rafmacalaba/datause-encoder-data huggingface dataset:
Table 5.1 <!-- Start of picture text --> Nat Capital Flows'<br>(billions of US _)<br>- __________________________ 1993 1899 I995 1 t997 1998 19SS 2000 2001 2t 2003<br>Cials Counbcl_s_a' _ _ _<br>PrFs Cfif nl n e 308 353 5658 74 3 - a6 -31 5 .139 -157 -16 2 -a4 .39<br>Pt -et wImVn,nT. 67 7 5 t3 1 7 102 1l1. 146 14 3 8.3 103 ¶06<br>PulvipuWdlokrhh not 250 t3&3 18s 26 9 9 -9 0 118 70 3 2 51 Is<br>Ottar pfS ram nitS -08 15.6 279 35 24 7 1 -404 -36 9 -27 7 -219 -tO<br>OleW lund_ 32 07 88 -47 137 170 -221 6 oe ¶4 33<br>Chinswn' -200 4 171 _ .481 4061 -69 -381 -224 III -11A 7 -121<br>Cwrut ame - 2 -9S .531 .255 697 627 471 326 231 179<br>'NM od fiW l rnf _t a itS .dat in rt.rn ndSpnlkro 9unt fal oarhs ri sllarrn rS imn m ta hLung dtieW arid pftbcwroa<br>wa. seda btld9w Star St tectnunr may inbtds sn odw fn<br>A rdfla in Indiezb m hncta<br>The asnm o the uTwt aooni biae natS pttve cw Sflu ndlows 3sl a1 nd ellAa in resea quade wvit ih. WcpSII algn<br>tu awn Ith casil un nld arms aid ondwasbt<br>chdi MtinLdau KXmas UMfqa Uhn Ptppo ad Th,a__d<br>Bcs IMF WcuWd Ecwned Outo AM 2002, TatI 1 5 29<br><!-- End of picture text --> So devastating and extensive was this reversal that the crisis came to be identified as a "capital account **crisis"** This is **in** contrast to the more typical "current account cnsis" which **is** characterized by poor macroeconomic fundamentals such as high uiflation and large fiscal deficits The East Asian economies pnor to the cnsis did not suffer in general from unsound macroeconomic policies as they had for the most part high savings ratios, sound fiscal positions, low inflation and high growth It **is** widely agreed that poor macroeconomic policy was not a pnmary factor in the crisis Frankel (2000) pomts out that Thailand made some macro policy mistakes in 1997 and "excessive expansion led to excessive current account deficits and excessive indebtedness " But he goes on to say, "statistical evidence suggests that a large current account deficit or-a hugh level of debt is not a hlghly significant predictor of crises ,125 More important **is** the fact that capital inflows compnsed primarily short-term foreign currency loans, which led to the double mismatch of maturity and currency as banks were lending long term and in local currency for nontradables (primanly real estate) This in turn led to twin crises **in** currency (resulting in devaluations) and m baning (resulting m banks becoming overburdened with non-performing loans causing many to eventually fail) Lookmg again at Table 5 1 above, we see that of the three types of capital flows, e , direct investment, portfolio investment, and other flows (which mcludes borrowmg), the last of these had the largest inflows before the crisis and outflows afterwards Many regional and intemational observers have speculated at length on the cause (or causes) of the crisis with no true consensus having been reached Some of these causes/vulnerabilities are lIsted in Table 5 2 45
<u>Are labor markets segmented?</u> The theoretical section of this paper maintained that developing country labor markets are segmented and that researchers therefore need to understand the workings of each segment and the linkages between segments. For labor market segmentation to exist, two conditions must hold: 1) Labor market returns (including earnings but also non-pecuniary aspects of jobs) differ across segments for identical workers, and 2) Employment in the better jobs segments is rationed.

How strong is the empirical evidence on the existence of labor market segmentation? In my view, the evidence is strong but not unassailable. Earnings differentials for observationally equivalent workers have been demonstrated repeatedly in a great many countries. Examples are Mwabu and Schultz (2000), who find a large union wage premium in South Africa, and Assaad (1997) and Rama (2003b), who find for Egypt and Sri Lanka respectively that public sector jobs are the better ones, leading to queueing for them. The effects of minimum wages in developing countries are surveyed in Cunningham (2007), Svejnar (2007), and Freeman (2007).

