rafmacalaba/datause-encoder-data huggingface dataset:
29

# Table IX

# Growth and Initial Financial Indicators

# Pooled Cross-Section Time-Series: Initial Decade Values

|Dependent<br>Variable|LLYI|BANKI|PRIVATEI|PRIVYI|
|---|---|---|---|---|
|GYP|0.034***<br>(0.009)<br>(0.001]|0.028**<br>(0.011)<br>(0.011]|0.016*<br>(0.009)<br>(0.071]|0.037***<br>(0.011)<br>(0.001]|
|R2s|0.42|0.40|0.39|0.42|
|GK|0.022***<br>(0.007<br>(0.0031|0.027***<br>0.009)<br>[0.003]|0.013*<br>(0.008)<br>[0.095]|0.028***<br>(0.009)<br>(0.002]|
|R<sup>2</sup>:|0.37|0.37|0.35|0.37|
|INV|0.108***<br>(0.023)<br>(0.001]|0.102***<br>(0.028)<br>[0.001]|0.043*<br>(0.024)<br>(0.068]|0.086***<br>(0.028)<br>(0.003]|
|R2<:|0.33|0.30|0.26|0.28|
|EFF|0.025***<br>(0.009)<br>(0.004]|0.020*<br>(0.010)<br>(0.058]|0.013<br>(0.009)<br>(0.144]|0.028***<br>(0.010)<br>(0.007]|
|R<sup>2</sup>:|0.33|0.31|0.30|0.33|
||(standa<br>(p|rd errors in par<br>-values in brack|entheses)<br>ets]|Observations<br>169|

* significant at the 0.10 level

** significant at the 0.05 level

* significant at the 0.01 level

- GYP = Real per capita GDP growth rate GK - Real per capita capital stock growth rate INV = Ratio of investment to GDP EFF = GYP - (0.3)*GK LLYI = Initial ratio of liquid liabilities to GDP BANKI = Initial deposit bank domestic credit divided by domestic bank domestic credit plus central bank domestic credit

- PRIVATEI = Initial ratio of claims on nonfinancial private sector to domestic credit

- PRIVY = Initial gross claims on the private sector to GDP

Other explanatory variables: Decade dummy variables, log of initial income, log of initial secondary school enrollment rate, initial ratio of government expenditures to GDP, initial inflation rate, initial ratio of exports plus imports to GDP.
<mark>Malaysia’ if things get a lot worse? Malaysia famously stopped foreign investors from taking their money out of the country during a crisis in 1998...”; and</mark> _<mark>Financial Times</mark>_ <mark>, August 15, 2013, “... the measure smacks more of desperation than of sound policy”).</mark>

# **6. Summary**

Due to steady liberalization of the capital account since the early 1990s and increased financial integration of the Indian economy over time, capital flows to India reflect broad global trends. Just like in other emerging markets, capital flows to India have been subject to a surge-and-stop cycle. The policy response has included reserve management, liquidity management, countercyclical capital flow measures and some monetary accommodation. Specifically, India eased money supply, accumulated reserves, accelerated the pace of liberalization of capital outflows, and slowed the pace of further liberalization of inflows during the “capital surge” episode of 2003-2008. It tightened monetary policy, used reserves to meet the demand for foreign exchange and avoid large exchange rate movements, and increased the pace of liberalization of inflows, while restraining outflows, during the periods of tepid inflows or outflows of capital, such as in 2008-09 or 2013.

Going forward, under the new inflation targeting framework, monetary policy will likely respond even more than before to meet the inflation target and adjust less than before to the capital flow cycles. One concern some people have with the move of a developing country such as India to inflation targeting is that it could result in greater exchange rate flexibility. Having liberalized the capital account progressively over the last two and a half decades, the scope to use capital flow measures countercyclically has perhaps diminished as well. Thus in years ahead, reserve management and macroprudential measures are likely to play a more significant role in helping respond to capital flow cycles, just as the policy makers and the economy develop greater tolerance for exchange rate adjustments.<sup>32</sup>

> 32 Pontines (2011) examines whether countries that target inflation experience higher exchange rate volatility. After accounting for the self-selection of countries into inflation targeting, he finds that the nominal and real exchange rate volatility is lower in inflation targeting countries, particularly in developing countries.

