rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Public and private shares of school enrolment by level of education, 1993-98

|(aspercent|ofgrossenrolments)|
|---|---|
|Typeofschool|Shareintotalenrolments|
||1993<br>1998|
|_Publicschools_||
|Primary|97.3<br>99.3|
|Lower secondary|97.6<br>96.6|
|Uppersecondary|95.7<br>83.0|
|_Privateschools_||
|Primary|0.9<br>0.3|
|Lower secondary|1.7<br>_0.5_|
|Uppersecondary|2.1<br>4.9|
|_Semi-public schools_||
|Primary|1.9<br>0.4|
|Lower secondary|0.7<br>2.9|
|Uppersecondary|2.1<br>12.6|

_Source:_ Author's estimates based on 1992-93 and 1997-98 VLSS and public expenditure data (from MOF) Turning to the secondary level, the most obvious explanation for the increased role of the private sector is the limited capacity by the public sector to provide education at that level. In Vietnam the private sector is still considered by households as offering an education that is of lower quality than that offered by the public sector. Demand for private and semi-public education at the secondary level has increased because many more children who want to attend secondary schools cannot gain admission to public schools due to limited space available at public school. -However, semi-public lower secondary schools developed faster than private ones perhaps as this is a better investment, compared to the pure private counterparts. In Vietnam, as the public sector cannot meet increasing demand for recurrent spending of schools, many schools were made semi-public, they could continue using public infrastructure (classrooms and facilities) but self-finance for most of its increased recurrent expenditure (such as salary for new hired teachers, top up salary for existing teachers) As a result, at lower secondary level, the private provision of education fell from 1.7 percent to a quite low level of 0.5 percent, while semi-public lower secondary school increased from 0.7 percent to 2.9 percent. Private schools at upper secondary level experienced a considerable increase from 2 % in 1993 to 5% in 1998, compared to a huge increase for semi-public schools from 2 % in 1993 to 13% in 1998.

The overall state budget expenditure for education and training (including funding from both central and local levels) increased from about 10 percent of current expenditures in 1993 to 17 percent in 1998. This implies that spending on education by the government increased from 1.8 percent of GDP in 1992 to 3.5 percent in 1998 (Table 7). Compared to 1993, budgetary spending on education has increased from 61 % of total public spending on education and training in 1993 to 76% in 1998 at the expense of spending on higher education and training. The largest share of the state budget for education and training sector (E&T), 36 percent, is allocated to primary education in 1998, an increase from 33 percent in 1993. The share of the budget expenditure on education for lower secondary schools in 1998 was the same as that in 1993 - 18 percent, while the budget share for upper secondary education increased slightly from 7 percent in 1993 to a little more than 8 percent in 1998. Government spending on higher education and 9
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**Table** 5. **Changes in Composition of Value-Added (evaluated at 1992 prices), by Region and Sector, Base Case: 1992-2005**

||||||_Sect_|_ors_|||||
|---|---|---|---|---|---|---|---|---|---|---|
|_Regions_|_PAgr_|_PFood_|_NRes_|_Text_|_WApp_|_LMnfc_|_TMEq_|_HMnfc_|_UH&CS_|_Svces_|
|Usc|-18|-20|2|-11|-19|-10|19|1|-1|0|
|EU|-22|-25|-9|-23|_-45_|-18|13|-14|-14|13|
|JPN|-9|-11|21|-16|-31|0|-9|3|7|1|
|KOR|-63|-35|7|-36|-60|20|-10|14|-12|21|
|TWN|-50|-32|17|48|-52|44|-37|62|11|-5|
|HKG|-35|91|36|64|-49|95|20|68|11|-12|
|CHN|-82|-9|43|47|23|82|34|113|31|4|
|IDN|-46|<br>-6|-37|9|-2|40|30|61|13|35|
|MYS|-64|-10|-47|3|30|49|-34|-12|-14|62|
|PHL|-14|-1|4|0|13|-4|-42|4|-7|10|
|THA|-77|-23|-67|0|-4|47|-19|23|0|40|
|LTN|-15|-11|18|-6|-6|-7|-36|-8|-22|12|
|SSA|12|17|-21|11|47|-5|-51|-13|-13|7|
|SAS|-30|1|22|23|21|38|33|38|44|-4|
|ROW|-23|-13|-18|-13|-20|-5|-9|-9|-3|11|
Table 10: Outcome of collective actions undertaken by SHGs

