rafmacalaba/datause-encoder-data huggingface dataset:
line should also come from the same base survey. This is an advantage of survey imputation methods because it helps preclude various data challenges such as obtaining the right consumption deflators for the target survey, or ensuring that various other variables in the target survey (such as the new poverty line, the new consumption basket, or the new consumption aggregate itself) have to be constructed in a comparable manner to those in the base survey.

When S tends to infinity (or is practically large enough), the third term on the right hand side in Equation (C.8) will vanish, thus the stated result follows. Consequently, as discussed above with Equation (C.7), we suggest that 1,000 simulations or more should be used to obtain the poverty estimates and their variances. Dang _et al._ (2019) further show that the proposed imputed method provides less bias and a better variance than wealth indexes and most proxy means testing methods, largely because it better takes into account the variances of the unobserved effects υc1 and ε1.

Making an additional, but standard, assumption that the variables to be standardized between the two surveys have a normal distribution, such that 2�, for survey _j_ . We can standardize the variables in survey 2 according those to survey 1 as follows 1 jj jj jj 2→1 2 −θ2) xx+ θ~NN�θ1 , σσ (C.8) 2 σσ xx = (xx σσ where 2→1 is the standardized variable in survey 2. The proof is given in Dang _et al._ (2017), who further suggest that we can relax the assumption of normality and we can just assume more generally that the distributions of xx _x1_ and _x2_ belong to the same location-scale family (see, e.g., Casella and Berger (2002, pp. 104)). In addition, Dang _et al._ (2017) also offer a formula to standardize the variables over multiple periods given further assumptions.

To make the notation simpler, we do not show the equations above with sampling weights. However, sampling weights can be straightforwardly incorporated in the formulas. For example, consider a commonly used stratified two-stage sample design where the population is divided into R strata, nr clusters are sampled out of a population total of Nr clusters from stratum _r_ in the first stage, and mrc households are sampled out of a population total of Mrc households from cluster _c_ in stratum _r_ in the second stage. Suppose that all individuals within a household share the same poverty status (i.e., poverty is measured at the household level), and household _h_ has mrch members. The formula for estimating the poverty rate at the _s_<sup>_th_</sup> random draw given in Equation (4) can be modified accordingly as P(y�2,s1 ≤z1) = ∑ ∑ ∑h=1r P(y�2rchi,s1 ≤z1) (C.9) RR nnrr mm rr=1 cc=1 r rrcch with the sampling weight = ww , and the subscript _i_ indexing individual _i_ in ~~�~~ �� r NNrr MM household _h_ . We provide a Stata user-written routine that automates the estimation procedures (Dang wwrrcch nnrr mm �mmrrcch and Nguyen, 2014).

Dang and Lanjouw (2023) discusses several scenarios where survey-to-survey imputation are most useful in the absence of consumption data. These include i) filling in missing data gaps (most 75
uncertainty with regrd to tho location, extent and priuing of risk and **as** a rult a sort of **inertia** or lethargy in properly appraising **the** risk involved in the light of potentially available information.8 This may lead to instability aid vulnerability of the international fiical sysem **as** a whole becaus of the inter-locking assets and liabilities of various financial institutions. Further, expectations tend to be based on what Keynes cals, 'avere opinion thinks about average opinion' or what Pigou calls, **eorrors** of optimism and pessimism,' resulting in inherent instaility of credit as Keynes christened itt9 Adequate prudential reulation of the national as well as the international financial system with lender of last resort provision is, thus, essetial for the stbility of the system as a whole. This is **the** rationale for an institution lik the international central bank.<sup>1 0</sup> **The** requirement for prudentl reulation is already recognized by the national authorities and an international agreement with regard to capital adequacy requirement for financial institutions is a pointer to what needs to be done at an international level. After this necessary digresion to pinpoint the vulnerability of the unrgulated international market, let as now revert to the nature of financial innovations that have been introduced in the international financial markets.

In the seventies, the OPEC countries preferred to keep their surpluses arising from the oil price stock-quadrupling of oil prices in 1973, which quintupled in 1979-as euro-dollar deposits with international banks. At the same time, there was instability in the interest rates in the various financial centers. These two events gave rise to the innovation of syndicatad banlk loans at variable interest rates. These loans were primarily made to the oil-importing developing countries and the **lack** of proper risk appraial by the international banks as well as the borrowing developing countries ultimately led to the debt crisis of 1982 and after- a cris which still remains to be resolved.

