rafmacalaba/datause-encoder-data huggingface dataset:
offsetting the fall in income. Identical shifts to non-farm activity have been observed in rural China.<sup>xxiv</sup> Rainfall shocks induce analogous behavioral patterns. In Brazil more work hours are allocated to nonagricultural activities at times of negative rainfall shocks, with stronger effects for households in lowincome municipalities (Branco and Feres, 2021).

In some cases, workers seem to reallocate time between leisure and labor. Workers in heat-exposed industries in the US reduce their daily labor supply by as much as 1 hour at temperatures above 85<sup>_◦_</sup> F (29<sup>_◦_</sup> C); this reduced work time is primarily replaced by indoor leisure (Graff Zivin and Neidell, 2014).<sup>xxv</sup> In another US study male workers labor 30 minutes longer on bad weather or rainy days (up to 48 minutes in dry regions), and reduce work time on the day following rainy day to make up for the previous day’s lost leisure (Connolly, 2008). It is unclear whether shifting activities across time is as common in developing countries, as the existing evidence is mixed. In rural China, more growing-season Harmful Degree Days – defined as days with temperatures above 32<sup>_◦_</sup> C – increases the probability of a time shift to leisure for men (Huang et al., 2020). The earlier mentioned DHS interviewers work more hours per day during hotter than usual periods.<sup>xxvi</sup> Labor supply response may also vary by gender. Women tend to have less opportunity to diversify into other tasks at times of weather shocks, possibly exacerbating existing gender gaps in the labor market.<sup>xxvii</sup> Together these studies suggest that workers may adapt their labor supply in the short term in response to changes in climate. Most of the current studies abstract from adaptation, which remains understudied.<sup>xxviii</sup> A growing literature studies the possible mechanisms behind the observed short-term changes in labor supply. Early work, primarily based on macro analysis, conjectured the decline in the temperature sensitive agricultural sector as a major factor (Jones and Olken, 2010; Dell et al., 2012). Recent evidence underlines the importance of health (Deschenes, 2014).

13
||Other<br>Income|M4<br>M6<br>(7)<br>(8)|_−_0_._095_∗∗∗_<br>_−_0_._077_∗∗∗_<br>(0_._032)<br>(0_._022)|_−_0_._169_∗∗∗_<br>_−_0_._148_∗∗∗_<br>(0_._053)<br>(0_._041)<br>_−_0_._245_∗∗∗_<br>_−_0_._221_∗∗∗_<br>(0_._075)<br>(0_._059)|521<br>730<br>0.166<br>0.200<br> other sources of income<br>f Mekong provinces that<br> M6 adds Soc Trang and<br>tively.|
|---|---|---|---|---|---|
||s and<br>ulture|M6<br>(6)|_−_0_._040_∗∗_<br>(0_._018)|_−_0_._078_∗∗_<br>(0_._035)<br>_−_0_._120_∗∗_<br>(0_._052)|753<br>0.271<br> agriculture, <br>o two sets o<br>nh Long, and<br> level, respec|
||Wage<br>Agric|M4<br>(5)|_−_0_._046_∗_<br>(0_._025)|_−_0_._085_∗∗_<br>(0_._044)<br>_−_0_._130_∗∗_<br>(0_._063)|532<br>0.262<br> wages and<br>(M6) refer t<br>hap and Vi<br> 5%, and 1%|
|Table 11<br>Impacts on Net Income<br>Alternative Samples|Non-Catfsh<br>Income|M4<br>M6<br>(3)<br>(4)|_−_0_._084_∗∗∗_<br>_−_0_._064_∗∗∗_<br>(0_._030)<br>(0_._021)|_−_0_._151_∗∗∗_<br>_−_0_._124_∗∗∗_<br>(0_._052)<br>(0_._040)<br>_−_0_._222_∗∗∗_<br>_−_0_._187_∗∗∗_<br>(0_._074)<br>(0_._059)|560<br>787<br>0.159<br>0.189<br>fsh income, non-catfsh income,<br>Mekong 4 (M4) and Mekong 6 <br>des An Giang, Can Tho, Dong T<br>: _∗_, _∗∗_, _∗∗∗_, signifcant at 10%,|
||Catfsh<br>Income|M4<br>M6<br>(1)<br>(2)|Low-Exposure<br>_−_0_._071_∗∗_<br>_−_0_._056_∗∗_<br>(0_._032)<br>(0_._023)|Mean<br>_−_0_._131_∗∗_<br>_−_0_._110_∗∗_<br>(0_._055)<br>(0_._043)<br>High-exposure<br>_−_0_._197_∗∗∗_<br>_−_0_._168_∗∗∗_<br>(0_._077)<br>(0_._064)|Observations<br>416<br>564<br>R2 (within)<br>0.239<br>0.264<br>Note: Estimates of income sources: cat<br>(livestock, silviculture, farm services).<br><br>specialize in catfsh production: M4 inclu<br>Tien Giang.<br>Robust standard errors within parenthesis|