Critics such as Rosenzweig (1988) are unpersuaded. He points to the possibility that such earnings differences could be due to compensating differentials, rewards for unmeasured skills, and/or compensation for differences in the disutility of the workplace. To the extent that these objections can be examined empirically, it would be useful to do so, paying careful attention to the methods used in such studies as Heckman and Hotz (1986) for Panama, Magnac (1991) for Colombia, and Pratap and Quintin (2006) for Argentina.

# <u>Why do people work informally?</u>

The early view of why people work informally was that no other work was available to them. The informal sector was equated with the “bad jobs” part of the economy. Such a view is maintained in many quarters at present, for example, by the ILO (2002a), which maintains that those working informally are part of a “decent work deficit” which must be remedied.

Other research, initially qualitative in nature, maintained that there exists a fundamental duality within the informal sector. On the one hand, there is a segment corresponding to the traditional view: people work informally because they can get no other work. On the other hand, there is also a segment in which people work by choice in 42
The debate between private and public schooling focuses on substitution between private and public enrollment, where increasing enrollments in one is seen as the decline in the share of the other. While the two are indeed alternate sources of education and hence substitutes at any given time, the success of private schools in Pakistan is based on their overwhelming reliance on locally based teachers who are female. But where do these locally based females with secondary education come from in the first place? Pakistan’s case adds a temporal dimension to the debate on public versus private schooling, introducing _complementarities_ between the two sectors. Girls educated in today’s public schools will become teachers in tomorrow’s private schools. Viewed in this light, a first priority should be the development of a cohort of educated women in every village who can then serve as catalysts in the process of educational reform. Dynamic complementarities between the public and private sector suggest that a first large investment, a “Big Push” in creating cohorts of educated women could then lead to self-sustaining growth in educational provision through the use of private schools. Alternatively, to alleviate the constraints of low cost teachers, it has to become easier for women from the outside to break into the village labor market. Educational policy in Pakistan needs to take into account the segmented nature of labor markets and the dynamic complementarities that exists between public and private schools.

Our focus on the supply constraints to education and the central role of women as teachers is not new. In the US there has been an extensive debate about the relationship between changing educational quality and the exit of women from the teaching profession. Two reasons—the opening up of alternative employment options and increased unionization—have been advanced as potential explanations for this compositional change in the teaching workforce. As the labor force participation of women in Pakistan increases, it is likely that similar patterns will emerge. Pakistan at this stage presents a fascinating glimpse of an environment that many higher-income countries witnessed 50 years ago.

There are important aspects of private schools that we have not touched on here. For instance, more than 92 percent of private schools in both rural and urban areas of Pakistan are coeducational. They are co-educational not only in the less conservative provinces such as Punjab (94 percent), but also in provinces with lower human-development indicators and typically more conservative attitudes, such as NWFP (88 percent) and Sindh (90 percent). A common wisdom in Pakistan is that girls will not go to school unless schools are single-sex. As Gazdar (2001) points out, this wisdom is not borne out by attitudinal surveys of parents towards primary schooling or systematic evidence from any part of the country. In fact, in certain cases, a large number of girls have started attending boys’ primary schools when allowed to do so—distance seems to be a 18
.3 Table 2: Percent of Children Aged 6-14<sup>and 15-19</sup> Enrolled in School, 1940-81

|Age GrouRs|1940|1961|1972|1981|
|---|---|---|---|---|
|Ages 6 to 14|||||
|Total|30<br>|58<br>|78<br>|90<br>|
|Males|34<br>|62<br>|82<br>|91<br>|
|Females|*26|53|75|88|
|Urban|-|-|90<br>|96<br>|
|Rural|-|-|63|79|
|Ages 15 to 19|||||
|Total|17<br>|33<br>|49<br>|56<br>|
|Males|23<br>|41<br>|57<br>|61<br>|
|Females|11|,6|41|52|
|Urban|-||54<br>|63<br>|
|Rural|-|-|17|24|

Source: Government of Peru 1981, Fernandez 1986.

