rafmacalaba/datause-encoder-data huggingface dataset:
|**Safety**<br>|||||||
|---|---|---|---|---|---|---|
|Amman - Men|-0.359***|-0.395*|-0.478*|-0.355**|-0.374|-0.526|
||(0.12)|(0.21)|(0.26)|(0.15)|(0.29)|(0.39)|
|Amman - Women|-0.254*|-0.067|0.473|-0.510***|-0.341|-0.323|
||(0.15)|(0.24)|(0.45)|(0.19)|(0.31)|(0.65)|
|Beirut - Men|-0.083|0.331|0.668|0.084|0.568|0.873|
||(0.28)|(0.38)|(0.46)|(0.46)|(0.65)|(0.74)|
|Beirut - Women|-0.038|0.119|-0.215|-0.446|-0.264|-0.314|
||(0.23)|(0.31)|(0.56)|(0.29)|(0.43)|(0.89)|
|Cairo - Men|0.445|0.594|0.529|0.799|1.077*|1.07|
||(0.45)|(0.49)|(0.63)|(0.51)|(0.55)|(0.78)|
|Cairo - Women|0.389|0.764|0.716|0.221|0.751|0.45|
||(0.49)|(0.53)|(0.71)|(0.57)|(0.61)|(0.89)|
|N|5912|3404|1810|3242|1886|929|
|R2|0.293|0.309|0.272|0.359|0.378|0.354|
|Adj.R2|0.28|0.287|0.228|0.338|0.343|0.284|

Notes: *, **, *** denote significance at the 10 percent, 5 percent and 1 percent levels respectively. Robust standard errors in parentheses. N: number of observations.

In Amman, safety at public transport stops appears to be a strong predictor for women’s likelihood to participate in the labor force. For the sample of women living at home since birth,<sup>33</sup> a one percentage point increase in the safety at a public transport stop increases the chance of a woman actively looking for a job by 0.95 percentage points. This is in line with the findings reported by Aloul et al., (2018) suggesting that harassment is a significant concern for women in transportation, and that the main locations of harassment are in the street or waiting for the public transport at the transit stops.

While safety appears to matter for the sample of all women living in the same home since birth, when we restrict the sample to women who hail from the low-income group, the relationship with safety disappears and instead accessibility becomes a strong predictor for women’s likelihood of labor force participation. For low-income households, a one percentage point increase in accessibility increases women’s likelihood of actively looking for a job by 1.23 percentage points. This is consistent with the explanation that women in need will brave unsafe conditions to access jobs which imposes a negative externality on them. Availability of public transport options in close proximity to the residential location does not appear to impact women’s labor force participation.

While the accessibility and safety play a role in determining women’s labor force participation, they appear to have overall little impact on women’s employment probability. This is consistent with the idea that while public transport plays a critical role in improving women’s access to

> 33 Our most robust and restrictive sample (this is also the sample of women who are on average younger and more likely to be single).

31
# **3. Method**

# _3.1 Constructing Household Food Expenditure and Diet Quality_

We first constructed household-level food expenditure. Using data from the FIES 2015 household expenditure module, we grouped individual food items surveyed in FIES 2015 into the same 19 food groups in NNS 2013.<sup>2</sup> Then, at the household level, we aggregated total consumption and total expenditure for each food group. Units were standardized to grams and pesos for consumption and expenditure, respectively. To determine household-level values, total expenditures were divided by total consumption to find the unit value of each food group. The unit value of a food group calculated by this approach was essentially the average prices of all food items within that food group weighted by their quantities consumed. This method of deriving unit values of food groups follows Deaton (1987).

