rafmacalaba/datause-encoder-data huggingface dataset:
to tax, their ownership of significant assets that can serve as collateral, and their capacity to pool the resources needed to execute large-scale projects (Martinez et al. 2023).

# **Figure 1. Public investment in EMDEs**

<!-- Start of picture text --> A Public investment, percent of GDP B. Public investment, percent of total investment<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP Percent of total investment<br>12 50<br>10 40<br>8<br>30<br>6<br>20<br>4<br>10<br>2<br>0 0<br>EMDEs AEs LICs Com. Com. EMDEs AEs LICs Com. Com.<br>Exp. Imp. Exp. Imp.<br>C. Public investment growth D. Public investment growth by EMDE group<br>Percent Percent<br>1990-2022 average 1990-2022 average<br>20<br>12<br>16<br>8 12<br>8<br>4<br>4<br>0 0<br>EMDEs EMDEs excl. World LICs Commodity Commodity<br>China exporters importers<br>1990-99 2000-09 2010-22 1990-99 2000-09 2010-22 1990-99 2000-09 2010-22 1990-99 2000-09 2010-22 1990-99 2000-09 2010-22 1990-99 2000-09 2010-22<br><!-- End of picture text --> _Sources:_ Haver Analytics; IMF Investment and Capital Stock Dataset (IMF 2021a); WDI (database); World Bank. _Note:_ AEs = advanced economies; EMDEs = emerging market and developing economies; Com. Exp. = commodityexporting EMDEs; Com. Imp. = commodity-importing EMDEs; LICs = low-income countries. A.B. Bars show means and whiskers show interquartile ranges for 2010-22 by group. Sample includes up to 36 advanced economies and 126 EMDEs.

C.D. Average annual public investment growth calculated with countries’ real public investment in constant international dollars as weights. Sample includes up to 162 economies, of which 126 are EMDEs, 23 are LICs, 76 are commodity exporting EMDEs, and 50 are commodity-importing EMDEs.

Over the past three decades, public investment patterns in EMDEs have evolved notably. In the 1990s, public investment in EMDEs was growing at a rapid pace largely on account of robust growth in China. This was followed by exceptionally high public investment growth in the 2000s which witnessed a period of macroeconomic stability, rapid economic integration, and reduction of poverty, amid elevated commodity prices. The latter also resulted in accelerated public investment growth in commodity-exporting EMDEs (figures 1.C and 1.D).

14
28

# **Table 4: Correlates of contract choice**

|Dependent variable: Ch|ose variable p|ay contract|||
|---|---|---|---|---|
||I|II|III|IV|
|Pro-poor preference|-2.091**|-2.170**|-2.407**|-2.093**|
|Donation topoor school/total donation|(0.85)|(0.99)|(0.97)|(0.85)|
|Ability (score)||0.318*|0.391**|0.481***|
|Score in ability round||(0.16)|(0.16)|(0.17)|
|Ability (time)||-0.001**|-0.001**|-0.002***|
|Time taken in abilityround(in seconds)||(0.00)|(0.00)|(0.00)|
|Occupation: Midwife|||-0.443|-0.097|
||||(0.73)|(0.90)|
|Occupation: Other|||1.030|0.689|
||||(0.87)|(1.28)|
|Female||||-0.070|
|1 = Female||||(1.12)|
|Age (years)||||0.055<br>(0.07)|
|Income (in CFA)||||-0.00001<br>(0.00)|
|Current state of personal finances||||0.624|
|4 = Excellent||||(0.65)|
|Confidence in payment to schools||||0.301|
|5 = Strongly Agree||||(0.30)|
|Interest in task||||0.994***|
|5 = Veryinteresting||||(0.34)|
|Constant|2.883***|2.331|2.059|-4.801*|
||(0.60)|(1.76)|(1.86)|(2.61)|
|Pseudo R2|0.029|0.123|0.153|0.266|
|Log Likelihood|-47.23|-42.66|-41.16|-35.7|
|P|0.013|0.004|0.013|0.000|
|Observations|100|100|100|100|

Notes: Logit regressions. Dependent variable is health worker contract choice (=1 if the worker chose the variable pay contract). * 10%, ** 5%, *** 1% significance level. Clustered standard errors (by day) in parentheses.

saw 5.30 cases on average, approximately half a case more, but not significantly different from, the 4.89 cases seen on average by workers who chose the flat salary contract (two-sample t-test: p=0.33). Those who chose the salary contract saw a slightly higher fraction of poor patients: 42 percent versus 40 percent for the workers choosing a piece rate (twosample t-test: p=0.85).
42 The other extreme case ignores the issue of whether one is able to perfectly identify the poor or not, and simply transfer to _all_ househoids an amount that would completely eliminate poverty. Under such a scheme, the total cost would be much larger than under a scheme where transfers are only given to the poor. This "universalistic" program is depicted in Figure 2. The figure shows that to each and every household an amount Z is transferred such that there are no households with an income below Z, which explains why there is a parallel upward shift of the curve to a level Z.