31
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**Table 8: Trade Openness and Industry Labor**<sup>**Demand for White Collar Workers in Chile (1982-95)**</sup> **(standard errors in parenthesis)**

||Ln L t-l|Ln WBt|Ln WBt- I|Ln WWt|Ln WWt- I|Constant|Wald<br>Test: p-<br>value(a)|Sargan<br>Test<br>|Autoco.<br>Test<br>(2nd o.)<br>|No. Obs<br>(Plants)<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|Trade Policy|0.256(*)|-0.072|-0.046|0.012|-0.017|0.051|--|0.193|0.669|21014<br>|
|Specification|(0.052)|_(0.105)_|(0.100)|(0.097)|(0.071)|(0.073)||||(1501)|
|Variable* Tariff Rate|-0.007<br>(0.020)|0.001<br>(0.004)|-0.001<br>(0.003)|0.002<br>(0.006)|0.002<br>(0.005)||0.997||||
|Variable* Real|0.001|0.001|0.0003|-0.003(*)|0.001||0.002||||
|Exchange Rate|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)||||||
|Wald Test: p-value(b)|0.847|0.584|0.936|0.000|0.204||||||
|<br>Price-Cost-Margin|0.215(*)|-0.097(*)|-0.038|-0.410(*)|0.220(*)|-0.074(*)|--|0.341|0.913|21014<br>|
|Specification(c)|(0.040)|(0.033)|(0.036)|(0.026)|(0.037)|(0.012)||||(1501)|
|Variable* Price Cost|0.0004|0.003(*)|-0.038|0.0007|-0.004|-0.005|0.000||||
|Margin|(0.0005)|(0.001)|(0.036)|(0.0007)|(0.001)|_(0.005)_|||||

Notes: GMM estimates with first-differenced data.<sup>Time dummies were included but are here omitted. All variables are assumed exogenous except for lagged</sup> employment. Instruments are based on second and<sup>fuirther lags of employment and output. (*) Significant at the 1% level. (**) Significant at the 5% level. (***)</sup> Significant at the 10% level. (a) Wald Test of Joint<sup>Significance of the variables interacted with a given openness variable. (b) Wald Test of Joint Significance of openness</sup> variables interacted with a given variable. (c) Additional<sup>Instruments: second and further lags of price-cost-margins.</sup>
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# **4. Hypotheses Tests and Results**

Hypotheses 1 through 5 are testable using the available information in the surveys. The last hypothesis does not yield itself to statistical testing. The null hypothesis for each of the testable hypotheses is that _measures to correct for factors hypothesized to lower inequality will not lead to significant increase in inequality._ The alternative is _these measures will significantly increase inequality in the Azerbaijan data_ . As there is no established lower or upper limit to the level of inequality, a _significant_ level of increase would be one that makes the Gini coefficient “reasonable” for Azerbaijan and comparable to that of other countries in the region and with similar economic structure.

# **_Hypothesis 1_**

We test **hypothesis 1** by calculating consumption inequality with and without various forms of transfers such as social transfers (including pensions, state benefits and certain privileges), and transfers from family and friends abroad and within country. As can be seen in Table 5, over 90 percent of all households report receiving some kind of transfer income. Over half of urban and over 55 percent of rural households were reported to receive pension income. Transfers from friends and family members reached about 73 percent of Azerbaijan households, with nearly 80 percent of rural households reporting receipt of transfer income from friends and family members. Transfers also constituted a large share of household income. For example, in 2004 total transfer income accounted for a non-trivial 29 percent of total household income per capita nationwide.

