In Table 8, we show the evidence from our exploration of the productivity channel through the impact on TFP post-2008 for firms in treated woredas that do not export. There is some evidence that TFP, like labor productivity, was boosted by the introduction of 3G networks for nonexporting firms in woredas with expanded access to mobile telecommunications. The estimated effect of 73.4% is significant at the 1% significance level in the specification with firm controls.

26
larger percentage of “family workers” and the smaller percentage of “self-employed” in the lab sample than in the survey sample. The economic sectors of our games participants are statistically indistinguishable from those of the survey respondents. Descriptive statistics for “traders” are identical in both samples. There are slightly fewer agriculturalists in the game sample but the difference is small compared to the standard deviation. We included the category “housekeeping” as an economic sector in our survey of games participants but it was not included in the household survey, which, along with the slightly larger percentage of women among games participants, accounts for the slightly smaller number of agriculturalists in that group. The percentage of persons in the housekeeping sector is virtually identical to the percentage who reported being employed as family workers. In tables A.5 and A.6 we provide more information on the demographics of the treated and control villages.

# **7 Findings**

The estimated effect of the program on pro-social behavior in the lab was zero and the effect on network membership may have actually been negative. The survey measures, by contrast, show a strong positive mean effect of the program on self-reported pro-social action and on respondents’ characterizations of social cohesion in their communities. In the tables below we present OLS estimates of the mean of the dependent variable in the control community and the average treatment effect on the treated (ATT), that is the increment in the dependent variable in the treated group over or under the control-group mean.<sup>11</sup> We present these results for each individual measure and then combine the estimates of these individual effects into a single mean effect based on _z_ -scores of the estimated treatment effects from each individual measure, the same method used by Kling, Liebman and Katz (2007) and Casey, Glennerster

> 11Due to a high degree of geographic isolation of the villages we are not worried about the control villages being affected by the program through the presence of spillover effects.

23
# **Tables**

_Table 1. Distribution of PSCT relative to agricultural production value_

|Countr|AUC|Analysis||Policy expe<br>from ric|riment (moving<br>e to maizeprod|subsidies<br>uction)|
|---|---|---|---|---|---|---|
|y|<br>production<br>value|AUC PSCT|Diff AUCs|AUC PSCT<br>with policy|Diff AUCs<br>with policy|Policy<br>impact|
|Philippines|0.64|0.51|-0.13|0.62|-0.02|0.11|
|Mexico|0.58|0.52|-0.05|0.52|-0.05|0.00|
|Canada|0.59|0.54|-0.05|0.51|-0.08|-0.03|
|Japan|0.36|0.33|-0.02|0.69|0.33|0.36|
|Vietnam|0.47|0.46|-0.01|0.55|0.08|0.09|
|Türkiye|0.55|0.54|-0.01|0.54|-0.01|0.00|
|Argentina*|0.50|0.49|0.01|0.49|0.01|0.00|
|Korea, Rep.|0.51|0.51|0.01|0.55|0.05|0.04|
|China|0.58|0.60|0.02|0.59|0.02|0.00|
|Colombia|0.52|0.55|0.02|0.61|0.09|0.06|
|United States|0.50|0.55|0.05|0.55|0.05|0.00|
|Ukraine*|0.61|0.56|0.06|0.55|0.07|0.01|
|India*|0.57|0.51|0.06|0.49|0.08|0.02|
|Indonesia|0.63|0.71|0.08|0.72|0.08|0.01|
|Kazakhstan|0.67|0.77|0.10|0.74|0.08|-0.02|
|Brazil|0.48|0.62|0.15|0.64|0.16|0.02|

_Notes:_ Our analysis of PSCT distribution covered 16 countries and 23 crops. * identifies countries with negative PSCT values. _Diff_ = difference between. The analysis is flipped for these countries to be consistent with measured values for other countries. In the case of negative PSCT, differences between AUCs are multiplied by the negative one. The last column shows the spatial distributional impacts of an experiment where all rice support is shifted to maize.

