rafmacalaba/datause-encoder-data huggingface dataset:
# **Acknowledgements**

Thanks are due to the Department of Education (DepED) of the Government of Philippines who approached the World Bank to conduct a study on the implementation of School Based Management (SBM) in the Philippines. Thanks are due to AusAID for financing this study through the AusAID World Bank Philippines Development Trust Fund No. 071200 and the Basic Education Public Expenditure Review Trust Fund No. 099307. This Working Paper is one of a series of analytical pieces prepared as part of the overall study and the paper itself does not purport to represent the view of the World Bank or of AusAID.

The overall study was conducted by a World Bank and AusAID team under the leadership and guidance of Motoo Konishi, WB Country Director, Philippines; Xiaoqing Yu, WB Sector Director, EASHD; Octavia Borthwick, Minister-Counsellor, AusAID Manila; and Luis Benveniste, WB Sector Manager, EASHE. The WB team was led by Suhas Parandekar, Senior Education Economist and included Futoshi Yamauchi, Senior Economist, and Lynnette Perez, Senior Education Specialist. The AusAID team was led by Ken Vine, Senior Education Adviser and included Lea Neri, Senior Program Officer; Hazel Aniceto, Portfolio Manager, Education; Teresita Felipe, Education Specialist; and Quintin Atienza, Senior Program Officer.

The WB-AusAID team was supported by a team of consultants led by Vandana Sipahimalani-Rao and included Brian Gozun, Jason Alinsunurin, Rouselle Lavado, and Maria Alma Pineda. The TNS Philippines survey team did an excellent job of administering the school survey in three divisions and collating and cleaning the survey data. The team benefitted from excellent support of the World Bank Manila Office and the AusAID Manila Office. Kristine San Juan-Ante and Corinne V. Bernaldez, EACPF, provided excellent administrative and logistical support from Manila and Chandra Chakravarthi, Anna Coronado and Maya Razat provided administrative support and Takiko Koyama provided research assistance from World Bank headquarters.

Thanks are due to Futoshi Yamauchi, Harry Patrinos and Luis Benveniste for providing very useful comments and suggestion on earlier versions of this paper. Any errors and remaining defects are the responsibility of the author, who may be contacted at sparandekar@worldbank.org.

2
Table 3: Panel unit root tests in levels

||||CIPS|||
|---|---|---|---|---|---|
|Income group|GDP|Aggregate|Industry|Agriculture|Price|
|High income|_−_1_._70|_−_2_._31|_−_2_._15|_−_1_._94 (rw)|_−_2_._92<sup>_∗∗∗_</sup>|
|No. of Obs.|1645|1615|903|856|1050|
|Upper middle income|_−_2_._52|_−_2_._44|_−_2_._37|_−_2_._14 (rw)|_−_2_._77<sup>_∗∗_</sup>|
|No. of Obs.|570|562|463|443|309|
|Lower middle income|_−_2_._39|_−_2_._65|_−_2_._90<sup>_∗∗_</sup>|_−_2_._08|_−_1_._83|
|No. of Obs.|211|211|210|210|132|
||||DHT|||
|High income|5.53|2.03|3.20|-0.19 (rw)|3.12|
|Upper middle income|0_._55|1.19|0.62|_−_0_._92 (rw)|1.16|
|Lower middle income|_−_0_._87|0.96|0.30|_−_0_._38|1.13|

Note:<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> denotes significance at the 1%, 5%, and 10% level respectively. (rw) indicates a null hypothesis of random walk. In all other cases, the null hypothesis is a random walk with a drift. Number of observations for DHT test are the same as reported for the CIPS test.

