rafmacalaba/datause-encoder-data huggingface dataset:
|Brazil|1994|280|26.22|2.4|12.24|6,100|1,932|
|---|---|---|---|---|---|---|---|
|Chile|Jan.92|26|26.42|4.2|19.12|1,000|656|
|Colombia|May 94|15|9.59|1.6||1,400|613(in1998)|
|CostaRica|Jan.93|2|20.47|3.9|20|400|75|
|Dom.Rep.|Jun. 95||7.89|0.3||||
|Ecuador|Jan.93|9|1.52|0.2|25|250|200(in1998)|
|ElSalvador|Jan.96|7|1.54|0.7|11.4|100|277|
|Guatemala|Dec.95|10|8.3|0.6|22|110||
|Haiti|Nov.96|||0.1||||
|Honduras|Jan.96|17|7.6|0.3|40|60||
|Jamaica|Aug.94|20|39.4|2.4||110|251(in1998)|
|Mexico|1989|148|40.88|2.6|25.51|4,300|1,984|
|Nicaragua|Feb.94|7|2.04|0.4|19.48|40|310|
|Paraguay|1996|22|3.02|0.4||60|95|
|Peru|Feb.94|54|3.0|1.5|21.99|900|380|
|Uruguay|Aug.93|12|76.09|7.6||330|400|
|Venezuela|1994|32|5.91|1.7||1,000|600(in 1998)|
|Source:ITU||||||||

One of the most obvious explanations for the continued high costs is the fact that there still are strong impediments to effective competition. Anecdotal evidence would tend to confirm this line of reasoning. For instance, in Brazil, which counts about 50% of all Latin American users, the high delivery costs for IP service results from the near monopoly position of the incumbent on international gateways. This is a problem for most potential entrants and for users which requires the creativity of service providers in finding various ways to finance their services. One example is Bradeco Bank. It introduced a free access internet service in December 1999, in an attempt to adopt a business model based on advertising fees. The option that seems to be most widely adopted is revenue sharing with local telcos, following the European model. This solution will require some regulatory intervention since in many countries local telecoms are also ISPs<sup>9</sup> and unlikely to work out deals that may hurt that part of their business.

There are of course other issues revealed by Table 3. The clearest among those is that PC penetration continues to be quite low. The percentage of the population who owns a PC varies between 5 and 10% depending on the country. The equivalent share is about 50% in the US or around 20% in Spain or Portugal for instance.'<sup>0</sup> While cable TV does offer an alternative to phone connections to the internet, analysts do not expect this access mode to represent an immediate viable solution."'

> 9 Think of the case of Telefonica who owns Terra Livre, a free internet access provider.

> 10 Morgan Stanley Dean Witter (2000), The Latin America Internet Report, February.

> II Latincom quotes forecasts predicting a 20% share of potential internet access using cable TV by the year 2010; see Latincom (2000) ,"TV sector primed for growth as economic recession lifts", June 5h. The equivalent figure is currently 40% in the UK and already above 20% in Spain or the European Nordic countries.

7
the second shows how they impact on rural poverty. The third equation gives the impact on the population shift component of  log _P_ . We estimate (6.1) and (6.2).<sup>19</sup>

# **3. Data**

In addressing the questions posed in the Introduction, it is clearly desirable to have a reasonable long time series of household surveys; a short series can be deceptive for inferring a trend.<sup>20</sup> Amongst developing countries, India has the longest series of national household surveys suitable for tracking living conditions of the poor. At the time of writing, we can assemble distributional data on household consumption in India from 47 surveys spanning 19512006, though some of the earliest surveys had smaller sample sizes and covered shorter periods. However, the surveys are large enough to be considered representative at the urban and rural levels as well as nationally, and they appear to be reasonably comparable over time since the basic survey instruments and methods have changed rather little (though we note, and address, some comparability problems). India thus provides rich time series evidence for testing and quantifying the relationship between living standards of the poor and macroeconomic aggregates.