_Table 2. Distribution of MPS relative to agricultural production value_

|Country|AUCproduction value|AUC MPS|Diff AUCs|Diff AUCs from table 1|
|---|---|---|---|---|
|Philippines|0.64|0.50|-0.14|-0.13|
|Argentina*|0.50|0.56|-0.07|0.01|
|Japan|0.36|0.29|-0.06|-0.02|
|Kazakhstan|0.67|0.61|-0.06|0.10|
|Mexico|0.58|0.53|-0.05|-0.05|
|India*|0.57|0.62|-0.05|0.06|
|Vietnam|0.47|0.46|-0.01|-0.01|
|Korea, Rep.|0.51|0.52|0.01|0.01|
|China|0.58|0.59|0.02|0.02|
|Indonesia|0.63|0.69|0.05|0.08|
|Colombia|0.52|0.59|0.07|0.02|
|United States|0.50|0.63|0.13|0.05|
|Brazil|0.48|0.69|0.22|0.15|

_Notes:_ Our analysis of MPS distribution was limited to the 13 countries that have MPS and the eight crops covered by MPS. * identifies countries with negative MPS values. _Diff_ = difference between. The analysis is flipped for these countries to be consistent with measured values for other countries. In the case of negative MPS, differences between AUCs are multiplied by the negative one.

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34
From the definition of <sup>in(13),inasymmetricequilibrium= </sup> <sup>.Sub-</sup> stituting this expression in (18) yields Substituting (17) and (19) in (16) yields the maximum profit for an intermediateinput producer: which is the standard expression obtained in Romer-type innovation models.

For simplicity, we assume that intermediate-input producing firms last only one period, and that patents are auctioned off randomly to a new group of firms in each period. Thus, each producer of a new intermediate good holds a patent only for the period during which it is bought, implying monopoly profits during that period only; yet patents last forever.<sup>11</sup> By arbitrage, therefore, the patent price <sup></sup> <sup>is</sup>

# 3.4 Design Sector

The outcome of a successful activity is a design, or blueprint, for the production of a new intermediate good. Let  ∈ (0 1) denote the probability of success of a design activity. In a symmetric equilibrium,  is independent across designers and is therefore the same for all projects. The (expected) flow of new designs is given by where  is a productivity factor, common to all firms engaged in design activities. In addition, as in Dinopoulos and Thompson (2000), we also account for a dilution effect, which captures the fact that creating new designs becomes more difficult as the size of the market, measured by the size of the population, , grows.

In standard fashion, productivity itself is linearly related to the existing stock of 11See Agénor and Canuto (2012) for a more detailed discussion of this assumption.

16
24

# Footnotes

1. Consumption was estimated by combining W'orld Bank estimates of the share of consumption in GDII, with the Bank's estimates of CDP per capita at 1980 prices, converted to 1981/82 prices using the implicit GDP deflator (all necessarv data are containied in World Bank, 1988). Calendar figures were then converted to a two year moving average for comparison with the HES.

_2._ It appears that average income may well have been underestimated in 1481/82, with important implicationis for comparisons with the earlier surveys kOsmani, 1989). However, the same bias is not evident in the 1981/82 consumption data.

3. There are other applications of this idea; for example, Ravallion and Huppi (1989) use the same method to deriv3 a decomposition of observed changes in poverty between two dates into effects due to growth in the mean, holding the Lorenz curve constant, and the effect **of** shifts in the Lorenz curve. a4. BBS nominal poverty lines appear to have been constructed by graphing mean calorie intake against mean income based on grouped data and using this graph to find the income level at which households typically attain the predetermined caloric norm. There is nothing to guarantee that the poverty line so determined will have constant purchasing power in terms of any relevant bundle of goods, or correspond to any given reference "utility" level.

a1

|Real values of BBS||||
|---|---|---|---|
|poverty lines|1981/82|1Q83/84|1985/86|
|Urban Nominal|300|439|519|
|Real (CPI Dhaka)|300|364|353|
|KCpI food)|300|361|348|
|**\Rc_)**|360|367|376|
|Rural Nominal|192|300|331|
|Real (CPI rural)|192|252|235|

Although my rural poverty line is slightly higher than the BBS poverty line quoted in note 2 above, it actually gives a closer fit to the BBS (1988b) estimates of the rural headcount index for 1981/82, using the published grouped data and the Lorenz curve parameterization (Appendix). But one should not be too concerned about accuracy in choice of a poverty line in this context, given the level of accuracy of other relevant inputs to the calculations, such as the grouped data, the original survey data, and the