||(1) W<br>underto|ards where<br>ok collectiv|SHGs<br>e actions|(2<br>thep|) Wards wh<br>roblem app|ere<br>eared||(3) All war|ds|
|---|---|---|---|---|---|---|---|---|---|
||Wards<br>(#)|Solution<br>(%)|Promise<br>(%)|Wards<br>(#)|Solution<br>(%)|Promise<br>(%)|Wards<br>(#)|Solution<br>(%)|Promise<br>(%)|
|Public|92|42.5|46.8|135|20.0|23.7|141|17.1|19.7|
|infrastructure||(34.0)|(35.5)||(25.0)|(29.7)||(23.1)|(25.9)|
|Welfare schemes|60|59.2|30.5|113|25.5|13.4|141|14.0|7.7|
|||(42.1)|(37.6)||(37.3)|(28.0)||(25.0)|(19.2)|
|Alcohol problems|66|51.9|39.7|121|20.1|15.7|141|16.4|12.9|
|||(38.1)|(36.6)||(29.5)|(25.5)||(27.5)|(23.6)|
|School problems|35|58.2|27.5|98|13.6|5.6|141|5.6|2.5|
|||(40.5)|(33.2)||(27.5)|(14.9)||(16.7)|(10.3)|
|Forest issues|74|59.6|32.3|121|26.8|14.0|141|19.9|10.6|
|||(34.8)|(34.4)||(32.7)|(23.6)||(27.7)|(18.9)|

We asked SHG members whether they obtained what they requested thanks to their collective actions (“solution”) or, at least, received the promise that a solution was to be provided (“promise”). The columns (1) show summary statistics for wards where at least one SHG undertook collective actions for the issue. The columns (2) show the percentages over all the wards where the problem appeared (we asked SHGs whether they faced the problem in their ward). Finally, the columns (3) assume that all the problems appeared in all the wards. Standard deviations are given in parentheses. _Source_ : Authors’ analysis based on data described in the text.

# **V Conclusions**

We examine the impact of SHGs’ collective actions on the variety of problems dealt with by local governments. Using a theoretical model, we show that an elected official, whose aim is to maximise re-election chances, would exert higher effort in providing public goods when private citizens undertake collective action and coordinate their voluntary contributions towards the same. This occurs although government and private contributions are assumed to be perfect substitutes. We test the prediction of the model using first-hand data on SHGs in India. We find that WMs take care of a larger variety of ward issues when SHGs undertake collective actions, and that they start dealing with the public goods preferred by SHGs. In particular, WMs are more likely to deal with alcohol, forestry and school problems.

Our results are important in light of the attention given to SHGs in anti-poverty programs in India. For example, the National Rural Livelihood Mission puts forward the creation of groups as a first step in its poverty alleviation program. As shown by Desai and Joshi (2014), SHGs 45
identify "credible" treated and control units for causal identification of impacts. In addition, energy use patterns entail significant amount of seasonalities, hence, a simple beforeafter differences in consumption levels may not reflect the impact of the pandemic.

To address these challenges, we leverage the detailed (panel) nature of our datasets and model electricity consumption at the customer level before and after the roll-out of the COVID lockdowns (SAH) compared against the typical (average) consumption levels during the same period in the previous years (Irwin et al., 2021). Essentially, for each customer, we use the typical electricity consumption levels in the previous (nonCOVID) years as the counterfactual, i.e., the level of consumption in the absence of COVID. We include customer fixed effects and location-year fixed effects to purge customer specific and temporal factors that influence consumption. In addition, we explicitly control for monthly average temperature and total precipitation to absorb the effect of seasonal weather patterns on electricity consumption.