The period of analysis in Ravallion and Datt (1996) ended only two years after India’s process of economic reform had started. This paper updates Ravallion and Datt (1996) by incorporating an extra 14 rounds of the National Sample Surveys (NSS). The data are not, of course, ideal. Imperfect matching between the survey periods and the annual accounting periods used in the national accounts makes it harder to detect the true effect of aggregate growth on poverty. There are also long-standing concerns about the rising gap between aggregate household consumption as measured from the NSS and the “private consumption” component of domestic absorption in the national accounts (NAS). New problems also emerge in the post-1991 period, including changes in survey design that we address later.

Notwithstanding these issues, we believe there is now sufficient data for the “postreform” period to revisit the question of whether India’s higher growth rates have delivered the

> 19 Equation (6.3) need not be estimated separately since the parameters can be inferred from the estimates of (6.1) and (6.2) and (4) using the adding-up restriction.

> 20 For example, the first survey (1992) available in the post-reform period indicated a substantial increase in poverty and this fuelled much debate about the wisdom of reforms. We questioned this inference at the time arguing that the 1992 survey was deceptive about trends (Datt and Ravallion, 1997).

9
data used in the empirical exploration. The results of this empirical analysis are presented in section 4. Finally, section 5 concludes with a summary of findings, a discussion of how these results link to the reasons for the Arab Spring uprisings, and a few caveats.

# **2. Root Causes of Dissatisfaction with Life in Developing MENA Countries**

A look at the universal conditions for happiness, as presented in cross-country studies focusing on life satisfaction, provides limited understanding of the root causes of dissatisfaction with life in the Arab world. To understand the factors shaping the subjective well-being in the developing Arab countries prior to the Arab Spring, we must factor in explicitly the social context in these countries during this time period.

There is no consensus on the root causes for life dissatisfaction in the Arab world on the eve of the Arab Spring. Several explanations have been put forward: (1) limited freedom and voice in predominantly autocratic states; (2) dissatisfaction with standards of living; (3) unhappiness with persistent unemployment and lack of good jobs due to the growing informality of the private sector; and (4) dissatisfaction with corruption and cronyism, which limits opportunities for those who work hard. Each of these explanations is discussed in greater detail below.

# **Autocracy**

On the eve of the Arab Spring, most Arab states were longstanding autocracies (Chekir and Diwan 2012; Bromley 2014; and Cammett and Diwan 2013). Power was concentrated in the hands of one person or a small group of elites, backed by the military, who made decisions subject to few legal restraints and mechanisms of popular control. At the same time, the public had few if any channels of safe expression of opinions and grievances and opportunities to develop strong civil society. The longstanding regimes managed to stay in power through a combination of repressive practices and a social contract, which extended benefits such as free public education and health, energy, and food subsidies, and guarantees of public employment in exchange for political support (Bellin 2004; Bromley 2014; Cammett and Diwan 2013). Cammett and Diwan (2013) refer to this social contract as an ‘autocratic bargain,’ in which the middle class was lured with ‘material benefits’ in exchange for ‘political quiescence.’ Thus, despite human development and economic progress after independence, the developing MENA countries scored low in terms of economic and social freedoms and the Freedom House ranked the region as the most repressive in the world (Freedom House 2008).