Table 4: Panel unit root tests in

|||C|IPS|||
|---|---|---|---|---|---|
|Income group|∆GDP|∆Aggregate|∆Industry|∆Agriculture|∆Price|
|High income|_−_5_._35<sup>_∗∗∗_</sup>|_−_7_._22<sup>_∗∗∗_</sup>|_−_5_._25<sup>_∗∗∗_</sup>|_−_5_._43<sup>_∗∗∗_</sup>|_−_5_._11<sup>_∗∗∗_</sup>|
|Upper middle income|_−_4_._84<sup>_∗∗∗_</sup>|_−_5_._99<sup>_∗∗∗_</sup>|_−_5_._90<sup>_∗∗∗_</sup>|_−_6_._33<sup>_∗∗∗_</sup>|_−_4_._11<sup>_∗∗∗_</sup>|
|Lower middle income|_−_4_._66<sup>_∗∗∗_</sup>|_−_5_._52<sup>_∗∗∗_</sup>|_−_5_._55<sup>_∗∗∗_</sup>|_−_4_._85<sup>_∗∗∗_</sup>|_−_3_._10<sup>_∗∗∗_</sup>|
|||D|HT|||
|High income|_−_16_._46<sup>_∗∗∗_</sup>|_−_24_._92<sup>_∗∗∗_</sup>|_−_17_._78<sup>_∗∗∗_</sup>|_−_21_._08<sup>_∗∗∗_</sup>|_−_13_._43<sup>_∗∗∗_</sup>|
|Upper middle income|_−_9_._82<sup>_∗∗∗_</sup>|_−_17_._50<sup>_∗∗∗_</sup>|_−_16_._45<sup>_∗∗∗_</sup>|_−_20_._44<sup>_∗∗∗_</sup>|_−_7_._14<sup>_∗∗∗_</sup>|
|Lower middle income|_−_6_._33<sup>_∗∗∗_</sup>|_−_11_._04<sup>_∗∗∗_</sup>|_−_11_._38<sup>_∗∗∗_</sup>|_−_11_._96<sup>_∗∗∗_</sup>|_−_1_._47<sup>_∗∗∗_</sup>|

Note:<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> denotes significance at the 1%, 5%, and 10% level respectively. Number of observations are the same as reported in Table 3.

20
Policy Research Working Paper 4945

# **Abstract**

Since 1990 migration flows from Albania have been massive, relative to the size of the country and its population, but they have also fluctuated over time. This paper presents and discusses various descriptive trends, mainly in graphical form. The data come from the Albanian Living Standards Measurement Survey, 2005 round, and cover the period 1990–2004. The resulting observed trends reflect changing push and pull factors in Albania and the two main host countries, Greece and Italy. The paper also presents a hazard approach to modeling Albanian emigration and return migration. This analysis highlights, among other things, the relevance of networks in Albanian migration dynamics, both to promote emigration and to delay return.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to study the impact of migration on poverty and other outcomes in source countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gcarletto@worldbank.org and cazzarri@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Polloy Research<br>Publilo Economlos<br>WPS 1082<br><!-- End of picture text -->

> This paper **-** a product of the Public Economics Division, Country Economics Department **-** is part of

> a larger effort in the department to analyze public expenditure policies in developing countries. Copies of

> the paper are available free from the World Bank, **1818** H Street NW, Washington, **DC** 20433. Please contact Carlina Jones, room **NIO-063,** extension **37699** (February **1993,** 40 pages).

Devarajan, Swaroop, and Zou develop a simple analytical framework **that** shows how the composition of public spending affects economic growth.

Distinguishing between productive and unproductive government spending (that which complements private sector productivity and that which does not), they show that increasing the share of productive spending leads to a higher steady-state economic growth rate.

They use data from **69** developing countries over 20 years to determine which components of public spending are productive. They find that an increase in the share of current spending has

> positive and statistically significant effects on growth.

> Otherwise, the news is mainly negative. The relationship between the capital component of public spending and per capita growth is negative. The same is true of the share of spending on transport and communications. The

> shares spent on health and education have no significant impact, although parts of those shares **-** the parts spent on preventive care and "other

> education" **-** do.

These results raise tie question whether public spending actually leads to a flow of public goods and services.

The Policy Research Working Paper Series disseminates the findings of work under _way_ in the Bank. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced **by** the Policy Research Disseednation Center
inequalities in development outcomes, including those along gender lines, but very litle research has been able to open the “black box” of the _deliberative_ bodies that are at the core of India’s decentralization effort. In this paper, we do just that. By using _text-as-data_ methods on an original corpus of village assembly transcripts from rural Tamil Nadu, we show that these assemblies are not merely empty spaces where state officials bluster and read banal announcements; rather, they provide meaningful forums for citizens to challenge their elected officials, demand transparency, and provide information about very real local development needs — from water and sanitation issues, to wage payments and government service failures.

