rafmacalaba/datause-encoder-data huggingface dataset:
wages. However, with mobile labor, local wages will remain nearly unchanged in either of these cases. Before presenting the test based on local wages, we note that there is no direct restriction on mobility of labor within Sri Lanka in contrast to some other countries such as China (Hukou) and Vietnam (Ho Khau). Indeed, labor market in Sri Lanka has been characterized by considerable geographic mobility with a large proportion of workers employed in manufacturing in cities (e.g. garments) coming from rural areas. Data from census 2012 show that about 27 percent of population in two prominent destination districts (Colombo and Gampaha) are migrants. About a fifth of Sri Lanka’s total population are migrants. This evidence suggests substantial mobility of population within Sri Lanka.

With geographically mobile workers, the impact of land restrictions on local wage provides a credible test of whether migration channel is indeed driving the results reported earlier: there should be a negative effect on wages only if migration cost is the relevant channel. As discussed above, the estimates in Table 6 provide clear evidence of a negative effect of land restrictions on local wage, thus strongly reject both the rights insecurity and collateral mechanisms. Note that a lower wage is not consistent with a labor supply response to property rights insecurity, even when geographic mobility of labor is limited.

There are two additional pieces of evidence that contradicts the existence of a significant collateral and credit constraint effect. First, the evidence that land restrictions do not affect self-employment in non-agriculture contradicts a collateral and credit effect, because a reduction in credit availability should affect non-farm self-employment (entrepreneurship) the most. The second piece of evidence comes from the fact that manufacturing is the most capital intensive among all of the sectors in our context. Thus a decline in capital due to tighter credit constraint would mean a larger negative impact of land restrictions on employment probability in manufacturing than services. The evidence discussed above suggests the opposite, probability of employment in services declines by a larger magnitude than that in manufacturing. Direct evidence on outmigration from villages also supports the conclusion that migration costs is the mechanism mediating the effects of land restrictions. Taking advantage of the recently released migration data at the district level from the 2012 population census, we plot outmigration rates against proportion of land under restrictions in Figure 1. It is clear from Figure 1 that propensity of outmigration is lower in areas with higher proportion of land under restrictions.

Taken together, all of the different pieces of evidence above suggest strongly that the negative effects of 24
pension institutions. Recipients of lump sums can in principle purchase such products in the open market but are highly unlikely to do so because the terms of life annuities from the second pillar are more favorable than products available in the open market. In Chile, term annuities are not allowed but lifetime phased withdrawals are provided for and even mandated in the case of retiring workers with low balances on their individual accounts.

Term annuities are permitted in Australia, although like all types of annuities their use is very limited. Term annuities for up to 25 years are allowed in the occupational plans of Denmark and Sweden. These two countries do not have any detailed data on the pattern of payouts but available evidence from other sources suggests that the use of term annuities of between 5 and 10 years is widespread in occupational pension plans.

In both Denmark and Sweden term annuities take two forms: 'guarantee and bonus annuities' and 'unit-linked' annuities. In either case, monthly payments are determined on the basis of the term of the annuity and a stipulated interest rate. Then, monthly payments are adjusted once a year to reflect the performance of the fund in which the reserves are invested. The capital is exhausted at the end of the agreed term.

Phased withdrawals are a form of term annuities. They differ from them in that they can have either a fixed or a variable term and withdrawals can vary on the basis of a specified withdrawal rule. Phased withdrawals may follow the fixed benefit rule or the fixed percentage benefit rule. The latter is widely used by retirees who adopt self-annuitization plans. However, the most important type is the lifetime or life expectancy phased withdrawal where the withdrawal fraction is each year set equal to the inverse of the remaining life expectancy of the account holder or the remaining joint life expectancy of the account holder and spouse if a joint benefit is specified.

