rafmacalaba/datause-encoder-data huggingface dataset:
these districts was still lower than other regions in the country given the low base of violence.

Although there are a number of potential explanations for households fleeing before violence begins, such as households fleeing the humanitarian situation caused by violence as opposed to violence itself, it is difficult to distinguish between all the potential explanations because one typically does not have welfare information collected from households _before_ they become displaced. However, given the unique data used here, we are able to demonstrate that there was very little difference in pre-displacement food access between households that fled significant violence and those that fled only future violence<sup>4</sup> , which suggests that many of these potential explanations are unlikely to fully explain this pattern.<sup>5</sup> However, one possibility that we are unable to rule out is that some households were more averse to a potentially poor security situation than households that became displaced long after violence escalated.

Our results contribute to the literature describing the causes of forced displacement. First, we demonstrate that there is a great deal of variation in when and how violence forces displacement, which is consistent with qualitative and more descriptive evidence on the complexity of displacement decisions (e.g., Verwimp and Maystadt 2015; World Bank 2017a; etc.). The two sources of this heterogeneity emphasized here- type of violence and aversion to a potentially poor security situation- have not, to our knowledge, been previously emphasized in other settings following the onset of a conflict.<sup>6</sup> In the Republic of Yemen, the sheer size of the population that fled little-to-no violence and the size of the population that continued to live in their homes in the midst of tremendous violence further illustrate how the heterogeneity emphasized here and potentially other factors are likely important to deciding whether and when to become displaced.

> 4The differences were low in magnitude and the estimates were precise enough to rule out economicially meaningful differences between the households 5In light of these food access patterns, there are other potential explanations that are unlikely to fully explain a large share of households fleeing before violence begins, such as only households of sufficient means can flee early or afford to flee uncertain increases in violence.

> 6There are numerous examples of large displacement crises occurring just before groups take control of territory. Both the recent Taliban capture of Afghanistan in 2021 and the large displacement crisis at the beginning of the Republic of Yemen’s conflict before violence escalated even further are examples (e.g., TFPM 2016; Sayad et al. 2021; etc.)

4
EMs and DMs as reported in this section. This finding has important implications for the design of NIFs and policies to reduce imperfect substitutability of the funds and its component assets traded in the home market.

# **IV** - **IPOs OF NIFs**

The theoretical and empirical literature dealing with the underpricing of 1POs of individual U.S. firms is extensive.'<sup>3</sup> These authors contend that underpricing results from information asymmetry and gaming strategies among various IPO participants. On the other hand, Mauer and Senbet (1992) develop an equilibrium model of IPO's that suggest that the socalled underpricing is a fair price differential based on incomplete access and imperfect substitution of the IPO in the secondary market. Recently, Peavy (1990) tests the IPOs of closed-end funds, and reports a mean initial return not significantly different from zero and attributes it to knowledge regarding the value of the underlying assets i.e. low information asymmetry. His conclusions also hold for the subset of international closed-end funds if three special-access funds that prohibit direct portfolio investments by U.S. investors are excluded. He also reports significantly negative after market returns. In a similar vein, Bailey and Lim (1990) also report statistically insignificant initial returns on average with high positive returns on funds specializing in Pacific Rim and Eastern Europe. With respect to aster market returns, they report poor performance except in the case of Pacific Rim countries.

> '<sup>3</sup> See for example, Ibbotson (1975), Ritter (1984) and Rock (1986).

