rafmacalaba/datause-encoder-data huggingface dataset:
it exceeds 3 percent. The significant cross-country variation is accompanied by substantial time-variation.

Table 2 shows that financial repression has been as high as 40 percent of government revenue in Mexico (where in 1987 it was as high as 83 percent of GDP, see below for discussion), and about 20 percent in India, Pakistan, Portugal, Sri Lanka, and Zimbabwe. In general, tables 1 and 2 suggest that in many countries a financial liberalization will generate a substantial budgetary problem, and should be accompanied by a fiscal reform aimed at substituting for the revenue loss.<sup>27</sup> The possible effects of financial liberalization can be gauged from table 3, reporting the breakdown of ownership of domestic government debt. For completeness, the table includes centralbank holdings, even though those are not included in the calculation of the financial-repression tax base. The second column in the table includes holdings of deposit money banks only-a subset of domestic financial intermediaries. The table shows that, for the years where we have data, on average 24 percent of total government domestic debt, and 37 percent of domestic debt in the hands of the public, is held by deposit money banks. The holdings of other financial institutions are included in the column labelled "other", but unfortunately could not be singled out for lack cf data. Financial intermediaries, and among them deposit money banks, typically offset artificially low rates on their assets by paying low rates on their liabilities-savings and time deposits: this is an example of the translation of the financial repression tax. In these cases the effect of a financial liberalization is an increase in the cost of liabilities to financial intermediaries, since domestic residents can access higher-yielding portfolio investments elsewhere. In absence of an increase in interest rates on government securities, financial intermediaries would thus risk bankruptcy, and would possibly require public bailout. Hence the likely budgetary impact of financial liberalization is either the cost of the bailout of these intermediaries, or an increased cost of debt servicing, which would occur if government securities were mostly short-term.

2 7 0n this topic, see, for example, Thirsk (1990). This conclusion is based on the assumption that the world rate of interest paid by the government on **its** foreign debt is exogenous, i.e. the small country assumption.

15
# **1. Introduction**

The impact of infrastructure endowment and quality on growth and development indicators is well documented as are the complementary roles of the public and private sectors in service provision.<sup>2</sup> The role of the private sector as an operator of infrastructure assets, and an investor, part-owner or partial-financier of capital assets has become a common and growing feature of infrastructure provision in developing countries over the last twenty years— particularly in electricity generation and supply, telecommunications, ports, rail and airports. Private financing and operations of highways, bridges and road networks are also becoming more commonplace. To a lesser degree, urban services such as water supply and sanitation and Bus Rapid Transit systems have also benefited from private operations and investment.<sup>3</sup> There has been an important increase in infrastructure PPPs--herein referred to as “private participation in infrastructure (PPI)”—so as to capture management contracts on one extreme of the risk spectrum and asset sales, auctions and privatizations on the other--over the last two decades. Annual commitments<sup>4</sup> to PPI projects have more than doubled over the last five years from levels seen in the previous ten years. For the period 2006-2010, total commitment to PPI projects reached US$757 billion (Figure 1).

Despite the overall growth trends, levels of investment in PPI vary across countries. There are countries of similar GDP with huge disparities in the levels of private infrastructure investment. For example, Angola benefited from US$ 2.0 billion in PPI between 2000 and 2010 while Tunisia attracted US$ 5.5 billion<sup>5</sup> . Likewise, Peru received more than US$10 billion in PPI over that period while Azerbaijan received less than US$0.8 billion in the same period.

> 2 Serven & Calderon (2004); Straub (2008)

> 3 See the PPI Database home page at ppi.worldbank.org.

> 4 Investment in this paper refers to the resources the project company commits to invest in facilities during the contract period. Investments can be either in new facilities or in expansion and modernization of existing facilities. Data entry varies across sectors: For projects other than telecommunications and large energy utilities, the total cost of developing or expanding the facility during the contract period is entered as investment data in the year of financial closure (for which data are typically available). For telecommunications projects and some large energy utilities, annual investments on facility expansion and modernization are entered as investment data in the year of investment when information is publicly available. Investments are recorded in millions of US dollars in either the year of financial closure or year of investment as indicated above.