**Table 8: Types of Term Annuities and Phased Withdrawals**

||Types andTerms|
|---|---|
|Australia|Term annuities for 5 to 25 years<br>Allocated annuities (lifetimePWs)|
|Chile|LifetimePWs|
|Denmark:ATP|Not allowed|
|Denmark: Other|Termannuitiesmainlyfor5 to10 years|
|Sweden:PPM|Not allowed|
|Sweden: Other|Termannuitiesmainlyfor5 to10 years|
|Switzerland|Not allowedinthemandatory pillar|

Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

Use of lifetime phased withdrawals is compulsory in Chile for retirees with low account balances, i.e., balances that cannot purchase an annuity that is at least equal to the PBS. In these cases, the monthly withdrawal equals the PBS and when the account balance is exhausted, the government steps in and takes responsibility for continuing payments for the remaining life of beneficiaries. The 2008 amendments to the pension law require the creation of reserves in individual accounts that cover a much higher than average life expectancy. This will lower the probability of triggering the PBS and limit the exposure 19
The empirical specification of the exchange rate pass-through on food prices entails running a regression of the log food price index, _food t_ on the log nominal effective exchange rate and log effective foreign producer price index ( _efppi_ ) . Model selection led to the following auto regressive specification<sup>13</sup> ,

**_The long-run elasticity for food turns out to be 0.59_**<sup>14</sup> . For the purposes of this paper, this elasticity is used to represent the exchange rate pass-through for all tradable goods. This passthrough estimate will be used in the subsequent analysis and as a test of sensitivity; the results are also reproduced assuming long-run NEER elasticities for tradable goods at 0.5 and 0.7.

# **4. Estimating the Fiscal Impact of a Nominal Exchange Rate Devaluation**

The aim of this section is to estimate the first order effect in the short-term of a nominal exchange rate devaluation in Djibouti. It first outlines the major channels through which currency devaluation is expected to influence the fiscal sector<sup>15</sup> . It then calculates the changes in real revenue and expenditure in response to a nominal exchange rate devaluation of 30 percent, using 2004 as a base year. As it will be illustrated, the magnitude of the short-term of the fiscal effect of the currency devaluation will depend on the size of the devaluation, the assumptions on exchange rate pass-through and whether or not the government introduces wage indexation.

## **4.1. Fiscal Adjustment to Devaluation: Main Channels**

The first-order responses of real fiscal revenue and expenditure to changes in the real exchange rate are examined as follows.

### **_4.1.1. Revenue Effects_**

In Djibouti, the main sources of government revenue are taxes, official grants and military-related revenues. Taxes are earned on imported goods (indirect taxes) and on wages, income and profits (direct taxes), which are assumed to mainly originate in the non-tradable sector such as the public sector. Real government revenue can be written as,

> where R is real government revenue;<sup>_t_</sup> _n_<sup>and</sup><sup>_t_</sup> _m_<sup>are the tax rates applied in the non-tradable</sup> sector and imports respectively, _E_ is the currency price of a unit of foreign exchange or the nominal exchange rate, _M_ * is the foreign value of imports, _F_ * is the flow of official grants and

> 13 See full results in appendix, Table A.1.

> 14 This estimates results from using the formula, _B(L)/(1-A(L))_ = (0.143+0.264)/(1-0.315).

> 15 This section draws on Yiheyis (2000) and expands the fiscal framework to incorporate varying degrees of exchange pass-through and public wage-indexation.