18
Table 2: Estimations of main effects of state political reservations on women's economic activity

||Log count of<br>new women-<br>owned<br>establishments<br>in unorganized|Log<br>employment in<br>new women-<br>owned<br>establishments<br>|<br> <br> <br>Log count of<br>women-owned<br>establishments<br>in unorganized<br>sector|Log women<br>employed|Log women<br>employed in<br>male-owned<br>establishments<br>in unorganized|Log women<br>employed in<br>women-owned<br>establishments<br>in unorganized|Log women<br>employed in<br>organized<br>sector|
|---|---|---|---|---|---|---|---|
||sector|in unorganized|||sector|sector||
|||<br>sector<br>|<br>|||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||||A|. Base estimatio|n|||
|(0,1) state has political reservations|0.391+|0.421+|0.215|0.015|0.075|0.137|0.039|
||(0.214)|(0.242)|(0.289)|(0.311)|(0.266)|(0.299)|(0.154)|
|Observations|3606|3606|3606|3606|3606|3606|3606|
|Adjusted R-squared|0.733|0.701|0.818|0.825|0.817|0.808|0.838|
|<br>Controls|||State x industry|and Industry x y|ear fixed effects|||
|||B. Panel|<br>A including male|<br>analogue of act|<br>ivity in state-ind|ustry-year||
|(0,1) state has political reservations|0.333+|0.359|0.233|0.089|0.122|0.001|-0.004|
||(0.194)|(0.216)|(0.262)|(0.205)|(0.182)|(0.200)|(0.110)|
|DV analogue for male-owned|0.118+++|0.111+++|0.216+++|0.442+++|0.389+++|0.515+++|0.366+++|
|establishments in state-industry-year|(0.024)|(0.024)|(0.030)|(0.048)|(0.055)|(0.043)|(0.048)|
|Observations|3606|3606|3606|3606|3606|3606|3606|
|Adjusted R-squared|0.737|0.706|0.824|0.853|0.840|0.865|0.855|
|Controls|||State x industry|and Industry x y|ear fixed effects|||

Notes: Regressions quantify adjustments in women's entrepreneurship and employment in India surrounding the implementation of state-level political reservations for women. Column headers indicate dependent variables. Regressions contain 3606 state-industry observations and include state x industry and industry x year fixed effects. Panel A includes a modified indicator variable for the period after a state implements political reservations. The modification allows for fractional values in two states that had pre-existing reservations that were below the level mandated by the Amendments. Panel B further includes a control for the analogue of the dependent variable for male-owned establishments. Regressions are weighted by log initial employment in state-industry in 1994 and cluster standard errors by state. +++, ++, and + indicate statistical significance at a 1%, 5%, and 10% level, respectively.
emigrate and realize returns to their human capital elsewhere, as they might be expected to send remittances back to the source economy.

Second, brain waste may occur when highly skilled migrants cannot find employment commensurate with their skills in the destination country. This is sometimes exemplified by Eastern European physicists driving taxis in New York. This might arise for various reasons. Their qualifications might not be recognized, such as foreign doctors who cannot obtain licenses. They might not be trained in location-specific skills, such as lawyers trained in a legal tradition that is different from the US legal regime. Finally, migrants may have the bureaucratic qualifications (the diplomas), but lack the actual human capital since their education is below the standards demanded in the destination labor markets. Mattoo et al. (2008) show that highly skilled immigrants to the USA from Eastern Europe and Latin America tend not to be able to find employment for which they are trained, whereas immigrants from OECD countries and Asia are more likely to find such employment. Their analysis shows that the main reasons are the lower quality of education in the origin countries, as well as selection effects. Since it is easier for Latin American migrants to enter the USA illegally or through family reunification programs, their average human capital level might be lower than that of Asian migrants who are forced to enter through more demanding employment-based preferences.

In addition to census-based global bilateral databases, multi-topic surveys are well suited to study brain drain, brain gain, and brain waste from the source-country perspective. Through an education module, one can characterize the educational background of a country’s (or region’s) out-migrants relative to the remaining population (Glewwe, 2000). To study brain gain, one can attempt to relate past migration variables 37
21 tend to have more assets as well as access to finance, making more cash available. As Table 6 shows, the percentage of female-headed households was small, particularly in India. Guatemala was the only country that had data on whether the head of household was indigenous. The coefficient was significant, large, and negative for selection, and insignificant for consumption.