_<u>Table 1. FBDG Recommended Food Intake</u>_

||_Children_<br>_1-6_|_Children_<br>_7-12_|_Teens_<br>_13- 19_|_Adults_|_Elderly_|_Pregnant_<br>_&_<br>_Lactating_<br>_Women_|
|---|---|---|---|---|---|---|
|**Rice, Rice Products,**|||||||
|**Corn, Root Crops,**<br>**Bread, Noodles**|162.5|250|350|325|262.5|306.25|
|**Vegetables**|42|42|300|300|300|375|
|**Fruits**|150|100|300|250|200|200|
|**Eggs**|25|N/A|50|50|50|50|
|**Fish, Shellfish, Meat &**|||||||
|**Poultry, Dried Beans**<br>**& Nuts**|58.5|108.5|125|140|150|225|
|**Milk & Milk Products**|240|240|240|240|170|240|

Note: FNRI only recommended number of servings instead of number of grams in their pyramid. Therefore, the authors have to make assumptions about serving size for each food group in order to convert number of servings to number of grams. The recommendations were given by FNRI separately for the pregnant and for the lactating <u>women; we combined the two groups by taking the average recommendations.</u> vegetables, and other vegetables. The CPIs of fruits were assigned to expenditures on vitamin-C rich fruits and other fruits. The CPI for meat was assigned to expenditures on meat and meat products and poultry. The CPI for eggs, cheese and milk was assigned to expenditures on eggs, whole milk and milk products.

2 The 19 food groups are: (1) rice and rice products, (2) corn and corn products, (3) other cereal products, (4) starchy roots and tubers, (5) sugar and syrups, (6) dried beans, nuts, and seeds, (7) green leafy and yellow vegetables, (8) other vegetables, (9) vitamin-C rich fruits, (10) other fruits, (11) fish and fish products, (12) meat and meat products, (13) poultry, (14) eggs, (15) whole milk, (16) milk products, (17) fats and oils, (18) beverages, and (19) condiments and spices.

10
region or population group (Mogadishu, other urban, IDPs in settlements and nomads) largely explain inequality in consumption.

<!-- Start of picture text --> Figure 10: Cross‐country comparison of poverty Figure 11: Inequality<br>and inequality<br><!-- End of picture text --> <!-- Start of picture text --> 60 50<br>Overall average<br>55<br>40<br>50 RWA<br>30<br>MWI SSD<br>45<br>UGA<br>20<br>40<br>TZA<br>35 10<br>SOM<br>ETH<br>30 0<br>15 35 55 75 Mogadishu Other Rural IDPs in Nomads<br>Poverty incidence (% of population) urban settlements<br>Source: Authors' calculations. Source: Authors' calculations.<br>GINI Index (0‐100)<br>GINI Index (0‐100)<br><!-- End of picture text -->

**Table 11: Inequality decomposition**

Theil GE(1) inequality index

|Decomposition|By population type|By region|
|---|---|---|
|Between group|0.002|0.005|
|Within group|0.208|0.205|
|Total|0.210|0.210|

Source: Authors' calculations.

The consumption distributions of the different population groups are relatively similar. The largest differences between rural and urban areas, as well as between IDPs in settlements and nomads, are found below the poverty line (Figure 12). A considerable share of 10 percent of the non‐poor population is clustered within 20 percent of the poverty line. This population is susceptible to fall into poverty in case of an unexpected decrease in their consumption levels.

**Figure 12: Consumption distribution**

35
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- Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. _Scientific Data_ , _7_ (1). 61

- Lizzeri, A., & Persico, N. (2004). Why did the Elites Extend the Suffrage? Democracy and the Scope of Government, with an Application to Britain’s “Age of Reform”. _The Quarterly Journal of Economics_ , _119_ (2), 707–765. Publisher: Oxford Academic. 4, 7

- Lowes, S., Nunn, N., Robinson, J. A., & Weigel, J. (2017). The Evolution of Culture and Institutions: Evidence from the Kuba Kingdom. _Econometrica_ , _85_ (4), 1065–1091. 5

- Martinez-Bravo, M., Mukherjee, P., & Stegmann, A. (2017). The Non-Democratic Roots of Elite Capture: Evidence From Soeharto Mayors in Indonesia. _Econometrica_ , _85_ (6), 1991–2010. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA14125. 6

- Michalopoulos, S., & Papaioannou, E. (2013). Pre-Colonial Ethnic Institutions and Contemporary African Development. _Econometrica_ , _81_ (1), 113–152. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA9613. 1, 5

- Migdal, J. S. (1988). _Strong societies and weak states: state-society relations and state capabilities in the Third World_ . Princeton, N.J: Princeton University Press. 1