11
**<u>Table 3: Key characteristics of panel farms by initial farm size</u>**

||**Period**|**All**|**200-500**|**500-1000**|**1000-1500**|**Size in ha**<br>**1500-2000**|**2000-3000**|**3000-5000**|**>5000**|
|---|---|---|---|---|---|---|---|---|---|
|**A. Per ha output/cost (US$)**||||||||||
|Profit/ha|2001/11|31.32|6.75|23.56|34.22|30.95|38.33|40.40|54.49|
||2001/06|-0.17|-36.14|-12.62|-0.21|2.98|11.19|14.79|23.79|
||2007/11|73.95|47.31|71.54|83.41|73.67|79.96|80.30|84.99|
|Output/ha|2001/11|268.50|275.35|237.79|257.15|267.97|273.45|277.51|351.94|
||2001/06|184.24|174.31|158.17|176.99|187.65|196.61|200.45|226.79|
||2007/11|382.54|370.90|343.39|371.62|390.69|391.26|397.57|476.28|
|Cost/ha|2001/11|237.18|268.60|214.23|222.92|237.02|235.11|237.11|297.45|
||2001/06|184.41|210.45|170.79|177.20|184.67|185.43|185.65|203.00|
||2007/11|308.54|323.59|271.85|288.22|317.02|311.30|317.27|391.28|
|**B. Sample composition (%)**||||||||||
|<br>Stayed from beginning|2001/11|0.173|0.048|0.095|0.138|0.181|0.255|0.316|0.333|
||2001/06|0.358|0.195|0.261|0.344|0.363|0.419|0.468|0.502|
||2007/11|0.559|0.265|0.533|0.510|0.616|0.714|0.768|0.734|
|Incumbents exited in ...|2001/11|0.424|0.328|0.450|0.444|0.438|0.457|0.428|0.314|
||2001/06|0.415|0.502|0.505|0.424|0.379|0.390|0.347|0.276|
||2007/11|0.198|0.281|0.268|0.187|0.181|0.138|0.116|0.104|
|Entered & staid|2001/11|0.283|0.453|0.316|0.289|0.255|0.212|0.178|0.269|
||2001/06|0.171|0.203|0.163|0.174|0.200|0.146|0.148|0.193|
||2007/11|0.213|0.401|0.172|0.263|0.177|0.141|0.096|0.148|
|Churned|2001/11|0.119|0.171|0.140|0.129|0.126|0.076|0.078|0.085|
||2001/06|0.057|0.101|0.070|0.057|0.057|0.045|0.037|0.029|
||2007/11|0.030|0.053|0.027|0.041|0.027|0.008|0.020|0.014|
|No. of farms|2001/11|16724|2076|3381|3419|2368|2726|1973|781|
|No. of obs.|2001/11|92324|8167|16400|18336|13829|16989|13265|5338|
||2001/06|53095|3782|9073|10631|8196|10312|8123|2978|
||2007/11|39229|5014|7853|7245|5200|6299|4855|2763|

_Source:_ Own computation from form 50SG.

20
61

# _a. Parameter Sensitivity_

For purposes of simplicity as well as economy of computer resources, the parameter sensitivity analysis of MULTLMOD has been limited, in the current study, to the money demand equations, which have the following form ln(M/P) ' mo+ m1 ln(GNP) + m2 RS + msRS-I+ m4ln(M_1 /P_l) where M/P denotes real balances and RS denotes the short-term interest rate. Parameter perturbations are calculated by comparing a baseline simulation to a series of simulations in which the money demand coefficients are perturbed, one at a time, by I percent.<sup>5</sup> The elasticity responses to perturbations of the five coefficients are exhibited in Table 14 for GNP and PGNP price deflator (the results for three other endogenous variables are exhibited in Appeudix B), and in each case, for three different blocks. With some exceptions (e.g., GNP deflators), the elasticity responses reached their respective first peaks in the second period. The GNP deflators reached their first peaks in the third period. The majority of elasticities damp down beyond the seco4d and third periods. In some cases, after experiencing a trough, the elasticity responses reached a second peak in period 8 or 9 (e.g., GNP deflator).

As is evident from Table 14, the properties of the dynamic coefficients are of primary importance in determining the elasticities. The endogenous variables listed in the table respond with greater elasticities and higher oscillations to the perturbation of m4 the coefficient of the 3kuh and Neese (1979) examined perturbations of the order of 0.001, 0.01, 0.1, 1, 10, and 20 percent. But, after weighing conflictingnumerical and analytical considerations, they finalyselected a perturbation by I percent as being nearly optimal, because it was sufficientlylarge to produce changes in the time paths of endogenous variables which were clearly distinguishable from rounding errors without being too large to invalidate the arc approximations to the actual elasticities. Further, it was sufficientlysmall so that the higher order terms arising out of nonlinearities could be considered negligible.
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Benchmarking ICT indicators**