The cost/benefit of both schemes is apparent; in the perfect targeting case total transfers needed to eliminate poverty are less, but there is a cost in identifying the poor that could easily outweigh the benefits from the smaller transfers. Under a universalistic program, total transfers are higher but there is no a _priori_ cost of identification.36 From an operational perspective, the perfect targeting scheme is impossible to apply, and Besley and Kanbur (1988) suggest that universalistic schemes are probably more realistic and have indeed been applied.<sup>37</sup> But as Thorbecke and Berrian (1988) suggest, an intermediate solution is to use socio-economic indicators that allow policy makers to reduce the size of transfers under a universalistic program.

**36** For a more detailed analysis on the cost of identification see Kanbur and Besley (1988). See also World Development Report (1990) for a general discussion on implementation costs. 37 See Anand and Kanbur (1987).
> **Table 10. Economic Sector. Matching group: Age 17-65. Three nearest neighbors based on propensity**

|_Cohort_|_Agric_<br>_Control_<br>**_Group_**|_ulture, fish_<br>_CONAL_<br>_EP_|_ing, etc._<br>|_Control_<br>_Group_|_Extractio_<br>_CONAL_<br>_EP_|_n_<br>_Dif_|_M_<br>_Control_<br>_Group_|_anufactu_<br>_CONAL_<br>_EP_|_ring_<br> <br>_._|<br>_Control_<br>_Group_|_Construct_<br>_CONAL_<br>_EP_|_ion_<br>|_Electri_<br>_Control_<br>_Group_|_city, gas,_<br>_CONAL_<br>_EP_|_and water_<br>_Diference_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|90 - 93|2.3|0.8|-1.4|0.1|0.0|-0.1|22.2|29.8|7.6|3.0|1.2|-1.8|0.8|0.8|0.0|
|91-94|1.3|2.8|1.5|0.1|0.4|0.2|20.4|33.3|12.9|2.5|2.1|-0.4|0.4|0.4|-0.1|
|92-95|2.1|0.4|-1.7|0.1|0.8|0.7|24.4|35.7|11.3|2.4|3.1|0.7|0.2|0.4|0.2|
|93-96|2.5|0.4|-2.1|0.1|0.4|0.3|25.7|32.1|6.4|2.6|4.0|1.3|0.7|0.4|-0.3|
|94 - 97|1.5|0.9|-0.6|0.1|0.0|-0.1|25.8|38.5|12.6|2.5|3.0|_0.5_|0.1|0.4|0.3|
|Total'|1.9|1.1|-0.8|0.1|0.3|0.2|23.6|33.8|10.2|2.6|2.7|0.1|0.5|0.5|0.0|
|_ENE 982_|20.3|||0.4|||18.1|||_5.51_|||0.5|||
|_ENE 98, LS'_|17.0|||0.1|||31.8|||4.57|||0.2|||
|_ENE 98, US'_|6.3|||0.5|||19.7|||2.22|||1.3|||

**1. Sample: Workers** in **the matching** group.

**2. Sample: All workers.**

3. **Sample: Workers with lower-secondary complete and 3 years of experience ( 8 and 19 years old).** 4. **Sample: Workers with upper-secondary complete and 1-5 years of experience (22-26 years old).**

