rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Determining the optimal level and composition of public education spending is a key policy question in many low-income countries. Many education advocates believe that low-income countries will fall short of achieving enrollment and learning goals without substantial increases in public spending on education (UNESCO, 2014); others argue that public sector inefficiencies leave considerable room for improvement within existing education budgets, and that fiscal constraints make it imperative to improve the efficiency of public expenditure (World Bank, 2010). However, the data to assess the relative importance of these contentions remains sparse, in part, due to the difficulty in detecting and measuring inefficiencies in public spending.

In this paper, we study one striking measure of public sector inefficiency - teacher absences - with panel data collected 7 years apart in India at a time of sharp increases in education spending. A large portion of this increase was accounted for by the salary cost of hiring teachers to reduce the student-teacher ratio in public schools. As a policy alternative to hiring more teachers, we show that reducing teacher absences by increasing school monitoring would be ten times more cost effective at reducing _effective_ student-teacher ratio (net of teacher absence). We also find that hiring more teachers _increases_ the teacher absence rate, which further increases the costs of teacher hiring once behavioral responses are adequately accounted for. Thus, while the default approach to improving education in low-income countries is input-augmentation, our results suggest that investing in reducing inefficiencies may yield much greater returns.

India presents a particularly salient setting for our analysis. It has the largest primary education system in the world, catering to over 200 million children. Further, over the past decade, the Government of India has invested heavily in primary education under the Sarva Shiksha Abhiyan (SSA) or “Universal Education Campaign.” Partly financed by a special education tax, this national program sought to correct historical inattention to primary education and led to a substantial increase in annual spending on primary education across several major categories of inputs including school infrastructure, teacher quality, studentteacher ratios, and school feeding programs.<sup>1</sup> However, the public education system in India also faces substantial governance challenges that may limit the extent to which this additional spending translates into improved education outcomes. Our indicator of systemic inefficiency - teacher absence - presents a particularly striking indicator of weak governance. A nationally-representative study of over

> 1Total spending on education more than doubled between 2004 and 2009. In the year 2004-2005, India’s education budget was Rs.1,528 billion ($25 billion) and it increased to Rs.3,783 billion ($60 billion) in 20092010 (Pratham, 2010).

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**Table 7: Results using cell averages at the City times Industry level**

|Dependent variable:_Employment_<br>_Growth_ (logdifference, %, annual)|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Informal Competition (City-Industry<br>level cell average)|-6.997**<br>(3.386)|-6.747**<br>(3.419)|-5.803**<br>(2.533)|-5.796**<br>(2.455)|
|Initial Employment (logs)|-4.100***|-4.100***|-4.174***|-4.361***|
||(0.375)|(0.374)|(0.367)|(0.369)|
|Age of Firm (logs)|||-2.973***|-2.963***|
||||(0.637)|(0.618)|
|Manager Experience (logs)|||0.123|0.039|
||||(0.403)|(0.402)|
|Exports (proportion of sales)|||2.669*|2.486*|
||||(1.424)|(1.416)|
|Female Top Manger Y:1 N:0|||-1.275<br>(1.390)|-1.262<br>(1.330)|
|Multi-establishment firm Y:1 N:0|||4.380***|4.209***|
||||(0.885)|(0.864)|
|Crime losses Y:1 N:0||||0.344<br>(1.258)|
|Courts obstacle Y:1 N:0||||-0.391<br>(0.673)|
|Power outages (hours)||||-0.004<br>(0.005)|
|Finance obstacle (0-4 scale)||||-0.219<br>(0.232)|
|Overdraft Y:1 N:0||||2.942***<br>(0.589)|
|Industry fixed effects|No|Yes|Yes|Yes|
|City fixed effects|Yes|Yes|Yes|Yes|
|Constant|18.576***|11.524***|18.266***|18.227***|
||(1.890)|(2.830)|(3.459)|(3.565)|
|Number of observations|19,513|19,513|19,513|19,513|
|R-squared|0.157|0.158|0.180|0.186|

Huber-White robust standard errors and clustered at the city times industry level in brackets. Significance is denoted by *** (1%), ** (5%), * (10%).