**Table 5.** Most Azeri households report receipt of transfer income in 2004

||**_Azerbaijan_**|**_Baku_**|**_Non-Baku Urban_**|**_Rural_**|
|---|---|---|---|---|
||**Sha**|**re of househ**|**olds receiving transfers**||
|All transfer|91.9|84.3|91.3|96.3|
|Social transfer|78.4|66.8|77.7|84.9|
|_of whichpensions_|52.7|45.6|54.8|55.2|
|Transfers from family/friends|73.0|64.1|69.2|79.9|
||**Shar**<br>|**e of transfer**<br>|**s in the household incom**<br>|**e**<br>|
|Total transfer|29.1|23.7|29.0|32.5|
|Social transfer|13.3|11.2|13.0|14.8|
|_of whichpensions_|10.9|9.5|11.0|11.7|
|Transfers from family/friends|15.8|12.5|16.1|17.7|

Source: Azerbaijan 2004 HIES data.

Inequality increases substantially after subtracting transfers from household per capita consumption. Table 6 presents Gini coefficients without transfers over time and by area of residence. This holds true nationally as well as at regional levels. Nationally the inequality increases by over 73 percent in 2002, by over 81 percent in 2003 and by over 94 percent in 2004 if no transfers were available for consumption (Table 6). If only social transfers were subtracted from consumption, the Gini coefficient increases by about 50, 53 and 67 percent in 2002, 2003 and 2004, respectively. The findings suggest that social transfers had welfare equalizing effect, which is reflected in lower inequality with transfers than without transfers. There are evidences 8
page 13 Table 1. Estimation of the Average Number of Life-Days Saved Per Vaccination from Ghanaian Data

|Vaccination/|Life-<br>Days-|Prop.<br>at|Poten-<br>tial|Prop.<br>Prdcng|LDS<br>Per|Distribut<br>Vaccina|ions<br>of<br>tions:|
|---|---|---|---|---|---|---|---|
|Dose|Lost|Risk|LDS|Im'ty|Vac.|Theory|Obsrvd.|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|1. Measles|23.36|.039|599.0|.60|359.38|.191|.128|
|2. Tuberculosis|11.01|1.00|11.01|.90|10.45|.142|.152|
|3. Polio/l|1.20|.038|15.8|.90|.57|.111|.180|
|4. Polio/2|||7.9|.90|.29|.111|.096|
|5. Polio/3|||7.9|.90|.29|.111|.084|
|6. Diptheria/l|.014|.077|.086|.90|7.08|.037|.060|
|7. Diptheria/2|||.078|.90|6.65|.037|.032|
|8. Dipthena/3|||.018|.90|1.57|.037|.028|
|9. Pertussis/l|4.65|.078|23.8|.90|21.46|.037|.060|
|10.Pertussis/2|||23.8|.90|21.46|.037|.032|
|ll.Pertussis/3|||11.9|.90|10.73|.037|.028|
|12.Tetanus/1|4.47|.961|2.2|.90|1.99|.037|.060|
|13.Tetanus/2|||2.2|.90|.22|.037|.032|
|14.Tetanus/3|||.2|.90|.22|.037|.028|
|TOTALS|44.70||706.0|||75.4|53.8|

- SOURCES: Table 1 of Ghana Health Assessment Team (GHAT) (1981) and the appendix to it distributed by R. Morrow, WHO, Geneva.

- Column (2): Expected life-days-lost per capita in entire population from GHAT, Table 1, column (10).

- Column (3): Proportion of entire population which is at risk from this disease and thus can benefit from the vaccination. Measles - pop. 1-2 assumed 39/1000 (GHAT Appendix); TB - entire population; polio - pop. 2-3 assumed 38/1000 (GHAT Appendix); Dip. - pop. 1-3 assumed 77/1000; pert. - pop. 0-2 assumed 78/1000; Non-neonatal tet. - pop. older than 1 yr. assumed 961/1000.

- Column (4): Column (2) / columr (3). Quotient is allocated among multiple doses as follows: polio: 50%, 25%, 252; dip.: 47.3%, 47.5%, 5%; pert.: 40%, 40%, 20%; tet.: 47.5Z, 47.5%, 5%. (Morrow, 1984, personal communication!