Since that time educational opportunities have expanded considerably. Appendix figures 1A-3B show<sup>the rapid rise in enrollment and in</sup> gross enrollment ratios between 1950 and<sup>1980. At the primary level the rise</sup> in girls' enro.lment came after the<sup>increase in boys' enrollment. In 1950</sup> only 69 percent of all girls were enrolled<sup>compared to 100 percent of the</sup>
# **3.5 Ad-valorem vs. Specific Transportation Costs**

As discussed above, the cost of transporting goods can be expressed in an ad-valorem (or iceberg) form as a percentage relative to the value of the transported goods: _τ_ = Freight Charges Value of Transported Goods<sup>. Al-</sup> ternatively, it can also be expressed in a specific form, as transportation charges per unit or weight: = Freight Charges _f_ Quantity (or Weight) of Transported Goods<sup>; or as a mix of ad-valorem and specific components of trans-</sup> portation cost. An empirically-relevant way to distinguish between these forms is by evaluating the price elasticity of freight rate, _∂_<sup>_∂_</sup> _p_<sup>_<u>f</u>_</sup> _<u>pf</u>_<sup>, where</sup><sup>_p_is the factory (i.e., exclusive trade costs) price of the</sup> good and f is the per-unit freight rate as defined above. The unitary elasticity corresponds to the ad-valorem form, zero elasticity—to the specific form, and the elasticity between zero and one—to having both components present. This section will discuss the literature utilizing both measures and the importance of the functional form of transportation cost for international trade.

For many decades, the workhorse empirical model to estimate the effect of trade costs on bilateral trade was the gravity equation. A rich line of literature derives the gravity equation from a variety of theoretical models (e.g., Krugman, 1980; Eaton and Kortum, 2002; Melitz, 2003). These models assume a convenient ad-valorem form of transportation costs, also known as the Samuelson iceberg. Therefore, the ad-valorem formulation became a natural default for transportation costs in the empirical literature. In addition, most datasets with the direct measure of transportation charges report the value but not the quantity of the imported goods.<sup>19</sup> This further strengthened the reasons to utilize the ad-valorem transportation cost as the per-unit measure could not be calculated without the information on the units.

The knowledge about the functional form of transportation costs has been greatly enriched by a seminal work by Hummels and Skiba (2004), who utilized the direct measures of transportation costs using the data from six countries (Argentina, Brazil, Chile, Paraguay, Uruguay, the US) importing from the rest of the world. They showed that the data do not support the widely assumed ad-valorem 19In the past, several studies have also employed measures of transportation costs constructed using matched partner c.i.f./f.o.b. ratios from the IMF Direction of Trade Statistics (DOTS) and UN COMTRADE data. Hummels and Lugovskyy (2009) found that the ratios from these sources are subject to significant measurement error and are unrelated to actual variation in shipping costs.

23
to work. In 1989, female labor-force participation had reached 72.8%, an increase of more than 50% compared to 1960 (35.9%) and by that time there were only slight differences in labor-force participation between men (77.2%) and women (72.8%) (Gjon ̧ca, Aassve and Mencarini, 2008; INSTAT, 2004).

After the fall of the communist regime, Albania went through many radical demographic, economic and political changes. During this transitional period, women in particular have faced a substantial withdrawal from the labor market due to several reasons. First, state enterprises, where women made up the greatest percentage of the workforce before 1990 collapsed due to their inefficiency and so did social protection associated with these jobs (Tarifa, 1994). Second, childcare deteriorated and at the same time many day-care nurseries and kindergartens were closed. For instance, compared to 1990, the number of kindergartens had fallen by 60% in urban areas and by 49% in rural areas in 2004 (IMF, 2006).<sup>8</sup> Third, during this period the country also witnessed a massive male emigration, which has left the wives at home taking care of the children, engaging in housework or informal market and thereby decreasing their working hours out of the house. Also, the internal migration to urban areas has damaged the labor supply of women since they face more problems entering the urban labor market in the current economic environment. Consequently, women’s schedules have required more accommodation giving rise to long-term structural discrimination in the labor market and high female unemployment rates.

Labor force participation has still remained low in Albania during the last decade, especially for young people and women. Statistics from INSTAT (2014) show that the labor-force participation rate for women between 15-64 years old is 50.1% whereas for men it is 70.2%. Employed women are still finding it more difficult to balance career and family as the availability of state financed social services is quite low and the number of pre-school facilities has not recovered to the levels previous to the collapse of the communist regime (see Figure 3). As a result, two decades after the fall of the communist regime, one may observe that the mutual support between family members has assumed a great role in attenuating the shock of poverty inherited from the communist regime, and that informal channels of support function better than the state social provisions and assistance (Danaj, 2014).