We also show, however, that _among citizens_ , inequalities in power and status meaningfully impact the citizens’ ability to be heard. More specifically, we show that across multiple measures of deliberative influence, women are at a considerable disadvantage. Tey are less likely to be heard, less likely to drive the agenda, and less likely to receive a relevant response from state officials. Indeed, even when we account for the particular issues raised, women still remain at a disadvantage — ofen ignored while their male peers receive a direct response. In the excerpt below, for example, a woman raises a genuine concern about the lack of ration shop facility in the village, to which the official does not respond; a man then raises the _exact same issue_ afer her, and receives an immediate response from the elected official, who promises to speak to higher-ups about what can be done to address the issue: _Female 1:_ In Patupalli village, so far, **there is no fair price shop.** Tey are keeping it in the Women’s Health Building. Women are quarrelling. Te village people want it built new. Tere is fight in the panchayat. So people are going to the neighboring village. But the pata [titled] land owners are preventing them from using their land for going to the next village, so resolution should be passed for construction of a ration shop here.

_Male 1:_ For so many years, **there is no ration shop here** . Only rental shops are here. So long, it was in rented place and now it is kept in Women’s Health Association. Now, women ask for the building and want a fair price shop built. So there is a lot of problem. Please establish for us a ration shop.

_Male (President):_ **Regarding this ration shop, we should talk with MLA** [Member of the Legislative Assembly] and BDO [Block Development Officer]. Te request will be made...

37
# **Table 2 (Continued)**

# <u>Notes:</u>

- (1) The sample is comprised of individuals 18 and older, and of those involved in agriculture. The estimates are weighted by the response weight. The agricultural parcels underlying the (individual-level) indicator definitions are those that are associated with the reference rainy season.

- (2) The rights-related variables are defined irrespective of the reported need to obtain consent/permission from anyone – a topic that the IHPS collected additional information on.

- (3) SDG owner is equal to 1 1 if the individual is a documented owner of any parcel or has the rights to sell or bequeath any parcel, and 0 otherwise.

- (4) Standard deviations in brackets. Adjusted Wald tests for equality of means were also conducted across T1 and T5, within the samples of women/men. Statistically significant differences (p<0.05) are in bold; all except two tests within the sample of non-HH headed women (exclusive economic ownership and SDG owner) were statistically significant at p<0.01.

- ***=p<0.01, **=p<0.05, *=p<0.10.

**Figure 2.  Exclusive Reported and Economic Ownership Among Self-Reporting Female Household Heads, by Survey Approach (T1/IHS4 vs T5/IHPS) and Age**

# <u>Notes:</u>

- (1) The sample is comprised of self-reporting female heads 18 and older, involved in agriculture.

- (2) The median age for female household heads was 46 years, and 20 percent of this sample was between 60 and 75 years.

- (3) The agricultural parcels underlying the (individual-level) indicator definitions are those that are associated with the reference rainy season.

Figure 3 looks more closely at how gender gaps between men and non-household head women vary by age. Individual interviews raise women’s exclusive reported ownership above that for men, among women less than 60 years old (about 88 percent of the sample). Women’s exclusive economic ownership is higher under both the business-as-usual and individual interview approaches, but the reverse gender gap is much greater under individual interviews. Individual interviews also narrow gender inequalities in rights, particularly among younger men and women. Gender inequalities do widen again in ownership and rights under both survey approaches for 15
# **Table A3: Extreme Observations Exclusion**

||**Extreme Value Obs**<br>|**ervation Exclusion: T**<br>**Duration**<br>|**otal Water Shortage**<br>|
|---|---|---|---|
||**Bottom 1%**|**Top 1%**|**Top and Bottom 1 %**|
||**coef/se**|**coef/se**|**coef/se**|
|Total duration of water shortages last month (in days)|-0.036***|-0.035**|-0.035***|
||(0.010)|(0.015)|(0.011)|
||**Extreme Value Ob**|**servation Exclusion:**|**Labor Productivity**|
||**Bottom 1%**|**Top 1%**|**Top and Bottom 1 %**|
||**coef/se**|**coef/se**|**coef/se**|
|Total duration of water shortages last month (in days)|-0.027***|-0.032***|-0.022**|
||(0.010)|(0.010)|(0.011)|

# **Table A4: Water Shortage and Correlates**

||**Total duration of water shortages last month (in**<br>**days)**|
|---|---|
||**coef/se**|
|Log of total number of employees in regular month|-0.217|
||(0.205)|
|Largest owner has secondary education or higher: Y/N|0.189|
||(0.188)|
|Manager experience (years)|0.011|
||(0.019)|
|Largest owner has a job in formal sector or looked for one over last 2 years Y/N|0.151|
||(0.148)|
|Largest owner started another business in the last three years Y/N|-0.172|
||(0.361)|
|Firm uses electricity for activity Y/N|-0.269|
||(0.309)|
|Firm uses machinery in its current operations Y/N|-0.090|
||(0.266)|