- Column (5): Makinen (1982) and Shepard, Sanoh and Coffi (1982a) have estimated the effectiveness of measles vaccine under field conditions in Cameroun and the Ivory Coast at 48.5% and 60% respectively. The second and more optimistic figure is used here. The other effectiveness proportions are conjectured to be obtainable in a well-managed EPI system.

- Calumn (6): Column (5) x column (4). The diptheria, pertussis and non-neonatal tetanus vaccines are administered in a single vaccine called "DPT."

- Column (7): The eight distinct applications of a vaccine to a "fully immunized" individual are: one each of measles and BC G, three of polio and three of DPT. The theoretical distribution of vaccinations across these eight distinct vaccination events is based on the calculations by P. Knebel of the Sahel Development Planning Team, Bamako, Mali as presented in Agency for International Development (1983). It assumes that all children receive all six vaccinations.

- Column (8): This distribution of vaccination types can be deduced from the data presented by Sanoh (1983) on aggregate vaccinations performed in the Abengourou region in 1981 and on estimated coverage of this rural population by each of the eight vaccination events.
A clear pattern as to the priorities of the population emerged. If resources were available, education, health, employment generation and nutrition programs should be the beneficiary programs. In the reverse case (Table 8) these were exactly the programs which were to be protected from cuts. Instead, the interviewed population suggested expenditures for sports arenas, the police, public transport and lighting to be cut – all of these four areas also fared at the bottom of the list when an expansion of programs was probed. From the results obtained it was obvious that all population groups in Cali much preferred an expanded education and health system than an underground metro (a policy proposal that was discussed by the administration at the time the EPSOC survey was conducted).<sup>22</sup> With few exceptions, priorities across income groups and regions were very similar as we had already observed above regarding the evaluation of city services. Social housing and nutrition programs fared somewhat higher on the list of priorities for the poor than for better off groups but overall differences in priorities were indeed small – which was a very positive result for social policy makers as the city population was not deeply divided among different alternatives in a scarce resource environment.

It is interesting to observe that priorities for municipal programs as shown in Tables 7 and 8 did not closely match with dissatisfaction levels of services reported above. One could hypothesize, for example, that education would not be the number one priority of families given that – overall – students were relatively satisfied with the service provided. But problem identification and priority articulation are indeed two different processes which need to be distinguished clearly. Identification of a problem, as related for instance to the sewerage service provided by the city, need not imply a subjective evaluation that investment in the service is more important than spending additional tax revenues on expanding a service which is rated as good. Further, when asked about spending priorities, respondents implicitly make an assumption about the efficiency of additional expenditures. Low efficiency expectaions of service provision could then explain why services with low satisfaction nevertheless obtain low budget priority ratings.

To sum up, problem identification through satisfaction ratings is important for the city to reform the services and, as such, can be a check on municipal performance.<sup>23</sup> Priority budget identification, on the other hand, provides a wider picture on the relative evaluation of the

> 22 From a financial point of view, Solans (2000) comes to the same conclusion.

22
45 account inflation in the country between the survey date and 2011, for which we need the country’s consumer price index (CPI). Thus, for a survey conducted in year _t_ , we compute the poverty line in LCUs using the formula: where PL������� is the poverty line expressed in $US (either $1.90 or $3.20 in the 2018 HEFPI data set), PPP� is the country’s implied PPP for year _t_ , PPP���� is the country’s PPP for 2011, CPI� is the country’s CPI in year t, and CPI���� is the country’s CPI in 2011. We thus convert prices in the survey year in the country in question to 2011 prices in that country, and then apply the 2011 PPP conversion rate to convert 2011 LCUs of that country into international dollars.