||Unit|Cape Verde|Cape Verde|Lower-middle-<br>income group|Sub-Saharan<br>Africa|Cape Verde|
|---|---|---|---|---|---|---|
|||2005|2008|2008|2008|2009|
|GSM coverage|% population|74|83|77|56|84|
|International bandwidth|Mbps/capita|35|310|153|34|604|
|Internet|Users/100 people|5|10|14|7|22|
|Landline|Lines/100 people|15|14|14|2|14|
|Mobile phone|Subscribers/100 people|17|56|47|33|68|
|Price of monthly mobile b|asket|18.1|16.7|8.4|11.8|20.5|
|Price of monthly fixed-lin|e basket|4.7|5.4|4.8|11.6|9.1|
|Price of monthly fixed bro|adband Internet package|—|45|31.4|100.1|32|
|Price of a call to the Unit|ed States ($/min)|2.0|1.3|—|0.7|0.86|
|||—|1.3|—|1.02|0.86|

_Source:_ Ampah and others 2009; World Bank ICT at a Glance Database, National Communications Agency (ANAC).Derived from the AICD national database (http://www.infrastructureafrica.org/aicd/tools/data).

_Note:_ ICT = information and communications technology; GSM = global system for mobile communications; Mbps = megabits per second. n.a. = Not applicable.

— = Not available.

# **Challenges**

Cape Verde faces the challenge of increasing ICT access as a small island developing state with only recent, limited experience in sector liberalization. While the experience of introducing competition into the mobile sector has been positive, with a significant increase in both penetration and coverage, access still lags behind other countries with similar levels of income. Nascent regulatory skills and the small size of the market constrain competition, resulting in relatively high tariffs (table 9, bottom). Fixed-line prices have been rebalanced, with subscription and local call charges rising sharply while international call charges have fallen. On the other hand, prepaid mobile prices have actually risen since the introduction of competition. This has been mitigated to some extent by the introduction of per-second tariffs—but at the cost of more expensive prices for heavy users. The one bright spot in this picture is broadband pricing, which has fallen.

To better utilize submarine cables, key access facilities must be prevented from establishing monopolistic bottlenecks. In the Internet market the incumbent controls the fixed-telephone network necessary for providing broadband access. Cape Verde has been fortunate to have a high-speed undersea fiber-optic connection through the Atlantis 2 cable since 2000. But the incumbent controls this and is also an investor in a second cable, the WACS (West African Cable System), scheduled for launch in 2011. Regulatory tools are needed to enhance access through these bottleneck facilities. Six of the most populated islands were connected by a submarine cable fiber-optic system in 2002, with the rest to be connected in 2011.

Competition in both mobile and broadband markets should intensify with the planned introduction of third-generation (3G) mobile networks and a third mobile operator. This should boost the country’s relatively high ICT position within the region (table 9) and enhance the possibility of meeting Cape Verde’s ICT development goals.

18
Table 2: Global Budget Shares

|Donor|Donors’ Share in Global Budget:<br>2010<br>2011|2012|
|---|---|---|
|JPN|15.7 (145)<br>13.2 (142)|13.8 (142)|
|USA|14.8 (140)<br>14.0 (135)|12.8 (136)|
|IDA<sup>_∗_</sup>|14.0 (79)<br>13.7 (80)|13.1 (81)|
|IEU|8.5 (149)<br>13.2 (143)|15.0 (142)|
|FRA|6.7 (134)<br>7.2 (130)|7.2 (137)|
|DEU|5.9 (137)<br>6.5 (132)|5.9 (128)|
|GLF|3.5 (98)<br>2.9 (97)|3.5 (99)|
|GBR|3.1 (123)<br>2.9 (120)|3.2 (129)|
|ADB<sup>_∗_</sup>|2.6 (27)<br>2.4 (30)|2.4 (29)|
|AfDF<sup>_∗_</sup>|2.1 (38)<br>2.4 (39)|2.2 (39)|
|IMF<sup>_∗_</sup>|1.9 (37)<br>1.9 (29)|1.9 (29)|
|ESP|1.7 (113)<br>1.1 (114)|0.6 (95)|
|AUS|1.6 (103)<br>1.5 (114)|1.7 (122)|
|DNK|1.4 (89)<br>1.3 (84)|1.2 (72)|
|AFESD|1.4 (12)<br>1.1 (11)|1.1 (12)|
|NOR|1.4 (103)<br>1.2 (106)|1.2 (107)|
|NLD|1.3 (88)<br>1.2 (85)|0.8 (79)|
|CAN|1.2 (123)<br>1.2 (124)|1.1 (114)|
|KOR|1.1 (133)<br>1.1 (133)|1.2 (133)|
|SWE|1.1 (102)<br>1.2 (105)|1.0 (109)|