Therefore, a distinguishing feature of this study is that it is quite interesting to analyze the effect of fertility on parental labor force participation in a developing country characterized by a combination of low levels of employment and also low fertility rate. Figure 2 presents these trends since 1990 until 2012 for the case of Albania, where we can clearly observe a decrease in both variables. The timing of these events suggests that there might exist a causal relationship between them.

> 8See Figure 3 for more detail on the situation of pre-school in Albania during the period analyzed in this study.

8
12 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

# _4.4 Measuring institutional fragmentation and metropolitan coordination_

Based on the literature desk review and the available data, we construct a database with a series of variables to characterize the functional areas included in this study, in terms of institutional fragmentation and metropolitan coordination. We use the administrative boundaries of local governments that conform the metropolitan areas and distributed population data to calculate institutional fragmentation measures using geoprocessing tools in ArcGIS. Administrative boundaries were obtained from OpenStreetMap<sup>3</sup> (April 25<sup>th</sup> 2017) and the World Bank LAC Spatial Database (Branson et al., 2016). We projected the administrative boundaries and the urban extents to the UTM coordinate system to calculate areas in square kilometers. Population counts at the administrative unit and urban extent levels were estimated using the Global Human Settlement Layer (GHS) distributed population grids produced by the Joint Research Centre (JRC) of the European Union (Freire and Pesaresi, 2015; Pesaresi et al., 2016). These layers show population counts for each pixel at 250 meters of spatial resolution, and were produced for the years 1975, 1990, 2000 and 2015. We used the 2000 layer to account for the population in that same year, and the 2015 layer as proxy for the population in year 2010.

Metropolitan coordination variables were obtained through a number of official information sources to account for the presence of a metropolitan governance body and public services single purpose districts (see Table A1). Tables 3 and 4 present the list of available variables for institutional fragmentation and metropolitan coordination respectively. The descriptive statistics are presented in Table A2.

# _4.5 Control variables_

In order to isolate the predictive power of the variables describing urban form and to reduce omittedvariable bias, we include in the model a number of control variables including city size, locational variables, natural and urban amenities, as well as country fixed effects. A number of different data sources are used to compute control variables. Population data in gridded format for 2010 were 3 http://www.openstreetmap.org/copyright
<!-- Start of picture text --> 5.0 1400<br>Foreign direct investment, net inflows (% of GDP)<br>4.5<br>1200<br>Foreign direct investment, net inflows (index, 1986=100)<br>4.0<br>3.5 1000<br>3.0<br>800<br>2.5<br>600<br>2.0<br>1.5 400<br>1.0<br>200<br>0.5<br>0.0 0<br>1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Figure 2: Pre- and post-NAFTA FDI in Mexico _Source:_ WDI.

the FDI scenarios that follow should be viewed as a “best-case” scenario, although, for example, Medvedev (2006a) suggests that many countries have been able to reap even larger FDI gains from preferential liberalization.

_Bilateral CAFTA and increased FDI inflows (IBL)_ . The first simulation in this section adds the FDI shock described above to the _CBL_ scenario described in section 6.1. For the moment, we leave aside the issues of dynamic growth effects of FDI as well as potential productivity spillovers, and focus only on the level effects. We also assume that new FDI is an addition to, rather than a replacement of, domestic investment. This does not need to be the case; for example, evidence from Mexico shows that while the FDI-to-GDP ratio rose significantly after NAFTA (Figure 2), total investment to GDP ratio rose only slightly.<sup>43</sup> Since it is difficult to predict _a priori_ whether potential new FDI in Honduras is likely to be a substitute or a complement to domestic investment, we allow the new FDI to become a net addition to total investment in the current scenario and explore the implications of relaxing this assumption in one of the following simulations.

_Increased FDI inflows (I_ 00 _)_ . This decomposition scenario separates the effects of increased FDI inflows from the effects of trade liberalization by imposing the FDI shock of the previous simulation ( _IBL_ ) without any reductions in the level of tariffs.