47
# **I. Introduction**

Many countries, particularly in the developing world, suffer from volatile revenue sources, which make it difficult to finance essential projects. They also have large numbers of poor people and there are concerns that revenue increases will negatively impact those people. Accordingly, attempts are often made to increase public revenues, while at the same time trying to minimize the impact of these revenue increases on the poor. The general intuition is often that increases in regressive taxes, such as the sales tax, will have a more negative impact upon the poor than will increases in progressive taxes, such as the corporate income tax. In this paper, we will analyze the problem of how to increase tax revenues while trying to minimize the impact of this revenue increase on the poorest segment of society. We will use a numerical analysis calibrated and applied to Pakistan, a country that faces both problems of poverty, as well as very poor public revenue performance. Our results will indicate that the standard intuition, at least in this case, may often be incorrect.<sup>2</sup> Pakistan is in a delicate fiscal situation, with a budget deficit of around 5.8 percent of GDP for 2014 and with tax revenues amounting to only 10.3 percent of GDP. Tax reform is therefore high on the political agenda.<sup>3</sup> Although reforms may seek to raise revenue or improve efficiency, in particular by reducing tax evasion, changes in the tax code will also affect the income distribution and the welfare of households. The objective of this paper is to shed light on

> 2 Many authors have examined the relationships between tax regimes and poverty.  See, for example Tanzi and Zee (2001, 2000), and Keen and Simone (2004) for a number of general insights, and Lustig (1998) for an application to the case of Mexico.  Bird (2005) looks at the redistributive role of the personal income tax in developing countries.

> 3 Fiscal data obtained from the World Bank’s Spring 2014 South Asia Economic Focus _Time to Refocus_ .

2
mechanisms, and with more effective external auditing systems. These findings – particularly on kickbacks – are robust to the inclusion of numerous controls and to a range of sensitivity tests.

Another procurement indicator, on the appropriateness of the legal and regulatory framework, is not associated with firms’ kickbacks or participation in procurement markets. That measure is primarily a _de jure_ indicator of official rules regarding transparency, competition, etc., in contrast to the other more _de facto_ indicators of actual systems and practices.

We expect better procurement systems to matter more for smaller firms’ participation in procurement markets and payment of kickbacks to obtain contracts. The information and transactions costs incurred in learning about bidding opportunities and fulfilling bidding requirements (including paying fees and deposits) should be more onerous for smaller firms. Larger firms are more likely to have the necessary expertise and connections to meet bidding requirements while avoiding the need to pay bribes. Dividing the sample into three groups of small, medium-sized, and large firms, these expectations find support in the data.