- **Urban residence** . Urban residence increased the probability of selecting LPG in four countries and decreased it in Pakistan (where two-thirds of urban residents were connected to natural gas). Once having selected LPG, however, urban households in Pakistan consumed more LPG than their rural counterparts, reversing the sign of the coefficient in the consumption equation. The coefficients were comparable in magnitude in Guatemala, India, and Sri Lanka for selection and in India, Pakistan, and Sri Lanka for consumption.

- **Electricity connection** . Access to electricity increased the probability of selection in all countries except Pakistan (possibly because electrification is closely linked to urban residence), and the coefficients were large and close in magnitude. This variable was significant only in Pakistan and Sri Lanka in the consumption equation. One possible interpretation is that electrification connection is indeed a good proxy for having adequate infrastructure for LPG distribution and that households with electricity have better access to LPG. It is also possible that electrification serves as another asset indicator and that households with electricity have greater cash income.

Table 7 summarizes the marginal effects calculated from the estimations presented in Table 5. The differences between the conditional and unconditional effects were large in some cases, for example for income elasticity in Guatemala, India, and Sri Lanka. Focusing on unconditional marginal effects, which combine three effects as explained in appendix 3, income elasticities varied between 0.3 and 0.7. Own price elasticities were close to 1 in India, Pakistan, and Sri Lanka. The price elasticity for kerosene was high in India and Pakistan, and that for subsidized kerosene was an order of magnitude larger than that for market-priced kerosene in India. Household size generally had a negative effect on LPG consumption.

Table 7: Conditional and unconditional marginal effects

|**Independent variable**|**Guatemala**|**India**|**Indonesia**|**Kenya**|**Pakistan**|**Sri**<br>**Lk**|
|---|---|---|---|---|---|---|
||**Conditional**<br>|**margina**<br>|**l effect**<br>|||**ana**<br>|
|Log of household expenditure|0.044|0.28|0.29|0.27|0.70|0.26|
|Logof LPGprice|–0.41|–0.96|–0.74|–0.74|–0.75|–0.91|
|Log of firewood price|0.042|0.13|—|—|—|0.074|
|Logof charcoalprice|—|—|—|0.040|—|—|
|Log of kerosene price<sup>a</sup>|—|0.51|0.033|—|0.91|–0.001|
|Logof keroseneprice,other<sup>b</sup>|—|0.073|—|—|—|—|
|Number of rooms<sup>c</sup>|—|—|0.00008|0.011|–0.023|–0.0002|
|Dummyfor house ownership|0.11|—|—|—|0.19|—|
|Dummy for car ownership|—|0.044|0.023|—|0.23|0.063|
|Household size|0.009|0.053|0.038|–0.016|–0.036|0.041|
|Household size squared|—|–0.002|–0.001|—|—|–0.00002|
20 Figure 10: Spillovers from U.S. uncertainty shocks

