rafmacalaba/datause-encoder-data huggingface dataset:
Household expenditures on electricity in Jordan are substantial, being more important for poor households from the standpoint of budget shares. Households spent an estimated 359 million JD on electricity in 2013 (using extrapolated data from HEIS 2010), an amount higher than that spent on LPG, diesel and kerosene put together, but lower than expenditures on gasoline. Households from the lowest quintiles spend less on electricity in absolute terms. For instance, households from the poorest quintile spend about a little less than a third on electricity than do the wealthiest quintile (annually about JD 30 per capita compared to JD 105 of the wealthiest quintile). However, the budget shares of electricity are higher among the poorest households; the poorest households spend about 3.5 percent of their budgets on electricity compared to 2.4 percent for the richest households (figure 4). Consequently, poor households can be highly vulnerable to higher tariffs on electricity.

**<u>Figure 4. Annual expenditure on electricity in JD and budget share in %</u>**

<!-- Start of picture text --> 110 Yearly electricity expenditure per capita, JD 4.0<br>Share of electricity in total expenditures, %<br>100 3.5<br>90<br>3.0<br>80<br>2.5<br>70<br>2.0<br>60<br>1.5<br>50<br>1.0<br>40<br>30 0.5<br>20 0.0<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Consumption per capita quintiles<br>% of total expenditure<br>Per capita electricity expenditures, JD<br><!-- End of picture text --> Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Note: Population quintiles based on spatially adjusted consumption per capita before the reform.</u> The distribution of households with different electricity consumption across quintiles is presented in table 5. Poor households consume less electricity and as a result pay lower tariffs. For instance, 41 percent of the poorest households consume between 161-300 kWh/month compared to 15 percent among the wealthiest households from the top quintile. Nevertheless, the relationship between electricity consumption and welfare is not perfect. There are rich households with low electricity consumption as well as poor households with high electricity consumption, although this may be partially attributed to richer households having smaller household size.

8
for part of the recurring expenses. But if help from donors and exporters is not forthcoming, smallholder farmers may find it difficult to obtain certification. Given our results, the costs of compliance during the first year are more than twice the net gain of first-time adopters.

As a result, as predicted by several authors and the Kenyan press (see Farina and Reardon, 2000 and the article by Mungai in the Daily Nation) most Kenyan exporters have reduced their involvement with small-scale growers after the introduction of EurepGap (Graffman, Karehu and MacGregor, 2007).

According to an independent survey fielded by International Development Research Center (IDRC) in November 2004 in the same region where DrumNet operates, farmers reported having heard about the EurepGap requirements although they were unable to give specific details. Regardless, they seemed overconfident about their ability to obtain certification. Although EurepGap compliance was made mandatory in January 2005, it was not until mid 2006 that the exporter in partnership with DrumNet ceased to purchase the produce from DrumNet SHGs since they lacked certification. In the next section we describe the fate of DrumNet SHG after European export markets became inaccessible.

# 8. Conclusion and Epilogue

We examine whether an intervention to help smallholder farmers access export markets can change farmer practices and improve household income. We find that the program succeeds in getting farmers to switch crops, and that the middle income farmers were the most likely to take-up (relative to low-income and high-income).

22
connection charge data obtained from websites archived by the Wayback Machine. Including an inspection fee, in 2016 Umeme charges 139,300 Ugandan shillings (US$40) for a connection that does not require a pole, and 367,300 Ugandan shillings (US$100) for connection with a pole.<sup>19</sup> Before 2011 the Umeme connection charge without a pole was much higher—about 237,000 Ugandan shillings.

Connection charges for other firms are higher than those for Umeme. Ferdsult charges about 342,200 Ugandan shillings for connections without a pole.<sup>20</sup> . Wenreco is reported to charge at least 1.5 million Ugandan shillings for a connection with a pole.<sup>21</sup> For the distribution firms for which no information is available, we assume the connection costs are equal to those of Ferdsult.

Monthly price data for other energy commodities in Uganda are published by the Uganda Bureau of Statistics in their Consumer Price Index reports. These include price data in seven cities for propane, gasoline, diesel, firewood, charcoal, and kerosene. There is some geographical dispersion in energy prices although the correlation of price changes is high.