Six main results emerge from the paper. First, residential electricity consumption during the pandemic increased in both Ghana and Rwanda, albeit the rate of increase was higher in the former. Specifically, average household consumption increased by about 23% in Ghana compared to 4% in Rwanda. Second, consumption in the non-residential sector declined by about 11% in Rwanda, while rising marginally in Ghana by about 6% in Ghana. Third, industrial consumption declined in both countries, highlighting the effect of COVID-induced business closure and supply chain disruptions on the activities of the industrial sector. Fourth, the effect of COVID on electricity consumption was pronounced in lockdown and non-lockdown communities, as well as both rural and urban communities. This highlights the effects of the pandemic across various segments of the population. Fifth, pandemic relief measures such as electricity subsidies administered in Ghana played a key role in raising consumption. While the subsidies were important to sustaining households’ consumption from falling below pre-pandemic levels, they also induced an unintended consequence: excess consumption among households. Finally, contrary to earlier criticisms that the design of the subsidy program in Ghana was regressive (Berkouwer et al., 2022), our back-of-the-envelope calculations suggest that the total subsidies to low-income (lifeline) customers were about 67% higher than total subsidies received by middle/high-income (non-lifeline) households (customers). This is largely driven by the extension of the subsidy for longer duration for low-income households.

This paper contributes to two strands of the literature. First, the broader literature on the effects of COVID-19 on households and firms in emerging economies. These studies range from the effects on food security(Amare et al., 2021; Abay et al., 2020), wel2
food basket without the requirement to attend educational sessions<sup>5</sup> ; a group (T2) that received the food basket with the requirement to attend educational sessions; and a group (T3) that received the equivalent value of the food basket in cash (or $150 Mexican pesos) accompanied with the requirement to attend educational sessions. The control and the three treatment groups were surveyed before and after the implementation of the transfer program.

One key advantage offered by the evaluation design of the program is that we can also examine directly differences in the impacts of receiving cash instead of in-kind food transfers on food and total consumption. In principle, there is a variety of reasons why the impacts of inkind and cash transfer may differ even among infra-marginal households. Women, for example, may have more control over in-kind food transfers while men may have more control over cash transfers. In this case, the impacts of cash and in-kind transfers may differ because of differences in the preferences between men and women. It is also possible that in-kind transfers may have more of social stigma attached to them than cash transfers. In-kind transfers may also affect the preferences of households in the sense that they feel socially obliged to consume everything they receive. The PAL data offer the ideal setting for testing whether indeed the impacts of the transfer in-kind differ from transfers in cash of equivalent value.

In our empirical analysis we apply the difference-in-differences estimator on repeated observations from households and their members in treatment and control villages surveyed for the purpose of evaluating the impact of the PAL program. Specifically, we investigate a number of interrelated questions. First, we examine whether the transfer and the type of transfer (in kind or in cash) affects total consumption and food consumption in particular. We find that the transfer has a positive impact on total and food consumption and confirm there are

> 5 As discussed in more detail below, the treatment in the communities of group T1 was contaminated since these communities also received educational sessions.

5
(Al.l) Fi = FGi - RESi + £(FXji - FXij); for i+j+t and i=c,d,o,b,p,t (Al.2) dFGi = K dFGi + CC dFGj; for<sup>i=c,d,o,b,p,t</sup> (Al.3) dFX = K dFXj3 + CC dFXi; for(i,j)=(d,c),(o,c),(b,c),(p,d) (Al.4) dRESi K Ki dRESi + CC dRESj; for i = c, d with Fi = net foreign liability position<sup>of sector i, FGi=external</sup> debt position of sector i, RESi= reserve<sup>position of sector i, FXij</sup> foreign exchange deposits of sector<sup>i at sector j. d stands for</sup> flow data, the prefix K is for the flows<sup>from the capital account</sup> of the balance of payments and the prefix<sup>CC indicates the cross-</sup> currency effect.