_Bilateral CAFTA and increased FDI inflows with FDI a substitute for domestic investment (IBL_<sup>_sub)_.Thissimulationisidenticaltothe</sup><sup>_IBL_scenarioexceptfortheinvestmentclosure.Inthe</sup> _IBL_<sup>_sub_scenario,theinvestment-to-GDPratioremainsunchangedaftertheFDIshock,whichmeans</sup> that the new FDI replaces domestic private investment rather than complementing it.

_Multilateral CAFTA and increased FDI inflows (IML)_ . In this simulation, tariff reductions are

> 43WDI data indicate that in the three years after NAFTA (1995–1997), the average investment-to-GDP ratio declined by half a percentage point relative to the average ratio three years before NAFTA. However, this decline mostly reflects the 2 percentage point drop in the ratio in 1995 as a result of the crisis. Looking at five-year averages shows that the post-NAFTA investment-to-GDP ratio rose by 1.6 percentage points, although this ratio has been declining since reaching a peak in 1999 and is currently identical to the 1992–1993 investment-to-GDP share.

33
||Inequality<br>Index|(4)||-0.008<br>[0.002]***|5,240<br>-0.119<br>[0.043]***|5,240|bles reported at the|anel B reports the|enforcement. City|city, its square and|teractions with the|city level controls||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|nequality (2000)|Unemployment<br>Rate|(3)||0.003<br>[0.001]***|5,240<br>0.093<br>[0.026]***|5,240|gression of the city varia|ty level characteristics. P|tate as an instrument for|ance to the state capital|n costs, its square and in|itude. It also includes the||
|ployment, and I|l<br>Poverty Rate|(2)||-0.009<br>[0.001]***|5,240<br>-0.052<br>[0.023]**|5,240|d coefficients of the re|ariables and several ci|ectors per firm in the s|stitutional quality, dist|uality, city transportatio|ty latitude and city long||
|Table 4: Enforcement of Labor Regulation and City Level Informality, Poverty, Unem|Dependent Variable:<br>Share Informa<br>Workers|(1)|Panel A: OLS|Log Inspected Firms per firm in city<br>-0.018<br>[0.002]***|Observations<br>5,240<br>Log Inspected Firms per firm in city<br>-0.150<br>[0.043]***<br>Panel B: IV|Observations<br>5,240|Standard errors in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%. Panel A reports the estimate|top of each column on the log of the number of inspected firms per firm in the city, controlling for state level dummy v|instrumental variable estimates using city distance to the nearest labor office interacted with the number of state level insp|level characteristics include distance to the nearest labor office, its square and interactions with state level measures of in|interactions with the number of inspectors per firm in the state and interactions with state level measures of institutional q|number of inspectors per firm in the state and interactions with state level measures of institutional quality, city altitude, ci|for income per capita, population, average schooling and share of urban population in 1991, 1980 and 1970.|
_38_

## _WATER WHEN IT COUNTS_

in Mozambique. While generalized recommendations appear sufficient to reduce water scarcity in ways that are consistent with improved scheduling rules being the mechanism, we do not find that these improvements are associated with significantly higher revenue for farmers. Continued water monitoring will reveal whether profits may improve as farmers better optimize their cropping strategies to reflect the newly more efficient allocations of water. What we learn from this experiment is that information campaigns may be instrumental in achieving water savings over periodic dry spells.

# **REFERENCES**

- **Aker, Jenny C.** 2011. “Dial A for agriculture: a review of information and communication technologies for agricultural extension in developing countries.” _Agricultural Economics_ , 42(6): 631–647.

- **Aker, Jenny C., Ishita Ghosh, and Jenna Burrell.** 2016. “The promise (and pitfalls) of ICT for agriculture initiatives.” _Agricultural Economics_ , 47(S1): 35–48.

- **Alcott, H., and T. Rogers.** 2014. “The Short-Run and Long-Run Effects of Behavioral Interventions: ExExperiment Evidence from Energy Conservation.” _American Economic Review_ , 104(10): 3003–3037.

- **Allen, R., L. Pereira, D. Raes, and M. Smith.** 1998. “Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements.” Food and Agriculture Organization of the United Nations FAO Irrigation and Drainage Paper 56, Rome.

- **Beaman, L., A. BenYishay, J. Magruder, and A. M. Mobarak.** 2015. “Can Network Theory-based Targeting Increase Technology Adoption?” University of California, Berkeley Working Paper.

- **BenYishay, A., and A. M. Mobarak.** Forthcoming. “Social Learning and Incentives for