rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3b: Poverty Dynamics - Synthetic vs. Actual Panel Data for Alternative Poverty Lines, Nicaragua 2001 - 2005 <!-- Start of picture text --> Poor in 2001 and Poor in 2005 Poor in 2001 and Non-poor in 2005<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0.5 1.5 2.5 3.5 4.5 5.5 6.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5<br>Poverty Line (USD at 2005 PPP) Poverty Line (USD at 2005 PPP)<br>Non-poor in 2001 and Poor in 2005 Non-poor in 2001 and Non-poor in 2005<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0.5 1.5 2.5 3.5 4.5 5.5 6.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5<br>Poverty Line (USD at 2005 PPP) Poverty Line (USD at 2005 PPP)<br>Lower Bound Actual Upper Bound<br>Poor, Poor<br>Poor, Non-poor<br>Non-poor, Poor<br>Non-poor, Non-oor<br><!-- End of picture text --> Data source: EMNV 2001 and 2005.

Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Upper bound estimations are based on 50 repetitions.

39
Table 1: Summary statistics on agricultural production

|||Staples|||Ho|rticultu|re|
|---|---|---|---|---|---|---|---|
||Staples<br>(1)|Maize<br>(2)|Beans<br>(3)|Bananas<br>(4)|All<br>(5)|Rainy<br>(6)|Dry<br>(7)|
|Yield|302|318|285|273|575|588|566|
|Hired labor (days)|37|37|37|9|61|66|57|
|Hired labor expenditures|28|28|28|7|45|49|42|
|HH labor (days)|266|248|260|101|417|414|420|
|Inputs<br>|19|35|16|3|50|50|50|
|Profts||||||||
|Shadow wage = 0 RwF/day|256|255|241|263|481|489|475|
|Shadow wage = 480 RwF/day|128|136|117|214|280|290|273|
|Shadow wage = 800 RwF/day|43|56|34|182|147|158|139|
|Sales share|0.19|0.30|0.14|0.46|0.62|0.60|0.63|
|Irrigated|0.02|0.02|0.02|0.02|0.65|0.25|0.93|
|Rainy|0.99|1.00|1.00|0.50|0.42|1.00|0.00|
|log area|-2.44|-2.26|-2.47|-2.10|-2.71|-2.83|-2.62|
|Share of obs.|0.65|0.13|0.42|0.19|0.12|0.05|0.07|

_Notes:_ Sample averages of outcomes by crop per agricultural season are presented in this table. Yield, inputs, hired labor expenditures, and profits are reported in units of ’000 RwF/ha, labor variables are reported in units of person-days/ha, and log area is in units of log hectares. All other variables are shares or indicators. For reference, the median wage in our data is 800 RwF/person-day.

**Stylized Fact 1.** _Irrigation in Rwanda is primarily used to cultivate horticulture in the dry season._

Farmers in our data rarely irrigate their plots in the rainy seasons, and almost never use irrigation when cultivating staples or bananas (only 2% of plots cultivated with staples or bananas use irrigation in our data). In contrast, 93% of plots cultivated with horticulture in the dry season use irrigation. This stylized fact makes agronomic sense as the rainfall in rainy seasons in this part of Rwanda is usually sufficient for either staple or horticultural production (and in wet years may be harmfully excessive for horticulture). Additionally, as staples do not have a sufficiently short cycle to permit cultivation during the relatively short dry season (while horticulture does), it is not agronomically feasible to use irrigation to cultivate staples during the dry season.

**Stylized Fact 2.** _Horticultural production is more input intensive than staple cultivation, which in turn is (much) more input intensive than banana cultivation._

The mean horticultural plot uses about 420 days/ha of household labor, 60 days/ha 13
# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Investment Incentives in Developing Countries: Types of Instruments<br>and Frequency of Use|2|
||Some Issues of General Relevance|2|
||Types of Instruments to Encourage Investment|4|
|Ill.|The Economiuc Rationale for Investment Incentives|7|
||The Role of Corporation Income Taxes|7|
||<br>The Efficiency of Capital Markets|14|
||The Role of Investment Incentives|25|
|IV.|The Conceptual Impact of Investment Incentives|30|
||Measuring Incentive Effects Using METRs|32|
||<br>Using METRs to Evaluate Investment Incentives|52|
|V.|The Impact of Tax Incentives on Investment: A Brief Survey of the Empirical||
||<br>Approaches and Applications in Developing Countries|73|
||Surveys of Firms|73|
||<br>Estimation of Ad Hoc/Eclectic Equations|74|
||<br>Investment Models|75|
||The Production Structure Approach|77|
||<br>Computable General Equilibrium Models|81|
|VI.|Concluding Remarks|82|
|Tables||85|
|Referen|ces|89|
|Annex|Tables|96|
_Figure 6. Cost decomposition of landed duty-paid value of exports in the US (2015)_