Equation A1.1 states th^ ;.ne net foreign<sup>liability position</sup> of any sector in the economy equals its external<sup>debt position</sup> minus reserve holdings and minus net foreign<sup>exchange deposits at</sup> other domestic sectors. In this way, we deal explicitly<sup>with the</sup> difference between the external debt position<sup>and the net foreign</sup> asset position of each sector. Clearly,<sup>for the economy as a whole</sup> there is no difference between the net<sup>external debt and the net</sup> foreign asset position. Equation A1.2<sup>decomposes the stock change</sup> of external debt into the actual flow from<sup>the capital account of</sup> the balance of payments and the "cross-currency<sup>effect" due to</sup> changes in exchange rates among the foreign<sup>currencies that compose</sup> the stock of debt. Equation A1.3 and A1.4<sup>do the same as equation</sup> A1.2 for foreign exchange deposits and<sup>reserve holdings.</sup> The formulation of equation A1.4 implicitly<sup>assumes that</sup> there are no sales or purchases of reserves<sup>between the different</sup> sectors of the economy (other than related<sup>to balance of payments</sup> 6
# **References**

Bagwell, Kyle and Robert W. Staiger (1990) “A Theory of Managed Trade,” _American Economic Review_ 80(4), 779–795.

Bown, Chad P. (2012) _Temporary Trade Barriers Database_ , The World Bank. Available at <u>http://econ.worldbank.org/ttbd/ , May.</u> Bown, Chad P. (2011) "Taking Stock of Antidumping, Safeguards and Countervailing Duties, 1990-2009," _The World Economy_ 34(12): 1955-1998.

Bown, Chad P. and Meredith A. Crowley (forthcoming) “Self-Enforcing Trade Agreements: Evidence from Time-Varying Trade Policy,” _American Economic Review_ .

Bown, Chad P. and Meredith A. Crowley (2012) “Emerging Economies, Trade Policy, and Macroeconomic Shocks,” July.

Bown, Chad P. and Meredith A. Crowley (2010) “"China’s Export Growth and the China Safeguard: Threats to the World Trading System?" _Canadian Journal of Economics_ 43(4): 1353-1388.

Bown, Chad P. and Meredith A. Crowley (2007) "Trade Deflection and Trade Depression," _Journal of International Economics_ 72(1): 176-201.

Council of Economic Advisers (2011) _Economic Report of the President_ , February.

Finger, J. Michael (1992) “Dumping and Antidumping: The Rhetoric and the Reality of Protection in Industrial Countries,” _World Bank Research Observer_ 7(2): 121-144.

IMF (2011) _World Economic Outlook_ . Washington, International Monetary Fund, September.

Kee, Hiau Looi, Cristina Neagu, and Alessandro Nicita (forthcoming) “Is Protectionism on the Rise? Assessing National Trade Policies During the Crisis of 2008,” _The Review of Economics and Statistics_ .

Messerlin, Patrick A. (2004) “China in the World Trade Organization: Antidumping and Safeguards,” _World Bank Economic Review_ 18(1): 105–30.

Prusa, Thomas J. (2001) "On the Spread and Impact of Anti-dumping," _Canadian Journal of Economics_ 34(3): 591-611.

Prusa, Thomas J. (1997) "The Trade Effects of U.S. Antidumping Actions,” in Robert C. Feenstra (ed.) _Effects of U.S. Trade Protection and Promotion Policies_ . Chicago, IL: University of Chicago Press, pp. 191-213.

