rafmacalaba/datause-encoder-data huggingface dataset:
**Chart 7. Market capitalization on the MSE to GDP**

<!-- Start of picture text --> 90%<br>80% 5.6%<br>70%<br>60%<br>50%<br>40% 79.1%<br>30% 7.0% 3.6% 3.0%<br>4.4%<br>20% 8.1%<br>27.0% 25.8% 29.8% 28.9%<br>10% 17.1%<br>0%<br>2005 2006 2007 2008 2009 2010<br>Market capitalization of shares/GDP<br>Market capitalization of listed bonds/GDP<br><!-- End of picture text --> Source: NBRM

# <u>Macroeconomic environment and monetary policy measures</u>

From 2004 until 2008, monetary policy went through several stages, accommodating changes in the macroeconomic environment and preserving price stability through the maintenance of exchange rate stability.

From the beginning of 2004 to September 2005, the monetary policy was conducted in conditions of growing deficit on the current account. In 2004 there was a more intensive growth of the domestic demand and export. Considering the high import component, the increased domestic export created higher import pressures. These developments contributed to significant widening of the current account deficit, from 4.1 percent of GDP in 2003 to 8.4 percent of the GDP in 2004. At the same time, the acceleration of the credit activities and the sharp deepening of the financial intermediation provided for additional financial support to the economy.

Unfavorable trends on the foreign exchange market, estimates on further aggravation of the current account position, and uncertainty about capital inflow dynamics required tightening of the monetary policy. This was also suggested by the relative indicators on foreign reserves, which were not favorable. In 2004, the reserves covered less than three months of the next year's imports of goods and services, indicating a need to increase the foreign reserves level. In such an environment, despite low inflation, a monetary policy response was necessary. It was aimed at relaxing the pressure on the current account and increasing the capacity to sustain the exchange rate stability. The Central Bank bills (CB bills) interest rate, as the main interest rate, generally had an upward trend and its cumulative growth was around 4 percentage points in this period. In February 2004 there was a shift in the type of the CB bills auctions, from ―interest rates tender‖ to ―volume tender,‖ with the latter having better signaling features. At the same time, in order to prevent additional pressure on the foreign exchange market, the portfolio of the NBRM instruments throughout which the excess of liquidity can be absorbed was expanded by the introduction of CB bills with 7-day maturity, besides the CB bills with 28-day maturity. In addition to the increase of the main interest rate, the bank's reserve requirements were increased, from 7.5 percent to 10 percent from the beginning of 2005, both for denar and foreign currency 9
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# **1. Introduction**

Accurate measurement of labor market activity is crucial for policy design, especially in developing countries, where households are disproportionately reliant on labor income. Current practice favors the use of a one-week reference period in labor market surveys, even though a shorter reference period, such as a day, might provide a sharper snapshot of labor market activity when such activity is short-lived and transitory, as is typical in large swaths of the developing world where self- and informal employment are the norm (Hussmanns, Mehran, and Verma 1992). At the same time, with the penetration of mobile phones in developing countries, new modalities of data collection have become feasible. These technological developments have also rejuvenated interest in the impact of survey mode on data quality.<sup>1</sup> This paper reports the findings of a randomized survey experiment conducted in urban Ghana to assess the impact of the reference period and survey modality (being interviewed over the phone) on labor statistics. The experiment tracked the labor market behavior of 954 respondents of the Ghana Urban Household Panel Survey for six months, using four monitoring instruments: (a) a baseline in-person interview, (b) a high-frequency sequence of interviews spanning 10 consecutive weeks, (c) an endline in-person interview conducted approximately three months after the baseline, and (d) a follow-up phone survey conducted three months after completion of the endline interview (i.e., six months after the start of the survey). Survey respondents were randomized into 3 ‘treatment’ arms for the high-frequency interviews which varied both the reference period and survey modality.<sup>2</sup> One-third of respondents was interviewed three times a week (using two separate 24-hour recall questions) by phone, while two-thirds were allocated to a weekly interview, but faced distinct survey modes: in one arm, respondents were

> 1 High-frequency labor supply data are also of policy interest, as recent work on labor supply responses to shocks has shown (see e.g. Heath et al. forthcoming).