Although we cannot fully rule out endogeneity bias in our estimated relationships between procurement systems and outcomes, there are several arguments for a causal interpretation. First, a causal argument from procurement systems to outcomes is consistent with the heterogeneous effects by firm size. Arguments for reverse causation, or for simultaneity bias, cannot easily predict stronger relationships for smaller firms. Second, any reverse causality is likely to bias key coefficient estimates downward (in absolute value) rather than upward. Poor outcomes in the form of corruption scandals or lack of competition are more likely than favorable outcomes to motivate reforms in procurement practices, particularly where pressure from donors is involved. This downward bias should apply particularly for our _de jure_ indicator (legal and regulatory framework for procurement), which is the only one that is never significant in our tests. Third, we report a series of falsification tests showing that other, non-procurement indicators from the PEFA assessments are not associated with procurement outcomes, and that the PEFA procurement indicators are not strongly associated with other “governance”-related outcomes in the firm surveys unrelated to procurement. Results from these tests do not support a view that our main findings merely reflect simultaneity bias, whereby procurement systems and outcomes indicators are both correlated with some broader, omitted aspects of “good governance.” Section 2 below summarizes related literature on participation and corruption in procurement markets. It also describes the indicators on procurement systems and outcomes, and briefly 3
15 percent of regulations modified within 24 months are linked to health care. Interestingly, it is not necessarily the sectors that dominate the production of legislation that show most legislative unpredictability, even though a very broad link between production and unpredictability seem to persist. Understanding the drivers behind this pattern will require further investigations.

# 6. DATA MAPPING IN MENA

Given the demonstrated value of legislative predictability indicators in Jordan, a similar approach could be applied to further countries in the MENA region. To establish the feasibility of such extension, we conducted a detailed mapping of data availability in Algeria, Djibouti, the Arab Republic of Egypt, Iraq, Lebanon, Morocco, Qatar, Saudi Arabia, Tunisia, and the United Arab Emirates. The goal of this mapping is to show the potential of our legislative predictability analysis beyond the Kingdom of Jordan and the possibility of a detailed cross-country analysis in the MENA region. Following our tested approach in Jordan, we thereby focused on publicly available data from parliamentary, governmental, or ministerial websites. Table 3 provides an overview of the time span, the number of observations and the source link for each country. The presence of N/A does not indicate missing data but rather difficulties in determining the number of observations on the source website.

**Table 3. Overview of legislative data sources reviewed**

|**Country**|**Coverage**|**Observations**|**Source**|
|---|---|---|---|
|Qatar|1961-2023|802|https://www.almeezan.qa/Default.aspx|
|Lebanon|1900-2023|3,788|http://www.legallaw.ul.edu.lb/LegisltaionSearch.aspx|
|Saudi Arabia|1930-2023|447|https://laws.boe.gov.sa/BoeLaws/Laws/|
|Tunisia|1957-2023|5,629|http://www.iort.gov.tn/|
|United Arab|1971-2023|223|https://elaws.moj.gov.ae/laws/search|
|Emirates||||
|Algeria|1964-2023|N/A|https://droit.mjustice.dz/ar/content/أساس�ة- قوانن -2|
|Egypt, Arab|1857-2023|9,813|https://www.cc.gov.eg/legislations|
|Rep.||||
|Iraq|2012-2023|N/A|https://moj.gov.iq/iraqmag/page_1/|
|Morocco|1964-2023|1,055|https://adala.justice.gov.ma/themes|
|Djibouti|N/A|N/A|https://preprod.ansie.dj/Ancienne.php|

Table 4 shows the structural results of this mapping exercise We focused in particular on variables that were available in Jordan and thus would allow a direct comparison with our findings on legislative predictability for Jordan (a detailed mapping of websites is available in Appendix B).