- Muralidharan, K., Niehaus, P., & Sukhtankar, S. (2016). Building State Capacity: Evidence from Biometric Smartcards in India. _American Economic Review_ , _106_ (10), 2895–2929. 4, 5, 7

- Myerson, R. B. (2011). Rethinking the Fundamentals of State-Building. _Working Paper_ . 5

- Olken, B. (2007). Monitoring Corruption: Evidence from a Field Experiment in Indonesia. _Journal of Political Economy_ , _115_ (2), 200–249. Publisher: The University of Chicago Press. 11

- Olken, B., & Pande, R. (2013). J-PAL Governance Initiative Review Paper. _Abdul Latif Jameel Poverty Action Lab_ . 6, 28

- Papaioannou, E. (2020). A Comment on: “State Capacity, Reciprocity, and the Social Contract” by Timothy Besley. _Econometrica_ , _88_ (4), 1351–1358. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA18028. 4, 13

- Pritchett, L., Woolcock, M., & Andrews, M. (2013). Looking Like a State: Techniques of Persistent Failure in State Capability for Implementation. _The Journal of Development Studies_ , _49_ (1), 1–18. Publisher: Routledge _eprint: https://doi.org/10.1080/00220388.2012.709614. 5

- Rajan, R., & Subramanian, A. (2007). Does Aid Affect Governance? _American Economic Review_ , _97_ (2), 322–327. 5, 7

- Rajan, R., & Subramanian, A. (2008). Aid and Growth: What Does the Cross-Country Evidence Really Show? _The Review of Economics and Statistics_ , _90_ (4), 643–665. Publisher: MIT Press. 5, 7

33
If we were using WITS to obtain information on tariffs and then estimate Equation (4), we would overstate the NTB effect of the trade agreement because the actually occurring tariff reduction cannot be identified due to the massive measurement error. Instead, the RTA dummy picks up both, the NTB effect and the tariff reduction. If tariff cuts are larger in deep trade agreements than in other ones, the measurement error creates an omitted variable problem that biases the NTB effect upwards: in the extreme, it could be possible that a positive and trade-creating NTB effect could be entirely due to tariff reductions. Therefore, it is crucial to use tariff data that adequately deals with the missing observations in the raw data.

The traditional approach to identifying the NTB effects of deep trade agreements does not control for time-varying unobservable country-pair-specific changes in bilateral trade costs such as general globalization trends that reduce the costs of international relative to domestic trade. As pointed out by Bergstrand et al. (2015), ignoring these forces overstates the effect of RTAs on trade. The resulting omitted variable bias is stronger for trade agreements that have been signed into effect in more recent years when these trends have been more prevalent. RTAs that entered into force more recently have a higher probability of being a deep trade agreement because the number of policy areas covered in trade deals increases with time: Hofmann et al. (2019) report that the average depth steadily increased starting from 1990 going from an average of 11 provisions for RTAs signed between 1990 and 1994 to 23 provisions for RTAs signed between 2010 and 2015.<sup>3</sup> Hence, not accounting for time trends in trade policy with the help of time-varying border dummies can lead to upwards-biased NTB effects for all RTAs, and the bias is larger for deep trade agreements than shallow ones. Following Yotov (2022), we add time-varying border dummies,<sup>L2015</sup> _t_ =2001 _Globijt_ , that equal one for international and zero for domestic trade flows to flexibly account for trends in trade policy.

# **2.3 Other Challenges to Identification**

Next, we discuss other challenges to identification when quantifying NTB effects and how we deal with them. These issues are not specific to our context but are a problem for any empirical strategy guided by structural gravity that aims at backing out trade policy effects.

Trade agreements are not randomly assigned across country pairs. Baier and Bergstrand (2007) argue that the inclusion of pair-specific fixed effects goes a long way in solving the potential endogeneity bias that results from the non-random treatment of country pairs by trade agreements. In fact, historical ties, geographical proximity, or cultural similarity, which are known to both foster trade and political proximity between countries, are fully accounted for by those terms, as are any other time-invariant country-pair-specific determinants of trade.

> 3 Dür et al. (2014) and Kohl et al. (2016) show similar patterns while using different definitions for depth.