37
|_R-squared_|0.314|0.319|0.348|0.390|0.315|0.337|
|---|---|---|---|---|---|---|
|_Number of countries_|70|43|51|38|84|41|
|_Country FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Time FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Standard errors in pare_|_ntheses_||||||
|_*** p<0.01, ** p<0.05,_|_* p<0.1_||||||

Table 11: Dependent Variable: Private employment

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|_General gov. (% LF)_|-0.106|-0.196|||||
||(0.598)|(0.627)|||||
|_Central gov. (% LF)_|||-0.547|2.181|||
||||(0.815)|(2.178)|||
|_Public sector (% LF)_|||||-0.150|-0.065|
||||||(0.472)|(0.660)|
|_Urbanization rate_|-10.657|102.695|80.421|3.972|6.315|118.322|
||(70.828)|(90.142)|(110.580)|(85.469)|(64.484)|(99.628)|
|_Openness_|-0.047|-0.006|-0.082|0.006|-0.029|0.016|
||(0.041)|(0.058)|(0.050)|(0.049)|(0.039)|(0.053)|
|_log of GDP per capita_|24.658***|32.782***|37.814***|27.785***|18.886***|27.231***|
||(7.318)|(8.574)|(9.307)|(8.809)|(6.571)|(8.950)|
|_Union density rate_||-0.640**||-0.201||-0.228|
|||(0.261)||(0.348)||(0.201)|
|_Constant_|-153.845|-302.752**|-351.076**|-221.599*|-104.241|-269.917*|
||(93.478)|(121.838)|(140.327)|(120.366)|(82.424)|(132.392)|
|_Observations_|116|61|87|59|131|62|
|_R-squared_|0.255|0.600|0.374|0.567|0.203|0.450|
|_Number of countries_|55|31|39|29|64|30|
|_Country FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Time FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Standard errors in pare_|_ntheses_||||||
|_*** p<0.01, ** p<0.05,_|_* p<0.1_||||||

# **3. Concluding Remarks**

We examined the determinants of public employment based on the ILO data set, which is the most complete source for analysis of public employment across countries, We found a negative association between the size of public employment and country size by population, and a positive association with 25
Table E5: Heterogeneous Impacts of GDP Decline in Firms’ Destinations by Worker Characteristics - Ecuador

||Average|months worked|Aver|age real wage|
|---|---|---|---|---|
|||Test eq. of coef||Test eq. of coef|
||Coefcient|t-test<br>p-value|Coefcient|t-test<br>p-value|
||(1)|(2)|(3)|(4)|
|GFC frm shock|-0.0652***||-0.0200||
||(0.0170)||(0.0137)||
|Observations||794,948||793,553|
|R-squared||0.1457||0.0752|
|GFC frm shock*Female|-0.1061***<br>(0.0264)||-0.0292**<br>(0.0116)||
|GFC frm shock*Male|-0.0478***|2.5076|-0.0157|0.7927|
||(0.0158)|(0.0122)**|(0.0170)|(0.4280)|
|Observations||794,948||793,553|
|R-squared||0.8902||0.3645|
|GFC frm shock*Higher education|0.0160||-0.0164||
||(0.0203)||(0.0225)||
|GFC frm shock*No higher education|-0.0734***|-4.0187|-0.0180|-0.0786|
||(0.0173)|(0.0001)***|(0.0134)|(0.9374)|
|Observations||794,948||793,553|
|R-squared||0.8905||0.3720|
|GFC frm shock*Young|-0.0698***<br>(0.0184)||-0.0183<br>(0.0127)||
|GFC frm shock*Middle-aged|-0.0555***|-0.8344|-0.0186|0.0116|
||(0.0201)|(0.4042)|(0.0269)|(0.9907)|
|GFC frm shock*Old|-0.0229|-1.3060|-0.0103|-0.4875|
||(0.0344)|(0.1917)|(0.0153)|(0.6260)|
|Observations||545,738||545,144|
|R-squared||0.8759||0.4205|
|2-digit industry X Year FE||Yes||Yes|
|State X Year FE||Yes||Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. All specifications control for vectors of worker and firm characteristics as of 2007 described in Section III. FE stands for fixed effects.