- (A) Impact of 10-percent rise in VIX on output growth

- (B) Impact of 10-percent rise in VIX on investment growth

<!-- Start of picture text --> Percentage points Percentage points<br>0.0 0.0<br>16-84 percent confidence bands<br>16-84 percent confidence bands<br>Median<br>Median<br>-0.1 -0.3<br>-0.2 -0.6<br>-0.3 -0.9<br>-0.4 -1.2<br>U.S. AEs EMDEs U.S. AEs EMDEs<br>(C) Impact of 10-percent rise in U.S. (D) Impact of 10-percent rise in U.S.<br>EPU on output growth EPU on investment growth<br>Percentage points Percentage points<br>0.2 16-84 percent confidence bands 0.2 16-84 percent confidence bands<br>Median<br>Median<br>0.0<br>0.0<br>-0.2<br>-0.2 -0.4<br>-0.6<br>-0.4<br>-0.8<br>-0.6 -1.0<br>U.S. AEs EMDEs U.S. AEs EMDEs<br><!-- End of picture text --> Sources: Haver Analytics, OECD, World Bank estimates. Note: Figures reflect cumulative impulse responses after one year on output and investment growth in the United States, 23 other AEs, and 18 EMDEs to a 10-percent increase in the VIX and U.S. EPU. Vector autoregressions were estimated for 1998Q1-2016Q2 with two lags. The model for the U.S. includes, in this order, uncertainty index (VIX or U.S. EPU), U.S. stock price index (SP 500), U.S. 10-year bond yields, U.S. real GDP, and investment growth. The model for AEs includes uncertainty indexes (VIX or U.S. EPU), MSCI Index for advanced economies (MXGS), U.S. 10-year bond yields, aggregate real output, and investment growth in 23 other AEs. The model for EMDEs includes uncertainty indexes (VIX or U.S. EPU), the MSCI emerging market equity price index, J.P. Morgan Emerging Market Bond Index (EMBIG), aggregate real output and investment growth in 18 EMDEs. G7 real GDP growth, U.S. 10-year bond yields, and the MSCI world equity price index are added as exogenous regressors.
- 47 -

The resolution of the apparently contradictory findings may lie in the question of whether or not a given budget deficit is sustainable. Deficit spending to finance productive infrastructure expenditures may be part of a strategy to speed up the rate of GDP growth. Governments may utilize this strategy so long as inflation remains in check. However, once prudent financing strategies are exhausted and inflation begins to rise, deficit increasing infrastructure expenditures may be scaled back in an effort to stabilize the economy.

# _8. External Balance_

The regression runs using the trade balance relative to GDP lagged one year (TBWTGDPL) provide no evidence that the external balance influences consolidated budget infrastructure expenditures. While the estimated sign of the coefficient is generally negative, it is never significant even at the 15 percent level, as can be seen from Tables 9 and 10 and Appendix C.

One's conclusion concerning the importance of the external balance for consolidated infrastructure expenditures is sensitive to how the external balance is measured, as can be seen from Table C5 in Appendix C. When the external balance is assessed on the basis of the black market foreign exchange premium, BLACK, a robust negative and highly significant relationship is found; The greater is the black market foreign exchange premium, the lower are consolidated budget per capita infrastructure expenditures. The value of the coefficient is about -.04, implying an elasticity of -0.07, so the impact is not very pronounced. This conclusion is further substantiated by the results of the regressions using DOLLAR4 as an index of external balance. Recall, that DOLLAR4 is actually an index of trade orientation, with low values (below 100) corresponding with inward orientation and high values (above 100) corresponding with outward orientation. One expects the external balance will be worse the lower is the value of DOLLAR4. If a poor external balance reduces infrastructure expenditures, then the estimated coefficient for DOLLAR4 should be positive. This is exactly what is found. The estimated coefficient for DOLLAR4 is positive and significant at the .05 level or better in all regressions. When DOLLAR4 increases by I unit (implying a very substantial movement towards outward orientation since this variable only ranges between 98 and 102), per capita consolidated budget infrastructure expenditures increase by between (1980) US $3.00 and $3.50. The corresponding elasticity estimate at the sample mean is 16.81.

An overall conclusion that the external balance does influence consolidated infrastructure expenditures appears warranted. Substantial infrastructure expenditures appear to be required to remain competitive internationally.

# _9. Size of the Foreign Sector_

There is considerable evidence that as the size of the foreign sector increases, per capita consolidated infrastructure expenditures increase, as hypothesized. The finding is not as robust, nor is the estimated impact as great, as it was for central government
# **2. Data and definitions**

The data used in the paper come from the World Income Distribution (WYD) database constructed to study the evolution of global inequality. The database is comprised almost entirely of micro data from representative household surveys from most of the countries in the world. For the year 2002, which is used here, the data come from 120 countries’ household surveys representing 94 percent of world population and 98 percent of world dollar income.<sup>14</sup> The geographical coverage is almost complete for all parts of the world except Africa (see Table 1).