> 2 There were also two control arms each comprising approximately 300 respondents. Phones were also provided to all respondents in one of the control arms. Since individuals in the control arms did not partake in the high frequency interviews, we do not analyze their reporting in this paper. Results which are available upon request from the authors suggest that receiving a phone or participating in the high-frequency surveys did not durably alter the labor market behavior of respondents.

2
(Balance Sheet data item #34) reported over the six year period from 2005 to 2010.<sup>21</sup> Figure 6B shows that both sales and short-term debt begin to recover in 2009. In our sample, the aggregate sales dropped by 12.15% from 2008 to 2009 but increased by 9.69% from 2009 to 2010. Debt in current liabilities, on the other hand, dropped by 28.88% from 2008 to 2009 and recovered by 3.94% from 2009 to 2010.

In unreported runs, we examine alternate measures of output at the firm level including Sales adjusted for Changes in total inventories, Sales adjusted for changes in finished-goods inventories and work in progress inventories, profitability as measured by earnings before interest, taxes, depreciation and amortization (EBITDA), and EBITDA adjusted for selling and general administrative expenses. In all instances, we find that output recovers in 2009 reaching pre-crisis levels of 2008 in 2010 in most cases. Overall, from Figures 6A and 6B we see that both output and credit in the US begin to recover simultaneously in 2009 although short-term credit levels are far below their pre-crisis peaks in 2008.

In Table 5, we present a detailed firm-level analysis using Compustat to understand how many firms recover and in how many cases recovery is in the absence of credit, suggesting potential phoenix miracles. In panel A of Table 5 we see that during the crisis period from 2008 to 2009, only 32.65% of firms had a positive increase in sales while 64.08% of firms had a decline in sales. By contrast during 2009 to 2010, 69.38% of the firms in our sample had a positive increase in sales and only 27.17% of the firms had a decline in sales. In unreported logit regressions, we find that the firms who had a positive increase in sales tend to be larger firms, in manufacturing (SIC codes 20-39), and with lower pre-crisis (in 2007) levels of short-term and long-term debt.

In panel B of Table 5 we repeat our analysis of Table 3 for the sample US firms using different measures of changes in financing as reported on the Balance Sheet and Statement of Cash Flows in Compustat. The Data Appendix provides details on how each of the variables is

> 21 The Compustat data download was in July 2011. So we may be missing data on some firms that have fiscal year ends in January-May2011 (which would count as fiscal year 2010 data) that may not have been updated in Compustat. Hence, we use a balanced sample because we are looking at aggregate sales and debt figures in the economy and we don’t want the year to year variation to be biased by non-missing data. However, to ensure that our results do not change if we were to include the non-missing data we re-do our analysis using quarterly statement data and do find a recovery in sales across all the firms in second quarter of 2009.

24
ratio but not to exchange rate movements, while rice prices do not respond to _S/U_ ratio but respond strongly to exchange rate movements. Second, from a methodological perspective, the rejection of cointegration may lead to the erroneous conclusion that there is no long-run relationship between food prices and fundamentals whereas such relationship not only exists but is very strong.

# **Conclusions and Further Research**

This paper uses a reduced-form price-determination model on 1960-2012 annual data of five food commodities (maize, wheat, rice, soybeans, and palm oil) to assess the relative contribution of various factors to their respective price changes. The factors include crude oil prices on the supply side, stock-to-use ratios, three macroeconomic indicators (exchange rate movements, interest rates, and inflation), and income on the demand side. The paper concludes that food commodity prices respond strongly to energy prices, stock-to-use ratios, and (in a mixed manner) to exchange rate movements. With a few exceptions, interest rates and income growth do not matter. Yet, crude oil prices mattered the most during the recent boom period because they experienced the largest increase. In terms of model performance, wheat and maize performed the best, while rice performed poorly, the latter a likely reflection of the fact that the rice market is subjected to policy distortions the most among the commodities analyzed.