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unprotected sectors. Ihe results of the Chow test show that there is indeed a statistcal differc in the behavior of protected sectors compared to unprotected ones.1

# VI. CONCLUSION

The effects of trade liberalization on total factor productivity (Cr in Morocco were estimaed using various measures of firm-level productivity, namely TFP from a fixed-offect modd esimating a production function by sector, TFP from a fixed-effect model imating a production function by sector and firm size (not reported here), and TFP from a difference model using instumeal variables to correct for simultaneity bias and measurement error in the factor inputs within a production function framework. These different models aimed at getting an accrae estimate of the TFP index. The results of the regressions linking trade and market structure variables to productivity showed litde variation across different TFP measures. In all cases, we get a strong posidve correlation between trade openness, as measured by export share in sales and import penrion, and firmlevel TFP. Moreover, by separating the sample into protected and unprotected sectors using the average tariff criterion, the results remained unchanged in terms of the signs of the coefficients of trade variables, although a difference in the magnitude of these coefficients was noticeable across the two categories. We therefore conclude with reasonable confidence that trade openness has had a significant positve impact on firm efficiency in the Moroccan manufacturing sector, this effect being present in aUl our models in a robust manner.

2'The F-statistc that we obtained with degrees of freedom (12 , 3905) is 5.42, falling above the critical value of 1.75.

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2007a, 2007b). The suburbanization of jobs along with that of residences thus changed commuting patterns: instead of traveling to the central city, workers increasingly traveled within the suburbs (Baum-Snow, 2010).<sup>10</sup> In Chinese cities as well, radial highways have led to the decentralization of both population and industries, (Baum-Snow _et al,_ 2015). Applying similar methods, Garcia-López _et al_ (2015) finds evidence of highways causing decentralization in Barcelona. For Spain as a whole, Garcia-López _et al_ (2014) also find that highways caused suburbanization, but to a lesser extent than in the US.

# **_Economic Activity, Income, and Employment_**

Increased trade and productivity results in greater production, improved labor market outcomes, and higher incomes. In the context of China, Banerjee _et al_ (2012) find that infrastructure (roads and highways) have a positive effect on per capita GDP at the county level. Also in China, Roberts _et al_ (2012) estimates a New Economic Geography model and finds that highways increased real incomes. In the case of Nigeria, Ali _et al_ (2015a) find that reducing transport costs significantly increases local GDP but note that the full impact of transport costs on incomes may only emerge slowly over time. Jedwab and Moradi (forthcoming) measure the impact of railway access in Ghana (constructed around 1901) on economic development (as measured by nightlights) and on cocoa production. They show that railway access positively affects economic activity, both in the short and long run (path dependency).<sup>11</sup> At the scale of Sub-Saharan Africa, Storeygard (2014) finds that cities close to a main port grow faster. In terms of employment, Duranton and Turner (2012) find that the initial stock of highways in the US caused higher city-level employment growth. The result is somewhat nuanced in a study on the UK in which Gibbons _et al_ (2012) find that although roads affect firm entry and exit, they do not affect employment in existing firms.

Regarding wages, Sanchis-Guarner (2012) highlights ambiguous possible effects from road construction resulting from two opposite forces: improved accessibility of firms that may result in higher productivity and thus higher wages, and increased competition among workers which can exert downward pressure on wages. As wages are found to

> 10 Baum-Snow (2014) finds that radial highways caused more decentralization of population than jobs (and a heterogeneous response across sectors as some have strong incentives to remain spatially clustered).

> 11 A similar study for Kenya (Jedwab _et al,_ 2014) finds further supportive evidence of those effects.

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_Table 3 Balancing between "training, no grant" vs "training and grant"_