19
# **LIST OF TABLES**

||Pag|e|
|---|---|---|
|Table 1|Indonesia: Manufactured Export Classified by||
||Resource Intensity ..............<br>................................|3|
|Table 2|Percent of Women Workers by Labor Intensive Sectors, 1993|4|
|Table 3|Percent of Women Workers by Labor Intensive Sectors||
||and Size of Company, 1993<br>.5||
|Table 4|Wage Increase 1989-1994<br>.6||
|Table 5|Sample Design .23||
|Table 6|Firms Sample by Area .24||
|Table 7|Characteristics of Respondent .25||
|Table 8|Receipt of UMR in Relation to Company Size .23||
|Table 9|UMR in Relation of Type of Industry|28|
|Table 10|UMR in Relation to Industry Type and Company||
||Scale Categories|29|
|Table 11|UMR in Relation to length of Employment .30||
|Table 12|Respondents Experience of Company Compliance||
||With Workplace Benefits .33||
|Table 13|Receipt of Entitlements in Relation To Company Size .34||
|Table 14|Receipt of Entitlements in Relation To Industry Type|35|
|Table 15|Receipt Benefit of Entitlements in Relation to||
||Length of Employment .................<br>..........................|35|
|Table 16|Provision Of Facilities in Relation to Company Size Category|37|
|Table 17|Access to Health and Safety Provisions .38||
|Table 18|Use of Provided Safety Equipment .39||
|Table 19|Reason For Not Using Safety Equipment .40||
|Table 20|Access to Safety Equipment in Relation to Company Size|41|
|Table 21|Health Problems Experienced By Respondent .41||
|Table 22|Knowledge of Standard Working Conditions, Wages and Benefits|42|
|Table 23|Knowledge of Entitlements in Relation to||
|**Appendix Table 9.1: Treatment I**|**mpacts on Willingness**<br>|**and Interest in**<br>|**Taking on Eq**<br>|**uity Investment**<br>||
|---|---|---|---|---|---|
||Interested in<br>equity investment|Maximum<br>equity share|Has specific<br>deal terms|Would consider <br>Royalties|Aggregate<br>Index|
|**Panel A: Impact at Six Months**||||||
|Assigned to Treatment|‐0.019|3.920|0.001|0.025|0.051|
||(0.066)|(3.169)|(0.061)|(0.065)|(0.094)|
|Sample Size|278|264|271|268|278|
|Control Mean|0.603|23.155|0.331|0.508|‐0.015|
|Control S.D.|0.491|23.439|0.472|0.502|0.764|
|**Panel B: Impact at Two Years**||||||
|Assigned to Treatment|‐0.034|‐2.175|0.050|0.105*|0.032|
||(0.055)|(2.972)|(0.051)|(0.056)|(0.084)|
|Sample Size|309|285|309|303|309|
|Control Mean|0.575|25.066|0.242|0.487|‐0.005|
|Control S.D.|0.496|26.591|0.430|0.501|0.783|

|**Appendix Table 9.2: Impacts on General**<br>|**Investabil**<br>|**ity**<br>|||||
|---|---|---|---|---|---|---|
|Number<br>Employees|Founder<br> full‐time|Positive<br> Revenue|Revenue<br> >10,000 euros|Positive<br>Profit|Sales<br>US/Europe|Aggregate<br>Index|
|**Panel A: Impact at Six Months**|||||||
|Assigned to Treatment<br>1.100|0.061|0.008|0.035|‐0.061|‐0.019|0.026|
|(1.215)|(0.052)|(0.061)|(0.068)|(0.059)|(0.060)|(0.085)|
|Sample Size<br>318|269|277|277|272|265|277|
|Control Mean<br>6.111|0.750|0.699|0.353|0.289|0.386|‐0.039|
|Control S.D.<br>10.596|0.435|0.461|0.480|0.455|0.489|0.634|
|**Panel B: Impact at Two Years**|||||||
|Assigned to Treatment<br>4.554**|0.018|0.032|‐0.017|0.085|0.051|0.089|
|(1.814)|(0.061)|(0.071)|(0.068)|(0.061)|(0.055)|(0.082)|
|Sample Size<br>291|291|232|242|276|310|291|
|Control Mean<br>4.683|0.620|0.526|0.361|0.482|0.340|‐0.058|
|Control S.D.<br>6.381|0.487|0.502|0.482|0.502|0.475|0.650|

68
Table A.5: Occupations and income of second-generation refugee, Albanian immigrants and Greek natives

**Panel A: Second-generation refugees and natives (born in 1923-1950)**

||Natives|Refugees|Difference|Difference|
|---|---|---|---|---|
||(1)|(2)|(2)-(1)|With age &gender FE|
|Occupation in last or current job:|||||
|Farmer|0.420|0.327|-0.092***|-0.105***|
||(0.014)|(0.031)|(0.035)|(0.035)|
|Low-skill non-farmer occupation|0.127|0.159|0.032|0.025|
||(0.009)|(0.024)|(0.024)|(0.024)|
|Skilled occupation|0.166|0.128|-0.037|-0.028|
||(0.010)|(0.022)|(0.026)|(0.026)|
|Deciles of household’s income|3.748|3.665|-0.083|-0.099|
||(0.050)|(0.134)|(0.134)|(0.130)|
|N|1,256|226|1,482|1,482|