From a methodological perspective, the research presented here can be extended in a number of ways. By applying the model to other commodities (either food or raw materials and metals), one could explore whether the drivers discussed here are relevant to these commodities as well. Extending the sample back to the 1950s would give us enough observations to examine the way in which food prices behaved during a period (that is, before 1973) when the macroeconomic fundamentals moved little or did not move at all. Reconciling the mixed evidence on the food-energy price relationship during the recent boom could give insights as to whether the expansion of biofuels has been driven solely by policies or profitability has played a role. Another direction could entail the use of alternative measures of the key drivers and examining whether they are associated with significant differences in the elasticity estimates (for example, using a broad index of currencies instead of the SDR as an exchange rate proxy or a policy rate instead of a US Treasury Bill as an interest rate proxy). In contrast, the differences between the limited and full sample models imply that a time-varying parameter (or a switching regime) model may yield further insights into the nature and timing of commodity price booms (and busts) and, perhaps, confirm whether the recent price increases signify a transition from a prolonged period of low or declining prices to a period of high prices. The role of policies is another important extension that can shed more light on the undershooting and overshooting observed before 2005 and after 2004 as well as the poor performance of the rice model. Last, measuring price volatility through the — 18 —
# outdetect set-up:

Normalization: natural logarithm Z-score: (x-mean)/std a=2.5 Outlier detection target: top (12447 observations are used)

## Incidence of outliers:

||Freq.|Percent|Share|
|---|---|---|---|
|Bottom|®|@.00|@.00|
|Top|157|1.26|100.00|
|Total|157|1.26|100.00|

Statistics for raw and trimmed pce:

||Raw|Trimmed|
|---|---|---|
|Summary<br>stats|||
|Mean|897.88|786.32|
|Median|648.73|641.44|
|SD|3926.19|521.38|
|CV(%)|437.27|66.31|
|IQR|550.12|533.24|
|Inequality|||
|Gini|@.4063|@.3328|
|MLD|@.2839|@.1814|
|Theil|@.5057|@.1840|
|Ccv2|9.5596|@.2198|
|A(@.125)|@.0541|@.0227|
|A(1)|@.2472|@.1659|
|A(2)|@.3776|@.2985|
|p90/p10|4.8185|4.6065|
damental differences in their magnitudes. We can then use statistical tests to determine whether the homogeneity assumption and the unity restrictions on the _βi_<sup>_gdp_</sup> and _βi_<sup>_de f_</sup> parameters across countries, which are imposed when the credit-to-GDP ratio is used on the left hand side of (11), are supported by the data. It is well known that imposing restrictions on a subset of parameters that are not supported by the data leads to substantial distortions in the estimates of the remaining unrestricted parameters.

# **3.1.3. Second-stage cross-country regression**

We are particularly interested in the cross-country variation of the long-run coefficients on real GDP and the GDP Deflator in (11); that is, the variation in the _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> and _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> coefficients and also the speed of adjustment coefficient _α_ ˆ _i_ which measures how fast deviations from the are eliminated. Once estimates have been we long-run equilibrium computed, proceed by relating the cross-country variation in _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> , _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> and _α_ ˆ _i_ to a set of development indicators. These are obtained from various sources, such as the FinStats database of AlHussainy _et al._ (2010) and the Financial Structure data set of Beck _et al._ (2000). We also add economic development indicators to this set. No well developed economic theory exists to guide us in the selection of possible drivers of the variation in _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> , _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> and _α_ ˆ _i_ . We therefore also consider traditional scale variables, such as the level of economic development (i.e., GDP per capita) a measure of overall GDP and population to control for an economy’s size, and the degree of openness. We further include data on financial sector supervisory structures from Melecky and Podpiera (2012). These contain measures of the degree of integration in prudential supervision, the pursuit and integration of business conduct supervision, and central bank independence. The Kaufmann _et al._ (2010) governance indicators are also included.<sup>16</sup> This yields a total of 42 economic, financial and institutional development indicators.