# **Woven men’s trousers (cotton)**

**Knitted women’s trousers (synthetic)**

<!-- Start of picture text --> 2016 2016<br>BangladeshCambodia China Ethiopia Kenya Lesotho Vietnam BangladeshCambodia China Ethiopia Kenya Lesotho Vietnam<br>unit value (fob) cif duty unit value (fob) cif duty<br>80 100<br>80<br>60<br>60<br>40<br>40<br>Price per unit (US$) Price per unit (US$)<br>20<br>20<br>0 0<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from USITC There is considerable variation among export destinations in this context. Lesotho has by far the greatest variety of exports going to South Africa, with only Namibia and Botswana exporting more. While the overall number of products has decreased between 2010 and 2015, the number of exports greater than $100,000 in value has increased. More significantly, while the number of products going to the European Union is greater than to the United States, over 30 products are greater than $100,000 for the United States, and only nine go to the European Union – this is a decline of 50% compared to 2010.

Most apparel export products to the United States had stagnant or declining market share over the last decade. For one, Lesotho has been losing market share in the United States in 10 of its most exported products against Asian and Latin American competitors such as Vietnam, Malaysia, Bangladesh and Cambodia. The potential expiration of AGOA could also result in a significant disadvantage for Lesotho’s apparel exports, as they would revert to the MFN tariff - which is slightly higher than 20% on average - since none of these products is granted duty-free status under the Generalized System of Preferences (GSP), the other major U.S. preferential program available to Lesotho.

Exports to the European Union are highly concentrated, with diamonds representing 98% of exports in 2015. The number of products exported to the European Union outside diamonds and the value of exports is very small. Fewer than 20 products over EUR 5,000 were exported to the European Union in any given year over the last decade and only four products recorded exports over EUR 1 million in 2015. Nondiamond exports were only able to surpass the EUR 3 million threshold twice in the last decade. Among non-diamond exports, the agricultural sector is the most important for the European Union. Surprisingly, apparel exports were less than half a million euros in 2015 and are not persistent (i.e. most products are not exported for more than two or three years in a row). This despite the fact that apparel products benefit from zero duties and generous rules of origin in the European Union as a result of the preferences under the Everything But Arms (EBA) agreement. Lesotho has recently concluded EPA negotiations with the European Union that improve market access and may enable Lesotho to diversify into new exports.

Exports to South Africa are more diversified than to the United States and the European Union but are still dominated by apparel. Apparel and machinery have traditionally accounted for the biggest share of exports 11
in harassment across spaces even at similar presence of men. This is not what we observe empirically (Figure 4). Another way sorting of men across spaces could manifest in our data would be a decrease in harassment in the public space as perpetrators are able to enter the reserved space, while harassment in the reserved space would increase as share of men in the public space goes down. Instead, we find no impact of presence of men in the reserved space on harassment in the public space, and vice versa (Figure A11). However, we cannot test whether perpetrators sort between cars when the rule _is_ well followed, because we do not observe situations with few men in the public space (Figure A3).

# **8.4 Congestion**

Congestion could play a role in two ways in our analysis. First, if congestion differed between the reserved and public spaces, this would create another mechanism affecting willingness to pay. However, both administrative and rider audit data show that congestion levels are almost identical across the reserved and public spaces (Figure A2). Our main estimates all control for above-median congestion.

Second, congestion could also create a type of measurement error in our analysis. At times of extreme congestion, participants may be physically unable to select the car they prefer, such that the car they select (which we observe) does not represent the car they prefer (unobserved). This would bias our estimates of mean takeup toward 13 - 17 percent for these observations (because one of each train’s six to eight cars is designated as a reserved space). This is well below the takeup rates for the “high level users” and above the levels for the other two thirds of the sample (Figure 3). This suggests that if anything this kind of measurement error could (a) bias the estimates for the high-level users downwards, and (b) reduce the estimated differences between the three groups. We explore this in Figure A12, which describes the relationship between congestion and (1) demand for the reserved space, and (2) the share of men in the women-reserved space. We notice that, at low levels of congestion, the share of men in the reserved space is flat, at about 20 percent. As congestion 31
and if the country can lend and borrow freely on international financial markets, then consumption can be smoothed out by lending and borrowing. The fluctuations in consumption will thus be substantially reduced and the cost of the risk correspondingly reduced.