||„training, n|o grant“|„training and|grant“|N|
|---|---|---|---|---|---|
||Mean|SD|Coeff|SE||
|Ind<br>|<br>ividual and h<br>|<br>ousehold char<br>|.<br>acteristics<br>|<br>||
|Age|27.570|4.691|5.594***|1.280|626|
|Married|0.606|0.489|0.160***|0.053|626|
|Employment status|0.656|0.476|0.134**|0.058|626|
|Business ownership|0.642|0.480|0.215***|0.049|626|
|Consumption food|5.390|1.150|0.908***|0.216|626|
|Consumption nonfood|2.398|1.322|0.676***|0.137|626|
|Formal bank account|0.421|0.494|0.137***|0.047|626|
|(Log) amount formal loans|‐0.338|1.756|‐0.140|0.171|625|
|(Log) amount informal loans|‐0.972|2.892|‐0.522*|0.267|614|
|Education<br>No education|0.173|0.379|‐0.056|0.044|626|
|level<br>Some Primary|0.308|0.462|0.078*|0.044|626|
|Some|0.399|0.490|0.164***|0.046|626|
|Secondary||||||
|Some University<br>or Higher|0.120|0.326|‐0.002|0.029|626|
|Literacy<br>No English|0.233|0.423|‐0.080*|0.042|626|
|Some English|0.269|0.444|0.131***|0.041|626|
|Good English|0.498|0.501|0.133**|0.056|626|
|Numeracy<br>Low|0.192|0.395|‐0.022|0.036|626|
|Medium|0.216|0.412|‐0.006|0.040|626|
|High|0.591|0.492|0.212***|0.058|626|
|Household size|7.058|3.215|1.648***|0.508|626|
|Number of children|3.171|2.239|0.628**|0.284|626|
|Number of elderly|0.072|0.332|0.039|0.036|626|
|Number of rooms|3.240|1.698|0.533***|0.191|626|
|Number of buildings|3.639|2.029|0.783***|0.293|626|
|(Log) distance to KCB branch|2.749|2.089|0.078|0.170|617|
|Conflict exposure 2011‐2014|0.208|4.393|-0.149|0.166|626|
|(300km buffer)||||||
|Conflict exposure 2015‐2017|0.136|3.728|-0.195|0.157|626|
|(300km buffer)||||||
||Stat|e at baseline||||
|Central Equatoria|0.188|0.391|0.019**|0.009|626|
|Eastern Equatoria|0.240|0.428|0.000***|0.000|626|
|Lakes|0.063|0.242|0.000***|0.000|626|
|Northern Bahr El Ghazal|0.125|0.331|0.178***|0.043|626|
|Western Bahr El Ghazal|0.091|0.288|‐0.004|0.004|626|
|Western Equatoria|0.293|0.456|‐0.009*|0.006|626|

_Note: Differences between treatment group participants that received that grant and those who did not use baseline characteristics. Column (1) reports mean values of baseline covariates for participants that received training but no grant". Column (2) reports OLS estimates on receiving "training and grant" and strata fixed effect. Standard errors are clustered at boma level and reported below coefficients in parenthesis.* (**, ***) indicates statistical significance at the ten‐percent (five‐ percent, one‐percent) level."_ 26
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- - that governmernt expenditures like exports should be included as separate entries in donestic production functions.'9 Levine and Renelt (1990) combine the Feder (1983) study of export growth and output growth with the Ram (1986) study of government spending growth and output growth and show that export growth enters insignificantly once government expenditure growth and the growth in government's share of GDP are included.

Turning from empirical to conceptual issues, causality is particularly problematic in cross-country regressions of output growth on export growth. For example, a shift in government expenditures from defense to education might stimulate long-run domestic production, including the production of exports. Attributing the growth in output to growth in exports would be inappropriate. Indeed, Jung and Marshall (1985) show that increases (or decreases) in the growth rate of exports are very poor predictors of increases (or decreases) in the growth rate of output.

Another weakness with cross-country growth regressions that focus on the relationship between exports and growth is that they generally do not examine specific proxies for trade policy and yet they tend to draw conclusions concerning trade policy in general and export promotion policies in particular.' Since (1) these studies do not include proxies for trade policy, (2) the causal relationship between export growth and output growth is ambiguous, (3) all the empirical relationships obtained by these studies using '9 Similarly, it is not difficult to envisage the argument that imports are an important source of knowledge spillovers and that imports too belong as separate entries in the production functions of domestically produced goods.