9
Figure 1. Marginal Impact of Financial Development (Private credit/GDP) on Poverty Depending on Country’s Strength in Property Rights <!-- Start of picture text --> Headcount poverty model Income of the poorest<br>RWASLEGNB B S EN WZ A<br>UGAUGAGMB<br>SEN<br>M ETH ZAFKEN TZACIV LSOG NGS MB A HDG LI WIA<br>TZBFETH A<br>CAF M CMRDGLIMLIGMBUGA<br>COG MRTBEN A TGOCIV CD FADI GAMR OZ N R<br>COG<br>BFA ETH MRT A TNGC CDG DIERIV MR OZ N O A<br>CAF M CMRDGLIMLIGMBUGA<br>ETHTZBF A<br>MZAFKEN TZAETHNGCIV GH S LSOMB A DG LI WIA<br>SEN<br>UGAUGAGMB<br>RWAGNBSLE<br>B S EN WZ A<br>2.5 3 3.5 4 4.5 2.5 3 3.5 4 4.5<br>Property right index (log) Property right index (log)<br>Povert gap model<br>RWASLEGNB<br>COG<br>BDITMRT ETHA NGCIV GCD FAER MR OZ N O A<br>CAF M CMRDGLIMLIUGAGMB<br>BFTZETH A<br>TZA ZMBKEN ETH MWILSO SCIVNG GHAF ALI DGA<br>SEN<br>UGAGMBUGA<br>B SW ENA Z<br>2.5 3 3.5 4 4.5<br>Property right index (log)<br>.1 .2<br>.15<br>0<br>.1<br>.05<br>-.1<br>0<br>Marginal impact of Private credit/GDP Marginal impact of Private credit/GDP<br>-.2<br>-.05<br>.1<br>0<br>-.1<br>-.2<br>Marginal impact of Private credit/GDP<br>-.3<br><!-- End of picture text --> 18
# **2. The Epidemic**

The prevalence of HIV/AIDS within the entire population of Colombia is low. Data from 2009<sup>20</sup> show an adult HIV prevalence of 0.59%—which represents about 140,125 people between the ages of 15 and 49 living with HIV or AIDS in the country. Preliminary epidemic estimates for 2010 point to an HIV prevalence of 0.57%—and to 144,290 people between the ages of 15 and 49 living with HIV or AIDS.<sup>21</sup> Using different data sources, the graph below shows the evolution of HIV prevalence for the general population in Colombia from 1991-2015:

**Figure 2: Evolution of HIV Prevalence for the General Population in Colombia, 19912015**

<!-- Start of picture text --> 1,00%<br>0,50%<br>0,00%<br>Data:<br>1991 a 1994 a 1996 a 1999 a 2003 a 2009 b 2010 c 2011 d 2012 d 2013 d 2014 d 2015 d<br>0,10% 0,30% 0,40% 0,49% 0,65% 0,59% 0,57% 0,57% 0,52% 0,50% 0,50% 0,49%<br>1991 1994 1996 1999 2003 2009201020112012201320142015<br>0,65%<br>0,59%<br>0,57% 0,57%<br>0,52%<br>0,49% 0,50% 0,50% 0,49%<br>0,40%<br>HIV Prevalence<br>0,30%<br>0,10%<br><!-- End of picture text -->

**Sources:** Adapted by Técnicas de Salud using data from the following sources:

- a. UNGASS 2012: p.28.

- b. UNGASS 2010: p.41.

- c. Personal communication from Dr. Luque, Asesor de la Dirección de Promoción y Prevención del Ministerio de Salud y Protección Social.

- d. Análisis estimaciones de la epidemia de VIH en Colombia 2011. Spectrum versión 4.47 generado el 30 de marzo de 2012. Ministerio de Salud y Protección Social.

> 20 UNGASS 2010: p.41

> 21 Personal communication from Dr. Luque, Asesor de la Dirección de Promoción y Prevención del Ministerio de Salud y Protección Social.