**Table 10. (cont.)**

|_Cohort_|_Commer_<br>_Control_|_ce, Restau_<br>_Hotels_<br>_CONAL_|_rants, and_<br> <br>_Diffe_|_Tra_<br>_Co_<br>_Control_|_nsportati_<br>_mmunica_<br>_CONAL_|_on and_<br>_tions_<br>_Difference_|**_Fin_**<br> <br>_ro_|**_ancial_**_Se_|_rvices_<br>_Dffe_|_Person_<br>_S_<br>_Group_|_nel, Com_<br>_ocial Serv_<br>_EP_|_mon, and_<br>_ices_<br>-<br>_Diference_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|90 - 93|27.8|29.8|2.0|4.9|4.9|0.0|2.4|4.1|1.7|36.5|28.6|-7.9|
|91 - 94|29.2|23.9|-5.3|4.9|2.5|-2.4|2.7|2.5|-0.3|38.5|32.3|-6.2|
|92-95|29.0|22.5|-6.5|5.6|3.5|-2.1|3.1|3.1|0.0|33.2|30.6|-2.6|
|93 - 96|27.0|22.2|-4.8|4.5|1.6|-3.0|2.0|1.6|-0.4|34.8|37.3|2.5|
|94-97|27.1|22.2|-4.9|5.3|2.6|-2.7|1.0|2.1|1.1|36.6|30.3|-6.2|
|Total'|28.1|24.1|-4.0|5.0|3.0|-2.0|2.3|2.7|0.4|35.9|31.9|-4.0|
|_ENE 982_|22.4|||4.4|||1.0|||27.5|||
|_ENE 98, LS'_|26.0|||3.6|||0.2|||16.5|||
|_ENE98, US'_|<br>31.6|||8.1|||2.9|||27.6|||

**1. Sample: Workers in the** matching group.

**2. Sample: All workers.**

3. **Sample:** Workers with **lower-secondary complete and** 3 **years of experience (18 and** 19 **years old).** 4. **Sample: Workers with upper-secondary complete** and **1-5 years of experience (22-26 years old).** 32
# **6. CONCLUSION**

The purpose of this document was to longitudinally describe and analyze HIV incidence trends in 20 Sub-Saharan African countries, using multiple sources and forms of data, empirical and modeled, and to assess how these data related to each other. It was found that modeled incidence trends are generally in congruence with nationally reported incidence proxy data from ANC and DHS/AIS reports, and both sets of data indicate that incidence has been on the decline in all countries analyzed here. However, measured incidence rates and prevalence coming from published studies and ALPHA network sites show variation from the modeled and nationally reported data, more often with higher values. A caveat is that these data come from discrete field sites and regions, and too few studies have provided temporal data across multiple sites for comparison with nationally reported data to help validate modeled HIV incidence trends. More focus should be given to efforts to longitudinally measure and monitor incidence within and across geographical areas.

This analysis also sought to investigate the dynamics of incidence trends prior to and during ART scale-up. In all countries, a downward trend in modeled incidence had begun prior to scale-up, suggesting other factors responsible for reduced incidence, such as HIV risk behavior changes or the natural rises and falls of an epidemic. While it cannot be ruled out that increased national ART coverage helped maintain downward modeled incidence trends, but concluding so was beyond the scope of this review. Furthermore, it is likely too early for the effects of ART scale-up to be observed at the population level, even for descriptive purposes, and current efforts are going into investigating what led to incidence declines prior to scale-up (previously mentioned Imperial College studies, funded by the World Bank).

In conclusion, while modeled incidence trends are encouraging and suggest a positive evolution towards lower national HIV incidence levels, maintaining this progress or rejuvenating efforts to break stagnant incidence trends will be helped with accurate and representative incidence data and a better understanding of how and why incidence began to decline within each country (and whether these declines have stabilized or continue to decline in recent years). This requires continual updates to Spectrum software based on the latest understanding of how various factors influence HIV epidemic dynamics (ART’s and other interventions’ effects on prevalence data and risk of transmission, biases from prevalence data, ART adherence and efficacy estimates, and changing/aging HIV+ population dynamics). Finally, increased availability and measurement of empirical HIV epidemiological data (as for instance through the ongoing TasP trials) will greatly improve the understanding of the HIV epidemic on the ground and provide further information to improve the Spectrum model.

51
# **I. Introduction**

The importance of women in economic development has grown, with their labor force participation rate increasing from 32.7 percent to 39.2 per cent over the 1980-90 period. It is estimated that the growth will continue so that the female participation rate will be 40.8 per cent of the labor force in 1993 and then increase to 44 per cent by 1998. The main reasons for the growth are the heightened awareness of intellectual life, the delay of marriage, increased social mobility, and the changing perception of women working outside the home (Manning 1993:65). Another factor is increased employment opportunities, especially because of growth in the laborintensive industries like textile, garment, and footwear. The growth of the above mentioned three industries has risen to 34 per cent p.a. and the share of labor intensive exports has gone up from 32 per cent to 45 per cent (see Table 1). The total number of workers in these industries (with the majority of them being women) increased sharply from 23 per cent of the total work force in the large- and mid-sized industries in 1985, to 30 per cent in 1990 (Manning 1993:65).