Even if the country cannot borrow, it can accumulate savings in buffer funds which can be drawn down in adverse times with a substantial reduction in the costs of risk. Papua New Guinea has followed a conservative policy of averaging export receipts from copper (and other less critical export crops) over a lengthy time horizon, with a buffer fund held in convertible currency. While it is rare for a developing country to resist the temptation to invest such funds in its own capital stock (thereby losing the international liquidity needed to buffer trade fluctuations), both the International Monetary Fund, through its Compensating Financial Facility, and the Lom6 countries, through STABEX, offer a similar buffering facility, being prepared to lend to countries at favorable rates when their export earnings drop sufficiently below trend.

Can a country optimally smooth consumption by borrowing and lending from overseas sources? Let us consider the most favorable and simplest case. Suppose that export revenue fluctuates from period to period in a serially uncorrelated way with no trend, and that the utility function is quadratic, and that the rate of pure time preference, 6, is equal to the rate of interest, r, abroad, then the country would have no motive for saving or borrowing other that to smooth consumption. We make this assumption to focus on the consumption smoothing aspect of international boffowing, and to 10
A single analysis for the Serbs, the comparison group, covers individuals in both cohorts with a dummy variable identifying the two cohorts.

The estimated models provide the setting for describing counterfactual outcomes. The main objective is to compare expected enrolment rates for the exposed cohort under the conditions (parameters) of the unexposed cohort. This way, we estimate the enrollment rate if those aged 16-26 years were living under the conditions of those aged 27-35 years. Formally, this implies comparing E[Eij' _I Xjl, (1]_ = E[ P _{Xjl,3l_ + uijl >0} with E[Ejj _I Xj14,_ (2] = E[ P{XQjl#2 + u1il >0}. For example, we estimate the enrollment equation (2) for the cohort aged 27-35 years (not exposed) and using these parameters, we predict the enrollment of the cohort aged 16-26 years.

# _Survival in Secondary and Higher Education_

Once individuals are enrolled in secondary education (grade 9 th), their performance throughout secondary education and higher education is modeled as a survival model where educational attainment (years of education) is the duration of interest.

The strategy actually mimics that described earlier in the paper, but a number of technical differences are worth noting. First, survival models connect the drop out (or hazard) rate at each education level (year) to observed characteristics of the individual and the household. For each Albanian cohort, c=1,2, we have: Estimating hc(.) requires imposing some assumption about the distribution of the unobserved variables. Choosing the appropriate hazard function (or its corresponding survival one) is driven by the features observed in the empirical survival (or hazard) function. The 15
this is tiat some potential policy measures may be warranted if the autiorities in a parficular developing country wish to enhance portfolio inflows (especially those with risk sharing characteristics such as equity portfolio investment).

# 4. CONCLUSIONS AND POLICY IMPLICATIONS

The conclusions in this paper are based on an econometric analysis of new quarterly World Bank data on gross portfolio investment flows for a sample of eight emerging markets forte period 1989QI-1993Q2. Four countries in each geographical region (namely, India, Indonesia, South Korea and Thailand in Asia, and Argentina, Brazil, Chile and Mexico in Latin America) were examined, all of which have been experiencing large portfolio investnent inflows in recent years.

According to the findings of this study, the perceived competition between developing countries for portfolio investments from abroad is, indeed, real and should not be considered to be merely a part of the general increase of portfolio allocations to emerging markets as a group from investors in industrialized countries. In addition, gross total portfolio flows to Latin America are found to be more significantly related to those going to East Asia (Indonesia, South Korea and Thailand in this sample) than those to South Asia (i.e. India in this sample). Hence, developing country policy makers must continue to provide the right signals to international capital markets, in tenms of economic and domestic institutional reforms that attract for portfolio investment from abroad, if they expect to compete successfully with other developing countries for the pool of private voluntary capital that is "allocated" by portfolio managers to the emerging markets group. Over the long term, this will also ensure the susinability of the portfolio flows into those "well performing" countries with improved creditwortiiness; they will benefit from increased interest of intemational institutional investors in allocating a share28
**Table 2. 1  Information requirements for opening a bank account in Colombia**

