rafmacalaba/datause-encoder-data huggingface dataset:
government spending on output. These short-run responses of output to government spending are potentially consistent with a variety of longer-run responses of output to aid.

The rest of this paper proceeds as follows. Section 2 sets out the empirical methodology and identification strategy, and Section 3 describes the construction of the instrument based on loan-level data from the DRS database. Section 4 contains my core results, and Section 5 subjects them to a variety of robustness checks designed to explore the plausibility of the identifying assumption. Section 6 investigates a number of potential sources of heterogeneity in multipliers, and Section 7 concludes.

# **2.  Empirical Strategy**

I estimate variants on this simple empirical specification to assess the short-run effects of government spending on output: (1) Here, and denote GDP and total government spending in country and year , both measured in constant local currency units; and the composite error term denotes all other sources of GDP fluctuations, such as other fiscal or monetary policy changes, terms of trade shocks, changes in productivity, natural disasters, and many other shocks. I sweep out the country-specific and yearspecific components of the error term, and , by including a full set of country and year effects in all specifications. The key parameter of interest is , which captures the short-run government spending multiplier, i.e. the contemporaneous change in output due to a change in government spending. As noted in the introduction, cannot be interpreted as a deep structural parameter. Rather, it should simply be thought of as a reduced-form empirical summary of the contemporaneous relationship between annual fluctuations in government spending and output.

The standard difficulty in statistically identifying is that changes in government spending are likely to be correlated with other contemporaneous shocks to output captured in the error term, so that OLS estimation of Equation (1) will be inconsistent. For example, if government spending increases endogenously in response to an economic downturn, perhaps due to the role of automatic stabilizers, then OLS estimates of the multiplier would be biased downwards. On the other hand, if government 7
# **Table 7:  Trends in Correlations Among Expert Assessments**

||**dri-eiu**|**1996**<br>**dri-prs**|**eiu-prs**||
|---|---|---|---|---|
|Voice and Accountability|..|..|0.82||
|Political Stability|0.71|0.59|0.66||
|Government Effectiveness|0.81|0.67|0.79||
|Regulatory Quality|0.70|0.56|0.55||
|Rule of Law|0.82|0.72|0.72||
|Control of Corruption|0.83|0.65|0.75||
|||**2005**|||
||**dri-eiu**|**dri-prs**|**eiu-prs**||
|Voice and Accountability|..|..|0.80||
|Political Stability|0.81|0.62|0.54||
|Government Effectiveness|0.74|0.62|0.84||
|Regulatory Quality|0.64|0.79|0.78||
|Rule of Law|0.84|0.70|0.77||
|Control of Corruption|0.81|0.70|0.84||
|||**Change**|||
||**dri-eiu**|**dri-prs**|**eiu-prs**||
|Voice and Accountability|..|..|-0.02||
|Political Stability|0.10|0.03|-0.12||
|Government Effectiveness|-0.07|-0.06|0.05||
|Regulatory Quality|-0.06|0.23|0.23||
|Rule of Law|0.02|-0.02|0.05||
|Control of Corruption|-0.03|0.06|0.09||
||**Sum**<br>|**mary of Cha**<br>|**nges**<br>||
||**dri-eiu**|**dri-prs**|**eiu-prs**|**overall**|
|Median Change|-0.03|0.03|0.05|0.03|
|Changes<0|3|2|2|7|
|Changes>0|2|3|4|9|

Note: Sample size = 103 for VA and 87 for remaining indicators. This implies standard errors for changes of 0.14 and 0.15.

42
The long-term variances in schooling for the farm and nonfarm households in China are: The multiplier effect of family background is much smaller in the case of China; the longterm variance in schooling is only about 10 percent higher than the variance of idiosyncratic shocks, and the estimates are virtually identical across the farm and nonfarm samples.

# **(5.2) Intercepts and Steady States**

As noted earlier, measures of relative mobility give us an incomplete, and sometimes misleading, picture of intergenerational mobility across groups such as farm and nonfarm households. A simple but important reason is that different groups may be converging to different steady states due to different intercepts in the intergenerational persistence equations. Perhaps more importantly, the theory in section (2) suggests that factors such as persistence in occupation choices, and expected returns to investment in schooling for children work through the intercept, leaving relative mobility as measured by IGRC and IRC largely unaffected.