Recent statistics from the Industrial Survey conducted by the Central Bureau of Statistics distinguish between woman and men workers. From Table 2 it can be observed that garments and footwear are export-oriented with a significant percentage of production being exported. While the percentage of export out of production for textiles is only 18 percent, indirect exports are in fact much higher. The three sectors absorb one third of total workers. It is clear that women workers predominate in the three sectors, accounting for 45 percent of total women workers in the manufacturing sector. The three sectors, especially garment and footwear, are also dominated by women workers with three quarters of total workers being women in the garment and footwear sectors and 53 percent of total workers being women in textiles.

It is also interesting to note that most workers are in the large scale companies (Table 3). Of course the industry survey does not include the very small scale and cottage industries (defined as employing less than 20 workers). Thus, in the formal sector most workers are in the medium-large companies.

The growth of women workers in the industrial sector has a number of implications. Firstly, it means the participation of women in paid activities results in an increased strengthening of the women's position in the family and in society due to their increased financial capacity. This in turn contributes to the development of the economy. On the negative side, the participation of women in the work force corresponds with a marked deterioration in working conditions, the emergence of more low-skilled jobs and an inadequate opportunity for
# 3.1 Volume of trade

Trade openness is commonly measured by the volume of trade, defined as real exports plus imports, over GDP. Overall, the volume of trade has sharply increased in most selected regions from 1990-94 to 2010-14 —except for oil rich countries that already have a high volume of trade (about 94-95 percent in periods 1990-94 and 2010-14). Trade openness grew by 13 percentage points of GDP for the whole region over the past two decades. The growth of trade as a percentage of GDP among non-resource rich countries was comparable to the regional average whereas it was faster than average among countries that are abundant in minerals and metals (see Table 1).

# 3.2 Trade Diversification

Looking at the volume of trade is not sufficient to understand the linkages between international trade integration and growth. Looking beyond the volume of trade, we examine the degree of trade diversification, as proxied by the extent of trade concentration across products and across markets. Earlier evidence shows that high levels of diversification in export baskets may result in higher growth (Lederman and Maloney, 2007), and reduce risk to external shocks and output volatility (Haddad et al. 2010). Diversifying the export basket is an important element of the antipoverty agenda as low-income countries tend to be specialized in few export products in highly volatile sectors (Cadot et al. 2013). Having said this, it is typically recommended for these countries to implement policies that diversify their export baskets and/or raise the productivity of the reduced product space through lower trade costs.

We use the Herfindahl index to compute the concentration of exports across products and markets. Here, lower values of our concentration measures can be interpreted as greater extent of export diversification. The Herfindahl index is computed as follows: where _x_ represents the amount of exports, the subscript i stands for a specific product (or market) and n is the total number of products (or markets). When a single export product (market) produces all the revenues, _H_ =1, as opposed to _H_ =0 when a large variety of products compose the revenues (Haddad et al. 2010; Lederman and Maloney 2012; Cadot et al. 2013). Higher (lower) values of the Herfindahl index, _H_ , imply lower (higher) degree of export diversification. We use COMTRADE data on exports by product at a 4-digit disaggregated level from the SITC Revision 1.0 to compute Herfindahl indices of export product concentration. Bilateral data from COMTRADE are also used to compute Herfindahl indices of export market concentration.

_Table 1_ displays the evolution over time of the Herfindahl indices of export product and market concentration in the periods 1990-94 and 2010-14. In the period under analysis, the region’s Herfindahl Index for export product concentration declined from 0.41 in 1990-94 to 0.3 in 2010-14 —thus, signaling an improvement in terms of trade diversification. In contrast, the region’s Herfindahl index of export market concentration remained invariant.

_page 12_
projections, the additional investment in the Panama Canal is estimated to reach just 8 percent of GDP during the peak spending year of 2010, while the average spending over the entire construction period is less than 4 percent of GDP.

At the sectoral level, the Canal expansion creates additional demand for only two types of activities in Panama: construction and capital goods. According to the initial structure of the investment demand, one additional Balboa spent of the Canal investment generates 64 cents of additional demand for construction (which is almost entirely domestic) and 36 cents for capital goods (which are mostly imported). Although these sectors generate demand throughout the economy (the multiplier effect), the limited linkages of the Canal with the rest of the economy restrict the ability of the investment spending to energize the entire economy.