> 5 The GDP per capita for Angola is US$ 4,422 and US$4,198 for Tunisia in 2010.

2
The domestic price of exported goods (PX) is given by PX = ER•PX<sup>*</sup> , (26) where ER is the nominal exchange rate and PX<sup>*</sup> the world price of exports (assumed exogenous).

The domestic price of imported goods is: where PM<sup>*</sup> the world price of exports (assumed exogenous).

The price of output is assumed to be numéraire,: PY = 1. (28)

# **III.6. Gross domestic product**

Nominal gross domestic product is the summation of private consumption (CP), public consumption (CG), public investment (IG), private investment (IP), all converted in nominal terms by multiplying them by PQ, and adding net exports to the summation (ER•(PX<sup>*</sup> •X – PM<sup>*</sup> •M)):

# **IV Simulation Results**

The model is applied to Ethiopia, one of the highly indebted poor countries in Sub-Saharan Africa. Ethiopia reached the HIPC Completion Point in 2004. Following that accession to debt relief, the ratio of net present value of its debt to exports declined from 284 at Decision Point to 150 after the Completion Point (World Bank, 2006).

This section is divided into three parts: the first one deals with the calibration of parameters underlying the proposed model. Then, the baseline simulation is discussed, followed by policy experiments.

# **IV.1 Calibration of parameters and projection of exogenous variables:** Most of the parameters used in

the simulations are taken from Agénor et al. (2008), because their model has been applied to the same country. Furthermore, by extending the application of their results, we assume that the parameters are relatively stable over time. The values of the parameters are given in Table 1A in the Annex. The behavioral equations are based on econometric regressions obtained using Ethiopia’s observable variables in the simulations. Equation (9) provides the estimate of private capital accumulation expressed as a percentage of GDP in real terms. The estimated parameters for Equation (9) are: PQ*IP/GDP = (0.03*Growth of Y + 0.3*(KG*PQ/GDP) - 0.003*(FdebtG.ER/GDP)).

Given that the base year is 2008 (because most complete observable data belong to that year), the projections are done for the period 2009-2030. The population growth rate (n) is taken equal to its 2008 11
<!-- Start of picture text --> 45.0%<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0% 2 3 a S<br>10.0%<br>5.0%<br>0.0%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>TMMicro @Small TMMedium ® Large<br><!-- End of picture text --> <!-- Start of picture text --> 60.0%<br>50.0%<br>40.0%<br>30.0%<br>x<br>g % oe<br>20.0% = &$z<br>3<br>10.0%<br>0.0%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>m<S =5-9 =10-14 15-19 m>=20<br><!-- End of picture text -->
-34-

CAS SlIDI WY lTh D>X0T!4 SYS7B f1. aiuS6

**Th-ir. Perfor_neend** Transfer **otf HInemAmt i ' Aseocltim**

Table **4: Cromelne Pattern,.** Iriation, end Yiel **the** _sn_ **IfT Irrination Dlitrict (19871**

|**Aree**|**a at**|**P.oduetlm**|**%**|**Yie**|**ds**||
|---|---|---|---|---|---|---|
|C-O|||||||
|Irite|Non-<br>leri|,tret<br>Irrite|Nan-<br>X<br>rrim|ti1;a*|**Non-**<br>Irri_ae||
|-___-_-------------|-----|_____------tons--|<br>--------<br>~|---------<br>ton/he|- ---------|--<br>8h-|
|Campairn<br>1987/I:|||||||
|Cotton<br>1719|199|4813|477|2.8|2.4|3500|
|Maize<br>335|264|1608|924|4.8|3.6|2100|
|Sorghum<br>973|1614|4135|5450|4.3|3.6|1700|
|Soya<br>1904|467|4188|840|2.2|1.8|1800|
|Tomatoes<br>11|6|275|132|28|22|3100|
|Passion<br>Fruit<br>460|166|6900|2158|18|13|3000|
|Pastures<br>51|1205|117|1052|2.3|0.9|1500|
|Crepes<br>352|-|6582|-|18.7|-|3700|
|Various<br>l16|_|290|-|2.8|-|2400|
|5921|3821||||||
|Campaign 1987/I,:|||||||
|Maize<br>14|103|391|204|8.8|3.0|2100|
|Sorghum<br>2062|1859|8886|7064|4.3|3.8|1700|
|Soys<br>2653|865|7428|1816|2.8|2.1|1800|
|Tomatoes<br>8|-|300|-|37.5|-|3100|
|Passion<br>Fruit<br>626|-|9890||15|-|3000|
|Pasture<br>16|1115|-|-|-|-|1500|
|Grapes<br>352<br>5745|3942|5984||17|-|**3800**|