# **4.4 Distribution utility financial data**

Umeme buys all of the electricity it sells from the grid operator, UETCL. The Electricity Regulatory Authority sets the price that Umeme pays for electricity. At the end of 2012, this price was 262 Ugandan shillings per kWh (10.4 US cents per kWh). For each kWh that a household consumes, Umeme has to buy more than one kWh from the transmission grid, to cover technical losses in the distribution network. The proportion of electricity lost due to these technical losses is assumed to be 15 percent.

In its financial statements, Umeme books both revenue from connection fees and the costs of providing connections, with the costs assumed to be equal to revenue. Based on this accounting convention, the cost of providing a connection was US$130 in 2012. For our analysis, we assume a higher connection cost of US$200. For the counterfactual analysis with alternative connection fees, we convert the di↵erence between the revenue from connection fees and the cost of providing a connection to monthly revenue (or loss) based on an annual discount rate of 5 percent.

A final requirement to calculate the profitability of connecting an additional customer is the variable component of administrative and network maintenance costs. By how much

> 19 `http://www.umeme.co.ug/about-umeme/yaka/new-connection.html`

> 20 `http://ferdsult.net/services.html`

> 21 `http://www.newvision.co.ug/new_vision/news/1193739/electric-poles-cost-sh2m-west-nile`

14
Table 6b: Table 6a using normalized input counts

||Organize|d sector|Unorgani|zed sector|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Log of district population|-0.001<br>(0.023)|0.100+<br>(0.052)|-0.044+<br>(0.024)|0.024<br>(0.033)|
|Log of district population density|0.048<br>(0.030)|0.102++<br>(0.044)|-0.114+++<br>(0.020)|-0.034<br>(0.030)|
|Age profile (demographic dividend)|-0.105+++<br>(0.036)|-0.068<br>(0.061)|0.027<br>(0.025)|-0.076+<br>(0.039)|
|Educated worker share (% pop graduate)|0.016<br>(0.040)|0.074<br>(0.056)|0.022<br>(0.027)|0.059+<br>(0.034)|
|Literacy rate|0.099++<br>(0.038)|0.132++<br>(0.051)|0.062++<br>(0.025)|0.095+++<br>(0.033)|
|Infrastructure: electricity access|0.038<br>(0.025)|0.106+++<br>(0.038)|0.047++<br>(0.019)|0.037<br>(0.031)|
|Infrastructure: paved roads|-0.039<br>(0.027)|-0.001<br>(0.046)|0.074+++<br>(0.019)|-0.026<br>(0.032)|
|Log travel time to closest large city|0.031+<br>(0.018)|0.070+++<br>(0.025)|-0.003<br>(0.013)|0.001<br>(0.017)|
|Strength of household banking environment|0.080+++<br>(0.026)|-0.053<br>(0.046)|-0.004<br>(0.019)|-0.016<br>(0.030)|
|Urbanization rate|-0.003<br>(0.046)|-0.131++<br>(0.063)|0.079+++<br>(0.029)|0.000<br>(0.040)|
|Log per capita consumption|0.212+|0.249|0.007|0.132|
||(0.123)|(0.168)|(0.075)|(0.097)|
|Log organized employment for district-industry|0.018<br>(0.012)|0.017<br>(0.012)|-0.020+<br>(0.012)|-0.025++<br>(0.012)|
|Log unorganized employment for district-industry|0.017<br>(0.013)|0.014<br>(0.013)|-0.009++<br>(0.004)|-0.015+++<br>(0.005)|
|HHI of local employment across mfg industries|-0.034+<br>(0.019)|-0.039+<br>(0.022)|0.028<br>(0.025)|0.044+<br>(0.026)|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|State fixed effects||Yes||Yes|
|Observations|3589|3589|10674|10674|
|Adjusted R-squared|0.119|0.148|0.058|0.083|