In the 2018 HEFPI database, we use the 2014 version of the 2011 PPP factors produced by the International Comparison Program (ICP). These PPP factors cover 199 countries and are expressed in terms of 2011 prices. We extract the PPP conversion factor series PA.NUS.PRVT.PP from the World Development Indicator (WDI) database. This conversion factor is for private consumption, i.e., household final consumption expenditure. Whenever possible, we rely on the CPI series used by PovcalNet.<sup>47</sup> When the country’s CPI series are not available, we rely on the International Monetary Fund’s World Economic Outlook (WEO) data or on World Bank’s WDI CPI series.

# _Computing the incidence of catastrophic and impoverishing expenditures_

The incidence rates of catastrophic and impoverishing health expenditures are computed using (the code underlying) the Stata module FPRO (Eozenou and Wagstaff 2018). Where health expenditure data are individual-level, the data are aggregated to the household level. A household is defined as incurring catastrophic expenses if its out-of-pocket health expenditures strictly exceed the 47 PovcalNet can be accessed at http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx.
|Good relationship<br>with how many|||||||||
|---|---|---|---|---|---|---|---|---|
|Students|349|E|0%|5%|53%|42%||All Students|
|Like more<br>|||||||||
|Involvement in||||||||Agree or|
|setting students'<br>learning goals|350|A|1%|2%|8%|54%|35%|Strongly<br>Agree|
|Workload is<br>manageable|350|A|1%|7%|6%|61%|25%|Agree or<br>Strongly<br>Agree|
|Working hours are<br>too long|349|G|2%|43%|55%|||No|
|_Panel B: Work_|||||||||
|_Environment_|||||||||
|||||||||Agree or|
|Student learning is<br>motivating|349|A|0%|5%|11%|56%|28%|Strongly<br>Agree|
|||||||||Agree or|
|Satisfied with<br>current job|350|A|8%|19%|23%|39%|12%|Strongly<br>Agree|
|||||||||Agree or|
|Happy with Career||||||||Strongly|
|Prospects|350|A|3%|11%|21%|50%|15%|Agree|
|||||||||Agree or|
|Satisfied with||||||||Strongly|
|current Salary|350|A|22%|32%|27%|17%|1%|Agree|
|Satisfied with||||||||Agree or<br>Strongly|
|current Benefits|350|A|15%|35%|27%|21%|1%|Agree|
|Like greater job||||||||Strongly|
|security|349|A|1%|0%|4%|38%|57%|Agree|
|Change current||||||||Agree or|
|job given the||||||||Strongly|
|<br>opportunity|350|A|5%|17%|23%|35%|21%|<br>Agree|

31
**Table 6c—Market Shares of Output Produced in Uganda in Sectors with FDI (in percentage).**

||Kenya|Tanzania|Uganda|Rwanda|COMESA|SADC|USA|EUR|China|ROW|
|---|---|---|---|---|---|---|---|---|---|---|
|**Business Services**|||||||||||
|Air Transport|0|0|5|0|0|0|72|19|0|4|
|Communication|0|0|1|0|0|55|0|14|0|31|
|Insurance|60|0|12|0|0|4|15|5|0|5|
|Business Services nec|0|0|78|0|0|0|9|9|0|4|
|Financial Services nec|0|0|50|0|0|0|50|0|0|0|
|Transport nec|0|0|67|0|0|0|18|8|0|7|
|Water Transport|0|0|39|0|0|0|61|0|0|0|

# **Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.**

**Table 6d—Market Shares of Output Produced in Rwanda in Sectors with FDI (in percentage).**

||Kenya|Tanzania|Uganda|Rwanda|COMESA|SADC|USA|EUR|China|ROW|
|---|---|---|---|---|---|---|---|---|---|---|
|**Business Services**|||||||||||
|Air Transport|0|0|0|5|0|0|2|92|0|1|
|Communication|0|0|0|54|0|36|11|0|0|0|
|Insurance|60|0|0|12|0|4|15|5|0|5|
|Business Services nec|0|0|0|78|0|0|6|15|0|0|
|Financial Services nec|0|0|0|50|0|0|26|24|0|0|
|Transport nec|0|0|0|66|0|0|6|20|0|7|
|Water Transport|0|0|0|40|0|0|13|0|0|47|

# **Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.**

66
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