Water Consumption Campaign **I: 14,68.000** a3 Campaign II: 11.757.10 3 26,593.700 .3 Average Consumption (m 3/e): 2730 (a) Of **the** 9742 **ha** that **are registered.**
<!-- Start of picture text --> yovy t<br>\ | \<br>«Soy\ 7FON %<br>/ \\/ \aman -©<br>/ > °<br>/<br>y+ |/Ps e * es ee<br>—e— --@--<br>— ee<br><!-- End of picture text -->

<!-- Start of picture text --> 1<br>0.9<br>S<br>Bo 08<br>A=i<br>2 07<br>2<br>s<br>Ss 06<br>aE<br>=° 05<br>Sswo<<8 04<br>2 03<br>0.2<br>0<br>German GUO German GUO French GUO British GUO<br>(low export share) (high export share)<br><!-- End of picture text -->
# **4.2 Estimating program impacts on refugee well-being**

The primary approach to estimate program treatment effects on the treated (TOT) is instrumental variables (IV) estimation, as represented by the following two equations: Equation (1) estimates the first-stage effect of assignment to treatment on the household’s takeup status (with results presented above), and equation (2) estimates the TOT effects. To enhance statistical power, data from the midline, endline, and follow-up surveys are combined in the main analysis when possible, as pre-specified. The data includes multiple rounds _t_ of survey data for each household, and the data are stacked. _Tic_ denotes the treatment take-up for household _i_ in community _c_ . _Zc_ is an indicator variable signifying whether community _c_ was randomly assigned to HSP treatment, where all eligible individuals were then assigned to the treatment. The outcome variable of interest is _yict_ , and the predicted treatment take-up, _T_<sup>�</sup> _ic_ , is derived from equation (1).

The vector _Xc_ encompasses community stratification variables used in the randomization, including indicators for whether a community is in Mafraq or Irbid and the community’s district’s population quartile<sup>12</sup> The vector _Wic_ includes individual baseline covariates, accounting for household-level demographic variables from the integrated assessment; in particular the vulnerability quartile, month of assessment, household size, number of children, and respondent gender and age. _μict_ is a vector of enumerator-round fixed effects. _ηic_ and _εict_ are error terms and are clustered at the community level, which is the level of randomization.

To assess dynamics, the analysis also estimates TOT effects separately with the midline, endline, and follow-up data, while the regression specification remains effectively the same as previously described, with enumerator fixed effects (rather than enumerator-round fixed effects). The

> 12As noted before, communities are defined by the Jordanian administrative unit “locality”. “District” is a more aggregated, administrative geographic unit used in Jordan.