' Balassa (1985) uses a measure of import penetration as a policy indicator. Pritchett (1990), however, demonstrates the problem with using import penetration as a measure of trade policy.
complete fertility histories for the entire cohort of women, data on maternal health care utilization are only available for a sub-sample who had children in the late 1980s and 1990s. Among the cohort of interest, women who had children later are likely to be systematically different to women who had children earlier and whose maternal care choices are not observed in the data used for this analysis. Women’s desire to start childbearing earlier may be associated with lower levels of education, as well as other observed and unobserved factors which determine women’s fertility and health care choices. Focusing on maternal care choices made by women who gave birth relatively later in life may lead to overestimation of the impact of schooling. I do not explicitly correct for selection bias in the maternal health care analysis but have chosen instead to assess the impact of schooling on the timing of women’s fertility decisions. I first estimate the impact of schooling on two related fertility outcomes: age at birth of first child and the probability of having had children by the age of 21. I then estimate the impact of schooling on women’s use of maternal care services, conditional on their having had children. The first analysis provides a measure of the extent to which schooling delayed childbearing among the cohort of interest and, consequently, the extent to which the effects of schooling on maternal health care use may be overestimated.

# **7. Empirical estimation and results**

There are three components to my empirical analysis. I begin by estimating the effect of the school construction program on education in order to construct the IV and assess the strength of its relationship with years of schooling. I then estimate the impact of schooling on the timing of women’s fertility choices. Finally, I estimate the maternal 29
1992, and for 2000 the average from 1998 through 2002. Quality of the business climate is measured using the average of the following governance indicators developed by Kaufman, Kraay and Mastruzzi (2004): voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. The indicators are available for alternating years in the 1996-2004 period. They range from –2.5 to 2.5 with higher numbers corresponding to higher quality of governance in the country. For 1990 we use the 1996 data and for 2000 we use 2000 data. The conflict indicator is an index constructed based on information available at www.prio.no, “Armed Conflict Version 2.1.” by Gleditsch, Wallensteen, Eriksson, Sollenberg and Strand (2002). The index takes on values from 0 (no conflict) to 3 (severe conflict) depending on the depth of the conflicts in which the country was involved during the 1990s. The distance between the US and the partner country is measured in miles and comes from Andrew Rose’s dataset available at http://faculty.haas.berkeley.edu/arose/.

In our instrumental variables approach, we use data on migrant stocks in the 15 countries of the European Union disaggregated by country of origin and education level, taken from Docquier and Marfouk (2005). We employ the information on passport fees from McKenzie (2005). We normalize the fees by the country’s GDP per capita expressed in US dollars.<sup>11</sup> Finally, we use the data on population density from the _World Development Indicators_ database _._ As additional instruments, we employ a dummy variable taking the value of one for countries in which women face restrictions on foreign travel and a dummy variable taking the value of one for countries placing other legal restrictions on emigration. There are only two countries in the sample which employ the first type of restrictions (Egypt and Saudi Arabia) and four countries with the second type of restrictions (Ecuador, Equatorial Guinea, Israel where government permission is required to travel and Singapore which restricts travel of citizens of national service age). Moreover, due to a regrettable mismatch in timing, as these variables pertain to year 2004/5, we use these additional instruments only in a robustness check. Both variables come from McKenzie (2005).

Table 1 presents summary statistics of all variables used in the study.

> 11 The information on fees pertains to 2005. To make the figures comparable to the GDP per capita data we express the fees in 1990 and 2000 US dollars, as appropriate.

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Table 8: Effect of Contract Enforcement on Rental Market Outcomes

||N|Control<br>mean|Treatment<br>coefcient|Standard<br>error|_p_-value|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Total rent from compound last month (US$)|525|62.575|23.880**|10.127|0.019|
|Average monthly rent paid by tenants (US$)|395|33.011|-1.845|2.576|0.474|
|Does compound have rental dwellings|568|0.589|0.135***|0.038|0.000|
|Number of rental units in compound|566|3.290|0.381|0.377|0.312|
|Property owner increased rent in the last 6 months|371|0.018|0.036*|0.021|0.091|
|Paying tenants have moved out in last 6 months|364|0.697|-0.059|0.053|0.264|

*** ** * _Notes_ . p<0.01, p<0.05, p<0.1. Sample includes all compounds that were included in the follow up survey: control and treatment compounds in disconnection-treatment clusters. "Have you increased rents" and "Paying tenants have moved out" are estimated only on the sub-sample of compound property owners that report having tenants. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated.