|Basic infor|mation requirements:|
|---|---|
|•<br>•<br>•|Complete name and last names<br>Identification number: citizenship number, identity card, or resident card<br>Date and place of birth|
|•|Mailing address and home telephone number|
|•|Job or occupation|
|•|Description of the occupation: Employed or autonomous, and position held|
|•|Monthly income and expenses|
|•|Other income, or income originated in activities other than main one|
|•|Total assets and liabilities|
|•|In case of a trust, identification of the good and type of resource that is being handed in|
|•|Disclosure authorization for control and reporting to risk management centers|
|•|Signature and fingerprint of the potential client|
|In case the|potential client’s activityinvolves transactions in foreign currency:|
|•|Type of foreign currency transaction that the person usually undertakes|
|•|Foreign currency current accounts that the person may have: account number, bank, city,<br>country and currency|
|•|If the potential customer does not undertake foreign currency transactions, a declaration<br>including this statement is required|
|Documents|that must be annexed to the application form:|
|•<br>•<br>•|Photocopy of the identification document (citizenship number, identity card, or resident card)<br>Proof of income (professional fees, tax retention certificate, or other document)<br>Tax filing declaration corresponding to last available fiscal year|
|•|Last available audited financial accounts,when applicable|

_Source: Superintendencia Bancaria, Circular Externa 025 de 2003_ 4. **Similarly, documentation requirements for credit applications require a high degree of formality.** The minimum information that needs to be provided by the applicant includes proof of identity, financial statements and the last rent payments if applicable, plus bank references and a positive reply from the credit bureau<sup>22</sup> . These requirements are clearly geared towards people who already are active in the formal financial sector. Requirements such as bank references and credit bureau information are difficult to fulfill by the unbanked and those mainly active in the informal sector.

**Figure 2. 1  Bank requirements for opening accounts**

> 22 Marulanda (2004)

31
movement of girls to be more restricted than that of boys. On the other hand, if the leading channel through which conflict decreases human capital accumulation is through direct engagement of children in fighting, then boys would be more negatively affected.

However, the general expectation that schooling should be disrupted in regions of more intense fighting may not be very well founded in Nepal. First, national trends do not indicate an increase in poverty coinciding with the conflict but a steady decrease in poverty instead (World Bank, 2005). Second, the insurgents have been reported to actively and coercively challenge traditions that tend to decrease educational attainment, namely discrimination against women, lower castes and indigenous ethnic groups (Hart, 2001; Lama-Tamang et al., 2003; Aguirre and Pietropaoli, 2008).

In Nepal, marriage is almost universal, arranged marriages are the norm, and early marriages are quite common, especially for girls. Nearly 80 percent of 20-year old women and of 25-year old men surveyed in the 2001 and 2006 Demographic and Health Surveys of Nepal were already married (Figure 1). Adverse economic conditions such as those potentially brought about by civil conflict and a potential shortage of husbands due to the fighting and migration are unlikely to affect the quasi-universality of marriage, but violent conflict could lead to an increase in the proportion of women who do not marry and are therefore likely to end up in submissive positions in the household of a relative. Younger brides are generally preferred by prospective grooms, so that the dowry attached to them is lower. Civil conflict may therefore also lead impoverished parents to favor early marriages. If the conflict affects female educational outcomes, it is also likely to have an impact on early marriage, as girls who leave school earlier are more likely to be married young. In addition, the strong focus on sexual purity, of unmarried girls especially, in most religious groups of Nepal, combined with the reported prevalence of rape during the conflict (Manchanda, 2004; Ariño, 2008), may have ruined the chances of marriage for a number of rape victims, or encouraged parents to marry their daughters early to prevent it. On the other hand, Maoist insurgents are reported to have opposed early marriage and even tried to abolish dowry (Hart, 2001; Manchanda, 2004; Aguirre and Pietropaoli, 2008), and Maoist recruitment targeted girls under 18, which may have contributed to increase age at marriage. Age at marriage matters insofar as women who marry younger are more likely to have an inferior position in the couple (Niraula and Morgan, 1996). Marriage practices largely condition the future life of the spouses, especially so for women, and so the impact of civil conflict on marriage is of major importance for the human capital accumulation, fertility, and long-term wellbeing of women.

4
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