Our goal here is to relate the cross-country variation in _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> , _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> and _α_ ˆ _i_ to the level of development of the economy of interest. Once the variation in these coefficients is linked to a set of relevant indicators, we will be able to determine equilibrium credit provision for a specific country based on its development stage, its financing needs, and the capacity of its financial sector to meet these needs. Our proposed framework will therefore enable macroprudential supervisors to gauge current credit provision in the economy against what is needed to maintain a financially stable economic growth path over the medium to long run.

The relationship between the coefficients and the considered development indicators 16A description of the explanatory variables that we use is provided in the Data Section.

14
Table 3: The determinants of disaster fatalities

||Stor|ms|Flo|ods|Earthq|uakes|
|---|---|---|---|---|---|---|
||3.1|3.2|3.3|3.4|3.5|3.6|
|Ln(_AIDj,t-1_)|0.794**||0.098||-0.960||
||(0.347)||(0.250)||(0.753)||
|Ln(_3yr._ _AIDj,t-1)_||0.761**||0.051||-0.991|
|||(0.308)||(0.164)||(0.744)|
|Ln(_GDPj,t-1_)|-0.552***|-0.548***|-0.135|-0.192|-0.951**|-0.969**|
||(0.137)|(0.136)|(0.152)|(0.120)|(0.479)|(0.460)|
|Ln(_POP jt)_|0.010|0.032|0.307***|0.317***|-0.997|-0.998|
||(0.307)|(0.313)|(0.116)|(0.116)|(1.120)|(1.052)|
|Ln_(POPDENSjt)_|-0.002|-0.006|-0.009|0.088|0.007|-0.006|
||(0.285)|(0.282)|(0.232)|(0.192)|(0.985)|(0.920)|
|_OPENjt_|0.003|0.003|-0.012***|-0.013***|-0.032***|-0.035***|
||(0.007)|(0.006)|(0.002)|(0.002)|(0.012)|(0.011)|
|_MAGNITUDEijt _|0.012***|0.012***|0.420***|0.430***|2.283***|2.266***|
||(0.003)|(0.003)|(0.126)|(0.126)|(0.281)|(0.282)|
|_NATHAZ – RISKj _|-0.179**|-0.179**|0.168|0.129|0.060|0.092|
||(0.075)|(0.074)|(0.168)|(0.091)|(0.327)|(0.292)|
|_LATITUDEj_|-0.014|-0.014|-0.034***|-0.028**|0.019|0.017|
||(0.017)|(0.017)|(0.012)|(0.009)|(0.091)|(0.080)|
|_ELEVATIONj_|-0.001***|-0.001***|0.001***|0.001***|-0.001**|-0.001**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|
|Obs.|292|292|1125|1125|367|367|
|Log pseudolikelihood|-1388.273|-1388.273|-5013.813|-5077.955|-1445.147|-1444.850|
|Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

22
Table B3: Concern and knowledge of malaria and past exposure

||Dependent va<br>(1)<br>OLS|riable: Conce<br>(2)<br>OLS|rn and know<br>(3)<br>IV|ledge of malaria<br>(4)<br>IV|
|---|---|---|---|---|
|Treatment|0.025***|0.025***|0.031***|0.031***|
||(0.009)|(0.009)|(0.011)|(0.011)|
|Some vegetation (middle past exposure)||0.044**<br>(0.021)||0.041*<br>(0.021)|
|Signifcant vegetation (high past exposure)||0.094***<br>(0.023)||0.097***<br>(0.023)|
|Controls|Yes|Yes|Yes|Yes|
|Observations|1486|1486|1351|1351|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. One observation per household. Concern and knowledge of malaria is an index computed by averaging 16 dummy variables representing information on whether respondents believe malaria is a problem in the community, are acknowledged of the malaria vector and are informed of the categories of individuals that are most affected by the infection. The index is equal to 1 if the respondent is concerned and fully aware of malaria. Columns (1) and (2) report the difference between treatment and control groups using OLS regression (model 2). Column (3) and (4) estimate the difference between households who report to have received the spraying campaign in the last 5 months and those who didn’t by instrumenting program participation with the treatment group indicator (model 3). Some vegetation and Significant vegetation are dummy variables indicating the vegetation level at sub-zone level in the period 2000-2009. Standard errors clustered at village level are reported in parentheses. All specifications include controls for gender, age, education and marriage status of the respondent, household size, tribe and religion, information about access to water, dwelling characteristics, sub-zone dummies and village characteristics (size and share of women living in the village).