Notes: See Table 6a. Normalized input counts are residuals from a regression of input counts on industry fixed effects and plant size dummies.
# Table of Contents

|1|. Introduction<br>........................................<br>I|
|---|---|
|H.|Data Sources and Compilation ...........................<br>2|
|HI.|Cross-Country Correlations . ............<br>................<br>4|
||A. Correlations inthe Period1984-1990<br>....<br>................<br>4|
||B. Correlations in the Period1978-1990<br>....................<br>_5_|
|IV.|Time-Series Correlations ..........<br>.............................<br>_6_|
|V.|Some Tentative Explanations for the Observed Patterns ..........<br>8|
|VI.|Concluding Remarks ................................<br>.<br>12|
|Refer|ences ...................................<br>.........<br>15|
|Appe|ndix:Country List<br>...................................<br>16|
|Table|s and Figures<br>.......................................<br>17|
Figure 3: Equity Issuance by Mexican Private Sector Companies (2000-2005) <!-- Start of picture text --> 0.8%<br>0.7%<br>0.6%<br>0.5%<br>0.4%<br>0.3%<br>0.2%<br>0.1%<br>0.0%<br>2000 2001 2002 2003 2004 2005<br>IPO - Mexico IPO - Abroad PO - Mexico PO - Abroad<br>% of GDP<br><!-- End of picture text --> Source: CNBV.

Note: Only non-financial private sector equity issuers are included in the above Figure. IPO is Initial Public Offering, while PO refers to secondary Public Offering (i.e. for firms that are already listed in the Bolsa). Both domestic and international listings are included.

# **_Debt Financing_**

Overall debt financing to the private sector excluding non-performing loans (NPLs) has increased relative to GDP between 2000 and 2005, while its quality has improved considerably. Several trends are evident in the figure below. First, total financing (excluding NPLs) has increased by almost 2 percent of GDP between 2000 and 2005. Second, the domestic portion of that financing (i.e. excluding foreign funding sources) has increased by almost 4 percent of GDP since 2000, primarily due to the substitution of external by domestic commercial financing and the significant expansion of consumer financing throughout this period, albeit from a low base. Third, the size of restructured performing loans is significant in the early part of the period<sup>7</sup> and helps to partly mask the strong underlying growth in housing finance since 2003<sup>8</sup> . Excluding all restructured loans 7 A large share of the restructured loans dates back to three main government-sponsored programs introduced after the 1995 Tequila crisis to avoid further deterioration of banks’ loan portfolios and to protect borrowers: FOBAPROA, UDIS and ADE. Under the FOBAPROA ( _Fondo Bancario para la Protección al Ahorro_ ) program, banks sold mostly impaired loans to FOBAPROA (the predecessor of _Instituto para la Protección de Ahorro Bancario_ or IPAB, the deposit insurance and bank restructuring agency) in return for 10-year non-tradable promissory notes, but retained a downside risk if collections fell short of the sale value. An agreement was reached in 2004 to convert remaining FOBAPROA notes into tradable IPAB securities. UDIS refers to the restructuring of housing, commercial and state/municipality loans by converting such financing to an inflation-indexed currency. ADE ( _Acuerdo de Apoyo a Deudores de la Banca_ ), which was paid out in 1997, was mainly targeted to individual debtors and provided a discount on consumer loan rates charged by banks; an additional program of rate discounts (“ _Punto Final_ ”) was later established and funded by banks and the government; see Graf P. (August 1999) for a description. 8 This is because new mortgage lending in pesos (i.e. financing ‘flow’) is partly offset by the decline in banks’ restructured UDIs-denominated performing mortgage portfolio over this period, resulting in a relatively small overall increase in outstanding housing finance (i.e. financing ‘stock’).

7
an overdraft facility (table 3). We recognize that this variable does not capture well the ease with which firms can obtain external finance for long-term investment.<sup>16</sup>

**_Labor market flexibility_ .** In the past, both India and China have suffered from excessive government-directed resource allocation. Their recent impressive growth is widely considered a result of the restoration of market-based resources allocation (Li 1997; Lin, Cai, and Li 2003). A key market-oriented reform has been the introduction of labor market flexibility.