6
Table 4: Intrahousehold Bargaining and Calorie, Protein, and Vitamin A Allocations

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Lcal|Lcal|Lprot|Lprot|Lanimprot|Lanimprot|Lvita|Lvita|
||_SURLS_|_SURHF E_|_SURLS_|_SURHF E_|_SURLS_|_SURHF E_|_SURLS_|_SURHF E_|
|L hast|0.001||-0.001||0.023**||0.015||
||(0.005)||(0.004)||(0.012)||(0.010)||
|L wast|-0.028**||-0.022**||-0.007||-0.014||
||(0.012)||(0.011)||(0.024)||(0.022)||
|_κ_|0.000|0.001|-0.019***|-0.017***|-0.005|-0.005|-0.004|-0.016***|
||(0.003)|(0.003)|(0.003)|(0.002)|(0.007)|(0.005)|(0.007)|(0.005)|
|L mcapx|0.435***|0.300*|0.478***|0.526***|2.074***|1.405*|0.509|0.019|
||(0.152)|(0.168)|(0.168)|(0.181)|(0.575)|(0.748)|(0.444)|(0.608)|
|(L macapx)<sup>2</sup>|-0.026**|-0.016|-0.030**|-0.032**|-0.126***|-0.089|-0.032|0.003|
||(0.011)|(0.012)|(0.013)|(0.013)|(0.043)|(0.055)|(0.033)|(0.045)|
|L landpc|0.052***|-0.067|0.025|-0.142|0.021|-0.236|-0.072|0.457|
||(0.018)|(0.107)|(0.017)|(0.113)|(0.057)|(0.436)|(0.055)|(0.407)|
|L hhsz|0.030*|0.048|0.041**|-0.032|0.074|-0.249|0.049|0.824*|
||(0.018)|(0.108)|(0.020)|(0.121)|(0.064)|(0.496)|(0.057)|(0.452)|
|round1|-0.021|-0.020|0.027*|0.019|0.166***|0.123**|0.020|0.051|
||(0.014)|(0.015)|(0.015)|(0.015)|(0.057)|(0.062)|(0.056)|(0.060)|
|round2|0.036|0.054**|0.057**|0.062**|0.177**|0.008|-0.206**|-0.239**|
||(0.023)|(0.025)|(0.024)|(0.026)|(0.081)|(0.089)|(0.085)|(0.095)|
|round3|-0.080***|-0.083***|-0.045**|-0.064***|-0.146**|-0.215***|-0.137**|-0.098|
||(0.018)|(0.020)|(0.020)|(0.021)|(0.067)|(0.079)|(0.065)|(0.075)|
|Constant|-2.413***|-2.309***|-1.303*|-2.192***|-5.716**|-4.652|-5.875***|-5.709*|
||(0.611)|(0.766)|(0.673)|(0.832)|(2.481)|(3.571)|(2.030)|(3.148)|
|Observations|10,555|10515|10,555|10515|10,555|10515|10,555|10515|
|test of equation|13 for L ha|st=L wast, fo|r :||||||
|boy|0.429|0.701|0.557|0.956|0.193|0.065|0.867|0.820|
|girl|0.282|0.103|0.610|0.103|0.221|0.059|0.220|0.513|
|madol|0.012|0.015|0.123|0.152|0.402|0.045|0.235|0.416|
|fadol|0.412|0.108|0.886|0.046|0.057|0.009|0.761|0.150|
|fadlt|0.189|0.084|0.283|0.064|0.279|0.196|0.404|0.318|
|preg, fadol|0.902|0.484|0.490|0.419|0.933|0.684|0.975|0.554|
|preg, fadlt|0.806|0.513|0.821|0.491|0.824|0.936|0.800|0.681|
|lact, fadol|0.679|0.669|0.894|0.702|0.213|0.126|0.482|0.881|
|lact, fadlt|0.742|0.533|0.370|0.813|0.350|0.324|0.771|0.705|

Note: Robust standard errors clustered at the household level are in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1; cal, calorie adequacy ratio; prot, protein adequacy ratio; animprot, animal protein intake/total protein requirement; vita, vitamin A adequacy ratio; L, natural log; madol, male adolescent; fadol, female adolescent; fadlt, female adult; preg, pregnant; lact, lactating; hast, 1+husband’s assets; wast, 1+wife’s assets; mcapx, monthly per capita expenditure; landpc, per capita land; hhsz, household size; _κ_ , individual health endowment; additional controls (not shown) are discussed in section 4.