Global budget shares reported for the years 2010, 2011, and 2012 exclude debt relief efforts, humanitarian and emergency aid efforts, administrative costs, and refugee costs in donor countries. The number of recipient countries with positive aid disbursements from a donor reported in brackets.<sup>_∗_</sup> indicates a donor using an official allocation formula with policyand poverty measures. Data Source: Credit Reporting System (OECD).

between 2010 and 2012.<sup>1</sup> Notably, the sectors “Government and Civil Society” and “Education” have consistently the highest average budget share, closely followed by “Health and Population Policy.” Notice that these measures, however, do not imply that these three sectors account for most aid disbursements. In parentheses, we report sector shares of total aid disbursements of all donors. For example, in 2012 “Education” received only 11.5% of total aid disbursements whereas “Health and Population Policy” with 15.7% received the most funds. Likewise, “Transport and Storage” received 13.5% of total aid disbursements whereas its share is only 5.8% for the average donor. The difference between these numbers in Table 1 emerges because of differences in priorities between small and large donors. Smaller donors seem to value “Education” more than “Health and Population Policy.” There are substantial differences in terms of the size of donors measured by

> 1We include all the current 29 DAC members except the European Union, Poland, Slovak Republic, and Slovenia.

6
Table A.20: Under-5 EFM, across states

||**Outcome: Deat**|**h before age 5**|
|---|---|---|
||Above median SRB|Below median SRB|
||(1)|(2)|
|_Firstborn girl_*_Female_|3.174***|2.050**|
||(0.794)|(0.830)|
|_Firstborn girl_*_Female_*_Post1_|-1.916*|-0.836|
||(1.047)|(1.035)|
|_Firstborn girl_*_Female_*_Post2_|-2.317**|-1.493|
||(1.021)|(0.975)|
|N|149,140|77,989|
|Mean state-level SRB|1.132|1.043|

NOTES: The table present estimates from the same specification as column (3) of Table 3. Columns (1) and (2) include states that have male-female sex ratio at birth (SRB) above and below median among all states, respectively. SRB is calculated using population counts of total number of children at age 0 in the state from the 2011 Census of India. Data: NFHS.

Table A.21: Number of births, across states

||**Outcome: Nu**|**mber of births**|
|---|---|---|
||Above median SRB|Below median SRB|
||(1)|(2)|
|_Firstborn girl_*_Female_|0.199***|0.137***|
||(0.016)|(0.019)|
|_Firstborn girl_*_Female_*_Post1_|-0.104***|-0.068**|
||(0.021)|(0.025)|
|_Firstborn girl_*_Female_*_Post2_|0.129***|-0.092***|
||(0.022)|(0.027)|
|N|69,254|49,409|
|Mean state-level SRB|1.132|1.043|

NOTES: The table present estimates from the same specification as column (1) of Table 6. Columns (1) and (2) include states that have male-female sex ratio at birth (SRB) above and below median among all states, respectively. SRB is calculated using population counts of total number of children at age 0 in the state from the 2011 Census of India. Data: NFHS.