China started liberalizing the labor market from the mid-1980s and deepened the liberalization with ownership and labor-restructuring reforms from the late 1990s. The reforms have given Chinese firms more flexibility than Indian firms have in adjusting staffing to meet changing economic conditions and to take advantage of technological developments (Ahya and Xie 2004; Dong and Xu 2009). The immediate consequence of China’s labor market reforms is that firms can hire temporary workers. Chinese firms have taken advantage of this flexibility by increasing the proportion of workers on temporary contracts.

In India, the existing labor codes require businesses that have more than a threshold of employees to seek permission from state governments for closing a business or downsizing. 17Permissions are rarely granted (Sachs, Varshney, and Bajpai 1999). Moreover, the threshold differs across states since both central and state governments are empowered to act on legislations related to trade unions and labor disputes (Besley and Burgess 2004). This is believed to have added significantly to the duration of insolvency procedures in the country, forcing firms to maintain suboptimal sizes. Related items of the Indian labor laws include the ―service-rules‖ provisions of the Industrial Employment Act of 1946 and the provisions of the Contract Labor Act of 1970. The Industrial Employment Act requires defining job content, employee status, and area of work by state law or by collective agreement, after which changes would not be made without all workers’ consent.<sup>18</sup> This has made it difficult for businesses ―to shift workers not only between plants and locations, but also between different jobs in the same plant‖ (Zagha 1999).

To circumvent the restrictions, Indian businesses may resort to contract workers per the provision of the Contract Labor Act. However, this law also gives state governments the right to

> 16 We have examined the robustness of this result with respect to sectoral composition, and find that the conclusion remains largely the same.

> 17 See also World Bank (2010) for details on labor regulation in India.

> 18 This applies to establishments with more than 100 employees. Zagha (1999) notes that some states have made the provisions mandatory to firms with 50 or more workers while other states have abolished the size limit altogether.

14
# 1 Introduction

The challenge of labor market policy is to devise a framework for contracting between employers and workers that is at once efficient and fair. Most people would agree that excessively rigid labor market regulations prevent the labor market from operating efficiently. The resulting losses to employment and productivity therefore reduce opportunities for workers to find good jobs. Most people would also agree that some regulation is necessary to codify and protect basic standards of fair treatment for workers. Regulations may also introduce a degree of predictability to, and reduce the cost of, contracting between employers and workers. The debate over the design of labor market policy centers on the question of how much and what kind of regulation is necessary to efficiently achieve these goals—and when regulation becomes "excessive".

There is considerable evidence that rigid labor regulations may prevent labor markets from operating efficiently. Botero, et. al. (2004), for example, find that countries with heavier labor-market regulations have lower rates of labor-market participation and higher levels of unemployment. Besley and Burgess (2004) find that "pro-worker" labor reforms in India negatively affected employment and productivity and increased poverty. Amin (2008) also shows that labor regulation in India has substantial and negative effects on employment. Heckman and Pagés (2000) find that job security legislation in Latin America reduces employment and increases inequality. Almeida and Carneiro (2008) find that increased enforcement of labor regulations constrains firm size and reduces employment. Ahsan and Pagés (2008) find that employment protection reduces output and employment in the formal sector without benefitting workers. Kugler (2004) finds that a reduction in firing costs in Colombia reduced unemployment. A more complete review of the literature on the effects of labor regulations in developing countries can be found in Djankov and Ramalho (2008).<sup>1</sup> There is also evidence that labor market regulations affect the labor market in ways that cannot be captured by aggregate employment statistics. Saavedra and Torero (2004), for example, find that increases in firing costs reduce labor demand. Although they could not observe worker flows, they did find that a reduction of firing costs in Peru led to a decrease in mean tenure of workers. Kugler (2004)

> 1 See Heckman and Pagés (2004) for an influential collection of 11 studies of the effects of labor regulations in Latin America.