8* _Pey S.._ Mis8 y -Globusation of Fdinaa Markets: Implicaions for Asian Developing counitris, _I, rradonai Journal of D opmt Banking,_ Volume 5, _No._ 2, (July 1987). 9. John Maynad Keyne. _A 2lh*i. on_ Not, Volume I, (London: Mac lilan and Co, 1930) p. 27. 10. John Hicks. _Crijicl Eu.ys in Monht.y Theory,_ (Oxford: Oxford University Press, 1967), pp. 172-73.
**<u>Appendix Table 4: Productivity Impact of Sharecropping Controlling for Observed Inputs</u>**

|||**Restricte**<br>|**d sample**<br>|
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
|Fixed rental dummy|-0.018|||
||(0.23)|||
|Only output sharing|-0.021|-0.017|-0.055|
||(0.66)|(0.42)|(1.26)|
|Output and input sharing|0.092|0.095|0.047|
||(1.52)|(1.22)|(0.58)|
|Farming is landlord‟s main activity (if sharecropped)|||0.086*|
||||(1.74)|
|No. of adult children per ha of rented out land|||0.004|
||||(1.08)|
|Number of years possessed|-0.000|-0.001|-0.000|
||(0.01)|(0.23)|(0.18)|
|Good soil quality|0.157***|0.195***|0.194***|
||(4.76)|(4.80)|(4.78)|
|Medium soil quality|0.098***|0.124***|0.123***|
||(3.09)|(3.19)|(3.18)|
|Flat land|<br>0.032|<br>0.026|<br>0.030|
||(0.64)|(0.42)|(0.48)|
|Gently sloped land|0.005|0.046|0.049|
||(0.09)|(0.70)|(0.75)|
|Irrigated land|<br>-0.016|<br>-0.047|<br>-0.049|
||(0.28)|(0.71)|(0.72)|
|Teff|0.045<br>|0.006<br>|0.006<br>|
||(0.86)|(0.09)|(0.10)|
|Wheat|-0.034|-0.019|-0.019|
||(0.66)|(0.31)|(0.30)|
|Sorghum|<br>-0.289***|<br>-0.323***|<br>-0.325***|
||(5.94)|(5.61)|(5.63)|
|Pulses|0.111**|0.128**|0.128**|
||(2.14)|(2.06)|(2.06)|
|Male adult labor (days per hectare)|<br>0.251***|<br>0.255***|<br>0.255***|
||(9.62)|(8.20)|(8.18)|
|Female adult labor (days per hectare)|0.086***|0.107***|0.106***|
||(4.33)|(4.42)|(4.38)|
|Hired labor (days per hectare)|0.015|0.036|0.037|
||(0.55)|(1.10)|(1.12)|
|Oxen (days per hectare)|0.286***|0.266***|0.266***|
||(13.35)|(10.03)|(10.05)|
|Chemical fertilizer (kg per hectare)|0.133***|0.121***|0.122***|
||(6.18)|(4.83)|(4.84)|
|Manure (kg per hectare)|<br>0.049**|<br>0.056**|<br>0.055**|
||(2.47)|(2.27)|(2.25)|
|Dummy female family labor<sup>a</sup>|0.331***|0.566***|0.566***|
||(4.69)|(6.31)|(6.32)|
|Dummy hired labor<sup>a</sup>|<br>-0.152*|<br>-0.153|<br>-0.149|
||(1.92)|(1.60)|(1.56)|
|Dummy chemical fertilizer<sup>a</sup>|<br>0.346***|<br>0.275**|<br>0.274**|
||<br>(3.38)|<br>(2.29)|<br>(2.29)|
|Dummy manure<sup>a</sup>|<br>0.245*|<br>0.280*|<br>0.277*|
||(1.80)|(1.68)|(1.67)|
|Constant|<br>4.885***|<br>4.853***|<br>4.852***|
||(25.07)|(20.59)|(20.59)|
|Number of observations|5443|3765|3765|
|Number of households|826|637|637|
|_R_<sup>2</sup>|0.367|0.370|0.371|

**Note:** Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. aThe value of the dummy is 1 if the input is not used, and the value is 0 if the input is used. All inputs are in logs.

29
figures are reported separately for boys and girls and for each grade from one to twelve.<sup>5</sup> In addition to identifying the district where a given school is located, the census asks a series of questions about the infrastructure of the school. These questions ask whether a school has each of the following amenities: a toilet, drinking water, electricity, a main gate, a boundary wall, a play ground, a library, laboratory facilities for physics, chemistry, biology, home economics and computers. Lastly for each school the analyst is provided with information on the number of teaching positions sanctioned by the educational authorities and the number of teaching positions that are currently filled.

In the main analysis that follows, schools form the unit of analysis. There are three main dependent variables of interest: girls’ enrollment, boys’ enrollment, and the ratio of filled teaching positions to sanctioned teaching positions. The availability of longitudinal data from two groups (schools in untreated districts and schools in treated districts) suggests a differencein-difference approach.<sup>6</sup>

# **5. Identification Strategy**

Consider the stylized example depicted in Figure 3. Data are available on an outcome (�) for schools from two sets of districts– those that receive the stipend treatment (����) and those that do not ������. The vertical dashed line at time t represents the implementation of the program. Consequently t-1 is the baseline year (2003) and t+1 is the post-program period (2004 to 2008). Outcomes for two groups are shown: the control group (solid grey line) and the treated

> 5 In addition there are two sets of students for whom enrollment is also reported but who do not constitute a formal part of the school’s student body – un-admitted students and students in the so-called _katchi_ (nursery) class. 6 The application of a strict district-level literacy cutoff in determining program eligibility suggests that the data could also be analyzed using a sharp regression discontinuity design (RDD). In the interests of space and because these estimates have limited external validity, I focus on the cutoff for descriptive analyses only in Figures 12 and 13.