19
Table 2: Impact of MNREGA on soil moisture

||Full year|January-April|
|---|---|---|
|Post-MNREGA|0.02|0.06|
||(0.01)|(0.01)|
|95% CI|[0.01,0.03]|[0.04,0.09]|
|N|6960|6944|
|Pre-MNREGA mean (m<sup>3</sup>/m<sup>3</sup>)|2.26|1.81|
|**Fixed Efects:**|||
|District|Yes|Yes|
|Year _×_ month|Yes|Yes|

Notes: Our outcome is the IHS transformation of soil moisture in the period specified in the column heading. Soil moisture is measured as cubic meters of water in a cubic meter of soil. We measure the district average level of soil moisture over the full year and the Rabi season for each year in our sample. All regressions include controls for temperature, a quadratic control for precipitation and a quadratic state _×_ year trend. Errors are clustered at the district level. Our estimates are based on the approach described in Callaway and Sant’Anna (2021) and implemented using their Stata package `csdid` .

Table 3: Impact of MNREGA on share of crop area irrigated

||Rice|Wheat|Cotton|Fruits &<br>Vegetables|
|---|---|---|---|---|
|Post-MNREGA|0.16|0.16|0.15|0.34|
||(0.04)|(0.04)|(0.05)|(0.08)|
|95% CI|[0.08,0.24]|[0.09,0.24]|[0.06,0.23]|[0.19,0.49]|
|N|6021|6021|6021|6021|
|Pre-MNREGA mean (000ha)|49.93|54.72|6.71|8.30|
|**Fixed Efects:**|||||
|District|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|

Notes: Our outcome is the area in each crop in each district-year that is irrigated. 100 _×_ the coefficient is approximately the percent point change in the area irrigated after the implementation of MNREGA. Errors are clustered at the district level. Our estimates are based on the approach described in Callaway and Sant’Anna (2021) and implemented using their Stata package `csdid` .

23
# _Economic growth in Egypt_

Economic growth in Egypt fluctuated between 1981 and 2015 (figure 8). It plummeted from about 7 percent in 2008 (before the global financial crisis) to about 1.8 percent in 2011, before beginning to recover thereafter. The recovery is expected to continue, as recent investment and industrial licensing laws are likely to boost investment. Security concerns and the political situation could slow growth, however. According to the government’s economic reports, the economy is expected to expand by 4.3 percent in 2018 and by 4.7 in 2019 (Egyptian Ministry of Finance 2017). In contrast to total growth, GDP per capita (measured in constant 2010 dollars) rose consistently over the last three decades, from about $1,154 in 1981 to about $2,907 in 2018 (WDI 2019).

# **Figure 8 GDP per capita in Egypt, 1981–2016**

<!-- Start of picture text --> 3,500<br>2907.3<br>3,000<br>2,500<br>2,000<br>1,500<br>1154.2<br>1,000<br>500<br>0<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> _Source_ : Data from IFS (2017). _Note_ : Figures are in constant 2010 dollars.

# **4. Econometric Methodology and Modeling Strategy: Auto Regressive Distributed Lag Model (ARDL)**

Economic analysis proposes that there is a long‐run relationship between variables and that the means and variances are constant and not dependent on time. In the case of time‐series variables, this stationarity of variables is not always satisfied. To account for nonstationarity, cointegration test are used to examine how time series data, which may be individually nonstationary and drift extensively away from equilibrium, can be paired so that the workings of equilibrium forces will ensure they do not drift too far apart.

15