18
# **Table 3: Summary Statistics**

||**Obs**|**Mean**|**sdtdev**|**min**|**max**|
|---|---|---|---|---|---|
|Firm purchased fixed assets Y:1 N:0|1228|0.42|0.49|0.00|1|
|Firm purchase of fixed assets amount in USD|1228|606,748|22,900,000|0|1,005,564,928|
|Mobile money for any financial transactions Y:1 N:0|1228|0.54|0.50|0.00|1|
|Mobile money to pay employee Y:1 N:0(0 if not adopted)|1228|0.09|0.29|0.00|1|
|Mobile money to pay supplier Y:1 N:0(0 if not adopted)|1228|0.24|0.42|0.00|1|
|Mobile money to pay utility bills Y:1 N:0(0 if not adopted)|1228|0.23|0.42|0.00|1|
|Mobile money to receive pymnts from customers Y:1 N:0(0 if||||||
|not adopted)|1228|0.36|0.48|0.00|1|
|% of total labor cost paid using mobile money (0 if not||||||
|<br>adopted or no payment)|1217|3.35|13.90|0.00|100|
|% of raw material cost paid using mobile money(0 if not||||||
|<br>adopted or no payment)|1194|3.90|10.97|0.00|100|
|% of utility bill paid using mobile money(0 if not adopted or||||||
|no payment)|1204|6.34|18.22|0.00|100|
|% of annual sales from customer payments using mobile||||||
|<br>money(0 if not adopted or|1186|5.53|12.19|0.00|90|
|Log of size|1228|2.71|1.08|0.08|7.78|
|Top manager experience in sector (years)|1228|14.96|9.61|1.00|57|
|Log of sales per worker (outliers removed)|1228|9.06|1.97|2.26|17|
|Log of age of firm|1228|2.48|0.80|0.00|4.65|
|Firm is part of a larger firm Y:1 N:0|1228|0.10|0.30|0.00|1|
|Exports 10% or more of sales Y:1 N:0|1228|0.15|0.36|0.00|1|
|Foreign ownership Y:1 N:0|1228|0.07|0.26|0.00|1|
|Firm with unfavorable credit record Y:1 N:0|1228|0.40|0.49|0.00|1|
|Establishment has a line of credit or loan at this time Y:1 N:0|1228|0.26|0.44|0.00|1|
|Firms use email to interact with clients/suppliers Y:1 N:0|1228|0.53|0.50|0.00|1|
|Establishment has checking or savings account at this time||||||
|Y:1 N:0|1228|0.87|0.33|0.00|1|
|Bank financing for working capital(%)|1228|11.52|22.03|0.00|100|
|Supplier Credit financing for working capital(%)|1228|9.93|19.83|0.00|100|
|Capital or main business city Y:1 N:0|1228|0.58|0.49|0.00|1|
|Establishment uses cell phone for operations Y:1 N:0|1228|0.90|0.30|0.00|1|
|Mean distance - bank (km)|1228|219.17|139.73|107.64|669|
|Firms has ISO certification ownership Y:1 N:0|1171|0.18|0.39|0.00|1|
|Senior management time spent in dealing government||||||
|<br>regulations|1004|6.11|8.94|0.00|48|
|<br>No. of Mobile Money Agents, 5 km radius|1228|1394|1357|0.00|5683|
|No. of MFIs, 5 km radius|1228|19.35|19.98|0.00|68.00|
|Manufacturing Firms|1228|0.34|0.47|0.00|1.00|
|Retail Firms|1228|0.26|0.44|0.00|1.00|
|OtherServicesFirms|1228|0.41|0.49|0.00|1.00|

21
current account surplus (large negative foreign savings) on the results of the simulation. Also, this modification is not far from the reality since Mount Lebanon back then did not have a monetary authority.

Table 4. Simulation of external shocks on the economy of Mount Lebanon in 1912 (change, in

|Percentage change compared to base year|percent)<br>Remitt.<br>-90%|Remitt.<br>+10%|Prices of<br>exports -<br>10%|Prices of exports -<br>10% and<br>consumption per<br>capita unchanged|
|---|---|---|---|---|
|Remittances|-90.0|10.0|0.0|16.1|
|Total Income|-39.7|4.5|-6.4|0.9|
|Private Consumption|-33.7|3.6|-4.3|1.6|
|Private Consumption/capita|-34.7|2.0|-5.7|0.0|
|Investment|-31.3|3.3|-4.0|1.5|
|Exports|7.9|-0.9|-0.1|-1.7|
|Imports|-37.0|4.1|-5.5|1.2|
|Demand of domesticgoods|-17.6|1.6|0.3|2.9|
|Price of domesticgood|-36.1|4.2|-9.4|-2.9|
|Real exchange rate|-36.1|4.2|-5.9|0.9|
|Real GDP at marketprice|-2.6|0.0|-0.1|0.0|

N.B: Per capita consumption is calculated taking into consideration an estimated 3 percent yearly natural increase in population. In the last scenario, I add an emigration rate of 1.4 percent. These assumptions are set according to tendencies observed during 1900-12. Note: Remitt. Stands for remittances. Source: author simulations.