8
**Table 9.  Base Interest Spreads Regressions**

|Explanatory Variable|OLS<br>(1)|Fixed effects<br>(2)|
|---|---|---|
|Overheads|1.7680***|1.7510***|
||(0.6352)|(0.3350)|
|Equity|-0.0061|0.2065**|
||(0.0198)|(0.0876)|
|Liquidity|0.0301|-0.0080|
||(0.0416)|(0.0367)|
|Loan-loss provisions|-0.0910|0.3761**|
||(0.2540)|(0.1740)|
|Market share|-0.1577|0.4170|
||(0.1439)|(0.5112)|
|Interest income|-0.1314|-0.0121|
||(0.1255)|(0.0867)|
|Intermediation|-0.0499|-0.1363**|
||(0.0475)|(0.0598)|
|Herfindahl index|-0.0425|0.0657|
||(1.0109)|(1.1181)|
|Real T-bills rate|0.1362|0.0611|
||(0.1529)|(0.1206)|
|Inflation|0.1676|-0.0294|
||(0.2909)|(0.2532)|
|Real Growth|-0.0058|0.1482|
||(0.2583)|(0.3309)|
|Private Domestic|0.0211*||
||(0.0120)||
|Government owned|0.0126<br>(0.1057)||
|Government-influenced|0.0525<br>(0.0362)||
|Constant|0.2064|0.1274|
||(0.1348)|(0.1541)|
|Observations|327|327|
|Number of banks|45|45|
|AdjustedR2|0.5634|0.7295|

Source: Own calculations, based on CBK data The Table 10 results show that the significant results from our base models are not a reflection of the banking sector as a whole, but are rather driven by specific subsets of banks. To see this, we re-run the base models in Table 9 with interaction terms for each of the three ownership categories that appeared in the original regression (private domestic, governmentowned, and government-influenced). In this way the association between each explanatory variable and the interest spread is allowed to differ by ownership type. The coefficients on the non-interacted variables therefore summarize the effects for foreign banks, our omitted ownership category. Rather than present the full specification, we present the effect of each 21
Policy Research Working Paper 10193

# **Abstract**

Childcare services enable women who were previously unable to work due to taking care of their children to join the labor market. If some women are more productive in market work, rather than unpaid household work, the availability of childcare can potentially improve the allocation of talent across different occupations, triggering an increase in productivity. This paper tests this hypothesis using a survey of manufacturing plants and data on preschool expansion in Indonesia. The analysis relies on a triple difference estimation comparing plants in sectors with different degrees of female labor at baseline. The results suggest that between 2002 and 2014, when a rapid preschool expansion took place in Indonesia, an additional preschool per 1,000 children increased the total factor productivity of manufacturing plants by 11 percent for plants with an average fraction of female workers. The paper provides suggestive evidence that these effects were driven by better labor market matching, enabled by the expansion of female labor supply, and greater job stability for female employees. The results unveil a novel short-term economic impact of childcare services, which complements the longterm growth impact through human capital accumulation.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at eperova@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 --> 7<br>a=<br><!-- End of picture text --> <!-- Start of picture text --> 4000<br>3500 MMM r=8-v<br>3000 HEB Distribution of¥ (truncated)<br>HEE Distribution ofB<br>2500<br>2000<br>1600<br>1000<br>500<br>a6 -4 2 a 2 4 6 fa<br>EB=H=1; EV=0.5<br><!-- End of picture text -->
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32
We conclude from this exercise that the presence of reticent respondents in the survey potentially matters for rankings of questions about sensitive behaviors. Regional comparisons may therefore be misleading without adjusting the sample for the share of reticent respondents.

# **6. Conclusion**

We apply the Azfar and Murrell (2009) method of detecting reticent respondents to a large survey of businesses in Nigeria. Whereas AM's implementation in Romania was in the context of a survey wholly focused on corruption, the Nigerian survey was focused more broadly on business operations, in which corruption questions were a small part of the questionnaire. The results of the current paper therefore provide insights into the effectiveness of AM's method when applied in a more general context. While our results are broadly similar to those of AM, they also provide new insights into ways in which respondents identified as reticent answer survey questions differently from those classified as possibly candid.

We are able to identify 13.1% of respondents as reticent in Nigeria. Those so identified are only a subset of all reticent respondents, which we estimate to be more than 30% of the sample, probably considerably more. The most important individual-level correlate of reticence is education, with the more educated being more likely to be more reticent. Firms in the retail sector are more likely to be reticent. Regional variation is very important with some regions having very small amounts of reticence while in other regions identified reticent respondents are in a majority. We show that this matters for regional rankings of responses to sensitive questions.

When examining how reticent respondents respond differently from other respondents, we find that the reticent are less likely to admit having done sensitive acts, when it is clear that the survey questions specifically implicate the reticent respondents. Hence, the coin-toss question originally proposed by AM in Romania is an effective tool for identifying reticence. However, as the phrasing of the survey questions becomes more general, with inferences less likely to indict the respondent, the answers of the reticent and possibly candid populations become more similar. Indeed, for one set of 15