The estimated intercepts of equations (12)-(13) and (14)-(15) above are reported in Table 2. The point estimates show that the intercept of the IGRC equation in India is significantly _higher_ for the _farm_ households (p-value 0.013). The evidence for the intercept of the IRC equation is similar (p-value 0.006). When considered along with the evidence that the slope estimates (IGRC and IRC) are smaller for the farm households in India, the evidence implies a set of interesting conclusions. First, whether the sons born to fathers in farm or nonfarm occupation enjoy educational advantage depends on the level of their fathers’ education with a switching threshold of 9-10 years of schooling. Please see figure 3. An interpretation of the evidence is that the national public examination administered at 10th grade (known as Matriculation examination, or all India Secondary School Examination (SSC)) represents a bifurcation point. The children of non-farm fathers with Matriculation or more schooling are expected to achieve better schooling attainment when compared to the children of farmer fathers with similar educational credential, but the children of nonfarm fathers with lower education (and probably unskilled nonfarm jobs) are likely to be worse-off when compared to the children of low educated farmer fathers (who likely own land). Second, the steady state level of education is not substantially different across farm and nonfarm households: 10.81 years of schooling (farm) and 11.20 years of 19
business. Minas Fácil is a one-stop-shop system, where firms obtain municipal, state, and federal tax registrations simultaneously instead of having to request these from separate offices. However, despite these efforts, survey data from 2009 revealed that 72 percent of firms were still informal. As a result, the State Government wanted to test several competing mechanisms for reducing formality.

A listing survey was used to identify potentially informal firms, which were then randomized into four treatment groups and a control group. Survey data revealing a lack of knowledge about how to formalize motivated the first treatment, which was to provide information about how to register by means of a glossy brochure and a dedicated helpline. A second treatment coupled this information with an exemption in the registration fees and free use of mandatory accounting services for a year to test if reducing registration costs would induce formalization. The third treatment randomly assigned municipal inspectors to firms, to see whether increased enforcement would get firms to formalize. The final treatment consists of having a neighboring firm visited by an inspector, to test whether there is a spillover impact of inspection on the formalization behavior of other firms.

We find that efforts to help firms formalize by giving them information, and by reducing the initial cost to zero along with offering a free accountant resulted in an increase in knowledge about the role of accountants in the formalization process, but did not lead to firms formalizing. In fact, it resulted in a small reduction in firms registering through a separate formal category – that of individual entrepreneur – which the information campaign did not target and which had its eligibility criteria relaxed during the course of our study. Moreover, firms which were assigned to either of these two treatments expressed less trust in government in our follow-up survey.

In contrast, assigning firms to receive a visit from a municipal inspector did result in an increase in municipal registration, although the impact was much less than anticipated by the government, with only an additional 3 percent of those assigned to treatment formalizing. This low rate results from the inspectors finding some firms closed, not finding others, and from some firms in our sample already being formal to start with. An instrumental variables estimate suggests that the impact of actually receiving an inspector visit is much higher – resulting in a 21 to 27 percentage point increase in registration. We find no evidence of spillovers on neighboring firms, perhaps due to the relatively low increase in inspections and to many firms saying they do not communicate very much with neighboring businesses. Cost-benefit analysis suggests the revenue benefits to the government of bringing these firms into the formal system are likely to outweigh the costs of the added inspections needed to get them to formalize, suggesting that more inspections of informal firms of this size would be beneficial from a fiscal standpoint.

3
|**Dodoma**(no risk)|||
|---|---|---|
|Low risk|0.0367 (5.553)|-14.85 (13.19)|
|Moderate risk|2.223 (8.459)|-7.760 (13.80)|
|High risk|0.0032 (7.184)|-13.88 (12.77)|
|Very high risk|12.70 (12.16)|1.813 (14.57)|
|**Tanga**(no risk)|||
|Low risk|13.54 (17.02)|10.62* (5.888)|
|Moderate risk|1.477 (7.758)|15.46** (7.36)|
|High risk|2.971 (18.03)|24.81** (11.1)|
|Very high risk|-18.2*** (5.5)|_empty_|
|**Region**(Dar es Salaam)|||
|Dodoma|-2.552 (9.423)|10.01 (13.63)|
|Tanga|2.657 (7.357)|-7.372 (5.974)|
|Firm age|0.310* (0.186)|0.278 (0.184)|
|Number of employees|0.01***(0.001)|0.0105***(0.00124)|
|Log (total operational costs, in $/month)|-1.001 (1.014)|-0.891 (1.071)|
|**Time since last serious disaster**(>12 months)|||
|<2 month|-10.17* (5.15)|-10.12 (6.332)|
|2–12 months|-0.588 (3.539)|0.473 (3.571)|
|**Sector**(Accommodation and food service|||
|activities, Construction)|||
|Communication and other services|15.87*** (4.3)|17.22*** (3.693)|
|Manufacturing, Mining, Water, Energy,|||
|Agriculture|2.869 (3.056)|1.386 (2.845)|
|Transportation and storage|19.31*** (7.6)|20.73** (8.113)|
|Wholesale and retail trade, Repair of|||
|motor vehicles and motorcycles|17.54***(3.58)|17.62***(3.373)|
|Constant|17.73*(9.618)|12.74(8.244)|
|Observations|576|576|
|R-squared|0.091|0.083|