**Figure 1: Expenditures for the Canal expansion are spread over 2007-2014**

<!-- Start of picture text --> 1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014<br>Years<br>Millions of Balboas<br><!-- End of picture text --> Source: Panama Canal Authority (2006) Master Plan, Chap.9

# **3. A CGE-Microsimulation Model for Panama**

Given the vast differences in earnings across sectors and the semi-isolated status of the Canal in Panama‘s economy, this paper adopts a structural macro-micro model to capture both the direct and indirect impacts of the Canal expansion on the income distribution. The expansion directly affects those who receive an income from the construction and operation of the Canal, but this group represents a fairly small portion of the total employment and includes very few poor and no indigenous persons. Thus, the potential impacts of the Canal expansion on the income distribution are likely to be mostly second-order, general equilibrium effects. These can be grouped into four major categories: a) changes in real income growth, b) changes in factor markets (employment, wages, and rental rates), c) changes in prices of consumer goods, and d) use of the government receipts from the new Canal.

The methodological approach of this paper can be best described as a two-step process. In the first step, a CGE model is used to create two scenarios, one with a new expanded Canal and the other without. In the second step, the four sets of general equilibrium effects identified above are mapped to households in a microsimulation model. This procedure generates macro and micro counterfactuals which can then be used to estimate the effects of the Canal expansion on the distribution of incomes.

5
The “grievance” approach (Posen, 1993; Gurr 1994; Petersen, 2002) asserts that civil conflict is fueled primarily by a failure to peacefully resolve political grievances, ordinarily caused by ethnic or social cleavages that are held by a sub-section of the population. Economic factors can still have an important effect on insurgency by fueling these grievances, but only indirectly. Grievance theories predict that development projects should not affect violence in so far as they do not affect underlying social or ethnic tensions or contribute to the resolution of the resulting grievances.

Finally, the “hearts and minds” theory (Berman, Shapiro, and Felter, 2011) asserts that the level of violence is, in part, determined by general attitudes towards the government. Increased support for the government makes it more difficult for insurgents to recruit additional members, thereby tightening insurgents’ labor constraints (Condra et. al.., 2010), while also making it easier for the government to gather intelligence, locate insurgents, and disrupt insurgent movements. These two effects reduce violence, but differ in timing, with the information-sharing effect almost immediately apparent, with the recruitment effect taking longer. Thus, according to the hearts and minds theory, development projects which increase support for the government can reduce violence.

# **II.2. Empirical Evidence**

Recent research has attempted to empirically test the aforementioned theories of conflict using data on ongoing insurgencies in Afghanistan, Iraq, and the Philippines.<sup>3</sup> Berman, Felter, and Shapiro (2009) test the “opportunity cost” theory, examining the correlation between unemployment rates and insurgent attacks in Iraq and the Philippine. Contrary to the predictions of the theory, they observe a negative relationship between unemployment and attacks against the government and allied forces and no significant relationship between unemployment and attacks that result in civilian fatalities. On the other hand, Iyengar, Monten, and Hanson (2011) provide

> 3 A number of studies that draw on data from the Israeli-Palestinian conflict also provide relevant evidence though admittedly more linked to terrorism than insurgency. Berrebi and Klor (2008) and Gould and Klor (2010) find that terrorist attacks have a significant and long-lasting effect on the preferences of the Israeli electorate, whereas Jaeger et. al (2012) show that the effect of local Israeli violence has only a temporary effect on Palestinian civilian political preferences. Benmelech, Berrebi, and Klor (2012) show that high levels of unemployment enable terror organizations to recruit terrorists with higher human capital, leading to more important Israeli targets getting attacked.

6
<!-- Start of picture text --> AGO<br>oi<br>ANY AL Eee°<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Discovery _———— Arms imports<br><!-- End of picture text --> <!-- Start of picture text --> MOZz<br>ee |i<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Discovery _———— Arms imports<br><!-- End of picture text -->

<!-- Start of picture text --> AZE<br>in<br> |) a<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Discovery _———— Arms imports<br><!-- End of picture text -->

<!-- Start of picture text --> SDN<br>o<br>lw o.<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Discovery _———— Arms imports<br><!-- End of picture text -->