62
36 Selowsky. The distribution of service connections<sup>for electricity, water and sewerage is pro-rich,</sup> with the percentage of households in any income quintile<sup>receiving services increasing with the</sup> size of the urban area. Meerman also finds that the<sup>percentage of households connected to these</sup> services is greater for Chinese and Indian households<sup>than for Malay households.</sup> Maddock and Castano examine the compensated<sup>variation associated with block pricing</sup> of electrical utility services in Medellin, Colombia.<sup>While they fmd the practice of charging</sup> higher prices for large electricity users is pro-poor,<sup>their empirical work focuses on electricity</sup> usage and does not account for households without electrical<sup>services or for households with</sup> illegal connecdons to electrical services (see Maddock<sup>and Castano, p. 6). Thus, the Maddock</sup> and Castano study cannot be taken as evidence of<sup>a pro-poor distribution of net benefits from</sup> electrical service. From the evidence available, the<sup>working hypothesis must be-that lower</sup> income and rural households receive the least total subsidy<sup>for these public services.</sup> The Whittington et al. (1988), Whittington et al. (1989)<sup>and Altaf et al. (1989) studies</sup> offer insight into why low income households do<sup>not connect to the public water system, even</sup> when they apparently can afford to do so. These studies<sup>suggest changes in program design that</sup> miight change low-income households demand for connection<sup>to the public water system.</sup> Altaf et al. fmd in Pakistan that, contrary to conventional<sup>wisdom, the annual or recurrent</sup> charge for water and not the one-time connection charge<sup>is the deterrent to the poor connecting</sup> to public systems. The availability of alternative water<sup>sources also influences the probability</sup> of connecting to public systems.

The two studies by Whittington et al. raise similar<sup>issues for two regions in Nigeria. In</sup> both regions the poor have altemative sources of water,<sup>which is from trucks that bring water to</sup> the village. Some low-income households purchase water<sup>directly from the truck, while others</sup> purchase the water from intermediaries who have purchased<sup>water from the truck. In both cases,</sup> households do not want the commitnent of paying<sup>a fixed monthly charge for water, or they</sup> want the option of paying ondy for water during<sup>the dry season. Thus, households prefer a</sup> metered system to that of unlimited use and a fixed<sup>monthly charge. The fact that households</sup>
Our first concern is that the MPS might be driven by other economic variables that are correlated with the ER regime or shock persistence. In particular, the literature stresses that low institutional quality and financial frictions can lead to procyclicality. In addition, we include the log of real GDP per capita to control for the level of income and development. Results with all three controls are shown in Panel B of Table 4, and we keep these controls for all other robustness tests.<sup>54</sup> The main results remain unchanged, except that all MPS coefficients are slightly higher (more pro-cyclical). In addition, 1P in column (6) increases from 0.12 to 0.19 GG and is estimated less precisely so that we can only reject the null hypothesis that ββ 1P 1 at the 10% level (versus 1% in the benchmark). GG FFLLNNAAFF A second concern involves the classification of ER regimes, as many countries ββ = ββ lie close to the benchmark cutoff, and the ER regime might be classified with error. In Panel C of Table 4, we classify countries as having a fixed ER if the ER index is equal to or less than seven (versus three in the benchmark).<sup>55</sup> Results are generally similar, though the size and significance of some coefficients increase.

Although our paper focuses on EMDEs, some commodity exporters are developed economies. Panel D of Table 4 tests whether our results generalize to all commodity exporters by added Australia and Norway to the sample. The results remain generally similar, except we fail to reject 1 1P at conventional FFLLNNAAFF GG levels. ββ = ββ The third concern is that the results might be driven by influential observations, countries, or years and do not reflect a general relationship. To test this, Panel E adds the extreme outliers that we dropped from the benchmark regressions (see Appendix Table 8 for a list of outliers). The results are generally similar, except that the estimates are less precise – with higher standard deviation of coefficients – and the first-stage F-statistics fall substantially.

Appendix Figures 7 and 8 test the sensitivity of the results to individual countries or years by dropping them one by one from the fully specified regression for EMDEs (column (6) of Table 4). While there is little evidence that specific years

> 54 Appendix Table 9 shows results when controls are added one by one.

> 55 See Appendix Table 6 for details about the ER classification.

43