_Note:_ Standard errors are clustered on the ward level in parentheses: *** p<0.01 ** p<0.05 * p<0.1

**Table D.3: Regression results for supply network extent**

|||Distance to avera<br>|ge supplier (km)<br> <br>|
|---|---|---|---|
||(1)|(2)|(3)<br>(4)|
|**Direct risk perception**(no risk)||||
|Low risk|12.543||9.837|
||(8.622)||(8.407)|
|Moderate risk|-1.081||-3.603|
||(8.926)||(10.731)|
|High risk|6.013||6.843|
||(13.444)||(15.098)|
|Very high risk|20.469||13.486|
||(20.067)||(17.414)|
|**Indirect risk perception**(no risk)||||
|Low risk||15.462|12.774|
|||(10.636)|(9.872)|
|Moderate risk||14.596|12.777|

41
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observations because there are gaps between surveys. Assuming a common slope for each change in output per capita we obtain the following regression: Again we see from Table 5 that only the growth in the primary sector reduced rural poverty. In fact the order of magnitude of the coefficient on the primary sector component (- 10.83) is quite similar to the corresponding parameter estimated by Ravallion and Chen (2007) using national data (-8.06). This result is not affected by the exclusion of the municipalities from the sample. Additionally, the parameter for the primary sector is significantly different from that for industry or services. However, we cannot reject the null hypothesis that the parameters on the secondary and tertiary sectors are equal, consistently with Ravallion and Chen (2007). However, as shown in column 3, when we impose the hypothesis that both parameters are the same we find that the common parameter is not significantly different from zero. All the results carry over the sample that excludes municipalities and to the regressions that use the overall (rural plus urban) headcount index. Therefore, this estimator shows again that only the growth rate of agricultural output matters for poverty reduction in rural areas.

We cannot rule out the possibility that secondary or tertiary sector growth is having an indirect effect via primary sector growth. However, we would also note that the development literature has tended to emphasize the spillover effects from agriculture to other sectors, not the reverse.<sup>11</sup> In the case of China there is evidence of quite strong externalities in the rural economic growth process, whereby agricultural growth has second-round effects in stimulating growth in other sectors (Ravallion, 2005, using micro panel data for southwest China in the 1980s).

Given that we find so little evidence that secondary or tertiary sector growth has helped directly reduce poverty in China, the issue of a trade-off between a more balanced pattern of growth across sectors and a higher overall growth rate does not arise. As we noted in the introduction, the non-market processes influencing the pattern of growth in China warn against assuming that higher agricultural growth would have come at the expense of growth in the other sectors. However, even if that was the case, there is no sign here of a trade-off from the point of view of poverty reduction.

> 11 For a recent overview of this literature see Bezemer and Headey (2008).

11
<mark>Table 3 reports regression results for the two specifications, with the samples restricted to countries where price-taking behavior dominates (the second column) and countries where the</mark> price-making behavior dominates (column 3).

In the second column of Table **_3_** 3 the relative price has the wrong expected sign in the long-run – i.e. an increase in export prices relative to domestic consumer prices leads to a _reduction_ in exports. The third column, however, shows that the relative price has the expected sign, but is not statistically significant at 1% - an increase in the export price relative to the world priced _reduces_ exports. Running separate country regressions confirms the ambiguity with the export equation specification as many of the signs and statistical significance depends on which relative price measure is used. Interestingly, the ECM coefficient suggests that it takes approximately 10 years � for exports to equilibrate. The short-run elasticity is much smaller than the long-run elasticity ���<sup>�</sup> �� ~~�~~ which is close to 1.

|**Tabl**|**e 3: Export volume coeffici**|**ents**|
|---|---|---|
|Long-run|ln~~�~~<sup>P�,���</sup><br>�<br>~~�~~|ln~~�~~<br>P�,���<br>�<br>�|
||P�,���<br>�<br>|E�,���P�,���<br>∗<br>|
|xmkt�,���|0.87***|0.92***|
|Long-run relativeprice|-0.12***|-0.06|
|θ�<sup>�</sup>|-0.12***|-0.11***|
|Short-run (average over country<br>list)|||
|∆�xmkt�,��|0.03|0.03|
|Short-run relativeprice|0.20**|-0.30***|
|α��|0.24***|0.13***|
|n∗t|2224|2215|
|Notes:*,**,*** signify|statistical significance at 10%, 5% a|nd 1%, respectively.|

Most of the short-run elasticities fall within the expected ranges for the estimates in column 3. The slow equilibrating process is highlighted by the center of mass in Figure **3** 4 with the smallest ECM at around-0.5 (i.e. it takes two years to equilibrate). It is maintained that the price taking assumption holds for most of the developing countries in MFMod. In the case of MFMod, outliers from Figure 4 are restricted to the 80 and 20 percentile bounds and in the case of positive relative prices we set the parameter value equal to 0. Appendix B summarizes the short-run and ECM parameters for the countries that hit the bounds in the sample.