40
Table 1: Summary Statistics

||Obs|Mean|S.D.|Min|Max.|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Log Inspected Firms per firm City|5,505|0.94|0.99|0.00|4.78|
|Log Inspectors per firm in the state|5,513|1.693|0.53|1.07|2.96|
|Distance to the nearest labor office (hours)|5,287|1.96|1.73|0.00|13.91|
|City distance to the State capital city (hours)|5,272|4.50|2.56|0.00|14.99|
|City transportation costs|5,495|5.89|0.78|0.39|8.69|
|City Latitude|5,507|-16|8|-34|5|
|City Longitude|5,507|46|6|32|73|
|City Altitude|5,507|412|293|0|1628|
|Log City Geografical Area|5,507|6.20|1.28|1.06|11.99|
|Access to Justice City|5,506|0.90|0.83|0.00|3.00|
|Governance City|5,505|3.17|0.91|1.00|5.85|
|Political Concentration City|5,504|0.23|0.10|0.07|1.00|
|Share Informal Workers City|5,507|0.74|0.17|0.22|1.00|
|Poverty Rate City|5,507|0.46|0.23|0.03|0.93|
|Unemployment Rate City|5,507|0.11|0.06|0.00|0.59|
|Theil Inequality Index City|5,507|0.52|0.11|0.19|1.27|
|Share Population Jobless|5,507|0.37|0.09|0.00|0.78|
|Share Population Formal Jobs|5,507|0.14|0.09|0.00|0.51|
|Share Population Informal Jobs|5,507|0.16|0.06|0.01|0.49|
|Share Population Self-Employed|5,507|0.20|0.09|0.00|0.70|
|Log wages in formal sector|5,497|5.93|0.35|3.69|7.65|
|Log wages in informal sector|5,507|5.73|0.42|4.47|7.38|
|Log wages self-employed|5,506|6.00|0.58|3.77|8.27|
|Log GDP per capita City|5,507|8.08|0.76|6.14|12.13|
|Log population City|5,507|9.36|1.11|6.68|16.16|
|Share migrants City|5,507|0.44|0.22|0.03|1.00|
|Log number firms City|5,505|5.09|1.52|0.00|13.05|
|Log Av. Firm size City|5,505|3.29|0.82|0.73|7.49|
|Share GDP Agriculture|5,492|0.29|0.19|0.00|0.86|
|Share GDP Manufacturing|5,507|0.20|0.17|0.00|0.95|
|Share GDP Services|5,507|0.52|0.16|0.03|0.97|
|Years schooling formal sector|5,504|6.18|1.42|0.00|11.16|
|Years schooling informal sector|5,507|5.29|1.39|1.52|10.80|
|Years schooling self-employed|5,506|4.45|1.59|0.32|10.29|

Source: Brazilian Ministry of Labor (2002), Population census (2000), IPEA, IBGE.
To summarize, the systematic combination of quantitative and qualitative methods helps evaluators to optimize the likelihood that their findings (and interpretations of those findings) will lead to accurate inferences about the effectiveness of interventions and the prospects of those interventions if implemented elsewhere (or at a larger scale). It achieves this primarily by using the strengths of one approach to offset the weaknesses of the other (Rao, 2002; Rao & Woolcock 2003). Specific instances where quantitative and qualitative methods can be combined in the evaluation process include:

- Generating hypotheses about an intervention’s effectiveness from theory, experience and qualitative research and then testing their ability to be generalized with quantitative techniques.

- Identifying contextual factors, processes and causal mechanisms via qualitative methods and assessing them further via quantitative methods (e.g., Ludwig et al, 2011) and/or additional qualitative analysis.

- Applying quantitative sampling techniques to units of qualitative data collection, and/or findings from qualitative analysis and using them to inform the design of quantitative data collection tools (i.e., household or firm surveys).

- Using qualitative findings to see if they support, explain, qualify or refute quantitative findings regarding an intervention’s impact (Ananthpur et al, 2014).