# **Panel B: Albanian immigrants and natives (all ages)**

||Natives|Albanians||Differences|
|---|---|---|---|---|
||(1)|(2)|(2)-(1)|With age &gender FE|
|Occupation in last or current job:|||||
|Farmer|0.230|0.091|-0.139***|0.008|
||(0.007)|(0.025)|(0.037)|(0.035)|
|Low-skill non-farmer occupation|0.103|0.318|0.215***|0.239***|
||(0.005)|(0.041)|(0.028)|(0.028)|
|Skilled occupation|0.313|0.038|-0.275***|-0.383***|
||(0.008)|(0.017)|(0.041)|(0.040)|
|Deciles of household’s income|4.313|3.731|-0.582***|-1.111***|
||(0.036)|(0.154)|(0.198)|(0.194)|
|N|3,378|132|3,510|3,509|

**Notes** . The table presents the average occupation in the current or last job, and the self-declared household income in brackets corresponding to deciles. In the fourth column of Panel A, the table reports regression estimates of the gap in the outcome between natives and refugee (or between natives and Albanians in Panel B) after controlling for age and gender dummies. Standard errors in parenthesis; *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. Source: Author’s elaboration on the European Social Survey.

36
Access to goods and services also varies by income. The access gap between rich and poor citizens is large and statistically significant.<sup>23</sup> The Access Gap is estimated in the same way as Gender Gap; while measuring difference in “Access” we conditioned difference in “Need” as reported by the wealthy and the poor. The gap is largest for services and the smallest for goods.<sup>24</sup> The poor are 12 percentage points less likely to be able to apply for a driver’s license (Figure 9). The income gap is 12 percentage points for utilities too. By contrast, the gap is small — though statistically significant — for government run schools and health care. This could imply that states have been relatively more successful in delivering schools or hospitals to poorer citizens. Another possibility is that public schools or hospitals have _giffen_ qualities — the rich do not apply for government run schools or hospitals because they prefer private alternatives. In the absence of private alternatives, such as in the case of driver’s license or land or property registration, access for the relatively wealthy continues to remain significantly higher than access for the poor.<sup>25</sup>

**Figure 9: “Income Gap” In Access to Goods and Services**

<!-- Start of picture text --> Healthcare*<br>Public School*<br>Utilities*<br>Register Land/Property*<br>Driving License*<br>0% 2% 4% 6% 8% 10% 12% 14%<br>Income Gap in Access in Percentage Points<br><!-- End of picture text --> _Note_ : This figure plots the difference between the likelihood that those coded wealthy had access to goods and services and the likelihood that those coded poor had access to goods and services. For example, the income gap for a driver’s license is 12 percentage points, which implies that the wealthy are 12 percentage points more likely than the poor to be able to apply for a license. An asterisk indicates that the Income Gap was statistically significant.

> 23 World Bank (2004, Page 39) shows that in India, the poorest fifth of the population consistently receive less than 10% of health expenditure. In contrast, the richest fifth of the population receive more than 30% of health expenditure. For more, see Chapter 1 “Services Can Work For Poor People But Too Often They Fail”, World Bank (2004)

> 24 This consistent with Paul et al.’ (2004, Page 932) assessment: “ _Primary education and drinking water are two services that have done reasonably well in terms of access for the poor. The poor are worse off in terms of access with respect to the other three services._ ”

> 25 For more examples, see Deverajan and Reinikka (2004), Yazbeck et al. (2002)