11
question of limited absorptive capacity, which to a large extent is linked to decreasing marginal returns of aid.

Following Kaufmann and Wang (1995), Isham, Kaufmann and Pritchett (1997), Isham and Kaufmann (1999) and Levin and Dollar (2005), we use data from the projects funded by the World Bank and evaluated by the Independent Evaluation Group (IEG) of the World Bank. Whereas these four studies support the view that better policies and institutions result in a higher economic rate of return and a higher probability of success of projects, we provide micro economic evidence on the influence of instability on aid effectiveness.

A main difference between our methodology and that of the authors of the previously mentioned four studies is that they all aggregate project data to the national level to obtain average economic rates of return or rates of success, while we run our regressions at a micro economic level using each project as an observation. This allows us to directly use a larger part of the available information: our explained variable is the outcome rating of the projects which focuses on the achievement of development objectives. Given that the outcome is a six scale notation, ranging from "very unsatisfactory" to "very satisfactory", our econometric model is an ordered logit. The success of each project is explained by the characteristics of the project (sector...), initial characteristic of the countries at project implementation (level of GDP, education, aid or institutions) and average characteristics of the countries during the realization of the project (level of exports, variability of exports...). Nonetheless, as a robustness check, we also aggregate the data to the country-level to confirm that the results are not sensitive to a change in the methodology.

The structure of the paper is as follows. In the next section we examine the consistency between the macro economic finding that aid dampens the negative effects of instability and the micro evidence of a lower decline of the success of aid projects (when aid increases) in an unstable environment: the stabilizing impact of aid is considered as a positive externality of aid projects. In section 3 we present our econometric model of the determinants of the success of projects where the macro economic variables of interest are the instability of exports, the amount of aid, and a variable multiplicative of the two previous ones likely to capture the stabilizing impact of aid. Section 4 is devoted to the estimation of the model and to the presentation of the results, the robustness of which is then checked.

3
Table A5: Impact on Women's Participation and Candidate Choice

||All|_t_<br>Women|_P-_<br>_value_<br>_of t-_<br>_est (1)-_<br>|Not vote|<br>d in the|_P-value_<br>_of t-test_<br>past|Vote|d in the|_P-value_<br>_of t-test_<br>past|
|---|---|---|---|---|---|---|---|---|---|
||T|CN|_(2)_|T|CN|_(4)-(5)_|T|CN|_(7)-(8)_|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Percentage of women who||||||||||
|Voted for the same party as male head|44.6|41.7|0.500|13.5|11.6|0.326|57.25|56.94|0.950|
|Voted for different party from head|10.1|2.8|0.010|2.2|1.4|0.647|13.15|3.47|0.000|
|Voted but male head did not|6.1|3.7|0.220|1.7|2.7|0.274|7.89|4.17|0.090|
|Did not vote|38.6|50.9|0.010|82.0|84.2|0.526|21.04|34.03|0.010|
|N. Observations|1,870|434||539|146||1,331|288||

Note: P-values are from regressions with village fixed effects, woman characteristics as controls and robust standard errors clustered at the geographic cluster level. The symbols *, **, *** represent significance at the 10, 5 and 1 % respectively.
KCPE score popularly perceived to constitute “passing” the examination is empirically the most important for boys, while a slightly lower cutoff is more relevant for girls. This is consistent with a recent survey of local secondary school administrators, who report lower admissions criteria for girls.

At the admissions cutoff, I find a 15 percent jump in the probability of completing high school. This is a large effect compared to many commonly used instruments for education. I perform relevant specification tests, and find that this effect is significant and stable across a range of specifications, bandwidths, controls, and sample restrictions.

Students on either side of the admissions cutoff are very similar demographically, and in a neighborhood of the test score cutoff, admission to secondary school is “as good as randomized” (Lee 2008). This allows me to treat the rise in schooling at the admissions cutoff as a source of exogenous variation for estimating the impact of secondary school. I find that completing secondary school has a substantial impact on human capital accumulation, as measured by performance on vocabulary and reasoning tests in adulthood. I estimate a performance improvement of 0.6 standard deviations attributable to the completion of secondary school. This is the first paper to show such positive effects of secondary schooling in Africa.

For labor market outcomes, I consider rates of employment and lowskill self-employment. I find clear causal effects: for men in their midtwenties, completing secondary school decreases the probability of low-skill self-employment by roughly 50 percent. There is also suggestive evidence of a 30 percentage point increase in the probability of formal employment, though this is not significant in all specifications. It is important to note that 4