Qualitative methods are especially useful when the interventions to be evaluated increase in complexity (i.e., require many discretionary and face-to-face transactions, and are contentious<sup>8</sup> ), when the ‘context’ itself is highly variable (and perhaps ‘fragile’), when the quality and availability of existing data is poor, and when insights are sought on specific types of impacts on specific groups (e.g., the effectiveness of a project for ethnic minorities, informal firms or illegal immigrants, who may not be adequately represented in formal surveys). Qualitative methods can also be useful when evaluating small-N interventions such as regulatory reforms at the national level, or automation of procedures in one single agency.<sup>9</sup>

> 8 Thus delivering the mail is a ‘simple’ (logistical) task while promoting women’s empowerment in rural Pakistan is a highly ‘complex’ one (see Andrews, Pritchett and Woolcock 2013).

> 9 Small N cases are those in which insufficient units are available to be assigned to comparison groups to get the sufficient statistical power to run an experimental or quasi-experimental design.

7
12 percent of the US population.

The findings suggest that an improvement in the gender wage ratio among women in urban settings is mainly associated with a reduction in homicides and that impacts are primarily focused among younger women and in municipalities with a lower human development index. The impacts of changes in the gender wage ratio on less severe forms of violence appear to depend on the context. In municipalities in which women’s police stations have been established, a decrease in the gender wage ratio is associated with a reduction in reports of violence. In municipalities without women’s police stations, the analysis found the opposite effect, a decrease in the gender wage ratio is associated with a rise in reports of violence. Taken together, the results suggest that, while women’s economic empowerment alone may contribute to a reduction in rates of the most severe violence, its effects on less severe forms of violence largely depend on the context. If not paired with civil and legal infrastructure that protects women, represented in the case of Brazil by women’s police stations, women’s economic empowerment appears to trigger a backlash among men. However, in conjunction with protective mechanisms targeted at women, women’s empowerment tends to lower the incidence of less severe forms of violence as well. Overall, the study confirms that policies aimed at narrowing the malefemale wage gap reduce the likelihood of violence in Brazil, an upper-middle-income developing economy. However, the findings also highlight the importance of additional policies specifically aimed at the protection of women against violence to maximize the positive impact of advances in gender equality in the labor market on the reduction of violence.

# **Appendix**

The analysis uses the RAIS to generate the gender wage ratio. This matched employer-employee data set contains data on all the labor market contracts in the formal sector in Brazil. However, in Brazil, 20 percent of laborers work in the informal sector.<sup>13</sup> In this section, the nationally representative household survey, PNAD, is used to compare the gender wage ratios in the formal and informal sectors and to discuss how the differences might affect the main results.

The 2011–15 editions of PNAD are used with a few sample restrictions. First, the focus is on individuals ages 15–49, which is the age-group of interest in the main analysis. Second, the following categories of workers are excluded from the validation exercise: the

> 13 The rate depends on the definition of informality. Here, the self-employed are not counted as informal workers. If they were so counted, the rate of informality would nearly double, to around 40 percent. 19
# **1. Introduction**

Corporate savings is an important component of the overall savings in Egypt, representing about 50% of total savings (Figure 1). It is important to understand why companies in Egypt save so much, what types of companies are most likely to save, and how financial savings interact with firm real decisions on investment into productive assets and with external finance constraints. This analysis may suggest more effective policies to support higher growth and development in Egypt. This paper uses two datasets - Investment Climate Assessment and data for publicly listed firms on EGX to analyze the determinants of corporate savings behavior.