1
with father, whether currently working, household ownership of DVD, television, a MP3 player, cellular phone, computer, calculator, whether English is spoken and whether the student aspires to study in a university. Preprogram variables measuring school quality from the Ministry of Education 911 forms (formats filled out by schools) include: student-teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade and average teacher schooling levels. These variables correspond to characteristics of the primary school attended in 2007-2008. In addition, we merge the locality-level margination index and its components to our data file. Community-level control variables include the margination index, the proportion of the population that is illiterate, the proportion of households without electricity, the proportion of households without sewage drainage, and the proportion of households without access to piped water. This set of control variables is used to estimate the propensity score model, additionally exact matching on gender and state is carried out. The type of matching is nearest neighbor matching with 1 match.<sup>8</sup>

# **V. Results**

We present results based on two main samples: 1) sixth grade students in the school year 20072008 who attend primary schools in localities of medium, high or very high levels of margination<sup>9</sup> and 2) sixth grade students in the school year 2007-2008 who are enrolled in indigenous primary schools. For each sample we present results on enrollment, type of school and achievement tests in mathematics and Spanish.

We present three sets of results for each indicator:

- 1) State fixed-effects models with individual, school and locality level control variables

- 2) School fixed-effects models with individual control variables and

- 3) Nearest-neighbor matching estimates using individual, school and locality variables for the propensity score and exact matching on gender and state.<sup>10</sup>

We begin by presenting descriptive statistics for our samples.

8 We also carried out matching estimations using 2 and 3 neighbors respectively. Results were very similar, and for brevity we provide only those based on 1 neighbor.

> 9 The majority of rural Progresa recipients live in communities with medium to very high levels of marginality. We thus focus our analysis on primary schools located in these areas. Nationwide, about 70% of all Progresa sixth grade students attend schools in these areas, consistent with about 70% of the PROSPERA population living in rural areas. If the program increased the likelihood that sixth grade students traveled to schools outside to non-poor areas, this might impart a selection bias to restricting the sample in this way. We have also replicated the results here using the set of nationwide sixth grade students which necessarily includes urban students but avoids selecting the sample based on potential school selection and we obtain similar although slightly smaller results.

10 For the matching estimators for all students, we use a random sample of 20% of all schools because of computation issues for the matching.

14
<!-- Start of picture text --> Internet users, high-income countries, 1990-2015<br>(Population %)<br>© tele<br>© opt3.0 Flgriti 8 | |<br>> § ef ii y i | s .<br>< i!leq!’rei} i 33<br>it 13 ete<br>N ire i 23°<br>e i] r i t 7?<br>o 7 8 | i 8s<br>1990 1995 2000 2005 2010 2015<br>year<br><!-- End of picture text -->

<!-- Start of picture text --> year<br><!-- End of picture text --> Internet use = 1 (1 + e~0.213491x(t-16.14261) ) Internet use = .7954979 (1 + e7-3914878x(C-13.5291)) t=0
# Table E: The consistency between the AG and the labor module in Malawi

||Inconsistent responses|Observations|
|---|---|---|
||(Share of household members that<br>worked on a plot, but did not report in the<br>labor module having engaged in HH<br>farmingin the last 12 months)||
|Average|7,85%|25,919|
|Number of plots|||
|1|9,71%|14,670|
|2|5,93%|8,317|
|3|4,13%|2,155|
|4|3,30%|575|
|5|5,59%|143|
|6|0,00%|36|
|7|0,00%|19|
|8|0,00%|4|
|Activities|||
|Land preparation and planting (1)|25,77%|617|
|Weeding/fertilizing (2)|30,24%|377|
|Harvesting (3)|36,70%|1,395|
|(1) and (2)|8,00%|1,925|
|(1) and (3)|12,79%|876|
|(2) and (3)|13,63%|851|
|(1), (2) and (3)|4,36%|19,878|
|Total hours worked on all plots<sup>1</sup>|||
|<50|18,37%|6,365|
|50 ‐ 119|6,02%|6,557|
|119 ‐ 226|4,37%|6,482|
|>226|2,87%|6,514|
|Sex|||
|Men|7,89%|12,045|
|Women|7,81%|13,872|
|Age|||
|5‐14|20,74%|4,892|
|15‐25|6,92%|7,104|
|25‐65|3,62%|12,248|
|>65|4,96%|1,671|

Source: Based on 2016/17 Malawi LSMS‐ISA data. Note:<sup>1</sup> Thresholds are set so that the four groups have a similar number of workers.

38