The first column summarizes the effect of a decline in remittances by 90 percent. The impact on income is strong and generates a sharp decline in consumption and investment. This is consistent with historical developments: when remittances inflow to Mount Lebanon halted in 1914-18 because of WWI, the country fell into a disastrous famine. Imports, including food imports, collapsed. The RER decreases strongly, due to a collapse in the demand and prices of domestic goods. The decline in the RER boosts exports by 8 percent. The increase in exports keeps the decline in real GDP moderate at a negative 2.6 percent. In reality, history shows that exports stopped in 1914-18 because WWI interrupted all trade flows.

The second column simulates the effect of a 10 percent increase in remittances. Income, consumption, and imports increase while real GDP remains unchanged. The country loses in competitiveness since RER appreciates due to higher demand and prices of domestic goods, and exports decline. This result meets the observations on the increase in the prices of land, a key nontradable good, with the rise in remittances to Mount Lebanon.

The third column assesses the impact of a decline by 10 percent in the prices of exports. Income declines and triggers a decrease in consumption. Imports also decrease since there is less foreign financing available due to the drop in exports. Real GDP regresses slowly while RER declines due to lower prices of domestic goods. The decline in the RER is however less marked than the drop in the prices of domestic goods since the relative price of domestic non-traded goods to traded goods declines moderately. Indeed, in the denominator, part of the prices of traded goods (exports) has also declined. Also in this case, history reports on the negative impact of a decline in the international prices of silk on income and consumption.

7
<mark>Gibson, J., McKenzie, D., Rohorua, H., & Stillman, S. (2018). The Long-term impacts of international migration: Evidence from a lottery.</mark> _<mark>The World Bank Economic Review</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(1), 127-147.</mark> **<mark>[</mark> 2]** Grace, K., & al., e. (2018). Examining rural Sahelian out-migration in the context of climate change: An analysis of the linkages between rainfall and out-migration in two Malian villages from 1981 to 2009. _World Development, 109_ , 187-196. **[1]** <mark>Gray, C., & Bilsborrow, R. (2013). Environmental influences on human migration in rural Ecuador.</mark> _<mark>Demography</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>(4), 1217-1241.</mark> **[1]** <mark>Gray, C., L., & Mueller, V. (2012a). Drought and population mobility in rural Ethiopia.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>40</mark>_ <mark>(1), 134-145.</mark> **<mark>[</mark> 1]** <mark>Gray, C. L., & Mueller, V. (2012b). Natural disasters and population mobility in Bangladesh.</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>,</mark> _<mark>109</mark>_ <mark>(16), 6000-6005.</mark> **<mark>[</mark> 1]** <mark>Helbling, M., Auer, D., Meierrieks, D., Mistry, M., & Schaub, M. (2021). Climate change literacy and migration potential: micro-level evidence from Africa.</mark> _<mark>Climatic Change</mark>_ <mark>,</mark> _<mark>169</mark>_ <mark>(1), 1-13.</mark> **<mark>[</mark> 1]** <mark>Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa?</mark> _<mark>Journal of Development Economics</mark>_ <mark>, 124, 60-82.</mark> **<mark>[</mark> 1]** <mark>Hirvonen, K. (2016). Temperature changes, household consumption, and internal migration: Evidence from Tanzania.</mark> _<mark>American Journal of Agricultural Economics</mark>_ <mark>,</mark> _<mark>98</mark>_ <mark>(4), 1230-1249.</mark> **[1]** Hoffmann, R., Dimitrova, A., Muttarak, R. _et al._ A meta-analysis of country-level studies on environmental change and migration. (2020). _Nature Climate Change,_ 10(10), 904-912. **[1]** <mark>Hunter, L. M., Murray, S., & Riosmena, F. (2013). Rainfall patterns and US migration from rural Mexico.</mark> _<mark>International Migration Review</mark>_ <mark>,</mark> _<mark>47</mark>_ <mark>(4), 874-909.</mark> **<mark>[</mark> 1]** <mark>International Organization for Migration (IOM) (2108).</mark> _<mark>Data Bulletin Series: Informing the Implementation of the Global Compact for Migration</mark>_ <mark>. Available at:</mark> <u><mark>https://publications.iom.int/books/data-bulletin-series-informing-implementation-global-compactmigration</mark></u> **<mark>[</mark> 2]** IPCC (2014). Summary for policymakers. In: Edenhofer O, Pichs-Madruga R, Sokona EFY, Kadner S, 43
- Fernandes, A. M. (2007). Trade policy, trade volumes and plant-level productivity in Colombian manufacturing industries. _Journal of International Economics_ , 71(1), 52– 71.