67
# **Table 3 (continued): first stage results**

||Dep|<br>endent variabl|<br>es:|
|---|---|---|---|
|First stage results:|ln(Transportati<br>on cost)|ln(No. of<br>fatalities near<br>market)|<br>ln(No. of<br>fatalities near<br>households)|
|ln(natural historicalpath)|0.977***|0.061***|-0.141***|
||(300.450)|(9.950)|(-21.15)|
|Fractionalization near market|-0.047|-0.858***|1.292***|
||(-0.51)|(-5.85)|(5.240)|
|Square of fractionalization near market|-0.218***|1.352***|-1.384***|
||(-2.25)|(7.930)|(-5.25)|
|Fractionalization near households|0.264***|1.532***|-0.708***|
||(3.690)|(13.130)|(-3.7)|
|Square of fractionalization near households|-0.058|-1.973***|1.235***|
||(-0.8)|(-15.4)|(6.090)|
|ln(Distance to east border from household in km)|-0.016***|-0.279***|-0.282***|
||(-2.09)|(-16.62)|(-15.86)|
|ln(Distance to east border from market in km)|-0.043***|-0.477***|-0.166***|
||(-3.02)|(-17.18)|(-3.52)|
|Angrist Pischke test of weak identification||||
|F(5,6523)|9559.55|179.92|121|
|Prob > F|P-value=.0000|P-value=.0000|P-value=.0000|

34
# **Table 12: Pairwise Correlations, Rank of Country Fixed Effects & Doing-Business Rank Indicators Sample Restricted to non-OECD, Young firms (2284 obs.)**

||Overall Ease of<br>Doing Business<br>(rank)|Employing<br>Workers (rank)|Starting a<br>Business (rank)|Construction<br>Permits (rank)|Registering<br>Property (rank)|Getting Credit<br>(rank)|Protecting<br>Investors (rank)|Paying Taxes<br>(rank)|Trading Across<br>Borders (rank)|Enforcing<br>Contracts (rank|)<br>Closing a<br>Business (rank)|New Product<br>specification:<br>Rank of Country<br>FE|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Overall Ease of Doing Business (rank)**|1***|0.44***|0.69***|0.58***|0.68***|0.66***|0.63***|0.45***|0.49***|0.52***|0.39***|0.22*|
|**Employing Workers (rank)**|0.44***|1***|0.52***|0.38***|0.29**|0.14|0.34***|0.12|0.01|0.24*|0.29**|0.11|
|**Starting a Business (rank)**|0.69***|0.52***|1***|0.42***|0.3**|0.34***|0.52***|0.12|0.22*|0.32**|0.19|-0.02|
|**Construction Permits (rank)**|0.58***|0.38***|0.42***|1***|0.37***|0.15|0.29**|0.21|0.37***|0.11|0.16|0.14|
|**Registering Property (rank)**|0.68***|0.29**|0.3**|0.37***|1***|0.4***|0.24*|0.25*|0.17|0.49***|0.26**|0.34***|
|**Getting Credit (rank)**|0.66***|0.14|0.34***|0.15|0.4***|1***|0.44***|0.14|0.35***|0.26**|0.03|0.29**|
|**Protecting Investors (rank)**|0.63***|0.34***|0.52***|0.29**|0.24*|0.44***|1***|0.3**|0.23*|0.15|-0.04|0.05|
|**Paying Taxes (rank)**|0.45***|0.12|0.12|0.21|0.25*|0.14|0.3**|1***|0.11|0.16|0.11|0.21|
|**Trading Across Borders (rank)**|0.49***|0.01|0.22*|0.37***|0.17|0.35***|0.23*|0.11|1***|0.02|0.11|-0.1|
|**Enforcing Contracts (rank)**|0.52***|0.24*|0.32**|0.11|0.49***|0.26**|0.15|0.16|0.02|1***|0.35***|0.21|
|**Closing a Business (rank)**|0.39***|0.29**|0.19|0.16|0.26**|0.03|-0.04|0.11|0.11|0.35***|1***|0.03|
|**New Product specification: Rank of Country FE**|_0.22*_|_0.11_|_-0.02_|_0.14_|_0.34***_|_0.29**_|_0.05_|_0.21_|_-0.1_|_0.21_|_0.03_|**_1***_**|
|**New Process specification: Rank of Country FE**|_0.23*_|_0.09_|_0.05_|_0.17_|_0.3**_|_0.27**_|_-0.02_|_0.12_|_-0.11_|_0.23*_|_0.15_|**_0.48***_**|

Note: *** significant at 1% level, ** significant at 5% level, * significant at 10% level.

40