36
of production inputs ( _capital and labor - resource movement effect_ ) and therefore permanently crowds out the declining<sup>22</sup> traditional export sector by the other two booming sectors of the economy. The Dutch Disease, by reallocating resource may be doing what the government would have been otherwise reluctant to do, therefore facilitating the transformational process of the economy.

Going back to the original context of Dutch Disease, the term refers to the fears of de-industrialization that gripped the Netherlands as a result of the appreciation of the Dutch currency that followed the discovery of natural gas deposits. The appreciation of the Dutch currency that followed the gas export boom reduced the profitability of manufacturing and service exports<sup>23</sup> . Total exports decreased markedly to GDP during the 1960s. Expansion of petroleum exports in the 1960s not only crowded out other exports, actually reduced other exports disproportionately and fueled the fears of dire consequences for Dutch manufacturing.

In principle the Dutch Disease<sup>24</sup> can be seen as a matter of one sector benefiting partly at the expense of others therefore not an impediment _per se_ ; however the disease is potentially harmful if there is something special about the reallocation of resources between sectors; for example from high-tech service industries to low primary production (Gylfason 2001). The oil boom would therefore induce a decline in a technologically leading sector of the economy with adverse consequence for long-term growth: The resource shift can then become a true disease (Sachs 2007).

In the case of Equatorial Guinea the secondary sector represents about 2 percent of the GDP and the manufacturing almost does not exist; in average from 2000 to 2005 the manufacturing sector represented less than 1 percent of GDP. The negative impact of Dutch Disease in that sense will be limited<sup>25</sup> given the structure of the economy and on the contrary may even be a good thing in that context, because it fuels the structural transformational process of the economy which was needed in Equatorial Guinea.

> 22 The Traditional export sector has been in difficulties for years even before the coming on stream of important oil fields due to structural deficiencies and mostly labor.

> 23 Thorvaldur Gylfason. Lessons from the Dutch Disease: Causes, Treatment, and Cures - Institute of Economic Studies - University of Iceland. August 2001.

> 24 The Dutch disease is a matter of concerns mainly because of its potentially deleterious consequence for economic growth.

> 25 Apart from the domestic food production: the real exchange rate appreciation has made imported foods cheaper hence; has increased the dependency on imported foods to the detriment of domestic food production sector. This short-term macro-adjustment consequent can be corrected through increased productivity and reallocation of resources in the sector.

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Table 2: Heterogeneous Impacts of GDP Decline in Firms’ Destinations by Worker Characteristics

||Average|months worked|Average real wages|
|---|---|---|---|
|||Test eq. of coef|Test eq. of coef|
||Coefcient<br>(1)|t-test<br>p-value<br>(2)|Coefcient<br>t-test<br>p-value<br>(3)<br>(4)|
|GFC frm shock|-0.0357***<br>(0.0073)||-0.0116***<br>(0.0030)|
|Observations|3|,046,074|3,030,524|
|R-squared||0.1274|0.2757|
|GFC frm shock*Female|-0.0454***<br>(0.0098)||-0.0105***<br>(0.0039)|
|GFC frm shock*Male|-0.0328***<br>(0.0080)|1.250<br>(0.2114)|-0.0114***<br>-0.194<br>(0.0033)<br>(0.8459)|
|Observations|3|,046,074|3,030,524|
|R-squared||0.8543|0.6046|
|GFC frm shock*Higher education|-0.0128<br>(0.0111)||-0.0005<br>(0.0061)|
|GFC frm shock*No higher education|-0.0378***|-2.142|-0.0126***<br>-1.890|
||(0.0077)|(0.0322)**|(0.0031)<br>(0.0588)*|
|Observations|3|,046,074|3,030,524|
|R-squared||0.8547|0.6089|
|GFC frm shock*Young|-0.0334***<br>(0.0073)||-0.0123***<br>(0.0031)|
|GFC frm shock*Old|-0.0762***|2.460|-0.0158***<br>0.7257|
||(0.0182)|(0.0139)***|(0.0050)<br>(0.4680)|
|Observations|3|,046,074|3,030,524|
|R-squared||0.8546|0.5974|
|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.

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