18
level the average STR is 14:1, but the average student/class ratio is more than double (37:1), 29 which implies that the average class is taught by 2.6 teachers .

|Table13 Student-Teacherand Student-Classratios2003-2004<br> <br>||
|---|---|
|**Level of Education:**<br>**Student -Teacher Ratio**|**Student -Class Ratio**|
|Primary<br>20|25|
|Junior Secondary<br>14|37|
|Senior Secondary<br>12|36|
|**Average:**<br>**17**|**28**|

_Source:_ MoNE, 2004.

The bottom line from a financing perspective is that the oversupply presents a significant costburden. Using realistic STRs 30 that follow international best practice and are in line with the regional average, Indonesia shows a teacher oversupply of approximately 21 percent (Annex 6) Even when using a conservative estimate and taking into account the large part-time teacher workforce, the cost for primary and junior secondary schools alone reaches over Rp. 5 trillion, or approximately 8 percent of the total education budget. This cost will be exacerbated when teacher wages are significantly increased due new incentives specified in the 2005 Teacher Law (discussed in detail in the next section).

A more efficient teacher supply method in the longer term may be to provide funds to schools on a per pupil basis and then allow the schools to hire the number of teachers they believe are required. While the current method of providing teacher salaries through the DAU encourages schools and districts to request more teachers from the central government, having the teacher salary come out of the school’s budget would encourage schools to be efficient in the number of teachers hired. It would help in creating a more equitable distribution since salaries would only be available at the school level. It would also in part address issues of teacher absenteeism and hiring of part-time teachers, because schools would have the incentive to use their resources efficiently and ensure that their teachers are performing their assigned tasks. This mechanism, however, bypasses district governments and might make local responsibilities in terms of providing educational services more diffused. Schools would also require additional financial and administrative capacity.

> 29 This is partly because many secondary schools employ subject experts. Often these teachers are hired on a full-time basis, but only teach part-time because they can only teach in their subject area.

> 30 A proposed entitlement is a minimum of 4 teachers in every primary school and a target STR of 32:1 in primary and a minimum of 7 teachers in every junior secondary school and a target STR of 28:1, which results in an actual STR of 26:1 and 22:1 respectively.

21
# **4. Main results**

Here I report and discuss main results from OLS estimations at the couple-level. In couple-level estimations, I compare couples who started childbearing before and after 1994. First, I test the effects of the Islamic inheritance exclusion rule on son and fertility preferences. Next, I determine whether couples who have stronger incentives for sonpreference follow a fertility stopping behavior. As a last step, I examine whether couples who have incentives to seek having a son end up with larger families.

# **4.1. Effects on the ideal proportion of sons and the ideal number of children: Muslim vs. non-Muslim differentials in son and fertility preferences**

I start by identifying the characteristics of couples who have a strong son preference. Specifically, I model the effects of the Islamic inheritance exclusion rule on women’s and men’s ideal proportion of sons as specified in eq. [1]. Muslim couples in which the man has at least one surviving brother and who started childbearing before 1994 are expected to desire having more sons. Therefore, I test for a positive coefficient on the double interaction _M i_<sup>_S_</sup> _i_ and a negative coefficient on the triple interaction _M i SiT_ . The baseline group is non-Muslim couples in which the man has no surviving brothers at the time of first child birth and who started childbearing before the compilation.

Expressed son preference is common, with 71 percent of women and 68 percent of men reporting that they would desire at least half their children to be boys. Overall, women wanted 0.4 percent more sons and men wanted 9.6 percent more sons than daughters.<sup>9</sup> However, I find that the average ideal proportion of sons across all women and men is considerably balanced, 0.42 and 0.41 respectively.<sup>10</sup> Son preference is not general but focused in the group that was expected to have stronger motivations to seek a son.

> 9 Women wanted at least 1.39 sons and 1.42 daughters. Men wanted at least 1.49 sons and 1.41 daughters. 10 This may be explained in part by data construction. The survey asks for the number of children the couple desires to be boys, girls or any. The ideal proportion of sons is calculated as the ideal number of boys over total ideal number of children, including children of either gender. Women and men are similar in their preferences for total number of sons but differ in their preferences for children of any sex.

13