20
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Benin. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, water and sanitation—and different policy areas—including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Benin, making it possible to benchmark the country’s infrastructure situation against that of its African peers. Given that Benin is a poor but stable country (per capita income of $690 in 2008), two sets of African benchmarks will be used to evaluate Benin’s situation: nonfragile low-income countries and middle-income countries. Detailed comparisons will also be made with immediate regional neighbors in the Economic Community of West African States (ECOWAS).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, it was necessary to standardize the indicators and analysis so that everything was done on a consistent basis. That means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

Between 2002 and 2006 Benin’s growth rate, at 3.3 percent per year, slowed from the rates of 4.9 percent per year registered between 1997 and 2001. Both levels are less than the 7 percent that would be needed to significantly reduce poverty. Nevertheless, the overall net contribution of infrastructure to Benin’s growth in the early 2000s was the highest, at 1.6 percentage points, of the countries of West Africa (figure 1a). As elsewhere, the ICT sector was responsible for most of this contribution, adding almost 1 percentage point to the per capita growth rate, while the road sector actually held back per capita growth by –0.5 percentage points. Looking ahead, if Benin could improve its infrastructure to the level of the middle-income countries of the subcontinent, growth performance could be enhanced by as much as 3.2 percentage points per capita (figure 1b).

2
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6 of nontradables, then the real wage will rise following a real devaluation.

In the very short-run, when all factors are specific, the if : effect of a real depreciation is to increase the returns to factors engaged in tradable production and decrease incomes in the nontradable sector. If factor returns are fixed in the very short run, then extraordinary profits will accumulate to the owners of tradable production.

There are a number of reasons why the standard dependent ?conomy model may not be a sufficiently realistic representation of an economy. First, there are usually more than two factors of production. Many developing countries export goods using their comparative advantage not in abundant labor but in specific factors related to the production of agricultural exports or mineral exports. Thus the model for these countries should include three factors: capital, labour, and a specific f-ctor such as land or minerals.<sup>8</sup> A second example is that many countries require intermediate imports as a factor of production.<sup>9</sup> A third example is a labor force that is not homogenous, implying at least two additional factors of production: skilled and unskilled labor.

There is a large literature on the generalization of the model to many factors and many goods, which will not be reviewed here.<sup>1 0</sup> The Stolper-Samuelson theorem is not fundamentally altered when there are many goods and two factors. However, when there are also many factors, the results are less specific, although it can still be shown that there is a positive correlation between 8See Krueger (1983) and Jones (1971).

9 Lal (1983, 1984) describes a two good (tradables and nontradables), three factor (labor, capital, intermediate imports) model and applies this model to the case of Philippines. **10** See the survey by Ethier (1984).
33 high unemployment and<sup>a large real appreciation (with the US</sup> dollar appreciating during<sup>that period relative to other</sup> currencies and, additionally,<sup>Chilean inflation still higher than</sup> US inflation). Domestic<sup>monetary and fiscal policies were not out</sup> of line. To some extent,<sup>domestic prices and wages were slow to</sup> adjust to reduced inflation<sup>because of lagged wage indexation.</sup> But such slowness in price<sup>and wage adjustment in response to</sup> disinflationary policies<sup>could be expected even if there had not</sup> **been** formal wage indexation.<sup>In addition, some degree of real</sup> appreciation was also to<sup>be expected because of massive capital</sup> inflow. In my view this<sup>much-analyzed episode cannot really be</sup> considered a failure. The subsequent<sup>problems of Chile arose</sup> because of excessive<sup>private borrowing during that brief period</sup> as well as the decline in<sup>the terms of trade and rise in real</sup> interest rates.

It must be observed that<sup>the Chilean exchange rate commitment</sup> for two years was only a nominal<sup>anchor insofar as the government</sup> chose to adhere to it. The<sup>fundamental commitment in Chile was</sup> to the objective of reducing<sup>inflation just as in the five Asian</sup> countries it has been<sup>to the objective of keeping inflation</sup> low 18 The true anchor<sup>is the belief by the policy makers-</sup> usually rooted in and<sup>backed by widespread community beliefs-</sup> in the undesirability of inflation.<sup>Perhaps a fixed exchange rate</sup> has a role in signalling<sup>this anti-inflationary commitment to</sup> 18 A critique of the nominal<sup>anchor approach as applied to</sup> Chile can be found in Balassa<sup>(1985b, pp. 203-8).</sup>