29
# **Poverty and Income Seasonality in Bangladesh**

## **1. Introduction**

Smoothing consumption is a major problem for rural households in many agrarian societies. There is a large body of literature on seasonality in income, consumption, and poverty. Household incomes vary seasonally, often quite sharply.<sup>2</sup> Like income, household consumption levels also vary seasonally (Chambers, Longhurst and Pacey 1981; Chaudhuri and Paxson (2001); Sahn 1989; Paxson 1993; Dercon and Krishnan 2000). It is frequently asserted that the observed seasonality in consumption is largely driven by the seasonal variation in income, and that lack of proper credit markets impedes consumption smoothing. Consumption seasonality may also be pronounced due to non-credit factors such as seasonal variation in prices, preferences, labor efforts and precautionary savings motives (e.g., Chaudhuri and Paxson 2001; Paxon 1993).<sup>3</sup> There is an extensive literature that shows that households adopt a wide range of strategies to undo or lessen the adverse affects of seasonality in income – counting on their own food and other asset stocks, remittance, receipts from the safety-net programs, selling assets, advance sale of labor and/or crops, and borrowing from formal and informal sources, etc. (e.g., Alderman and Paxson 1992; Besley 1995; Deaton 1992; Jalan and Ravallion 1999). Land fragmentation, storage of grain, accumulation of buffer stock, and mutual insurance (provided by family or friends) are some of the traditional risk management devices. Often

> 2 For example, in the ICRISAT sample of Indian villages used in Chaudhuri and Paxson’s (2001) study, agricultural households, on an average, received 75 percent of their annual income in a three-month period. 3 For instance, Paxson’s (1993) findings suggest that in rural Thailand the observed seasonality in consumption patterns results from variation in prices or preferences, which are common to all households, rather than from the inability of households to use savings or borrowings to smooth consumption.

2
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 17

# **Table 8.  Proportion of total land area, by land category and low population density**

||**Land ca**|**tegory**|||||||
|---|---|---|---|---|---|---|---|---|
||**Suitab**<br>**rainfed**<br>**not for**|**le for**<br>**crops,**<br>**ested**|**Unsuita**<br>**rainfed**<br>**not for**|**ble for**<br>**crops,**<br>**ested**|**Suitab**<br>**rainfed**<br>**fores**|**le for**<br>**crops,**<br>**ted**|**Unsuita**<br>**rainfed**<br>**fore**|**ble for**<br>**crops,**<br>**sted**|
||**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|
|Argentina|37%|36%|60%|60%|1%|1%|2%|2%|
|Bolivia|31%|30%|28%|28%|37%|37%|4%|4%|
|Brazil|35%|34%|15%|15%|35%|35%|15%|15%|
|Chile|5%|5%|68%|67%|4%|4%|23%|23%|
|Colombia|21%|20%|20%|19%|28%|28%|31%|30%|
|Costa Rica|10%|10%|18%|18%|17%|17%|54%|48%|
|Cuba|75%|66%|3%|2%|14%|13%|9%|8%|
|Ecuador|28%|25%|37%|35%|14%|14%|21%|21%|
|Guatemala|10%|9%|24%|15%|31%|31%|34%|26%|
|Guyana|10%|10%|2%|2%|63%|63%|25%|25%|
|Honduras|12%|12%|25%|24%|17%|16%|45%|44%|
|Mexico|18%|16%|58%|56%|9%|9%|15%|15%|
|Nicaragua|23%|21%|16%|15%|32%|32%|28%|28%|
|Paraguay|54%|53%|25%|25%|18%|17%|4%|4%|
|Peru|5%|5%|41%|40%|23%|23%|31%|31%|
|Panama|21%|19%|18%|17%|18%|17%|44%|42%|
|Uruguay|97%|97%|3%|3%|0%|0%|0%|0%|
|Venezuela|37%|35%|7%|6%|28%|28%|28%|28%|
|_LCR Totals_|_30%_|_29%_|_29%_|_29%_|_25%_|_25%_|_16%_|_16%_|

Sources: 1) Population density is from CIESIN Gridded Population of the World 3.

2) Rainfed crop suitability data are from FAO/IIASA Global Agro-Ecological Zones. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total area as given by the tabulated cells, and excluding those few cells with omitted country codes.

4) “Unsuitable” includes GAEZ classifications of ‘marginal’, ‘very marginal’, and ‘not suitable’.