- Galindo, A., Schiantarelli, F., & Weiss, A. (2007). Does financial liberalization improve the allocation of investment?: Micro-evidence from developing countries. _Journal of Development Economics_ , 83(2), 562–587.

- Greenaway, D., Guariglia, A., & Kneller, R. (2007). Financial factors and exporting decisions. _Journal of International Economics_ , 73(2), 377–395.

- Hart, O. & Moore, J. (1994). A theory of debt based on the inalienability of human capital. _The Quarterly Journal of Economics_ , 109(4), 841–879.

- Hauner, D. & Prati, A. (2008). The interest group theory of financial development: Evidence from regulation. forthcoming IMF Working Paper.

- Helpman, E., Melitz, M. J., & Yeaple, S. R. (2004). Export versus FDI with heterogeneous firms. _American Economic Review_ , 94(1), 300–316.

- Henry, P. B. (2000). Stock market liberalization, economic reform, and emerging market equity prices. _Journal of Finance_ , 55(2), 529–564.

- Hsieh, C.-T. & Klenow, P. J. (2007). Misallocation and manufacturing TFP in China and India. NBER Working Paper No. 13290.

- Hsu, T. (2006). Financial market imperfections, firm heterogeneity and the impact of trade on entrepreneurial selection and industry productivity. mimeo.

- Hur, J., Raj, M., & Riyanto, Y. E. (2006). Finance and trade: A cross-country empirical analysis on the impact of financial development and asset tangibility on international trade. _World Development_ , 34(10), 1728–1741.

- IMF (2001). _Structural Conditionality in IMF-Supported Programs_ . Washington D.C.: International Monetary Fund.

- Jorgenson, D. W. & Vu, K. (2007). Information technology and the world growth resurgence. _German Economic Review_ , 8, 125–145.

- Kiyotaki, N. (1998). Credit and business cycles. _Japanese Economic Review_ , 49(1), 18–35.

- Kletzer, K. & Bardhan, P. (1987). Credit markets and patterns of international trade. _Journal of Development Economics_ , 27(1-2), 57–70.

- Manova, K. B. (2006). Credit constraints, heterogeneous firms and international trade. mimeo.

- Melitz, M. (2003). The impact of trade on intra-industry reallocations and aggregate industry productivity. _Econometrica_ , 71(6), 1695–1725.

- Pavcnik, N. (2002). Trade liberalization, exit, and productivity improvement: Evidence from Chilean plants. _The Review of Economic Studies_ , 69(1), 245–76.

37
18a Figure 4 a, COLOMBIA - CPI INFLATION AND REAL EFFECTIV **E** XR <!-- Start of picture text --> INFLATION RER<br>35 - - 170<br>1 160<br>30 9<br>250 g !<br>125 - - REALI; E X R 1<br>15E . . o<br>-100<br>10 ,/<br>5- 8<br>74 7576 747 76 78 80 8 84 5 86 88 90<br><!-- End of picture text --> <!-- Start of picture text --> INFLATION<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE 4<br>6. MEXICO -REAL EXR, CPI INFLATION, AND EXR DEPRECIATION<br>REAL EXR INFL, EXR DEPR.<br>150-<br>125 ............... REA-L-EXR ..... I. ..............<br>1oo0 . ............. ~.. .....<br>I~~~~~~'<br>I~~~~~~'<br>74 75 76 77 78 79 80 i1 82 83 84 85 66 87 88 89 90<br><!-- End of picture text --> I-RERIRAL2 -. CPINF -- EXRDEP