Table 1. Population and income coverage of the surveys (in %)

||Africa|Asia|Latin<br>America|E.Europe<br>and CIS|WENAO|World|
|---|---|---|---|---|---|---|
|Population|77|96|96|97|99|94|
|Income|71|95|95|99|100|98|
|Number of|||||||
|countries|29|23|21|26|21|120|

Source: World Income Distribution database. See http://econ.worldbank.org/projects/inequality. Note: WENAO is Western Europe, North America and Oceania (Australia and New Zealand). CIS = Commonwealth of Independent States. Eastern Europe includes formerly Communist countries.

For the vast majority of surveys (115 surveys) we had access to micro data which means that any type of distribution (by decile, ventile, percentile; by households or individuals) could have been created. In order to limit the number of data points and make the analysis manageable we have limited the number of data points per country to 20 ventiles (each ventile contains 5 percent of country’s population). All individuals in a survey are ranked from the poorest to the richest according to their household per capita income (or expenditures, depending on what welfare aggregate is used in the survey). Since not all countries produce annual surveys, we had to use a “benchmark” year (2002 in this case), that is, try to get 2002 household surveys for as many countries as possible, but where there were no surveys conducted in 2002, to use a year as close to 2002 as possible. In the event, 79 country surveys were conducted in the benchmark year or one

> 14 We cannot express the share of the included countries in terms of $PPP income because for most of the countries for which we lack surveys, we also lack PPP data (e.g.,  Afghanistan, Iraq, Sudan etc.) The dollar incomes however are typically available.

8
- 59 -

establish lines of credit or MDF's specially when subloans were to be used for capital improvements. However, the largest share of resources were usually provided through appropriations from the government budgets or direct allocations of a fixed percentage from the revenue of particular taxes. Because the initial capital is usually unrequited and the mix, origin and actual cost of subsequent contributions frequently unpredictable, "spreads" remain somewhat arbitrarily determined and are usually insufficient to assure significant internal generation of resources over time. Past experience with MDF's in Latin American countries (Bolivia, Brazil, Colombia, Costa Rica, Dominican Republic, Guatemala, Honduras, Mexico, and Venezuela) show that they generally failed to become self-supporting or to achieve a significant scale of operation. The three case studies reviewed

||Table<br>4.14|:<br>CEN|TRAL GOV|ERNMENT<br>OF|LENDING<br> TOTAL|MINUS RE<br>EXPENDITU|PAYMENT<br>RE_/|AS PERCE|NTAGE|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|Average<br> <br>-|
|LAC SAMPLE b/|12.8|11.6|14.1|12.6|13.1|12.6|15.4|13.6|16.6|14.9|<br>13.7|
|HARKET ECONOMIES<br>----|--<br>5.6|4.4|6.8|6.4|6.2|5.9|5.2|5.2|4.7|4.5|<br>5._.|

--------------------------------------------------------------------

__-------__-----------------------_a/_ Total expenditures Lnclude expenditures and lending minus repayment. hi Weighted averages. Comparability over time affected by the availabilLty of data.

Source: IMP, Government FLnance Statistics, various years.

in Box 4 are rather typical of the use of institutional credit in LAC countries. Most subnational governments (especially the smaller municipalities) are yet unaccustomed to servicing debt and are reluctant to adopt borrowing as an instrument of budgetary policy. The overwhelming dominance of traditional patterns of revenue sharing systems has in fact discouraged them from doing so and has fostered their chronic dependence on decisions taken in the upper levels of government. Nevertheless, the acute shortage of fiscal resources to increase grants to the desired level over the 1980's has revived interest in the MDF's as an alternative means to finance urban investments. The international lending agencies (especially IBRD and IDB) have partly encouraged this trend because it affords a wholesale solution to the problem of supporting a spatially diffused pattern of small investments, which could not be funded through conventional project lending. Consequently, external financial assistance tends to associate