**Figure 1. Composition of Savings in Egypt**

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>95/96 96/97 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07<br>Household savings Corporate savings<br>Government Savings Total savings (% of GDP)<br><!-- End of picture text --> Source: WB Staff calculations based on National Accounts

# **2. Defining Corporate Savings**

Figure 2 presents a rough outline of the flow of funds in firms, which is helpful in defining corporate savings measures. Sales revenues minus costs comprise gross operating income (GOI), which is a very crude proxy for the cash flow available to firms. Out of the GOI the firm must pay interest on loans and taxes (and perhaps subtract other expenses not included in the GOI cost calculations). Then we arrive at the net income, which is a proxy for cash flows available to firms.<sup>1</sup> The net income can be either paid to firm equity holders as a dividend distribution or retained in the firm in the form of retained earnings. In turn, retained earnings can be allocated to

> 1 Depreciation of existing assets is considered a non cash expense. It usually is added back to net income for purposes of cash flow calculations. Changes in working capital can also be taken into account for cash flow calculation.

2
**Table 5: Multivariate Regressions**

|**Dependent variable:**|**IDEB**|**Secondary S**|**chool Score (**|**0‐10)**||**Child M**|**ortality**|||**Homic**|**ide Rate**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Results Agreements and Variable Pay**|0.34|0.32|0.24|0.26|‐6.51|‐1.76|‐2.67|‐0.20|‐7.47|‐14.78|‐10.84|‐14.78|
||(0.07)***|(0.09)***|(0.07)***|(0.07)***|(1.98)***|(1.88)|(1.47)*|(1.43)|(2.38)***|(2.89)***|(2.12)***|(2.53)***|
|**Management Shock**||0.07||‐0.08||‐10.73||‐6.41||8.90||5.31|
|||(0.07)||(0.07)||(1.37)***||(1.08)***||(2.12)***||(1.93)***|
|**Sector Expenditure per Capita**|||0.0004|0.0004|||0.001|‐0.0004|||‐0.002|‐0.003|
||||(0.00)*|(0.00)|||(0.01)|(0.004)|||(0.01)|(0.01)|
|**Poverty**|||‐0.02|‐0.02|||0.55|0.47|||‐0.56|‐0.53|
||||(0.00)***|(0.004)**<br>*|||(0.06)***|(0.06)***|||(0.07)***|(0.07)***|
|**GDP per Capita (constant 2000)**|||0.0001|<br>0.00|||0.001|0.001|||‐0.003|‐0.003|
||||(0.00)**|(0.00)**|||(0.001)**|(0.001)|||(0.001)**<br>*|(0.001)**<br>*|
|**GPD per Sq. Km**|||0.00|0.00|||0.00|0.00|||<br>0.00|<br>‐0.002|
||||(0.00)***|(0.00)|||(0.00)|(0.00)|||(0.00)|(0.01)***|
|**LN Population**|||0.22|0.21|||‐1.47|‐1.77|||6.08|6.31|
||||(0.75)***|(0.08)***|||(1.43)|(1.33)|||(2.08)***|(2.05)***|
|**Population Density (per sq. km)**|||‐0.004|‐0.004|||‐0.12|‐0.09|||0.05|0.03|
||||(0.003)|(0.00)|||(0.06)*|(0.06)|||(0.09)|(0.09)|
|**Constant**|3.07|3.05|1.88|1.97|21.84|23.64|11.82|18.33|28.75|27.71|23.70|‐14.78|
||(0.01)***|(0.01)***|(0.69)***|(0.60)***|(0.32)***|(0.35)***|(10.47)|(9.85)*|(0.39)***|(0.45)***|(15.22)|(12.54)**<br>*|
|**N**|243|243|243|243|270|270|270|270|270|270|270|<br>270|
|**N Groups**|27|27|27|27|27|27|27|27|27|27|27|27|
|**R Square**|0.08|0.07|0.50|0.511|0.03|0.03|0.42|0.67|0.03|0.11|0.31|0.33|
|**F‐Ratio**|25.64|38.56|23.28|27.20|10.73|237.48|37.98|42.40|9.83|14.05|15.27|14.67|
|**Prob > F**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|

*** Significant at the 0.01 level (2-tailed);  ** Significant at the 0.05 level (2-tailed).

24